Editor-In-Chief Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE

International Journal of Recent Technology and Engineering (IJRTE)

Associated Editor-In-Chief Chair Prof. MPS Chawla

Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &

Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India

Dr. Vinod Kumar Singh

Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

Dr. Rachana Dubey

Ph.D.(CSE), MTech(CSE), B.E(CSE)

Professor & Head, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE),

Bhopal (M.P.), India

Associated Editor-In-Chief Members Dr. Hai Shanker Hota

Ph.D. (CSE), MCA, MSc (Mathematics)

Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science ,Technology and Maritime

Transport, Egypt

Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India

Dr. Shanmugha Priya. Pon

Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East

Africa, Tanzania

Dr. Veronica Mc Gowan

Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,

China.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India

Executive Editor Chair Dr. Deepak Garg

Professor & Head, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,

Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Dr.Ch.V. Raghavendran

Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.

Dr. Thanhtrung Dang

Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineeering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

Dr. Ch. Ravi Kumar

Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra

Pradesh), India.

Dr. Sanjay Pande MB

FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR)

Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University

Belgaum (Karnataka), India.

Manager Chair

Mr. Jitendra Kumar Sen Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Prof. (Dr.) Rahul Malhotra

Director – Principal, Department of Electronics & Communication, Swami Devi Dyal Institute of Engineering and Technology,

Barwala (Haryana), India.

Editorial Members Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. T. Sheela

Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India

Dr. Shivanna S.

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. H. Ravi Kumar

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. Pratik Gite

Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),

India

Dr. S. Murugan

Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. P. Malyadri

Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India

Dr. K. Prabha

Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India

Dr. Liladhar R. Rewatkar

Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India

Dr. Raja Praveen.N

Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education

University, Amman- Jordan

Dr. Balachander K

Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi

(Coimbatore), India

Dr. Sudhan M.B

Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,

Anna University, (Tamilnadu), India

Dr. T. Velumani

Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India

Dr. Subramanya.G.Bhagwath

Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai

Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

S.

No. Volume-8 Issue-9S, July 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication Page No.

1.

Authors: Hari Shankar, Ankush Kansal

Paper Title: MGF-based Analysis of Maximum Ratio Combining Receiver over Fisher-Snedecor

Composite Fading Channel

Abstract: Fisher Snedecor composite fading model is the combination of Nakagami-m and

inverse Nakagami-m distribution. The Nakagami-m is used to characterize the small scale

fading, whereas shadowing is modeled by inverse Nakagami-m distribution. In this paper,

the closed-form expression for moment generating function (MGF) of instantaneous signal

to noise ratio (SNR) over independent identically distributed (i.i.d) Fisher-Snedecor

composite fading channel using maximum ratio combining (MRC) diversity technique is

derived. By using newly derived MGF expression, we derive the closed-form expressions of

average bit error rate (ABER) or average symbol error rate (ASER) for different binary and

multilevel modulation schemes. The expressions for average channel capacity (ACC) under

two adaptive transmission protocols like optimum rate adaption (ORA) and channel

inversion with fixed rate (CIFR) are also derived using proposed MGF. Further, the

numerical results of newly derived expression are presented and compared with the results

of Rayleigh and Nakagami-m distribution which is the special case of Fisher Snedecor

composite fading model.

Keyword: Fading, shadowing, maximum ratio combining, channel capacity, error

probability.

References: 1. P.M. Shankar, Fading and Shadowing for Wireless system. New York: Springer, 2005.

2. M.K. Simon, M.S. Alouini, Digital Communication over Fading Channels. New York: John Wiley & Sons,

2005. 3. M.K. Simon, M.S. Alouini, A unified approach to the performance analysis of digital communication over

generalized fading channels. Proceedings of the IEEE, 1999; 86(9): 1860-1877.

4. M.S. Alouini, A. J. Goldsmith. (1999). A unified approach for calculating error rates of linearly modulated signals over generalized fading channels. IEEE Trans. commun. 47(9), pp.1324-1334.

5. K.A. Hamdi. (2008). Capacity of MRC on correlated Rician fading channels. IEEE Trans. commun. 56(5), pp.

708-711. 6. M.D. Renzo, F. Graziosi, F. Santucci. (2010). Channel capacity over generalized fading channels: A novel

MGF-based approach for performance analysis and design of wireless communication systems. IEEE Trans.

Veh. Technol. 59(1), pp. 127-149. 7. A. Abdi, M. Kaveh. (1998). K-distribution: an appropriate substitute for Rayleigh-lognormal distribution in

fading- shadowing wireless channels. Electron. Lett. 34(9), pp. 851-852.

8. P.M. Shankar. (2004). Error rates in generalized shadowed fading channels. Wirel. Pers. Commun. 3, pp. 233–38.

9. S.K. Yoo, S.L. Cotton, P.C. Sofotasios, S. Freear. (2016). Shadowed Fading in Indoor Off-Body

Communication Channels: A Statistical Characterization Using the κ-μ/gamma Composite Fading Model. IEEE Trans. Wirel. Commun. 15(8), pp. 5231-5244.

10. S.K. Yoo, S.L. Cotton, P.C. Sofotasios, M. Matthaiou, M. Valkama, G.K. Karagiannidis. (2017). The Fisher-

Snedecor F distribution: A Simple and Accurate Composite Fading Model. IEEE Commun Lett. 21(7), pp. 1661-1664.

11. T. Aldalgamouni, M.C. Ilter, O.S. Badarneh, H. Yanikomeroglu. Performance analysis of Fisher-Snedecor F

composite fading channels. IEEE Middle East and North Africa Communications Conference

(MENACOMM). Jounieh, Lebanon, 2018.

12. H.A. Hmood, Performance of cognitive radio systems over κ-µ shadowed with integer µ and Fisher-Snedecor F

fading channels. International Iraqi Conference on Engineering Technology and its Applications. Al-Najaf, Iraq, 2018.

13. J. Gong, H. Lee, J. Kang. (2018). Generalized moment generating function-based secrecy performance analysis

over Fisher-Snedecor composite fading channels. Electron. Lett. 54 (24), pp. 1381-1383. 14. S. Chen, J. Zhang, G.K. Karagiannidis, B. Ai. (2018). Effective rate of MISO systems over Fisher–Snedecor F

Fading Channels. IEEE Commun. Lett. 22 (12), pp. 2619-2622.

15. L. Kong, G. Kaddoum. (2018). On physical layer security over the Fisher- Snedecor F wiretap Fading Channels. IEEE Access. 6, pp. 39466-39472.

16. F.S. Almehmadi, O.S. Badarneh. (2018). On the effective capacity of Fisher–Snedecor F fading channels.

Electon. Lett. 54(18), pp. 1068-1070. 17. S.K. Yoo, P. C. Sofotasios, S.L Cotton, S. Muhaidat, F.J. Lopez-Martinez, J.M. Romero-Jerez, G.K.

Karagiannidis. (2019). A comprehensive analysis of the achievable channel capacity in F composite fading

1-9

channels. IEEE Access. 2019.

18. O.S. Badarneh, D.B. da Costa, P. C. Sofotasios, S. Muhaidat, S.L. Cotton. (2018). On the sum of Fisher-

Snedecor F variates and its application to maximal-ratio combining. IEEE Wirel. Commun. Lett. 7(6), pp.

966-969. 19. I.S. Gradshteyn, I.M. Ryzhik. Table of Integrals, Series, and Products. Academic Press, New York, 2007.

20. K.P. Peppas, H.E. Nistazakis, G.S. Tombras. An overview of the physical insight and the various

performance metrics of fading channels in wireless communication systems. Advanced Trends Wirel. Commun. In Tech. 2011.

21. S.P. Singh, S. Kumar. (2016). A MGF based closed form expressions for error probability and capacity over

EGK fading for interference limited system. Wirel pers commun. 91(2), pp. 577-816. 22. A. Hamed, M. Alsharef, R.K. Rao. MGF based performance analysis of digital wireless system in urban

shadowing environment. Proceedings of the world congress on engineering and computer science

(WCECS), San Francisco, USA, 2015

2.

.

Authors: Khushmeen Brar, Ashima Kalra, Piyush Samant

Paper Title: An Improved Technique to Diagnose Skin Cancer using Advanced Image Processing

and Machine Learning Techniques

Abstract: Skin cancer is being classified among the mortal and exaggerating forms of cancer

since decade. Notwithstanding, the early diagnosis of skin cancer is very important and it is

an extravagant procedure. The presence of human skin is tough to examine and to model.

This is because of its complex surface. The difficulty of the irregular edge, tone, appearance

of thick hair and other alleviating features generate the skin tough to be analyzed. Human

skin has some non-identical sorts of textures that diseased skin can characterize between the

textures of the healthy one. In consequence, considerable achievements have not been

brought into the evolution of diagnosis approach for skin cancer. On the other hand, skin

cancer diagnosis in dermoscopic portrayals is an exceptionally troublesome chore.

Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant

procedure. In consequence, considerable achievements have not been brought into the

evolution of diagnosis approach for skin cancer. For precise diagnosis and categorization of

skin cancer, particular features are recommended that one may categorize benign and

malignant illustration. A midst analysts, governing the efficacious mechanism of skin cancer

diagnosis is an all-important matter of contention. Ascertaining the more proficient methods

of diagnosis to minimize the amount of errors is a crucial subject among analysts. Image

processing is used to recognize the affected area by disease, its form and color etc. The

reason behind it is to minimize the percentage of miscalculations. Computer vision can bring

about influential contribution in skin cancer detection. Computer Aided Diagnosis provides

support in the premature diagnosis of skin cancer. Programmed skin abrasion segregation is a

stimulating chore because of the low contrast between abrasion and encompassing skin and

asymmetrical periphery. In this paper we present an amalgamation of statistical features,

GLCM features and GLRLM features. The recommended methodology is enacted on 100

images from PH2 database. In this work 2 segmentation tactics namely Otsu’s thresholding

and Region growing have been explored. The performance of two systems is measured in

terms of sensitivity, specificity, accuracy, precision, recall and f-measure.

Keywords: Biomedical image processing, Skin cancer, Gray-level Co-occurrence Matrix,

Grey-level Run Length Matrix, Region Growing, Otsu thresholding, supervised classification

References: 1. A. Rajesh, “Classification of malignant melanoma and Benign Skin Lesion by using back propagation

neural network and ABCD rule,” Cluster Comput., pp. 1–8, 2018.

2. S. Jain, V. Jagtap, and N. Pise, “Computer aided melanoma skin cancer detection using image processing,” Procedia Comput. Sci., vol. 48, no. C, pp. 736–741, 2015.

3. P. Dubai, S. Bhatt, C. Joglekar, and S. Patii, “Skin cancer detection and classification,” Proc. 2017 6th

Int. Conf. Electr. Eng. Informatics Sustain. Soc. Through Digit. Innov. ICEEI 2017, vol. 2017–Novem, pp. 1–6, 2018.

4. H. Alquran et al., “The melanoma skin cancer detection and classification using support vector machine,” 2017 IEEE Jordan Conf. Appl. Electr. Eng. Comput. Technol. AEECT 2017, vol. 2018–Janua, pp. 1–5,

2018.

5. S. M. Jaisakthi, P. Mirunalini, and C. Aravindan, “Automated skin lesion segmentation of dermoscopic

10-17

Authors: Kanupriya Verma, Sahil Bhardwaj, Resham Arya, Mir Salim Ul Islam, Megha

Bhushan, Ashok Kumar, Piyush Samant

Paper Title: Latest Tools for Data Mining and Machine Learning

Abstract: Nowadays, Data Mining is used everywhere for extracting information from the

data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns

which are used to extract information and knowledge for making decisions. Many open

source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for

Data Mining and predictive analysis. This paper discusses about different tools available for

Data Mining and Machine Learning, followed by the description, pros and cons of these

tools. The article provides details of all the algorithms like classification, regression,

characterization, discretization, clustering, visualization and feature selection for Data

images using GrabCut and k-means algorithms,” IET Comput. Vis., vol. 12, no. 8, pp. 1088–1095, 2018.

6. P. Bumrungkun, K. Chamnongthai, and W. Patchoo, “Detection skin cancer using SVM and snake

model,” 2018 Int. Work. Adv. Image Technol. IWAIT 2018, pp. 1–4, 2018.

7. S. Joseph and J. R. Panicker, “Skin lesion analysis system for melanoma detection with an effective hair segmentation method,” in International Conference on Information Science, ICIS, 2016, pp. 91–96.

8. N. Petrellis, “Using Color Signatures for the Classification of Skin Disorders,” 2018 7th Int. Conf. Mod.

Circuits Syst. Technol., pp. 1–4, 2018. 9. A. Fidalgo Barata, E. Celebi, and J. Marques, “Improving Dermoscopy Image Classification Using Color

Constancy,” IEEE J. Biomed. Heal. Informatics, vol. 2194, no. c, pp. 1–1, 2014.

10. M. N. Islam, J. Gallardo-Alvarado, M. Abu, N. A. Salman, S. P. Rengan, and S. Said, “Skin disease recognition using texture analysis,” 2017 IEEE 8th Control Syst. Grad. Res. Colloquium, ICSGRC 2017

- Proc., vol. 1, no. August, pp. 144–148, 2017.

11. C. Barata, M. Ruela, M. Francisco, T. Mendonca, and J. S. Marques, “Two systems for the detection of melanomas in dermoscopy images using texture and color features,” IEEE Syst. J., vol. 8, no. 3, pp. 965–

979, 2014.

12. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, “Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention,” 2014 IEEE Long Isl. Syst.

Appl. Technol. Conf. LISAT 2014, 2014.

13. N. F. M. Azmi, H. M. Sarkan, Y. Yahya, and S. Chuprat, “ABCD rules segmentation on malignant tumor

and Benign skin lesion images,” 2016 3rd Int. Conf. Comput. Inf. Sci. ICCOINS 2016 - Proc., pp. 66–70,

2016.

14. A. N. Hoshyar, A. Al-Jumaily, and R. Sulaiman, “Review on automatic early skin cancer detection,” 2011 Int. Conf. Comput. Sci. Serv. Syst. CSSS 2011 - Proc., pp. 4036–4039, 2011.

15. A. N. Hoshyar, A. Al-Jumaily, and A. N. Hoshyar, “The beneficial techniques in preprocessing step of

skin cancer detection system comparing,” Procedia Comput. Sci., vol. 42, no. C, pp. 25–31, 2014. 16. F. Adjed, S. J. Safdar Gardezi, F. Ababsa, I. Faye, and S. Chandra Dass, “Fusion of structural and

textural features for melanoma recognition,” IET Comput. Vis., vol. 12, no. 2, pp. 185–195, 2018.

17. R. Sumithra, M. Suhil, and D. S. Guru, “Segmentation and classification of skin lesions for disease diagnosis,” Procedia Comput. Sci., vol. 45, no. C, pp. 76–85, 2015.

18. R. Maurya, S. K. Singh, A. K. Maurya, and A. Kumar, “GLCM and Multi Class Support vector machine

based automated skin cancer classification,” 2014 Int. Conf. Comput. Sustain. Glob. Dev., pp. 444–447, 2014.

19. T. M. P. M. Ferreira1 and J. S. M. A. R. S. M. J. R. Abstract—The, “PH2 - A dermoscopic image

database for research and benchmarking*,” J. ACM, vol. 14, no. 4, pp. 677–682, 1967. 20. C. Hima Bindu and K. Satya Prasad, “An Efficient Medical Image Segmentation Using Conventional

OTSU Method,” Int. J. Adv. Sci. Technol., vol. 38, pp. 67–74, 2012.

21. S. Kamdi and R. K. Krishna, “Image Segmentation and Region Growing Algorithm,” Int. J. Comput.

Technol. Electron. Eng., vol. 2, no. 1, pp. 2249–6343, 2012.

22. J. Virmani and R. Agarwal, “ScienceDirect Effect of despeckle filtering on classification of breast tumors

using ultrasound images,” Integr. Med. Res., pp. 1–21, 2019. 23. P. Samant and R. Agarwal, “Machine learning techniques for medical diagnosis of diabetes using iris

images,” Comput. Methods Programs Biomed., vol. 157, pp. 121–128, 2018.

24. Kriti, Jitendra Virmani and Ravinder Agarwal, "Effect of despeckle filtering on classification of breast tumors using ultrasound images", Biocybernetics and Biomedical Engineering, Vol. 39, No. 2, pp. 536-

560, 2019.

25. Jitendra Virmani, Vinod Kumar, Naveen Kalra and Niranjan Khandelwal, "Neural network ensemble based CAD system for focal liver lesions using B-mode ultrasound", Journal of Digital Imaging, Vol. 27,

No. 4. Pp. 520-537, 2014.

26. Jitendra Virmani, Vinod Kumar, Naveen Kalra and Niranjan Khandelwal, "SVM-based characterization of liver cirrhosis by singular value decomposition of GLCM matrix", International Journal of Artificial

Intelligence and Soft Computing, Vol. 3, No.3, 2013, pp. 276-296. 27. P Samant, R Agarwal, "Comparative analysis of classification based algorithms for diabetes diagnosis

using iris images", journal of Medical Engineering & Technology, vol. 42, 2018.

3.

Mining and Machine Learning tools. It will help people for efficient decision making and

suggests which tool is suitable according to their requirement.

Keywords: Data mining, Open source tools, Licensed tools, Machine learning

References: 1. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” in

AI Magazine, 1996, vol. 17, no. 3, pp. 37–54. 2. J. Alcala-Fdez et al. “KEEL: a software tool to assess evolutionary algorithms for data mining problems,” in

Soft Computing, 2009, vol. 13, pp. 307-318.

3. R. Mikut, and R. Markus, “Data mining tools," in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2011, vol. 1, pp. 431-443.

4. A. M. Hirudkar, and SS. Sherekar, “Comparative analysis of data mining tools and techniques for evaluating

performance of database system,” in International Journal of Computer Science Appllications, 2013, vol. 6, pp. 232-237.

5. N. Sharma, A. Bajpai, and R. Litoriya, “Comparison the various clustering algorithms of weka tools,” in

facilities, 2012, vol.4, pp. 78-80.

6. https://rapidminer.com/glossary/data-mining-tools/

7. Q. M. Yas, A.A. Zaidan, B.B. Zaidan, B. Rahmatullah and H.A. Karim,“Comprehensive insights into

evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions,” in Measurement,2018, vol. 114, pp. 243-260

8. A. Jovic, B. Karla, and B. Nikola, “An overview of free software tools for general data mining,” in IEEE 37th

International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014, pp. 1112-1117.

9. N. Borude, C. Maher, V. Sarda, and A. Santra, “Generic binary classifier tool for diagnosis of patients

suffering from brain disorders in R,” in International Conference on Computing, Analytics and Security Trends (CAST), 2016, IEEE pp. 173-178.

10. B. Radim, K. Jan, S. Zdeněk, U. Václav, and D. Otto, “Rapidminer image processing extension: A platform

for collaborative research,” in 33rd International Conference on Telecommunication and Signal Processing, TSP, 2010, pp. 114-118.

11. R.M. Rahman, and F. Afroz, “Comparison of various classification techniques using different data mining

tools for diabetes diagnosis,” in Journal of Software Engineering and Applications, 2013, vol. 6, p. 85. 12. H. Solanki, “Comparative study of data mining tools and analysis with unified data mining theory,” in

International Journal of Computer Applications,2013, vol. 75, pp. 23-28.

13. K. Rangra, and K. L. Bansal, “Comparative study of data mining tools,” in International journal of advanced research in computer science and software engineering, 2014, vol. 4.

14. X. Wu, X. Zhu, G.Q. Wu, and W. Ding, “Data mining with big data,” in IEEE Transactions on Knowledge

and Data Engineering, 2013, vol. 26, pp. 97-107. 15. DK. Tayal, A. Jain, S. Arora, S. Agarwal, T. Gupta, and N. Tyagi, “Crime detection and criminal

identification in India using data mining techniques,” in AI & society, 2015, vol. 30, pp. 117-127.

16. FP. Steinmetz, CL. Mellor, T. Meinl, and MT. Cronin, “Screening Chemicals for Receptor‐Mediated

Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME

Workflow,” in Molecular informatics, 2015, vol. 34, pp. 171-178. 17. D. Chopra , N. Joshi , and I. Mathur, “Improving Quality of Machine Translation Using Text Rewriting,” in

Computational Intelligence & Communication Technology (CICT), Second International Conference on

IEEE, 2016, pp. 22-27. 18. S. Slater, S. Joksimovic, V. Kovanovic, RS. Baker, D. Gasevic, “Tools for educational data mining: A

review,” in Journal of Educational and Behavioral Statistics, 2017, vol. 42 ,pp. 85-106.

19. D. Wilk-Kolodziejczyk, K. Regulski, G. Gumienny, B. Kacprzyk, S. Kluska-Nawarecka, K. Jaskowiec, “Data mining tools in identifying the components of the microstructure of compacted graphite iron based on the

content of alloying elements,” in The International Journal of Advanced Manufacturing Technology, 2018,

vol. 95, pp. 3127-3139. 20. T. Siddiqui, and A. Ausaf, “Data mining tools and techniques for mining software repositories: A systematic

review,” in Big Data Analytics. Springer, Singapore, 2018, pp. 717-726.

21. R. Alcalá, MJ. Gacto, J. Alcalá‐Fdez “Evolutionary data mining and applications: A revision on the most

cited papers from the last 10 years (2007–2017),” in Wiley Interdisciplinary Reviews: Data Mining and

Knowledge Discovery, 2018 ,vol. 8 , e1239. 22. S. Yefimenko, “Advances in GMDH-based Predictive Analytics Tools for Business Intelligence Systems,” in

International Conference Proceedings, ACIT, 2018, pp. 254-257.

23. https://www.softwareadvice.com/bi/dundas-bi-profile/

18-23

Authors: Shagun Sharma, Mamta Nanda, Raghav Goel, Aashrey Jain, Megha Bhushan,

Ashok Kumar

Paper Title: Smart Cities using Internet of Things: Recent Trends and Techniques

4.

Abstract: A Smart Cities focuses on the way we live. Smart governments are also

acknowledged as augmentations of electronic governments based on the Internet of Things

(IoT). There are many existing challenges in the environment such as, research in gadgets,

framework and programming etc. Particularly, the Smart Cities are facing difficulties with

IoT frameworks, systems administration, independent registration, wearable sensors,

gadgets and systematization of aggregates including human beings as well as programming

specialists. This paper incorporates role of Smart Cities in various domains such as smart

infrastructure, smart building, smart security and so on. Moreover, the work depicts the IoT

technologies for Smart Cities and the primary components along with the features of Smart

Cities. This paper is based on technologies for Smart Cities which will benefit citizens by

facilitating a platform for integrating all the resources and prompt communication of

information. Furthermore, merits, demerits and main challenges of Smart Cities are

discussed.

Keyword: Smart Cities, IoT, Sensor System Integration, Smart technology, Smart citizens

References: 1. Scuotto, Veronica, Alberto Ferraris, and Stefano Bresciani. "Internet of Things: Applications and

challenges in Smart Cities: a case study of IBM Smart Cities projects." Business Process Management Journal 22.2 (2016): 357-367.

2. Fazio, M., Paone, M., Puliafito, A., & Villari, M. (2012, July). Heterogeneous sensors become homogeneous things in Smart Cities. In 2012 Sixth International Conference on Innovative Mobile and

Internet Services in Ubiquitous Computing (pp. 775-780). IEEE.

3. Bresciani, S., Ferraris, A., & Del Giudice, M. (2018). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) Smart Cities

projects. Technological Forecasting and Social Change, 136, 331-338.

4. Chatterjee, S., Kar, A. K., & Gupta, M. P. (2018). Success of IoT in Smart Cities of India: an empirical

analysis. Government Information Quarterly, 35(3), 349-361.

5. Zhu, H., Chang, A. S., Kalawsky, R. S., Tsang, K. F., Hancke, G. P., Bello, L. L., & Ling, W. K. (2017, October). Review of state-of-the-art wireless technologies and applications in Smart Cities. In IECON

2017-43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 6187-6192). IEEE.

6. Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie-Khah, M., & Siano, P.

(2016, June). Iot-based Smart Cities: a survey. In 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) (pp. 1-6). IEEE.

7. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for Smart

Cities. IEEE Internet of Things journal, 1(1), 22-32.

8. Deakin, Mark, and Husam Al Waer. "From intelligent to Smart Cities." Intelligent Buildings

International 3.3 (2011): 140-152.

9. AlEnezi, A., AlMeraj, Z., & Manuel, P. (2018, April). Challenges of IoT based smart-government development. In 2018 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1-6).

IEEE.

10. Balsamo, D., Merrett, G. V., Zaghari, B., Wei, Y., Ramchurn, S., Stein, S., & Beeby, S. (2017,

September). Wearable and autonomous computing for future Smart Cities: Open challenges. In 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-

5). IEEE.

11. Fan, Y. J., Yin, Y. H., Da Xu, L., Zeng, Y., & Wu, F. (2014). IoT-based smart rehabilitation

system. IEEE transactions on industrial informatics, 10(2), 1568-1577.

12. Roman, Rodrigo, Jianying Zhou, and Javier Lopez. "On the features and challenges of security and privacy in distributed internet of things." Computer Networks 57.10 (2013): 2266-2279.

13. Balakrishna, Chitra. "Enabling technologies for Smart Cities services and applications." 2012 sixth

international conference on next generation mobile applications, services and technologies. IEEE, 2012.

14. Winters, John V. "Why are Smart Cities growing? Who moves and who stays." Journal of regional

science 51.2 (2011): 253-270.

24-28

Authors: Harbinder Singh, P.N. Hrisheekesha, Gabriel Cristobal

Paper Title: Infrared and Visible Image Fusion Based on Nonparametric Segmentation

Abstract: Image fusion is a process of combining an image sequence of the same scene into

a single image for better human perception & targeting. The thermal energy reflected from

outstanding objects under poor lighting conditions and visible information that yield spatial

details needs to be fused for improving the performance of surveillance systems. In this

paper, we present a fusion technique that is helpful in surveillance systems for detecting

5.

targets when the background and the targets are of the same color. A nonparametric

segmentation based weight map computation technique is proposed to extract target details

from infrared (IR) imagery. The optimal threshold based on local features is selected

automatically for target detection. With this, the extracted salient information of targets is

blended to visible image without introducing distortions. The main advantage of the new

technique is that it is based on a single-scale binary map (SSBM) fusion approach. The

binary weight maps are computed for the fusion of separable IR target with visible imagery.

An extension to IR and visible color image fusion is also suggested for target localization.

Several simulation results are demonstrated for different data sets to support the validity of

the proposed technique.

Keyword: Infrared, visible image, night vision, image fusion, segmentation, histogram.

References: 1. A. Toet, J.K. IJspeert, A.M. Waxman, and M. Aguilar, “Fusion of visible and thermal imagery improves

situational awareness,” Displays, 18, pp. 85—95, 1998.

2. Kalman Tihanyi, Monash University, Archived from the original on 24 October 2003. Retrieved 15 March 2013.

3. Y. Niu, S. Xu, L. Wu, and W. Hu, “Airborne infrared and visible image fusion for target perception based

on target region segmentation and discrete wavelet transform,” Mathematical Problems in Engineering, vol. 2012, Article ID 275138, 10 pages, 2012.

4. H. Singh, V. Kumar, and S. Bhooshan, “Anisotropic diffusion for details enhancement in multiexposure

image fusion,” ISRN Signal Processing, vol. 2013, Article ID 928971, 18 pages, 2013. 5. S. Li, X. Kang, J. Hu, and B. Yang, “Image matting for fusion of multi-focus images in dynamic scenes,”

Information Fusion, vol. 14, no. 2, pp. 147–162, 2013.

6. P. K. Saho, S. Soltani, and A. K. C. Wong, “A Survey of Thresholding Techniques,” Computer Vision, Graphics, and Image Processing, vol. 41, pp. 233-260, 1988.

7. N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," in IEEE Transactions on

Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979. 8. R. C. Gonzalez, and R.E. Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2008.

9. C. Yang, J. Ma, S. Qi, J. Tian, S. Zheng, and X. Tian, “Directional support value of Gaussian

transformation for infrared small target detection,” Applied Optics, vol. 54, no. 9, pp. 2255-2265, 2015. 10. H. Singh, V. Kumar, and S. Bhooshan, “Weighted Least Squares Based Detail Enhanced Exposure

Fusion,” ISRN Signal Processing, vol. 2014, Article ID 498762, 18 pages, 2014. H. Singh, V. Kumar, and

S. Bhooshan, “Weighted Least Squares Based Detail Enhanced Exposure Fusion,” ISRN Signal Processing, vol. 2014, Article ID 498762, 18 pages, 2014.

11. Zhiqiang Zhou, Mingjie Dong, Xiaozhu Xie, Zhifeng Gao “Fusion of infrared and visible images for

night-vision context enhancement,” Appl Opt., vol. 55, no. 23, pp. 6480–6490, 2016. 12. D. P. Bavirisetti and R. Dhuli, "Fusion of Infrared and Visible Sensor Images Based on Anisotropic

Diffusion and Karhunen-Loeve Transform," in IEEE Sensors Journal, vol. 16, no. 1, pp. 203-209, Jan.1,

2016. 13. Durga Prasad Bavirisetti and Ravindra Dhuli “Two-scale image fusion of visible and infrared images

using saliency detection,” Infrared Physics & Technology, ISSN: 1350-4495, vol. 76, pp. 52-64, 2016.

14. H Singh, H. Fatima, S. Sharma and D. Arora, "A Novel Approach for IR Target Localization Based on IR and Visible Image Fusion," IEEE 4th International Conference on Signal Processing, Computing and

Control (ISPCC 2017), pp. 235 – 240, September 21-23, 2017.

15. Zi-Jun Feng, Xiao-Ling Zhang, Li-Yong Yuan, and Jia-Nan Wang, “Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images,” Mathematical

Problems in Engineering, vol. 2013, Article ID 720979, 7 pages, 2013.

29-35

Authors: Misha Kakkar, Sarika Jain, Abhay Bansal, P.S.Grover

Paper Title: Fuzzy Logic based model to predict per phase software defect

Abstract: Software reliability is expressed as the probability of software to function

properly under specified condition for a specified time period. A basic method to evaluate

the software reliability is to check the presence of defects in the software. The presence of

defect can be calculated as defect density measured defined as total number of defects

present in the software divided by the size of the software. The paper proposes a fuzzy logic

based model to predict per phase software defect density. The model uses 3 relevant

software metrics per SDLC phase. Defect density prediction is a useful measure, which

indicates the critical modules of the project and helps software teams to plan their resources

in an efficient manner. The proposed model results are better in comparison with existing

literature in the same domain when compared using MRE performance measure on 20

6.

project dataset.

Keyword: Defect Prediction, Fuzzy logic, Metrics, Phase-wise, SDLC.

References: 1. M. Xie and M. R. Lyu, “Handbook of software reliability engineering,” Software Testing, Verification and

Reliability, vol. 222, pp. 59–60, 1996.

2. IEEE. IEEE Standard Glossary of Software Engineering Terminology, Inst. of Electrical and Electronical Engineers, vol. 121990, 1990.

3. K.Y. Cai, C.Y. Wem, M.L. Zhang, “A critical review on software reliability modeling”, Reliab. Eng. Syst.

safety vol. 32, no 3, pp 357-371, 1991. 4. Kaner, C., & Bond, W.P. “Software Engineering Metrics: What Do They Measure and How Do We Know?”,

2004.

5. N. E. Fenton and M. Neil, “A critique of software defect prediction models,” IEEE Transactions on Software Engineering, vol. 25, no. 5, pp. 675–689, 1999.

6. T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, “A Systematic Literature Review on Fault

Prediction Performance in Software Engineering,” IEEE Transactions on Software Engineering, vol. 38, no. c, pp. 1–31, 2011.

7. Y. Jiang, B. Cukic, T. Menzies, and J. Lin, “Incremental Development of Fault Prediction Models,”

International Journal of Software Engineering and Knowledge Engineering, vol. 23, no. 10, pp. 1399–1425, 2013.

8. H. B. Yadav and D. K. Yadav, “A fuzzy logic based approach for phase-wise software defects prediction

using software metrics,” Information and Software Technology, vol. 63, pp. 44–57, 2015. 9. W. W. Agresti, W. M. Evanco, "Projecting software defects from analyzing software designs", IEEE Trans.

Software Engineering, vol. 18, pp. 988-997, Nov. 1992. 10. C. Smidts, M. Stutzke, R. W. Stoddard, "Software reliability modeling: An approach to early reliability

prediction", IEEE Trans. Reliability, vol. 47, pp. 268-278, Sept. 1998.

11. V. Cortellessa, H. Singh, and B. Cukic, “Early reliability assessment of UML based software models,” Proceedings of the third international workshop on Software and performance - WOSP ’02, no. May 2014, p.

302, 2002.

12. N. Fenton, M. Neil, W. Marsh, P. Hearty, Ł. Radliński, and P. Krause, “On the effectiveness of early life cycle defect prediction with Bayesian nets,” Empirical Software Engineering, vol. 13, no. 5, pp. 499–537,

2008.

13. Z. A. Rana, M. A. Mian, and S. Shamail, “Improving Recall of software defect prediction models using association mining,” Knowledge-Based Systems, vol. 90, pp. 1–13, 2015.

14. A. Tosun, A. Bener, B. Turhan, and T. Menzies, “Practical considerations in deploying statistical methods for

defect prediction: A case study within the Turkish telecommunications industry,” in Information and Software Technology, 2010, vol. 52, no. 11, pp. 1242–1257.

15. K. Dejaeger, T. Verbraken and B. Baesens, "Toward Comprehensible Software Fault Prediction Models

Using Bayesian Network Classifiers," in IEEE Transactions on Software Engineering, vol. 39, no. 2, pp. 237-257, Feb. 2013.

16. A. K. Pandey and N. K. Goyal, “Fault Prediction Model by Fuzzy Profile Development of Reliability

Relevant Software Metrics,” International Journal of Computer Applications, vol. 11, no. 6, pp. 975–8887, 2010.

17. G. Abaei, Z. Rezaei and A. Selamat, "Fault prediction by utilizing self-organizing Map and Threshold," 2013

IEEE International Conference on Control System, Computing and Engineering, Mindeb, 2013, pp. 465-470. 18. K. Gao, T. M. Khoshgoftaar, H. Wang, and N. Seliya, “Choosing software metrics for defect prediction: An

investigation on feature selection techniques,” Software - Practice and Experience, vol. 41, no. 5, pp. 579–

606, 2011. 19. M. Kakkar and S. Jain, “Feature selection in software defect prediction: A comparative study,” in

Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence

2016, 2016.

20. L. Ming and C. S. Smidts, “A ranking of software engineering measures based on expert opinion,” IEEE

Transactions on Software Engineering, vol. 29, no. 9, pp. 811–824, 2003.

36-41

Authors: Dr. Harpal Singh, Kanika Chhabra, Dr. Rinkesh Mittal

Paper Title: Resistivity Based Low Cost Mastitis Level Detection System

Abstract: Mastitis is universally considered the most wide spread and costly disease of the

dairy industry. It can be defined as an inflammation of mammary glands in the udder tissue.

Mastitis is a disease complex due to different causes, various degrees of intensity, variation

in disease duration and residual effect. India has ranked as the highest milk producing

country among the world. In a survey conducted by FAO for top milk producing countries,

it is estimated that by the year 2021 India will be ranked as the number one country for milk

production. In small areas of Punjab and Haryana where the farmers are illiterate and are not

7.

aware about the proper health conditions of cattle, then there are ultimate chances of cattle

to have mastitis. In this work we present low cost solution for detection of mastitis level on

the basis of resistivity of milk. The survey conducted in three different regions of Punjab

(Bathlana, Mansa, Badmajara) where cattle with mastitis and their milk was tested, tests

were also presented. It was found that milk production rate has been declined by the

unawareness of the dairy holders, as they don’t know much about the causes of mastitis. The

presented method provides comparable results for the detection of mastitis level at low cost

based on ionic concentration of milk.

Keyword: Mastitis, Resistivity, milk samples

References: 1. Marcuss Henningnson, Karin Ostergren and Petr Dejmek, “The electrical conductivity of milk – the effect of

dilution and temperature”, international journal of food and agriculture, vol 8,15-22,2005. 2. Ming-Chih Chen, Chein-HsingChen, and Chong-YuSiang, “Design of Information system for Milking Dairy

Cattle and Detection of Mastitits”, Mathematical Problems in Engineerig, Article ID 759019, 2014.

3. Francesca Gabriele, Thorgeir Lawrence, “Impact of Mastitisin Small Scale Dairy Production Systems”, Food

and Agriculture Organisation of the United States, 13, 2014.

4. Shagufta Fahmid, Eram Hassan, Hafsa Waeem, Spozhmai Barrech, Shazma Lodhi,and Sidra

Latif,”dtermination of mastiti by measuring milk electrical conductivity”, international journal of advanced research in biologicla sciences, vol 3(10), 1-4,2016.

5. Mandheer Kaur, Khush Preet Singh, Neha, Yash Hans, Amrit Pal Kaur, Palki Sahib Kaur, Ankit Magotra,

“Biotools for Early Diagnosis and Cure of Mastitis, 2016. 6. Jurjen Draaiyer, Brian Dugdill, Anthony Bennett, Jerome Mounsey,“Milk Testing and Payment Systems

Resource Book”, Food and Agriculture Organisation of United States, 2009. 7. I Panchal, I K sawhney,A K Dang, “Relation between electrical conductivity, dielectric constant, somatic cell

count and some other milk quality parameters in diagnosis of subclinical mastitis in Murrah buffaloes”, indian

journal of dairy science, 69 (3), 2016. 8. Daniella Flavia, Vilas Boas, Anibal Eugenio Vercesi Filho, Mariana Alencar Pereira, Luiz Carios Roma

Junior, and Lenira El Faro, “Association between electrical conductivity and milk productio traits in dairy gyr

cows”,Journal of Applied Animal Research, vol 45, no.1,227-233, 2017. 9. Francisco J. Ferrero, Gustavo Grillo, Perez M A,Juan C. Campo, “Design of a low cost mastitis detector in

cows b measuring electrical conductivity of milk”, IEEE Instrumentation and Measurement Technology

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8.

Authors: Harpal Singh, Ramneek Kaur Brar and Priyanka Kaushal

Paper Title: Performance Analysis of Fractional Redundant Wavelet Transform for

Watermarking Scheme

Abstract: In this paper, an algorithm for digital image watermarking which utilizes the

hybridization technique is presented. The hybrid technique is formulated by combining the

Redundant Wavelet Transform (RDWT) with Fractional Fourier Transform (FrFT) and

Singular Value Decomposition (SVD). In this technique, watermark information is

embedded in the low frequency band of Redundant Wavelet Transform. To increase the

robustness, FrFT is implemented on low frequency coefficients of RDWT. Experimental

results have been demonstrated on the basis of Peak Signal to Noise Ratio (PSNR),

Correlation Coefficient (CC), and Gradient Magnitude Similarity Deviation (GMSD). A

comparable improvement is witnessed from the results in terms of qualitative and

quantitative analysis. The experimental results prove to be robust against various image

processing and geometrical attacks applied on the standard test images.

Keyword: Logo Watermarking; Fractional Fourier Transform (FrFT); Redundant Wavelet

Transform (RDWT); Singular Value Decomposition (SVD), Gradient Magnitude Similarity

Deviation (GMSD).

References: 1. H. Tao, L. Chongmin, J. M. Zain, and A. N. Abdalla, “Robust image watermarking theories and

teciques: A review,” J. Appl. Res. Technol., vol. 12, no. 1, pp. 122–138, 2014.

2. I. J. Cox and M. L. Miller, “A Rewiew of Watermarking and the Importance of Perceptual Modeling,”

Proc. SPIE, Hum. Vis. Electron. Imaging II, vol. 3016, pp. 92–99, 1997. 3. J. Liu and X. He, “A Review Study on Digital Watermarking,” Inf. Commun. Technol., pp. 337–341,

47-52

2007.

4. J. Nin and S. Ricciardi, “Digital watermarking techniques and security issues in the information and

communication society,” Proc. - 27th Int. Conf. Adv. Inf. Netw. Appl. Work. WAINA 2013, pp. 1553–

1558, 2013. 5. F. Ernawan and M. N. Kabir, “A Blind Watermarking Technique using Redundant Wavelet Transform

for Copyright Protection,” no. March, pp. 9–10, 2018.

6. J.-G. Cao, J. E. Fowler, and N. H. Younan, “An image-adaptive watermark based on a redundant wavelet transform,” Proc. 2001 Int. Conf. Image Process. (Cat. No.01CH37205), vol. 2, pp. 277–280,

2001.

7. J. E. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Process. Lett., vol. 12, no. 9, pp. 629–632, 2005.

8. L. Chen and J. Zhao, “Adaptive Digital Watermarking Using RDWT and SVD Lei Chen and Jiying

Zhao School of Electrical Engineering and Computer Science , University of Ottawa,” pp. 0–4, 2015. 9. G. Bhatnagar and Q. M. Jonathan Wu, “A new logo watermarking based on redundant fractional

wavelet transform,” Math. Comput. Model., vol. 58, no. 1–2, pp. 204–218, 2013.

10. M. H. Vali, A. Aghagolzadeh, and Y. Baleghi, “Optimized watermarking technique using self-adaptive differential evolution based on redundant discrete wavelet transform and singular value decomposition,”

Expert Syst. Appl., vol. 114, pp. 296–312, 2018.

11. S. Lagzian, “Robust watermarking scheme based on RDWT-SVD : Embedding Data in All subbands,”

pp. 48–52, 2011.

12. M. T. Taba, “The fractional fourier transform and its application to digital watermarking,” 2013 8th Int.

Work. Syst. Signal Process. Their Appl. WoSSPA 2013, pp. 262–266, 2013. 13. S. Bansal, “On the security of robust reference logo watermarking scheme in Fractional Fourier

Transform Domain,” pp. 200–205, 2013.

14. F. Q. Yu, Z. K. Zhang, and M. H. Xu, “A digital watermarking algorithm for image based on fractional fourier transform,” 2006 1st IEEE Conf. Ind. Electron. Appl., 2006.

15. H. C. Andrews and C. L. Patterson, “Singular Value Decompositions And Digital Image Processing,”

IEEE Trans. Acoust., vol. 24, no. 1, pp. 26–53, 1976. 16. V. Aslantas, “An optimal robust digital image watermarking based on SVD using differential evolution

algorithm,” Opt. Commun., vol. 282, no. 5, pp. 769–777, 2009.

17. C. Chang, P. Tsai, and C. Lin, “SVD-based digital image watermarking scheme,” vol. 26, pp. 1577–1586, 2005.

18. M. Fan, H. Wang, and S. Li, “Restudy on SVD-based watermarking scheme,” vol. 203, pp. 926–930,

2008. 19. C. Lai, “A digital watermarking scheme based on singular value decomposition and tiny genetic

algorithm,” Digit. Signal Process., vol. 21, no. 4, pp. 522–527, 2011.

20. C. Lai, “An improved SVD-based watermarking scheme using human visual characteristics,” OPTICS,

vol. 284, no. 4, pp. 938–944, 2011.

21. J. Shi, N. T. Zhang, and X. P. Liu, “A novel fractional wavelet transform and its applications,” Sci.

China Inf. Sci., vol. 55, no. 6, pp. 1270–1279, 2012. 22. H. Singh, L. Kaur, and K. Singh, “Fractional M-band dual tree complex wavelet transform for digital

watermarking,” Sadhana - Acad. Proc. Eng. Sci., vol. 39, no. 2, pp. 345–361, 2014.

23. H. Singh, L. Kaur, and K. Singh, “A novel robust logo watermarking scheme using fractional M-band wavelet transform,” J. Commun. Technol. Electron., vol. 59, no. 11, pp. 1234–1246, 2014.

24. W. Xue, L. Zhang, X. Mou, and A. C. Bovik, “Gradient magnitude similarity deviation: A highly

efficient perceptual image quality index,” IEEE Trans. Image Process., vol. 23, no. 2, pp. 668–695, 2014.

25. R. Choudhary, “A Robust image Watermarking Technique using 2-level Discrete Wavelet Transform (

DWT ),” no. Ll, pp. 0–4, 2016. 26. N. Li, X. Zheng, Y. Zhao, H. Wu, and S. Li, “Robust Algorithm of Digital Image Watermarking Based

on Discrete Wavelet Transform,” pp. 942–945, 2008. 27. P. Saravanan, M. Sreekara, and K. Manikantan, “Digital Image Watermarking using Daubechies

wavelets,” 3rd Int. Conf. Signal Process. Integr. Networks, SPIN 2016, pp. 57–62, 2016.

28. N. Jindal and K. Singh, “Applicability of fractional transforms in image processing - review , technical challenges and future trends,” 2018.

29. N. Kashyap, “Image Watermarking Using 3-Level Discrete Wavelet Transform ( DWT ),” no. April, pp.

50–56, 2012. 30. Y. Chen, W. Yu, and J. C. Feng, “A digital watermarking based on discrete fractional Fourier

transformation DWT and SVD,” Proc. 2012 24th Chinese Control Decis. Conf. CCDC 2012, no. 3, pp.

1383–1386, 2012. 31. D. Hien, “RDWT Domain Watermarking based on Independent Component Analysis Extraction.”

Authors: Parneet kaur, Dr. Pooja Sahni and Dr. Sukhdeep Kaur

Paper Title: Leach Protocol in Wireless Sensor Networks using Matlab

9.

Abstract: Today, at this age of intelligent technology, the intelligent have entered

agriculture in a great way. The base of this study is WSN based irrigation system. In this

work various problems in the irrigation system based on traditional wireless sensor

networks. The flaw is related to the selection approach for CH (cluster head). The most

important technologies are used in this work i.e. to develop an automatic and intelligent

irrigation system. The use of a sensor monitoring system is an effective method to prevent

interference and to improve efficiency. The CH election approach is modified by enhancing

the list of network influencing factors. To sense the energy of the overall network, LEACH

and IMPROVED LEACH protocol are compared to have a better and efficient network.

MATLAB simulates and evaluates the performance of the communication system in terms

of network life. The results of the simulation show the different amount of sensors initial

energy in relation to the dead last node and the dead first node.

Keyword: WSN (wireless sensor networks), cluster head, MATLAB

References: 1. Shunmin Wang, “Application of high precision accuracy irrigation based on the fuzzy spatial data mining in

4G”, IEEE, International Conference on Intelligent Human-Machine Systems and Cybernetics, 2014. Sandeep Kaur, Deepali, “An automatic irrigation system for different crops with WSN”, IEEE,2017 pp 407-

411.

2. Jin Wei and Gihan J. Mendis, “A Deep Learning-Based Cyber-Physical Strategy to Mitigate False Data Injection Attack in Smart Grids”, IEEE, 2016.

3. Peng Zhang, Qian Zhang, Fusheng Liu, Changqing Song, “The Construction of the Integration of Water and

Fertilizer Smart Water Saving Irrigation System Based on Big Data”, IEEE, International Conference on Computational Science and Engineering, 2017.

4. Sandeep Kaur, Deepali, “An automatic irrigation system for different crops with WSN”, IEEE, pp 407-411,

2017. 5. Manpreet Kaur, Amarvir Singh, “Detection and Mitigation of Sinkhole Attack in wireless sensor network”,

IEEE, International Conference on Micro-Electronics and Telecommunication Engineering, 2016.

6. Ravi Kishore Kodali, Borade Samar Sarjerao, “A Low Cost Smart Irrigation System Using MQTT Protocol”, IEEE, 2017.

7. Mahammad Shareef Mekala, Dr P. Viswanathan, “A Novel Technology for Smart Agriculture Based on IoT

with Cloud Computing”, IEEE, 2017.

53-58

10.

Authors: Ranjeet Kaur and Amit Doegar

Paper Title: Localization and Classification of Brain Tumor using Machine Learning & Deep

Learning Techniques

Abstract: Digital image processing is a rising field for the investigation of complicated

diseases such as brain tumor, breast cancer, kidney stones, lung cancer, ovarian cancer, and

cervix cancer and so on. The recognition of the brain tumor is considered to be a very

critical task. A number of approaches are used for the scanning of a particular body part like

CT scan, X-rays, and Magnetic Resonance Image (MRI). These pictures are then examined

by the surgeons for the removal of the problem. The main objective of examining these MRI

images (mainly) is to extract the meaningful information with high accuracy. Machine

Learning and Deep Learning algorithms are mainly used for analysing the medical images

which can identify, localize and classify the brain tumor into sub categories, according to

which the diagnosis would be done by the professionals. In this paper, we have discussed

the different techniques that are used for tumor pre-processing, segmentation, localization,

extraction of features and classification and summarize more than 30 contributions to this

field. Also, we discussed the existing state-of-the-art, literature gaps, open challenges and

future scope in this area.

Keyword: Brain Tumor, MRI, Machine Learning

References: 1. K. Manju and Tamanna, “A Survey on Brain Tumor Detection Technique”, International Journal of

Computer Science & Management Studies (IJCSMS), vol. 15, no. 06, 2015. 2. L. Kapoor, S. Thakur, “A Survey on Brain Tumor Detection Using Image Processing Techniques”,

International Conference on Cloud Computing, Data Science & Engineering (ICCDSE), 2017.

59-66

3. Swathi, Anoop, “Comparison of different image preprocessing methods used for Retinal Fundus

images”, IEEE Conference on Emerging Devices and Smart Systems, 2017.

4. V. Y. Borole, S. S. Nimbhore, S. S. Kawthekar, “Image Processing techniques for Brain Tumor

Detection: A Review”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2015.

5. https://www.semanticscholar.org/paper/Novel-Hardware-Implementation-of-Adaptive-Median [last

accessed on 10-05-2019]. 6. http://www.owlnet.rice.edu/~elec539/Projects99/BACH/proj2/wiener.html[last accessed on 24-03-

2019].

7. https://forums.ni.com/t5/Machine-Vision/gaussian-filter/td-p/2441104 [last accessed on 24-03-2019]. 8. E. Hassan and A. Aboshgifa, “Detecting Brain Tumour from MRI image using Matlab GUI

programme”, International Journal of Computer Science, 2015.

9. M. Jain “Review of Image Classification methods and techniques”, International Journal of Engineering Research & Technology, 2013.

10. N. V. Shree, T. N. R. Kumar, “Identification and classification of brain tumor MRI image with feature

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11.

Authors: Akanksha Upadhay and Rajesh Khanna

Paper Title: A CPW-Fed Tomb Shaped Antenna For UWB Applications

Abstract: In this paper, a Coplanar Waveguide (CPW) fed tomb shaped antenna with novel

broad banding techniques is proposed for Ultra-Wideband (UWB) applications. The initial

design is taken as an elliptical radiator and techniques such as beveling of upper and lower

half of elliptical radiator and introducing staircase in the coplanar ground plane of the

antenna are used to significantly enhance the bandwidth. The proposed antenna has overall

size of 50×38×1.6 mm3 and possess good radiation characteristics and return loss < -10 dB

in the whole UWB spectrum from 2.8 GHz to 18 GHz. CST MICROWAVE STUDIO

SUITE is employed for the design, optimization and simulation of antenna, which is based

on method of finite integration technique (FIT). The performance of the proposed antenna is

validated by comparing simulated and experimental results which exhibit that the designed

antenna can be employed in various UWB applications.

Keyword: Coplanar Waveguide, Microstrip antenna, Planar antenna, Ultra-Wideband

antenna.

References: 1. Federal Communications Commission: First report and order in the matter of revision of part 15 of the

commission's rules regarding ultra-wideband transmission systems. ET-Docket 98-153, FCC 02-48, 2002.

2. S. R. Emadian, C. Ghobadi, J. Nourinia, M. H. Mirmozafari and J. Pourahmadazar, “Bandwidth Enhancement of

CPW-Fed Circle-Like Slot Antenna With Dual Band-Notched Characteristic,” in IEEE Antennas and Wireless Propagation Letters, vol. 11, 2012, pp. 543-546.

3. C. Deng, Y. Xie and P. Li, “CPW-Fed Planar Printed Monopole Antenna with Impedance Bandwidth

Enhanced,” in IEEE Antennas and Wireless Propagation Letters, vol. 8, 2009, pp. 1394-1397. 4. K. Chung, T. Yun and J. Choi, “Wideband CPW-fed monopole antenna with parasitic elements and slots,”

in Electronics Letters, vol. 40, no. 17, 2004, pp. 1038-1040.

5. S. R. Emadian and J. Ahmadi-Shokouh, “Very Small Dual Band-Notched Rectangular Slot Antenna With Enhanced Impedance Bandwidth,” in IEEE Transactions on Antennas and Propagation, vol. 63, no. 10, 2015, pp.

4529-4534,.

6. A. K. Gautam, S. Yadav and B. K. Kanaujia, “A CPW-Fed Compact UWB Microstrip Antenna,” in IEEE Antennas and Wireless Propagation Letters, vol. 12, 2013, pp. 151-154.

7. Z. J. Tang, J. Zhan and H. L. Liu, “Compact CPW-fed antenna with two asymmetric U-shaped strips for UWB

communications,” in Electronics Letters, vol. 48, no. 14, 2012, pp. 810-812. 8. J. Pourahmadazar, C. Ghobadi, J. Nourinia, N. Felegari and H. Shirzad, “Broadband CPW-Fed Circularly

Polarized Square Slot Antenna With Inverted-L Strips for UWB Applications,” in IEEE Antennas and Wireless

Propagation Letters, vol. 10, 2011, pp. 369-372. 9. N. Chen and Y. Liang, “Coplanar-Waveguide Fed Circular Disc Monopole Antenna with Improved Radiation

Characteristics,” in The Second European Conference on Antennas and Propagation, Edinburgh, 2007, pp. 1-6.

10. Chenn-Ming Lee, Tzong Chee Yo, Cheng Hsing Luo, Chih-Ho Tu and Ying-Zong Juang, “Broadband disk

67-72

monopole antenna with a circular CPW-feeding line,” in IEEE Antennas and Propagation Society International

Symposium, Honolulu, HI, 2007, pp. 773-776.

11. N. Ojaroudi and M. Ojaroudi, “Novel Design of Dual Band-Notched Monopole Antenna With Bandwidth

Enhancement for UWB Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 12, 2013, pp. 698-701.

12. R. Addaci, N. Hamdiken, T. Fortaki, F. Ferrero, D. Seetharamdoo and R. Staraj, “Simple bandwidth-

enhancement technique for miniaturised low-profile UWB antenna design,” in Electronics Letters, vol. 50, no. 22, 2014, pp. 1564-1566.

13. Xian-Ling Liang, Shun-Shi Zhong and Feng-Wei Yao, “Compact UWB tapered-CPW-fed planar monopole

antenna,” in Asia-Pacific Microwave Conference Proceedings, Suzhou, 2005, pp. 3. 14. I. B. Vendik, A. Rusakov, K. Kanjanasit, J. Hong and D. Filonov, “Ultrawideband (UWB) Planar Antenna with

Single-, Dual-, and Triple-Band Notched Characteristic Based on Electric Ring Resonator,” in IEEE Antennas

and Wireless Propagation Letters, vol. 16, 2017, pp. 1597-1600. 15. Anju A. Chandran, and Shiney Thankachan, “Triple frequency notch in UWB antenna with single ring SRR

loading,” in Proceedings of Computer Science, vol. 93, 2016, pp. 94-100.

16. L. Peng, B. Wen, X. Li, X. Jiang and S. Li, “CPW Fed UWB Antenna by EBGs With Wide Rectangular Notched-Band,” in IEEE Access, vol. 4, 2016, pp. 9545-9552.

17. Y. Kim and D. -Kwon, “CPW-fed right-angled dual tapered notch antenna for ultra-wideband communication,”

in Electronics Letters, vol. 41, no. 12, 2015, pp. 674-675.

18. A. Dastranj and H. Abiri, “Bandwidth Enhancement of Printed E-Shaped Slot Antennas Fed by CPW and

Microstrip Line,” in IEEE Transactions on Antennas and Propagation, vol. 58, no. 4, 2010, pp. 1402-1407.

19. J. Y. Siddiqui, C. Saha and Y. M. M. Antar, “Compact SRR Loaded UWB Circular Monopole Antenna With Frequency Notch Characteristics,” in IEEE Transactions on Antennas and Propagation, vol. 62, no. 8, 2014,

pp. 4015-4020.

20. D. M. Elsheakh and E. A. Abdallah, “Ultra-wide-bandwidth (UWB) microstrip monopole antenna using split ring resonator (SRR) structure,” in International Journal of Microwave and Wireless Technologies, vol. 10, no.

1, 2018, pp. 123–132.

21. M. S. A. Rani, S. K. A. Rahim, H. Rezaie, F. D. Dahalan, M. I. Sabran, M. Z. M. Nor, and A. Zainal, “Directional UWB antenna with a parabolic ground structure and split ring resonator for a 5.8 GHz band

notch,” in Journal of Electromagnetic Waves and Applications, vol. 27, no. 1, 2013, pp. 14-22.

12.

Authors: Ritika Sharma, Rajesh Khanna

Paper Title: A Novel UWB Antenna with Reconfigurable Notch Bands

Abstract: A UWB antenna with reconfigurable notch band characteristics is proposed in

this paper. The tunable notches are created using modified E shaped resonators that can be

reconfigured to modified C shape; etched on either side of the microstrip feed line of a

circular patch UWB antenna. The single and dual band rejection characteristics are created

by using C and E shaped structure respectively. Reconfigurability is achieved by using two

RF switches. By varying the ON and OFF states of the RF switches, two different notch

bands are created; single notch band from 4 to 6.2 GHz and an additional notch band from

7.6 to 10 GHz are achieved. These wide bandwidth rejection performance leads to notching

of WLAN, WiMAX, C-band frequencies and X band Satellite communication systems.

Keyword: Circular patch antennas, parasitic element, reconfigurable, resonator, RF switch,

UWB.

References:

1. US Federal Communications Commission, et al., “FCC revision of part 15 of the commission’s rules

regarding ultra-wideband transmission systems: First report and order,” Technical Report, Feb. 2002.

2. M. Ojaroudi, N. Ojaroudi, “Novel design of dual band-notched monopole antenna with bandwidth

enhancement for UWB applications”, IEEE Antennas Wirel. Propag. Lett., vol. 12, 2013, pp. 698–701.

3. L.Y. Cai, “Compact printed ultra-wideband antennas with band-notched characteristics,” in Electronics

Letters , vol. 46, no. 12, 2010, pp. 817-819. 4. R. Zaker, C. Ghobadi, and J. Nourinia, “Bandwidth enhancement of novel compact single and dual band-

notched printed monopole antenna with a pair of L-shaped slots,” IEEE Trans. Antennas Propag., vol. 57,

no. 12, Dec. 2009, pp. 3978–3983. 5. Nikolaou, S., Kingsley, N.D., Ponchak, G.E., et al., “UWB elliptical monopoles with a reconfigurable

band notch using MEMS switches actuated without bias lines”, IEEE Trans. Antennas Propagation., vol.

57, no. 8, 2009, pp. 2242-2251. 6. Homayoon Oraizi, “Frequency- and time-domain analysis of a novel UWB reconfigurable microstrip slot

antenna with switchable notched bands”, IET Microwaves, Antennas & Propagation, vol. 11, no. 8, May

2017 , pp. 1127-1132. 7. A. Tariq, H. Ghafouri-Shiraz, “Frequency-reconfigurable monopole antennas”, IEEE Trans. Antennas

Propag., vol. 60, no. 1, pp. 44–50, 2012.

73-79

8. D. E Anagnostou, “Reconfigurable UWB antenna with RF-MEMS for on-demand WLAN rejection,”

IEEE Trans. Antennas Propagation, vol. 62, no. 2, Feb. 2014, pp. 602–608.

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wideband slot antenna with switchable single/dual band notch functions,” IET Microwave Antennas Propagation, Vol. 8, No. 8, Nov. 2013, pp. 541–548.

10. W. A. E. Ali, and R. M. A. Moniem, “Frequency reconfigurable triple band-notched ultra-wideband

antenna with compact size,” Progress In Electromagnetics Research C, Vol. 73, Apr. 2017, pp. 37–46. 11. K. C. Gupta, R. Garg, I. J. Bhal, and P. Bhartia, Microstrip Lines and Slotlines, 2nd Edition,10–15,

Artech House, 1996.

12. Abhishek Viswanathan, Rajasi Desai, “Applying Partial-Ground Technique to Enhance Bandwidth of a UWB Circular Microstrip Patch Antenna,” International Journal of Scientific & Engineering Research,

Vol. 5, Issue 10, Oct. 2014.

13. A. Valizade, Ch. Ghobadi, J. Nourinia, N. Ojaroudi, M. Ojaroudi, “Band-notch slot antenna with enhanced bandwidth by using Ω-shaped strips protruded inside rectangular slots for UWB applications”,

Applied Computer Electromagnetics Society (ACES) J., vol. 27, no. 10, 2012, pp. 816–822.

14. S. Nikolaou, N.D. Kingsley, G.E. Ponchak, “UWB elliptical monopoles with a reconfigurable band notch using MEMS switches actuated without bias lines’, IEEE Transaction Antenna Propagation, vol. 57, no.

8, 2009, pp. 2242–2251.

15. Datasheet at http://astramtl.com/admin/uploads/switches/datasheets/1360083817_2551011.pdf

13.

Authors: Hardeep Singh Dhillon and Dr. Paras Chawla

Paper Title: Hybrid Model based on Peltier and Piezoelectric Human Energy Harvesting for

WBAN Application

Abstract: Wireless body area network (WBAN) is developed as a result of Wireless

personal area network (WPAN), in which various interconnected Body Node (BN)

communicates near and around human body. There are many differences between the

WBAN and WPAN i.e distribution, density and mobility. Due to redundant nodes, BN in

WBAN are less dense. In WBAN, Body node are implanted inside and on human body to

measure physiological signals using different sensors i.e Electro cardio graph (ECG),

electroencephalogram (EEG), Blood pressure, temperature etc) of body which collects data

and send it to sink node. Earlier researchers have used either piezoelectric harvester, solar or

temperature gradient based. But in this paper optimization technique using combination of

Peltier and Piezoelectric human energy harvesting is studied. By developing an algorithm,

extensive simulation can be performed considering four human body gestures (relaxing,

walking, running and fast running). Overall Quality of Service (QoS) including average

(packet loss, end-to-end delay, throughput) and overall detection efficiency is studied.

Keyword: Wireless body area network, QoS, Optimization, Piezoelectric, Peltier.

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international workshop on Wireless sensor networks and applications. ACM, 2002.

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Systems, Networks and Technologies (ANT 2013), 2013, Halifax, Nova Scotia, Canada, Procedia Computer Science, Volume 19, 2013, Pages 224-231, ISSN 1877-0509, http://dx.doi.org/10.1016/j.procs.2013.06.033.

Procedia Computer Science.

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Area Networks for Full-Body Motion Capture and Gait Analysis”. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM 2007), Washington DC, WA, USA, 26–30 November 2007;

pp. 3775–3780.

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39. S. K. Yee, N. E. Coates, A. Majumdar, J. J. Urban, and R. A. Segalman, “Thermoelectric power factor

optimization in PEDOT: PSS tellurium nanowire hybrid composites," Physical Chemistry Chemical Physics, vol. 15, no. 11, pp. 4024-4032, 2013.

40. C.-C.Wang, C.-I. Hung, and W.-H. Chen, “Design of heat sink for improving the performance of

thermoelectric generator using two-stage optimization," Energy, vol. 39, no. 1, pp. 236-245, 2012. 41. J. Xiao, T. Yang, P. Li, P. Zhai, and Q. Zhang, “Thermal design and management for performance

optimization of solar thermoelectric generator," Applied Energy, vol. 93, pp. 33-38, 2012.

42. V. Leonov, P. Fiorini, S. Sedky, T. Torfs, and C. Van Hoof, “Thermoelectric mems generators as a power supply for a body area network," in Solid-State Sensors, Actuators and Microsystems, 2005. Digest of

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294.

14.

Authors: Bishwajeet Singh, Mohit Arvind, Divya Asija, Pallavi Choudekar and Ruchira

Paper Title: Driver Alert System for Hilly Roads at Hair Pin Bends

Abstract: In this paper we have proposed the time-based system for alerting the driver

against other vehicle at hair pin bends to avoid accidents causing loss of human life. This

system is designed to provide safe journey to the local residents and tourists by alerting

them against blind spot curves and also reduced the possibility of accident at blind curves.

This paper is focused on the designing of automated system with the advent of new

technology and up gradation. The proposed automated system would remarkably reduce the

human efforts by utilizing the PLCs. Automated system is taking place of human due to

increased efficiency and quality output. Thus, the proposed system adopts the latest

automated technology including PLC to develop driver alert system which has application

on the hair pin bends of hilly roads.

Keyword: MCB, SMPS, PLC, Sensor, LED

References: 1. A. K. Gupta, G. Wable, T. Batra, “Collision Detection System for Vehicles in Hilly and Dense Fog Affected

Area to Generate Collision Alerts” in International Conference on Issues and Challenges in Intelligent

Computing Techniques (ICICT), 2014.

2. S. Boopathi, K. Govindaraju, M. Sangeetha, M. Jagadeeshraja ,M. Dhanasu “Real time based smart vehicle

monitoring and alert using GSM” in International Journal of Advanced Research in Computer and

Communication Engineering, vol. 3, Issue 11, November 2014. 3. https://www.hindustantimes.com/business-news/hp-leo-burnett-make-smart-poles-for-roads-that-honk-to-

alert-drivers-on-blind-turns/story-gxkiMJjHWfCyMWhFTpyrGJ.html.

4. K. Aravind , D. Hardley, Pradeep, T. Vijayan, B. K. Selvi, S. Latha, “Automation of space management in vehicle parking using PLC and SCADA” International Journal of MC Square Scientific Research vol.9, no.2,

2017.

5. S. P. Biswas, P. Roy, N. Patra, A. Mukherjee and N. Dey, ” Intelligent Traffic Monitoring System”, Second International Conference on Computer and communication Technologies, Advances in Intelligent Systems

and Computing .

6. M. H. Alsibai, S. A. Manap,”A study on Driver fatigue notification systems” , ARPN Journal of Engineering and Applied Sciences, vol. 11, no. 18, September 201

7. J. K. Hedrick, M. Tomizuka, and P. Varaiya,”Control Issue in Automated Highway Systems” IEEE Control Systems, December 1994.

8. Kanchan Manohar Sontakke, ”Efficient Driver Fatigue Detection and Alerting System”, International Journal of Scientific and Research Publications, vol.5, no. 7, July 2015.

85-90

Authors: Akanksha Sharma and Neeru Jindal

Paper Title: CBNWI-50: A Deep Learning Bird Dataset for Image Translation and Resolution

Improvement using Generative Adversarial Network

Abstract: Generative Adversarial Networks have gained prominence in a short span of time

as they can synthesize images from latent noise by minimizing the adversarial cost function.

New variants of GANs have been developed to perform specific tasks using state-of-the-art

GAN models, like image translation, single image super resolution, segmentation,

classification, style transfer etc. However, a combination of two GANs to perform two

different applications in one model has been sparsely explored. Hence, this paper

concatenates two GANs and aims to perform Image Translation using Cycle GAN model on

15.

bird images and improve their resolution using SRGAN. During the extensive survey, it is

observed that most of the deep learning databases on Aves were built using the new world

species (i.e. species found in North America). Hence, to bridge this gap, a new Ave

database, 'Common Birds of North - Western India' (CBNWI-50), is also proposed in this

work.

Keyword: Generative Adversarial Networks, Indian-Subcontinent, Bird Dataset, Image

Translation, Single Image Super Resolution

References: 1. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio,

Y., 2014. Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680). 2. Zhu, J.Y., Park, T., Isola, P. and Efros, A.A., 2017. Unpaired image-to-image translation using cycle-

consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision (pp.

2223-2232). 3. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J.,

Wang, Z. and Shi, W., 2017. Photo-realistic single image super-resolution using a generative adversarial

network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690). 4. Timofte, Radu and Agustsson, Eirikur and Van Gool, Luc and Yang, Ming-Hsuan and Zhang, Lei and Lim,

Bee and others, 2017. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. In

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 5. Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., & Perona, P. (2010). Caltech-UCSD

birds 200.

6. Van Horn, G., Mac Aodha, O., Song, Y., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2017). The inaturalist challenge 2017 dataset. arXiv preprint arXiv:1707.06642, 1(2).

7. Berg, T., Liu, J., Woo Lee, S., Alexander, M. L., Jacobs, D. W., & Belhumeur, P. N. (2014). Birdsnap: Large-

scale fine-grained visual categorization of birds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2011-2018).

8. Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., ... & Belongie, S. (2015). Building a

bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 595-

604).

9. Yi, Z., Zhang, H., Tan, P. and Gong, M., 2017. Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision (pp. 2849-2857).

10. Kim, T., Cha, M., Kim, H., Lee, J.K. and Kim, J., 2017, August. Learning to discover cross-domain relations

with generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 1857-1865). JMLR. org.

91-102

16.

Authors: Manbir Kaur, Rajesh Khanna and Atipriya Sharma

Paper Title: Radar Cross Section Reduction Techniques using Metamaterials

Abstract: In this paper, we have discussed and summarized the techniques of radar cross

section reduction (RCSR). RCSR has been prompted due to the evolution of military

technology. The paper reviews the basic concepts and characteristics of metamaterials, as

these are the most favorable development that impacts defense industry products and stealth

technology. This paper emphasizes the role of airpower and the ever-increasing demand for

stealth. Initially, the blending of the fundamental aspects of stealth technology through radar

signatures and methods of signature reduction are discussed. Then, the description of

metamaterials and detailed analysis of their properties is made. This paper review the

fundamental properties of metamaterials. It also explores the recent research activities on

metamaterials in various areas. Some existing researches techniques used for RCSR are

examined. The metamaterials are engineered media whose electromagnetic responses are

different from those of their constituent components. The general benefits of metamaterials

are pointed out in the paper. Metamaterials are mostly used in antenna configuration for

enhancing antenna performance such as realizing miniaturization, expanding the operating

band, enhancing gain as well as reducing RCS. These characteristics of the metamaterials

are basically the reason why metamaterials should be used in stealth technology. The

various categories of metamaterials used for RCSR are studied in the paper. In this paper,

we have also proposed a unit cell. The unit cell consists of a square loop and intersecting

strips at the edges of add shaped structure.

103-111

Keyword: Electromagnetic bandgap, Metamaterials, Microstrip Antenna, RCS, RCSR.

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Microstrip Antenna”, Microwave and Optical Technology Letters, pp1200-1204, vol. 56, no. 5, May 2014. 13. Mohsen Zahir Joozdani, Mohammad Khalaj Amirhosseini, and Ali Abdolali, “Wideband RCS Reduction of

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Antenna Radar Cross-Section Based on Frequency Selective Surfaces”, IEEE Antennas and Wireless

Propagation Letters, pp1456-1459, vol. 12, 2013. 15. Ying Liu, Yuwen Hao, Hui Wang, Kun Li, and Shuxi Gong, “Low RCS Microstrip Patch Antenna Using

Frequency-Selective Surface and Microstrip Resonator”, IEEE Antennas and Wireless Propagation Letters,

pp1290-1293, vol. 14, 2015. 16. Hao Jiang, Zhenghui Xue, Weiming Li, Wu Ren, and Meng Cao, “Low-RCS High-Gain Partially Reflecting

Surface Antenna With Metamaterial Ground Plane”, IEEE Transactions on Antennas and Propagation,

pp4127-4132, vol.64, no. 9, September 2016. 17. Ali Azarbar, Mostafa Mashhadi, “Broadband RCS Reduction using a Composite AMC Structure” Majlesi

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Antennas and Propagation Conference (LAPC ’10), pp. 453–456, November 2010..

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23. P. Salonen, F. Yang, Y. Rahmat-Samii, and M. Kivikoski, “WEBGA—Wearable electromagnetic band-gap

antenna,” in Proceedings of the IEEE Antennas and Propagation Society Symposium, vol. 1, pp. 451–454, Monterrey, Calif, USA, June 2004.

24. E. F. Knott, J. F. Shaefer, and. T. Tuley, Radar Cross Section, Scitech Publishing Inc, 2nd revised

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Scattering cross section.”, Microwave and Optical TechnologyLetters, vol 53, no 5, pp 1150-1154, 2011.

30. L I, Y., Zhang, H., F U, Y., Yuan, N., “RCS reduction of rigid waveguide”, IEEE Antenna and Wireless Propagation Letters, vol 7, pp 473-476, 2008.

31. R.L.Fante, M.T. Mc Cormack, “Reflection Properties of Salisbury Screen.”, IEEE Trans Antennas Propag,

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32. H.Mosallaei, Y.Rahmat-Samii, “RCS Reduction of Canonical Targets using Genetic Algorithm Synthesized

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Materials Design”, Electron.Lett., vol 41, no 17, pp 936-937, 2005. 35. M.Gustafsson,“ RCS Reduction of Integrated Antenna Arrays with Resistive Sheets”, Electromagnetic

Waves Appl, vol 20, pp 27-40, 2006.

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Applications, New York: Springer-Verlag, pp. 3–10, December 2008. 44. Smith, David R., "What are Electromagnetic Metamaterials?", Novel Electromagnetic Materials. The research

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reduction,” IEEE Trans. Antennas Propag., vol. 55, no. 12, Dec. 2007. 83. J. C. Iriarte, I. Ederra, R. Gonzalo, and P. de Maagt, “Dual-band RCS reduction using planar technology by

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2011, pp. 1322–1323.

Authors: Munish Mehta, Vijay Goyar, Vishnu Bairwa

Paper Title: Security and Authentication through text encryption and decryption based on

substitution method

Abstract: In order to maintain security, privacy and integrity of important information or

data is a great challenge in today’s world. Cryptography is a technique to prevent important

data from hackers and intruder by converting important information in encrypted text by

using an encryption algorithm. This paper is a review of various symmetric and asymmetric

cryptography techniques. In this paper some gaps have been identified after reviewing some

algorithms like they don’t work for case sensitive data but merely works for uppercase

alphabets, they don’t encrypt numerically, spaces and they don’t run on the special symbol.

In our proposed solution an effort to expand the original 5X5 character set of the Play Fair

algorithm will be done so as to include lower case alphabets(a-z), some symbols, numeric(0-

9) and a special character ‘\0’ for space. So, a new algorithm will encrypt lowercase,

numeric, symbols as well as spaces.

Keyword: Cipher, Cryptanalysis, Decryption, Encryption, Private Key, Public Key,

Substitution, Transposition.

17.

References: 1. Jitendra Choudhary, Prof. Ravindra Kumar Gupta, Dr Shailendra Singh, ” A GENERALIZED VERSION

OF PLAY FAIR CIPHER”, COMPUSOFT, An international journal of advanced computer technology, 2

(6), June-2013 (Volume-II, Issue-VI).

2. Mohammed Haris, Bhavya Alankar, “A Survey Paper on Different Modification of Playfair Cipher”, International International Journal of Advanced Research in Computer Science, Volume 8, No. 5, May-

June 2017.

3. Mohit Marwah, Rajeev Bedi, *Amritpal Singh, Tejinder Singh, “COMPARATIVE ANALYSIS OF CRYPTOGRAPHIC ALGORITHMS”, Singh et al., International Journal of Advanced Engineering

Technology, E-ISSN 0976-3945, Int J Adv Engg Tech/IV/III/July-Sept.,2013/16-18.

4. Ravindra Babu K¹, S. Uday Kumar ², A. Vinay Babu ³, I.V.N.S Aditya4, P. Komuraiah5, “An Extension to Traditional Playfair Cryptographic Method”, Volume 17– No.5, March 2011.

5. Sanjay Kumar Mathur1, Sandeep Srivastava2, “Extended 16x16 Play- Fair Algorithm for Secure Key

Exchange Using RSA Algorithm”, Volume: 4 Issue: 2. 6. V.Subhashini1, Dr.N.Geethanjali2, P.Vidyasagar3, P.Amrutha4 1Research Scholar, “A Novel Approach

on Encryption and Decryption of 5X5 Playfair Cipher Algorithm”, International Journal of Advanced

Scientific Technologies, Engineering and Management Sciences (IJASTEMS-ISSN: 2454-356X) Volume.3, Special Issue.1,March.2017.

7. S.S.Dhenakaran, PhD. Assistant Professor, M. llayaraja research socholar , “Extension of Playfair Cipher

using 16X16 Matrix”, International Journal of Computer Applications (0975 – 888) Volume 48– No.7,

June 2012. 8. Reena singh, Shaurya taneja, Kavneet kaur, “Modified Play-fair Encryption Method using Quantum

concept”, DIACom-2016; ISSN 0973-7529; ISBN 978-93-80544-20-5

112-115

18.

Authors: Munish Mehta, Rahul Sharma, Pawan Suthar

Paper Title: Field Worker’s Routine Behaviour for Efficient Time Utilization

Abstract: In this paper, we are proposing a monitoring technique that can predict the

appropriate time to meet the field officer with more accuracy as compared to general human

prediction. To get the routine behaviour of an individual we will keep a track of his online

presence on WhatsApp by the “Active Now” status shown beneath the user’s name in the

profile. This will help the Personal assistants, secretaries and receptionists to provide right

appointment schedule to clients or to those who want to meet a field officer. This all will be

done by scraping data from WhatsApp web portal using python modules, storing the judging

parameters like unique identification number, date and time stamp, duration of continuous

presence and number of sessions in a day, afterwards analyzing the dataset and providing an

appropriate routine behaviour prediction. For the sake of efficient utilization of time and

resources.

Keyword: Human behaviour analysis, Naïve Bayes, Time Utilization, Web scraping.

References: 1. Python Reference. [Online]. Available: www.python.org 2. (Basic Book) Al Sweigart. (2015,April,14). Automate the boring stuff with PYTHON (Second Edition).

Available: https://automatetheboringstuff.com/

3. Leonard Richardson. (2004-2015). Crummy, The Webspace by L.R. [Online]. Available: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

4. Mafrur, R., Nugraha, I.G.D. & Choi, D. Hum. Cent. Comput. Inf. Sci. (2015) 5: 31.

https://doi.org/10.1186/s13673-015-0049-7. 5. Harari G.M., Muller S.R., Aung M.S., Rentfrow P.J. Smartphone sensing methods for studying behaviour in

everyday life (2017) Current Opinion in Behavioral Sciences, 18, pp. 83-90.

6. Nikola Banovic, Tofi Buzali, Fanny Chevalier, Jennifer Mankoff, Anind K. Dey, Modeling and Understanding Human Routine Behavior, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems,

May 07-12, 2016, Santa Clara, California, USA [doi>10.1145/2858036.2858557].

7. Jing Wang, Yuchun Guo Sch, "Scrapy-Based Crawling and User-Behavior Characteristics Analysis on Taobao", 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery,

pp. 44-52.

8. Mizuno, H & Nagai, H & Sasaki, K & Hosaka, H & Sugimoto, Chika & Khalil, K & Tatsuta, S. (2007). Wearable Sensor System for Human Behavior Recognition (First Report: Basic Architecture and Behavior

Prediction Method). 435 - 438. 10.1109/SENSOR.2007.4300161.

9. Jian, An & Xiaolin, Gui & Jianwei, Yang & Yu, Sun & Xin, He. (2015). Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-Sense Service. Proceedings - 2015 IEEE International

Conference on Multimedia Big Data, BigMM 2015. 92-99. 10.1109/BigMM.2015.62.

10. Ashiwal Pratiksha, Tandan S.R., Tripathi Priyanka, Miri Rohit, Web Information Retrieval Using Python and BeautifulSoup, International Journal for Research in Applied Science & Engineering Technology (IJRASET),

Volume 4 Issue VI, June 2016, Available: https://www.ijraset.com/fileserve.php?FID=4999.

116-120

19.

Authors: Ms. Mamta Arora, Dr. Paras Chawla

Paper Title: Performance Enhancement of OFDM System using Hybrid Channel Estimation with

Pseudo Pilot

Abstract: For high spectral efficiency and robustness against intersymbol interference (ISI),

orthogonal frequency division multiplexing (OFDM) is the best suited technology for many

applications such as digital audio broadcasting (DAB), wireless networks, digital video

broadcasting (DVB) and LAN. For efficient recovery of the original transmitted signal we

require the coherent demodulator as the frequency selective fading channels affects the

transmitted signal in OFDM. At the receiver end for coherent demodulation we require

channel state information (CSI), which is done by channel estimation. To find the channel

characteristics which varies with time and frequency, a reference signal is transmitted with

payload signal know as pilot or training sequence and technique or process is known as

channel estimation. The use of pilots in the OFDM system leads to pilots overhead. So we

propose a new technique which will reduces the transmission delay, reduce pilot overhead,

increases transmission rate and improves the BER performance without any computational

complexity, which is implemented using the Hybrid algorithm for channel based OFDM

system. In this proposed technique, for the channel estimation in OFDM system over

AWGN fading channel pseudo-pilots are used. At the transmitter end, pseudo-pilots are

employed to breed bank of pseudo random symbol (PRS). In this OFDM system with

pseudo-pilot the channel estimation is done by the Hybrid (LS + RLS) estimation approach

to recover the original transmitted information. The performance of the proposed techniques

and the weighted scheme are compared and verified using computerized simulation carried

out using Matrix Laboratory (MATLAB) software.

Keyword: OFDM, Pseudo-pilots, Least Square (LS), Recursive Least Square (RLS).

References: 1. S. Haykin, M. Moher, Communication systems. Hoboken: John Wiley, 2010.

2. J. Proakis and M. Salehi,Digital communications. Boston: McGraw-Hill, 2008.

3. J. KIM, S. NAM and D. HONG, "Channel Estimation in Comb-Type Pilot Arrangements for OFDM Systems with Null Subcarriers", IEICE Transactions on Communications, vol. 89-, no. 12, pp. 3458-3462, 2006.

4. M. Ozdemir and H. Arslan, "Channel estimation for wireless ofdmsystems",IEEE Communications Surveys

& Tutorials, vol. 9, no. 2, pp. 18-48, 2007. 5. Ye Li, "Simplified channel estimation for OFDM systems with multiple transmit antennas", IEEE

Transactions on Wireless Communications, vol. 1, no. 1, pp. 67-75, 2002.

6. Y. Ma, "Pseudo-Pilot: A Novel Paradigm of Channel Estimation", IEEE Signal Processing Letters, vol. 23, no. 6, pp. 814-818, 2016.

7. N. Jindal and A. Lozano, "A Unified Treatment of Optimum Pilot Overhead in Multipath Fading

Channels", IEEE Transactions on Communications, vol. 58, no. 10, pp. 2939-2948, 2010. 8. S. Chouhan, "Channel Estimation Using LS and MMSE Estimators", International Journal of Scientific

Research, vol. 3, no. 8, pp. 136-143, 2012.

9. K. Kavitha and S. Manikandan, "LMMSE Channel Estimation Algorithm Based on Channel Autocorrelation Minimization for LTE-Advanced with Adaptive Guard Interval", Wireless Personal Communications, vol.

81, no. 3, pp. 1233-1241, 2014.

10. G. Li and G. Liao, "A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation", Algorithms, vol. 10, no. 1, p. 3, 2016.

11. T. J. Lee and Y. C. Ko, “Channel Estimation and Data Detection in the Presence of Phase Noise in MIMO-

OFDM Systems With Independent Oscillators”, in IEEE Access, vol. 5, pp. 9647-9662, 2017.

12. P. Aggarwal and V. A. Bohara, “A Nonlinear Downlink Multiuser MIMO-OFDM Systems” , in IEEE Wireless Communications Letters, vol. 6, no. 3, pp. 414-417, June 2017.

121-126

Authors: Ms. Mamta Arora, Dr. Paras Chawla

Paper Title: Performance Evaluation of OFDM System Using Pseudo-Pilots With Particle Swarm

& Moth Flame (Hybrid) Optimization

Abstract: This paper is focused on the advanced signal processing techniques for the multi-

carrier modulation especially on the orthogonal frequency division multiplexing (OFDM).

OFDM has high data rate and robust against the frequency selective multi-path channels.

Channel estimation is crucial for the receiver design in coherent detection. In this paper we

20.

are investigating and examining the pilot aided system for channel estimation and its special

effects on the performance of the OFDM based system. In this paper new technique is

proposed which make use of conventional Least Square (LS) and Recursive Least Square

(RLS) hybridization techniques for the channel estimation afterwards we apply Particle

Swarm Optimization (PSO) and Hybrid PSO (PSO+ Moth Flame Optimization(MFO))

optimization for obtaining more optimize techniques. In the proposed system, to estimate

channel impulse response we are using pseudo-pilots instead to pilots as it is useful to

overcome the pilot overhead and decrease the complexity by using pseudo random symbol

(PRS). The performance of the proposed techniques and the weighted scheme are compared

and verified using computerized simulation carried out using Matrix Laboratory

(MATLAB) software. It is demonstrated on the basis of graph between BER and signal to

Noise Ratio (Eb/NO), that the proposed technique reduces the execution time and increase

the transmission rate with low or zero overhead.

Keyword: OFDM, PSO, MFO, LS, RLS, Channel estimation.

References: 1. Ye Li, "Simplified channel estimation for OFDM systems with multiple transmit antennas", IEEE

Transactions on Wireless Communications, vol. 1, no. 1, pp. 67-75, 2002.

2. Y. Ma, "Pseudo-Pilot: A Novel Paradigm of Channel Estimation", IEEE Signal Processing Letters, vol. 23,

no. 6, pp. 814-818, 2016.

3. T. S. Rappaport, Wireless Communications, Principles and Practice 2nd ed., Pearson Edu., vol.-1, pp. 356–

376, 2002.

4. D. Qu , S. Lu and T. Jiang , "Multi-block joint optimization for the peak-to-average power ratio reduction

of FBMC-OQAM signals" , IEEE Trans. Signal Process. , vol. 61 , no. 7, pp.1605 -1613 , 2013.

5. S.Venkatachalam, T. Manigandan, “Optimization of OFDM Systems Using Genetic Algorithm in FPGA,”

– International Journal of Computing Science and Communication Technologies, VOL.5 NO. 2, PP-854-859, Jan. 2013.

6. P. Aggarwal and V. A. Bohara, "A Nonlinear Downlink Multiuser MIMO-OFDM Systems," in IEEE

Wireless Communications Letters, vol. 6, no. 3, pp. 414-417, June 2017.

7. T. J. Lee and Y. C. Ko, "Channel Estimation and Data Detection in the Presence of Phase Noise in MIMO-

OFDM Systems With Independent Oscillators," in IEEE Access, vol. 5, pp. 9647-9662, 2017.

8. E. Eraslan and B. Daneshrad, "Low-Complexity Link Adaptation for Energy Efficiency Maximization in

MIMO-OFDM Systems," in IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5102-5114, Aug. 2017.

9. R. Kaur and . Mittal, “Implementation of neural network for channel estimation in OFDM network,” 2nd

International Conference on Contemporary Computing and Informatics (IC3I), 2016.

10. Şimşir and N. Taşpınar, " Channel estimation using neural network in Orthogonal Frequency Division

Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) system," Telecommunications Symposium (ITS), 2014 International, Sao Paulo, pp. 1-5 2014.

11. S. Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm” ,

Knowledge-Based Systems (2015), doi: http://dx.doi.org/10.1016/j.knosys.2015.07.006” .

12. L. Zajmi, F. Ahmed and A. Jaharadak, "Concepts, Methods, and Performances of Particle Swarm

Optimization, Backpropagation, and Neural Networks", Applied Computational Intelligence and Soft

Computing, vol. 2018, pp. 1-7, 2018. Available: 10.1155/2018/9547212.

127-130

21.

Authors: Rishikesh Sharma, Abha Thakral

Paper Title: Identifying Botnets: Classification and Detection

Abstract: The past few years have witnessed the threats caused by the evolving of botnets.

It has been found that the nefarious network consisting of contagious systems called as bots

are operated by the botmaster. These botnets have been used for malicious activities. This

prevailing threat on the internet has led to spam, Distributed Denial of Service (DDoS)

attacks, phishing emails, and other cyber-attacks. The detection of such networks is very

important keeping the protocols and features they work upon. The paper talks about the

various detection techniques that can be adapted to evade the attacks of bots. The huge

amount of traffic created by bots can be studied and distinguished respectively to understand

the protocols used by the botmaster; which are further used to detect botnets based on the

signature and anomaly patterns. The attacks being done from different locations have made

it difficult for a botnet to be caught. It has been mentioned that a few networks provide the

bots with a nickname using which the detection can be done. The method has been

described thoroughly by also specifying how the bot-names of the same network are similar.

Nowadays, the number of botnets has increased with a fewer number of trained bots. These

network work upon the protocols like Command and Control (C&C), Internet Relay Chat

(IRC), HyperText Transfer Protocol (HTTP) and Peer to Peer(P2P). The detection of such

networks is being done classifying the traffic and analyzing the spam e-mails alongside the

respected IP address. Even the traps of honeynet are developed which motivate the

botmaster to take action and get caught. Such honeynet techniques along with the required

steps and the necessary precautions are also mentioned in the paper.

Keyword: Botnet, Honeynet, IP Address, Network Traffic Classification, Phishing emails.

References: 1. Zhao, D., Traore, I., Sayed, B., Lu, W., Saad, S., Ghorbani, A., &Garant, D. (2013). Botnet detection based on

traffic behavior analysis and flow intervals. Computers & Security, 39, 2-16.

2. https://www.knowbe4.com/gameover-zeus

3. Abu Rajab, M., Zarfoss, J., Monrose, F., & Terzis, A. (2006, October). A multifaceted approach to

understanding the botnet phenomenon. In Proceedings of the 6th ACM SIGCOMM conference on Internet

measurement (pp. 41-52). ACM

4. Karasaridis, A., Rexroad, B., &Hoeflin, D. A. (2007). Wide-Scale Botnet Detection and Characterization. HotBots, 7, 7-7.

5. Dagon, D. (2005, July). Botnet detection and response. In OARC workshop (Vol. 2005).

6. Goebel, J., &Holz, T. (2007). Rishi: Identify Bot Contaminated Hosts by IRC Nickname Evaluation. HotBots, 7, 8-8.

7. Sperotto, A., Schaffrath, G., Sadre, R., Morariu, C., Pras, A., & Stiller, B. (2010). An overview of IP flow-

based intrusion detection. IEEE communications surveys & tutorials, 12(3), 343-356. 8. Know you Enemy: Tracking Botnets ( https://www.honeynet.org/papers/bots )

9. Baecher, P., Koetter, M., Holz, T., Dornseif, M., &Freiling, F. (2006, September). The nepenthes platform:

An efficient approach to collect malware. In International Workshop on Recent Advances in Intrusion Detection (pp. 165-184). Springer, Berlin, Heidelberg.

10. Karasaridis, A., Rexroad, B., &Hoeflin, D. A. (2007). Wide-Scale Botnet Detection and

Characterization. HotBots, 7, 7-7. 11. Sperotto, A., Schaffrath, G., Sadre, R., Morariu, C., Pras, A., & Stiller, B. (2010). An Overview of IP Flow-

based Intrusion Detection. IEEE Communications Surveys and Tutorials, 12(3), 343-356.

12. Haddadi, F., Morgan, J., Gomes Filho, E., &Zincir-Heywood, A. N. (2014, May). Botnet behaviour analysis

using ip flows: with http filters using classifiers. In Advanced Information Networking and Applications

Workshops (WAINA), 2014 28th International Conference on (pp. 7-12). IEEE.

13. Choi, H., Lee, H., Lee, H., & Kim, H. (2007, October). Botnet detection by monitoring group activities in DNS traffic. In 7th IEEE International Conference on Computer and Information Technology (CIT 2007) (pp.

715-720). IEEE.

14. Strayer, W. T., Lapsely, D., Walsh, R., &Livadas, C. (2008). Botnet detection based on network behavior. In Botnet detection (pp. 1-24). Springer, Boston, MA.

15. Zeidanloo, H. R., Shooshtari, M. J. Z., Amoli, P. V., Safari, M., & Zamani, M. (2010, July). A taxonomy of

botnet detection techniques. In 2010 3rd International Conference on Computer Science and Information Technology (Vol. 2, pp. 158-162). IEEE.

16. Gu, G., Perdisci, R., Zhang, J., & Lee, W. (2008). Botminer: Clustering analysis of network traffic for

protocol-and structure-independent botnet detection. 17. Villamarin-Salomon, R., &Brustoloni, J. C. (2008, January). Identifying botnets using anomaly detection

techniques applied to DNS traffic. In 2008 5th IEEE Consumer Communications and Networking Conference (pp. 476-481). IEEE.

18. Feily, M., Shahrestani, A., &Ramadass, S. (2009, June). A survey of botnet and botnet detection. In 2009

Third International Conference on Emerging Security Information, Systems and Technologies (pp. 268-273). IEEE.

19. Li, C., Jiang, W., & Zou, X. (2009, December). Botnet: Survey and case study. In innovative computing,

information and control (icicic), 2009 fourth international conference on (pp. 1184-1187). IEEE. 20. Singh, K., Srivastava, A., Giffin, J., & Lee, W. (2008, June). Evaluating email’s feasibility for botnet

command and control. In Dependable Systems and Ntworks With FTCS and DCC, 2008. DSN 2008. IEEE

International Conference on (pp. 376-385). IEEE. 21. Dittrich, D., & Dietrich, S. (2008, October). P2P as botnet command and control: a deeper insight. In 2008

3rd International Conference on Malicious and Unwanted Software (MALWARE) (pp. 41-48). IEEE.

22. https://www.statista.com/statistics/420391/spam-email-traffic-share/ 23. McCallum, A., & Nigam, K. (1998, July). A comparison of event models for naive bayes text classification.

In AAAI-98 workshop on learning for text categorization (Vol. 752, No. 1, pp. 41-48).

24. Sahami, M., Dumais, S., Heckerman, D., & Horvitz, E. (1998, July). A Bayesian approach to filtering junk e-mail. In Learning for Text Categorization: Papers from the 1998 workshop (Vol. 62, pp. 98-105).

25. FineReader, A. B. B. Y. Y. 7.0 Professional

26. Dredze, M., Gevaryahu, R., & Elias-Bachrach, A. (2007, August). Learning Fast Classifiers for Image Spam. In CEAS(pp. 2007-487).

131-137

27. Thonnard, O., &Dacier, M. (2008). A framework for attack patterns' discovery in honeynet data. digital

investigation, 5, S128-S139.

28. Spitzner, L. (2003). The honeynet project: Trapping the hackers. IEEE Security & Privacy, 99(2), 15-23.

22.

Authors: Shanky Goyal, Dr. Sahshi Bhushan

Paper Title: A Optiminzed Model for Energy Efficiency on Cloud System using PSO &

CUCKOO Search Algorithm

Abstract: Cloud computing is one of the growing technologies, these days. Cloud

computing is a paradigm that is surrounded by multiple resources, which helps in resource

utilization. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a

Service (Saas) are named as services of cloud computing. In the IaaS models, users can rent

infrastructure of the data center as a service. Some of the examples of IAAS are Google

Compute Engine (GCE) and Amazon Web Service (AWS). In the PaaS models, users can

take services like operating system and database. Some of the examples of PAAS are

Microsoft Azure and Google App Engine. In the SaaS models, users can access and install

application software and databases via Internet. Examples of SAAS are Citrix

GoToMeeting and Google Docs. In this paper algorithms named as PSO and CSA are

discussed The objective of optimization for energy consumption on cloud has also been

discussed in the paper. Along with the optimization techniques, the detailed literature

reviews have been presented. To achieve the proposed work, CloudSim simulators and

standard programming languages have been used. The performance of the proposed work

will be analyzed by using the various performance parameters such as response time, energy

efficiency and execution time.

Keywords: Cloud Computing, Load balancing, Energy efficiency, Data center, CSA, PSO.

References: 1. Vanitha, P. Marikkannu (2017).Effective resource utilization in cloud environment through a dynamic

well-organized load balancing algorithm for virtual machines. Elsevier Ltd.

2. Shiva Razzaghzadeh, Ahmad Habibizad Navin, Amir Masoud Rahmani, Mehdi Hosseinzadeh. (2017). Probabilistic modeling to achieve load balancing in Expert Clouds. ElsevierB.V.

3. Mohammad Goudarzi, Mehran Zamani, Abolfazl Toroghi Haghighat.(2017). A fast hybrid multi-site

computation offloading for mobile cloud computing. Journal of Network and Computer Applications, Elsevier.

4. Muhammad Baqer Mollah, Md. Abul Kalam Azad, Athanasios Vasilakos.(2017).Security and privacy

challenges in mobile cloud computing: Survey and way Ahead. Journal of Network and Computer Applications, Elsevier.

5. Sungju Huh , Seongsoo Hong. (2017). Providing fair-share scheduling on multicore computing systems

via progress balancing. The Journal of Systems and Software, Elsevier. 6. Hassan Raei, Nasser Yazdani. (2017). Performability analysis of cloudlet in mobile cloud computing.

Information Sciences, Elsevier.

7. Piotr Nawrocki, Wojciech Reszelewski. (2017).Resource usage optimization in Mobile Cloud Computing. Computer Communications, Elsevier.

8. Wei Zhu, Yi Zhuang, Long Zhang. (2017). A three-dimensional virtual resource scheduling method for

energy saving in cloud computing. Future Generation Computer Systems, Elsevier. 9. SONG Ningning, GONG Chao, AN Xingshuo, ZHAN Qiang. (2016). Fog Computing Dynamic Load

Balancing Mechanism Based on Graph Repartitioning. China Communications.

10. Lei Zhang, Jiangchuan Liu, Edith Cheuk-Han Ngai, Wenwu Zhu.(2016). On Energy-Efficient Offloading in Mobile Cloud for RealTime Video Applications. IEEE.

11. Mohammad Mehedi Hassan, Majed Alrubaian, Atif Alamri. (2016). Effective QoS aware Novel Resource Allocation Model for Body Sensor-Integrated Cloud platform. ICACT.

12. Qi Liu, Weidong Cai, Jian Shen, Dandan Jin, Nigel Linge. (2016). A Load Balancing Approach B ased

on Modified K-ELM and NSGA-II in a Heterogeneous Cloud Environment. IEEE International Conference on Consumer Electronics.

13. Deepak KumarPatel, DevashreeTripathy, C.R.Tripathy. (2016). Survey of load balancing techniques for

Grid. Journal of Network and Computer Applications, Elsevier. 14. Ankita Choudhary, Shilpa Rana, K.J. Matahai. (2016). A Critical Analysis of Energy Efficient Virtual

Machine Placement Techniques and its Optimization in a Cloud Computing Environment. International

Conference on Information Security & Privacy, Elsevier. 15. Aarti Singh, Dimple Juneja, Manisha Malhotra. (2015). Autonomous Agent Based Load Balancing

Algorithm in Cloud Computing. ICACTA, Elsevier.

138-144

16. Ebin Deni Raj and Dhinesh Babu L.D. (2015). A Two Pass Scheduling Policy based Resource allocation

for MapReduce. ICICT, Elsevier.

17. Fatemeh Jalali, Kerry Hinton, Robert Ayre, Tansu Alpcan, and Rodney S. Tucker. (2014). Fog

Computing May Help to Save Energy in Cloud Computing. IEEE Journal.

23.

Authors: Komalpreet Kaur, Dr. Amanpreet Kaur

Paper Title: Ultra-wideband coplanar waveguide antenna with an improved gain using a

frequency selective surfaces (FSS)

Abstract: In this article, an ultra-wideband FSS reflector has been proposed to enhance the

gain of a CPW antenna for UWB applications. A CPW fed antenna having dimensions of

38mm×38mm×1.605mm and FSS unit cell having dimensions 14mm × 14mm × 1.605 mm

are presented in the paper. A rectangular slot and stubs are interleaved at the outer edges of

the patch for achieving desired characteristics of an ultra-wideband for the frequency range

of 3.39 GHz to 12.9 GHz. Simulation results carried out using the CST microwave 2016

version in the time domain are presented for the proposed antenna. An FSS unit cell

designed and simulated using periodic boundary conditions and floquet ports is presented.

The combined setup of an array of FSS reflector behind the antenna has been simulated in

the time domain. This set up shows an improved performance in terms of antenna’s gain. A

maximum and minimum gain of 8.14 dB and 4.98 dB has been observed with the presence

of FSS reflector behind the coplanar waveguide antenna. A significant improvement of 2.9

dB has been observed over the entire band of antenna’s operation.

Keywords: Frequency selective surface (FSS), reflector, reflection parameters, and

transmission parameters, gain.

References: 1. Ekta Saini, Richa Bhatia, Saurabh Prakash, “High-speed broadband communication system for moving trains

using Free Space Optics”, 2016 International Conference on Computational Techniques in Information and

Communication Technologies (ICT ICT),pp.1-4.. 2. Mahdi Ghorbani and Habib Ghorbaninejad,2017 “Design Of A High Gain Bandwidth Improved Aperture

Antenna Using A Frequency Selective Surface”, ACES Journal, Vol. 32, No.4

3. Valentino Trainotti, Gonzalo Figueroa 2010, “Vertically Polarized Dipoles and Monopoles, Directivity, Effective Height and Antenna Factor”, IEEE Transactions on broadcasting, VOL. 56, NO. 3.

4. M. T. Islam, M. N. Shakib, and N. Misran, 2009 "Multi-Slotted Microstrip Patch Antenna for Wireless

Communication", Progress in Electromagnetics Research Letters, Vol. 10, pp.11-18. 5. Giorgio Montisci, Zusheng Jin, Mingchao Li, Hu Yang, Giovanni Andrea Casula, Giuseppe Mazzarella, and

Alessandro Fanti, 2013"Design of Multilayer Dielectric Cover to Enhance Gain and Efficiency of Slot

Arrays", International Journal of Antennas and Propagation, pp.1-6 6. C. A. Balanis. Antenna Theory2015: Analysis and Design, Third Edition: John Wiley & Sons.

7. T.K. Wu,1995 Frequency selective surfaces and grid array, Wiley, New York

8. B.A. Munk, 2000 Frequency selective surface - theory and design, Wiley, New York, . 9. Y. Ranga, K. P. Esselle, L. Matekovits3, and S. G. Hay,2012 "Increasing the Gain of a Semicircular Slot

UWB Antenna Using an FSS Reflector”, IEEE, pp.478-481.

10. Silva MWB, Araujo HX, Campos ALPS,2018 “Design of a narrow band and wideband absorbers using resistive FSS concept for the X and Ku band application”, Microwave Optical Technology Letters, vol.60,

pp.2128–2132.

11. A.G. D’Assunc¸ão, Jr.2012, Analysis of integrated circuits and FSS using WCIP for applications at microwaves and terahertz bands (in Portuguese), Ph.D. Dissertation, Federal University of Campina Grande,

PB, Brazil.

12. Silva MR, N_obrega CL, Silva PHF, D’assunção AG.2013 , “Stable and compact multiband frequency selective surfaces with fractal configurations”, IET Microwave Antennas Propagation, pp.543–551.

13. Sanz-Izquierdo B, Parker EA.,2014 “Dual polarized reconfigurable frequency selective surfaces”, IEEE Trans Antennas Propagation, pp.764–771.

145-149

Authors: Sandeep S. Jain, Riya Gupta, Chetika Tiwari, Navnoor Kaur

Paper Title: Behaviour of Players in IPL Based on Fuzzy C Means

24.

Abstract: Clustering algorithms are being widely used in the field of data mining in order to

accumulate similar data in the form of clusters. Indian Premiere League(IPL) is one of the

most famous cricket league around the globe.In this paper, the dataset of IPL is used to

cluster the players on the basis of various attributes. The authors ought to analyse both

batsmen and bowlers in various clusters with the help of Fuzzy-c-means. The algorithm has

been implemented to group the players in different clusters based on their performance in

the IPL season of 2018. The pros and cons of the algorithm are also discussed and finally

the experimental results are shown to highlighttwo main clusters i.e. above average and

below average. The present work simulates the algorithm to distinguish only overseas

player. In future this work can be extended for every player and form a recommendation

model to identify best player or form best team.

Keywords: Clustering algorithm, Fuzzy-C-Means, Clusters, IPL Dataset.

References: 1. Swartz, T. B., Research directions in cricket. Handbook of Statistical Methods and Analyses in Sports.

Chapman & Hall/CRC Handbooks of Modern Statistical Methods: Boca Raton,2016, FL, 272.

2. Shah, S., Hazarika, P. J., & Hazarika, J.,“A Study on Performance of Cricket Players using Factor Analysis

Approach.”International Journal of Advanced Research in Computer Science, 8(3), 2017. 3. Mittal, A., Manavalan, A., “The IPL Model: Sports Marketing and Product Placement

Sponsorship”International Journal of Humanities and Social Science Invention, 6(2), 2017.

4. Hou, J., Gao, H., & Li, X.,“DSets-DBSCAN: a parameter-free clustering algorithm.” IEEE Transactions on Image Processing, 25(7), 2016, pp. 3182-3193.

5. Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., & Shao, L,“Real-time superpixel segmentation by DBSCAN clustering algorithm.” IEEE Transactions on Image Processing, 25(12), 2016, pp. 5933-5942.

6. Ienco, D., &Bordogna, G. ,Fuzzy extensions of the DBScan clustering algorithm. Soft Computing, 22(5),

2018, pp. 1719-1730. 7. Nayak, J., Naik, B., &Behera, H. S. ,“uzzy C-means (FCM) clustering algorithm: a decade review from 2000

to 2014.” Computational intelligence in data mining-volume 2, 2015, pp. 133-149.

8. Zheng, Y., Jeon, B., Xu, D., Wu, Q. M., & Zhang, H. ,“Image segmentation by generalized hierarchical fuzzy C-means algorithm.” Journal of Intelligent & Fuzzy Systems, 28(2), 2015, pp. 961-973.

9. Pourjabari, A. J., &Seyedzadegan, M.,” An improved method of fuzzy c-means clustering by using feature

selection and weighting.” International Journal of Computer Science and Network Security (IJCSNS), 16(10), 2016, pp.64.

10. Cebeci, Z., &Yildiz, F.,“Comparison of K-means and Fuzzy C-means algorithms on different cluster

structures.” AGRÁRINFORMATIKA/JOURNAL OF AGRICULTURAL INFORMATICS, 6(3), 2015, pp. 13-23.

11. Ludwig, S. A.,“MapReduce-based fuzzy c-means clustering algorithm: implementation and

scalability.” International journal of machine learning and cybernetics, 6(6), 2015, pp. 923-934. 12. Chirag Goyal, “IPL-2017 Cross Country Cluster Analysis.”International Journal of Computer Science Trends

and Technology (IJCST)5(4),2017, pp.117-124.

150-154

Authors: Arashpreet Kaur, Amanpreet Kaur

Paper Title: A Compact Rectangular Microstrip Patch Antenna Loaded with Stubs and Defected

Partial Ground Structure for UWB Systems

Abstract: This paper presents the prototype and simulations of a compact rectangular

microstrip patch antenna for ultra-wideband applications. The proposed antenna is printed

on FR4 (Flame Retardant) substrate with relative permittivity of 4.4, dielectric loss tangent

of 0.0024 and the dimensions of 57 × 25 × 1.57 mm3. The radiating patch of the antenna is

loaded with two rectangular stubs along its upper and lower edges and an equilateral

triangular notch is truncated from the reduced ground plane to achieve optimum results in

terms of bandwidth and reflection coefficient. It is fed along the centerline of symmetry by

50Ω microstrip feed line. The simulated return loss (𝐒𝟏𝟏) characteristics show that the

proposed antenna has a capability of covering the wireless bands from 0.17GHz to 7.25GHz

with impedance bandwidth of 7.08GHz and exhibits a peak gain of 5dB at 7.25GHz which

is acceptable for UWB systems.

Keywords: UWB patch antenna, DGS (Defected ground structure), VSWR (Voltage

Standing Wave Ratio), Smith Chart, Radiation Pattern, CST MWS’14.

25.

References: 1. Vuong, T.P., Ghiotto, A., Tedjini, S., et al. (2007). Design and characteristics of a small U-slotted planar

antenna for IR-UWB. Microwave and Optical Technology Letters, 49(7), 1727-1731.

2. Jung, J., Choi, W., Choi, J., (2005). A Small Wideband Microstrip-fed Monopole Antenna. IEEE Microwave

and Wireless Components Letters, 15(10), 703-705. 3. Kaur, S., Khanna, R., (2015). Design and analysis of stair-shape antenna with flowery DGS. International

Journal of Microwave and Wireless Technologies, 7(1), 53-60.

4. Balanis, C.A. (2005). Antenna Theory Analysis and Design, 3rd edition. Hoboken, NJ: John Wiley & Sons, Inc. 5. Abdelaziz, A.A. (2006). Bandwidth enhancement of microstrip antenna. Progress in Electromagnetics Research,

PIER 63, 311–317.

6. Kaur, A., Kaur, A. (2017). A compact staircase shaped slotted microstrip patch antenna with DGS for UWB applications. 5th International Conference on Advancements in Engineering and Technology, Sangrur, India,

66-69.

7. Lee, C.P., Chakrabarty, C.K. (2011) Ultra-Wideband Microstrip Diamond Slotted Patch Antenna with Enhanced Bandwidth. International Journal of Communications, Network and System Sciences, 4(7), 468-474.

8. Islam, M.T. (2009). Design analysis of High Gain Wideband L-Probe fed Microstrip Patch Antenna. Progress in

Electromagnetics Research, PIER 95, 397-407. 9. Kaur, A., Khanna, R., Kartikeyan, M.V. (2015). A stacked Sierpinski gasket fractal antenna with defected

ground structure for UWB/WLAN/radio astronomy/STM link applications. Microwave and Optical Technology

Letters, 57(12), 2786-2792. 10. Kaur, A., Khanna, R. (2017). Design and development of a stacked complementary microstrip antenna with a ‘

’-shaped DGS for UWB, UNII, WLAN, WiMAX and radio astronomy wireless applications. International

Journal of Microwave and Wireless Technologies, 2, 1-10. 11. Kaur, A., Kaur, A. (2017). An extended semi-circular microstrip patch antenna with DGS for UWB

applications. Journal of Microwave Engineering and Technology, 4(1), 13-18.

12. Wu, C.M., Chen, Y.L., Wen, C.L. (2012). A Compact Ultra-Wideband Slotted Patch Antenna for Wireless Dongle Application. IEEE Antennas and Wireless Propagation Letters, 11, 596-599.

13. Arya, A.K., Patnaik, A., Kartikeyan, M.V. (2013). Gain Enhancement of Microstrip patch antenna using

Dumbbell shaped Defected Ground Structure. International Journal of Scientific Research Engineering and Technology, 2(4), 184-188.

14. Woo D.J., Lee, T.K., Lee, J.W, Pyo, C.S, Choi, W.K. (2006). Novel U-Slot and V-Slot DGSs for band stop

filter with improved Q-factor. IEEE Transactions on Microwave Theory Techniques, 54(6), 2840-2847. 15. Reddy, B.R., Vakula, D. (2015). Compact Zigzag Shaped Slit Microstrip Antenna with Circular Defected

Ground Structure for Wireless Applications, IEEE Antennas and Wireless Propagation Letters, 14, 678-681.

16. Chen, H.J., Huang, T.H., Chang, C.S., Chen, I.S., Wang, N.F., Wang, Y.H, Houng, M.P. (2007). A novel cross-shape DGS applied to the design of ultra-wide stop band low pass filters. IEEE Microwave Wireless

Components Letters, 17(8), 586-588.

17. Chen, W., Lee, K.F., Dahele, J.S. (1992). Theoretical and experimental studies of resonant frequencies of the equilateral triangular microstrip antenna. IEEE Transactions on Antennas and Propagation, 40(10), 1253-1256.

18. Kaur, A., Khanna, R., Kartikeyan, M.V. (2017). A Multilayer dual wideband circularly polarized Microstrip

antenna with DGS for WLAN/Bluetooth/ZigBee/WiMAX/IMT band applications. International Journal of Microwave and Wireless Technologies, 9(2), 317-325.

19. Kelothu, B., Subhashini, K.R., Manohar G.L. (2012). A compact high-gain microstrip patch antenna for dual

band WLAN applications. 2012 Students Conference on Engineering and Systems, Allahabad, Uttar Pradesh, India, 1-5.

20. Agarwal, A., Kaur, A. (2017). A dual band stacked aperture coupled antenna array for WLAN applications.

Microwave Optical Technology Letters, 59, 648-654.

155-160

Authors: Sukhman Kaur

Paper Title: Energy Optimization Approach for Underwater Sensor Network Using

Nature Inspired Technique

Abstract-There is lots of energy optimization technique is used in underwater sensor

network but in this paper, nature-inspired technique, called Elephant Herding Optimization

(EHO), and is proposed for solving optimization tasks. The EHO method is inspired by the

herding behavior of the elephant group. In nature, the elephants having a place with various

factions live respectively under the initiative of female authority, and the male elephants will

leave their family bunch when they grow up. These two practices can be displayed into two

after administrators: group refreshing administrator and isolating administrator. In EHO, the

elephants in every faction are refreshed by its present position and female authority through

group refreshing administrator. It is found from the outcomes that the EHO based vitality

streamlining approach shows the outcomes compelling than established methodology as far

as numerous parameters. In this work, the advancement dimension of the vitality to over 11%

is accomplished utilizing Elephant Herd Optimization that is likewise utilized in numerous

other areas for designing enhancement.

26.

Keywords: Energy Optimization Underwater Sensor Networks, Elephants, Nature Inspired

Techniques.

References: 1. X. Gao, Z. Cui, “Theory and applications of swarm intelligence,” Neural Computing & Applications, vol. 21.

2. J. Kennedy, R. Eberhart, “Particle swarm optimization,” in Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, 1995.

3. S. Mirjalili, L. d. S. Coelh, G.-G.Wang, “Binary optimization using hybrid particle swarm optimization and

gravitational search algorithm,” Neural Computing and Applications, vol. 25. 4. A. H. Alavi, A. H. Gandomi, G.-G. Wang, S. Deb, “A hybrid method based on krill herd and quantum-

behaved particle swarm optimization,” Neural Computing and Applications, 2015.

5. G.-G. Wang, A. H. Gandomi, X.-S.Yang, A. H. Alavi, “A novel improved accelerated particle swarm optimization algorithm for global numerical optimization,” Engineering Computations, vol. 31.

6. X. Zhao, B. Song, P. Huang, Z. Wen, J. Weng, Y. Fan, X. Zhao, B. Song, “An improved discrete immune

optimization algorithm based on PSO for QoS-driven web service composition,” Applied Soft Computing, vol.12.

7. X. Zhao, “A perturbed particle swarm algorithm for numerical optimization,” Applied Soft Computing, vol.

10. 8. S. Mirjalili, A. Lewis, “S-shaped versus V-shaped transfer functions for binary Particle Swarm

Optimization,” Swarm and Evolutionary Computation, vol. 9. Cybernetics, vol. 26.

9. J. Jaen, K. Krynicki , J. A. Mocholí, "Ant colony optimization for resource searching in dynamic peer-to-peer grids," International Journal of Bio- Inspired Computation, 2014,vol. 6, no. 3, pp. 153-165.

10. D. Karaboga, B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization,2007, vol. 39, no. 3, pp. 459-471

11. X. S. Yang, S. Deb, "Cuckoo search via Lévy flights." pp. 210-214.

12. S. Debv, X.-S. Yang, “Cuckoo search: recent advances and applications,” Neural Computing and Applications, 2013 vol. 24.

13. X. Li, J. Wang, M. Yin, “Enhancing the performance of cuckoo search algorithm using orthogonal learning

method,” NeuralComputing and Applications, 2013,vol. 24, no. 6. 14. A. H. Gandomi, X. Zhao, G.-G. Wang, H. C. E. Chu, "Hybridizing harmony search algorithm with the

cuckoo search for global numerical optimization," Soft Computing, 2014.

15. G.-G. Wang, S. Deb, A. H. Gandomi, Z. Zhang, A. H. Alavi, “Chaotic cuckoo search,” Soft Computing, 2015.

16. A. H. Gandomi, G.-G. Wang, A. H. Alavi, X.-S.Yang, “A new hybrid method based on krill herd and

cuckoo search for global optimization tasks,” International Journal of Bio-Inspired Computation, 2012.

17. X. Li, M. Yin, “Modified cuckoo search algorithm with self- adaptive parameter method,” Information

Sciences,2015, vol. 298.

18. X. S. Yang, “Nature-inspired metaheuristic algorithms”, 2nd ed., Luniver Press, Frome, 2010. 19. S. M. Mirjalili, X.-S. Yang, “Binary bat algorithm,” Neural Computing and Applications, 2013,vol. 25.

20. X.-S. Yang, A. H. Gandomi, A. H. Alavi, “Mixed variable structural optimization using firefly algorithm,”

Computers & Structures,2011, vol. 89. 21. X. S. Yang, "Firefly algorithm, stochastic test functions, and design optimization," International Journal of

Bio-Inspired Computation, 2010, vol. 2.

22. G.-G. Wang, D. Wang, “An effective hybrid firefly algorithm with harmony search for global numerical optimization,” The Scientific World Journal, 2013.

23. S. Mirjalili, “The ant lion optimizer,” Advances in Engineering Software, 2015, vol. 83.

24. J.-W. Zhang, G.-G. Wang, “Image matching using a bat algorithm with mutation,” Applied Mechanics and Materials, 2012,vol. 203.

25. B. Chang, G.-G. Wang, Z. Zhang, "A multi-swarm bat algorithm for global optimization.”

26. A. Kaveh, S. Talatahari, "A discrete big bang-big crunch algorithm for the optimal design of skeletal structures,” ,2010, .vol. 11.

27. A. Kaveh, S. Talatahari, “Size optimization of space trusses using Big Bang–Big Crunch algorithm,”

Computers & Structures, vol. 87, 2009.

28. S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, 2010, vol.

21.

29. R. Sheikholeslami, S. Talatahari, “Optimum design of gravity and reinforced retaining walls using enhanced charged system search algorithm,” KSCE Journal of Civil Engineering, 2014,vol. 18, no. 5.

30. S. Talatahari, A. Kaveh, "Charged system search for the optimal design of frame structures," Applied Soft

Computing, 2012,vol. 12. 31. A. Kaveh, R. Sheikholeslami, S. Talatahari, M. Keshvari-Ilkhichi, “Chaotic swarming of particles: a new

method for size optimization of truss structures,” Advances in Engineering Software,2014, vol. 67.

32. S. M. Mirjalili, A. Hatamlou, “Multi-verse optimizer: a nature- inspired algorithm for global optimization,” Neural Computing and Applications, 2015.

33. A. H. Gandomi, A. H. Alavi, “Krill herd: a new bio-inspired optimization algorithm,” Communications in

Nonlinear Science and Numerical Simulation, 2012,vol. 17, no. 12 34. G.-G. Wang, A. H. Gandomi, A. H. Alavi, "A chaotic particle swarm krill herd algorithm for global

numerical optimization," Kybernetes, 2013,vol. 42.

35. A. H. Gandomi, A. H. Alavi, G.-G. Wang, “An effective krill herd algorithm with migration operator in

161-164

biogeography-based optimization,” Applied Mathematical Modelling, 2014,vol. 38.

36. A. H. Gandomi, G.-G. Wang, A. H. Alavi, “Stud krill herd algorithm,” Neurocomputing, 2014,vol. 128.

37. G.-G. Wang, A. H. Gandomi, A. H. Alavi, S. Deb, “A Multi-Stage Krill Herd Algorithm for Global

Numerical Optimization,” International Journal on Artificial Intelligence Tools, 2015. 38. A. H. Alavi, G.-G. Wang, L. Guo, H. Duan, “A new improved krill herd algorithm for global numerical

optimization,” Neurocomputing, vol. 138, 2014.

39. G.-G. Wang, S. Deb, Z. Cui, “Monarch butterfly optimization,” Neural Computing and Applications, 2015.

27.

Authors: Sukhman Kaur

Paper Title: Soft Computing Technique Based on Missing Value Treatment

Abstract: Missing value treatment is an actual yet challenging issue confronted in data

mining. In existing work missing value treatment is a procedure that replaces the missing

values in a dataset by some conceivable values. The conceivable values are generally

generated from the dataset using a statistical evaluation. These types of result do not give

accurate outcomes. In this paper, soft computing is used in the random forest approach using

for missing value treatment that is devised and implemented on the different types of social

media. Using random forest approach results are improved form existing technique.

Keywords: Missing value, Missing value treatment, Soft computing, Social media.

References: 1. Bifet, A., & Frank, E.. Sentiment knowledge discovery in Twitter streaming data. In International Conference

on Discovery Science. Springer Berlin Heidelberg, 2010. 2. Bollen, J., Mao, H., &Pepe, A.. Modeling public mood and emotion: Twitter sentiment and socio-economic

phenomena. ICWSM, 11, 2009,450- 453.

3. Bollen, J., Mao, H., &Pepe, A.. Determining the Public Mood State by Analysis of Microblogging Posts. In ALIFE2010, (pp. 667-668).

4. Asur, S., &Huberman, B. A.. Predicting the future with social media. In Web Intelligence and Intelligent Agent

Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). 5. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P... User-level sentiment analysis incorporating social

networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and

data mining (pp. 1397-1405). ACM, 2011.

6. Saif, H., He, Y., &Alani, H.. Semantic sentiment analysis of Twitter. In International Semantic Web

Conference (pp).508-524),2012. Springer Berlin Heidelberg..

7. Leong, C. K., Lee, Y. H., &Mak, W. K. Mining sentiments in SMS texts for teaching evaluation. Expert Systems with Applications, 39(3), 2584- 2589, 2012.

8. Wang, H., Cambria, E., Schuller, B., Liu, B., &Havasi, C.. Knowledge- based approaches to concept-level

sentiment analysis. IEEE Intelligent Systems, 28(2), 12-14, 2013. 9. Dong, H., Shahheidari, S., &Daud, M. N. R. B.. Twitter sentiment mining: A multi-domain analysis. In

Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference (pp.

144-149). IEEE, 2013. 10. Cambria, E., Fu, J., Bisio, F., &Poria, S.AffectiveSpace 2: Enabling Affective Intuition for Concept-Level

Sentiment Analysis. In AAAI (pp. 508-514), 2013.

11. Kotwal, A., Fulari, P., Jadhav, D., &Kad, R.. Improvement in Sentiment Analysis of Twitter Data Using Hadoop. Imperial Journal of Interdisciplinary Research, 2(7), 2014.

12. Poria, S. Cambria, E., Fu, J., Bisio, F., &. AffectiveSpace 2: Enabling Affective Intuition for Concept-Level

Sentiment Analysis. In AAAI (pp. 508-514), 2015. 13. Davis, B., Hürlimann, M., Cortis, K., Freitas, A., Handschuh, S., & Fernández, S.. A Twitter Sentiment Gold

Standard for the Brexit Referendum. In Proceedings of the 12th International Conference on Semantic Systems

(pp. 193-196). ACM, 2016.

165-169

Authors: Gagandeep Kaur, Amanpreet Kaur

Paper Title: A Circular coplanar waveguide fed Microstrip Patch Antenna with modified

triangular Ground for UWB applications

Abstract: This research article gives a detailed insight of the design, simulation of a

compact circular shaped microstrip patch antenna that is fed using a coplanar waveguide

feed (CPW for practical wireless communication applications). The antenna is typically

designed for Ultra wideband (1.46-6GHz), Bluetooth (2.4GHz), ZIGBEE (2.4GHz), WLAN

(5.15- 5.35 GHz and 5.725- 5.825), Wi-Fi (2.4-2.485GHz) and HIPERLAN-2(5.15 - 5.35

GHz and 5.470 -5.725GHz) wireless applications with stop band characteristics for the H

(partial C band). The proposed antenna has an overall packaged structure dimensions of 78

28.

x75 x1.605 mm3 and is fabricated on FR4 substrate as a circular patch antenna with a

coplanar ground .The commercially available laminate FR4 substrate that is used has a

dielectric constant of 4.4, height of 1.6mm and a loss tangent of 0.0024.The prospective

antenna shows a simulated impedance bandwidth of 4.54 GHz. The coplanar waveguide

feeding used with this antenna helps in improving antenna performance in terms of its

impedance bandwidth as this geometry helps in creating multiple current loops at the

antenna structure, thereby exciting nearby frequencies that merge to show a broadband of

operation. The antenna’s operational bandwidth is also improved by the concept of modified

ground, in which triangular and rectangular shapes are added symmetrically on both sides of

ground plane that provide a better fringing effect and hence an improved bandwidth.

Keywords: CPW fed, modified ground structure (MGS), ultra wide band (UWB), CST

MWS version 2017.

References: 1. J. Y. Sze and K. L.Wong ,Bandwidth enhancement of a microstrip line-fed printed wide-slot antenna, IEEE

Trans. Antennas Propag, Jul. 2001;49(7),pp. 1020–1024.

2. H. G. Schantz , A brief history of UWB antennas , IEEE Aerosp. Electron. Syst. Mag., April 2004 ; 19(4), pp. 22–26.

3. Suvadeep Choudhury ,Effect of Dielectric Permittivity and Height on a Microstrip-Fed Rectangular Patch

Antenna, International Journal of Electronics & Communication Technology, March 2014;5 (2),pp.129-130. 4. Amanpreet Kaur, Rajesh Khanna, and Machavaram Kartikeyan, A Stacked Sierpinski Gasket Fractal Antenna

With A Defected Ground Structure For UWB/WLAN/Radio Astronomy/STM Link Applications, Microwave And Optical Technology Letters, December 2015; 57(12), pp. 2786-2792.

5. Amanpreet kaur, Rajesh Khanna and Machavaram kartikeyan ,A multi layer dual wideband circularly polarized

microstrip antenna with DGS WLAN/Bluetooth/Zigbee/WiMAX/IMT band applications, International Journal of Microwave and Wireless Technologies , 2015;9(2), pp. 317-325.

6. Mohamaed Nabil Srifi, Symon K. Podilchak, Mohamed Essaaidi, and Yahia N. N. Antar , A planar circular

disc monopole antennas using compact impedence matching networks for ultra wide band (UWB) applications, IEEE Trans Antennas Propag , 2009 ,782-785.

7. X. Qing and Z. N. Chen, Monopole-like slot UWB antenna on LTCC, in Proc. IEEE International Conference

on Ultra-Wideband, 2008, pp. 121–124. 8. S. Cumhur Basaran , Dual wideband CPW fed split ring monopole antenna for WLAN applications, IEEE

Trans. Antennas Propagation, 2010; 978(1) ,pp. 174- 177.

9. Kirti Vyas and P. K. Singhal , Bandwidth Enhancement in CPW Fed Compact Rectangular Patch Antenna, International Journal of Electronic and Communication Engineering, 2014; 8(2): pp. 378-381.

10. M. Bod and M. M. S. Taheri , Compact UWB printed slot antenna with extra Bluetooth, GSM, and GPS bands,

IEEE Antennas Wireless Propag. Lett 11(2012), 531–534. 11. C.-L. Tsai and C.-L. Yang. Novel compact eye-shaped UWB antennas, IEEE Antennas Wireless Propag Lett

11(2012), 184–187.

12. Y. Sung, UWB monopole antenna with two notched bands based on the folded stepped impedance resonator, IEEE Antennas Wireless Propag. Lett 11(2012), 500–502.

13. S. R. Emadian, C. Ghobadi, J. Nourinia, M. Mirmozafari, and J. Pourahmadazar , Bandwidth enhancement of

CPW-fed circle-like slot antenna with dual band-notched characteristic, IEEE Antennas Wireless Propag Lett 11(2012), 543–546.

14. Dhirgham K. Naji,Compact Broadband CPW-fed Taper-shaped Monopole Antenna with L-slots for C-band

Applications, International Journal of Electromagnetics and Applications ,2013; 3(6), pp. 136-143. 15. A. Kaur , R. Khanna and M.V. Kartikeyena , A Stacked Rectangular MSA with Defected Ground Structure for

IEEE 802.11b/g Bands and Wi-Max Applications, International Conference on Microwaves, Antenna

Propagation and Remote Sensing (IEEE), 2014, pp. 266–270. 16. Gagandeep Kaur and Amanpreet Kaur, Design of a Slotted Micro-strip patch Antenna with DGS for an UWB

applications, International conference on advancements in engineering and technology, 2017, pp. 39-41.

170-174

Authors: Saloni Behal, Nitin Sharma, Simrandeep Singh

Paper Title: Enhanced Vehicular Ad hoc Network Protocol to Improve Quality of Service in

Vehicle to Vehicle Communication

Abstract: The vehicular ad-hoc networks (VANETs) are specific type or a sub form of

Mobile ad hoc networks (MANETs). However the main problem which is related to this

network is the Quality of Service (QoS) which mainly occurs due to rapid change topology

nature in the network and lack of stability of communication. Consequently, some of the

challenges that researcher focus on routing protocols for VANET. The problem which is

faced by this network with these protocols is the dynamic environment in their route

29.

instability. This paper approaches the combination of Dynamic Source Routing protocol

(DSR) and Particle Swarm Optimization Algorithm (PSO) to solve the problems of Routing

protocols which help to improve the Quality of service (QoS) in the network. The approach

which is introduced in this paper is to make use for making the better Quality of Service

(QoS) in the VANET. The simulation results in MATLAB exactly predict the overall

performances regarding the proposed work in terms of the packet drop ratio, transmission

delay, channel utilization, Throughput and Energy consumption under varying conditions.

Keywords: VANETs, Topology Routing Schemes, DSR and PSOA.

References: 1. Sharma, Aravendra Kumar, Sushil Kumar Saroj, Sanjeev Kumar Chauhan, and Sachin Kumar Saini. "Sybil

attack prevention and detection in vehicular ad hoc network." In Computing, Communication and Automation (ICCCA), 2016 International Conference on, pp. 594-599. IEEE, 2016.

2. Abbasi, Irshad, and Adnan Shahid Khan. "A Review of Vehicle to Vehicle Communication Protocols for

VANETs in the Urban Environment." Future Internet 10, no. 2 (2018):

3. Eze, Elias C., Sijing Zhang, and Enjie Liu. "Vehicular ad hoc networks (VANETs): Current state, challenges,

potentials and way forward." In Automation and Computing (ICAC), 2014 20th International Conference on,

pp. 176-181. IEEE, 2014. 4. Chaubey, Nirbhay Kumar. "Security analysis of vehicular ad hoc networks (VANETs): a comprehensive study."

International Journal of Security and Its Applications 10, no. 5 (2016): 261-274.

5. Ghori, Muhammad Rizwan, Zamli Kumal Z., Quosthoni Nik., Hisyam Muhammad., and Montaser, Mohamed.” Vehicular Ad-hoc Network (VANET): Review.” International conference on Innovative research

and Development (ICIRD). 2018.IEEE 6. Kabir, Md Humayun. "Research issues on vehicular ad hoc network." International Journal of Engineering

Trends and Technology (IJETT)–Volume 6 (2013).

7. Chadha, Divya. "Reena,“Vehicular Ad hoc Network (VANETs): A Review,”." Int. J. Innov. Res. Comput. Commun. Eng 3, no. 3 (2015): 2339-2346.

8. Rehman, Sabih, M. Arif Khan, Muhammad Imran, Tanveer A. Zia, and Mohsin Iftikhar. "Enhancing Quality-of-

Service Conditions Using a Cross-Layer Paradigm for Ad-Hoc Vehicular Communication." IEEE Access 5 (2017): 12404-12416.

9. Al-Kharasani, Nori M., Zuriati Ahmad Zulkarnain, Shamala Subramaniam, and Zurina Mohd Hanapi. "An

Efficient Framework Model for Optimizing Routing Performance in VANETs." Sensors 18, no. 2 (2018): 597. 10. Lugayizi, Francis L., Bukohwo M. Esiefarienrhe, and Akakandelwa Warren. "Comparative evaluation of

QoS routing in VANET." In Advances in Computing and Communication Engineering (ICACCE), 2016

International Conference on, pp. 183-188. IEEE, 2016. 11. Phakathi, Thulani, Francis Lugayizi, Bassey Isong, and Naison Gasela. "Quality of Service of Video Streaming

in Vehicular Adhoc Networks: Performance Analysis." In Computational Science and Computational

Intelligence (CSCI), 2016 International Conference on, pp. 886-891. IEEE, 2016. 12. Mchergui, Abir, Tarek Moulahi, Bechir Alaya, and Salem Nasri. "A survey and comparative study of QoS

aware broadcasting techniques in VANET." Telecommunication Systems 66, no. 2 (2017): 253-281.

13. Shaikh, Farheen Iqbal, and Hyder Ali Hingoliwala. "Path planning based QoS routing in VANET." In 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 37-43. IEEE, 2017.

175-181

30.

Authors: Vani Bhargava, S K Sinha, M P Dave

Paper Title: Optimal Integration of Distributed Generation in Primary Distribution System and

its Economics

Abstract: The paper analyses four types of DG (distributed generators) for their optimal

placement in primary distribution system. They are sited and sized optimally to obtain

maximum loss reduction. The ultimate objective of the adopted work in this paper is to

identify the size and location of distributed generators for their placement in primary

distribution network and to justify the economics of these placements. To serve the purpose,

an analytical method is used in this paper for determination of sizes and sites for four DG

types. The analytical method is considered suitable for the analysis purpose in proposed

work. The paper presented a comprehensive analysis for four types of DGs for their

placement in primary distribution system. The type 1 DG is capable of delivering real power

only whereas type 3 DG is capable of providing real as well as reactive power to the

distribution network. Out of other two types, type 2 can deliver only the reactive power

whereas type 4 supplies active power but at the same time it consumes reactive power. The

paper compares the economic feasibility of placement of four DG types in primary

distribution system.

Keywords: Distributed Generation, Distribution System, Economics, Optimal Placement

References: 1. Duong Quoc Hung and Nadarajah Mithulananthan,. “Multiple Distributed Generator Placement in Primary

Distribution Networks for Loss Reduction,” IEEE Transactions on Industrial Electronics, vol.60, no. 3, April

2013

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23. https://www.mnre.gov.in

182-190

Authors: Palak Thakur, Payal Patial, Dr. Prabhat Thakur

Paper Title: Performance analysis of Cognitive Radio based Internet-of-things Network for

energy efficiency and spectrum utilization

Abstract: Energy and Spectrum are the two basic requirements in the realm of Internet-of-

Things (IoT). The network of IoT is becoming larger day by day and the design of spectrum

and energy efficient solution is a quite challenging task because of the rapid increase of

connecting devices in IoT network. To make the system more energy and spectral efficient,

energy harvesting and cognitive radio (CR) are the proficient solutions, respectively. This

31.

paper introduces a spectral and energy efficient design for CR based sensor networks. We

present a network architecture, in which nodes or other sensing devices can use the

spectrum opportunistically and energy harvesting can be done from different ambient

sources. We then propose an 1) energy balancing scheme for heterogeneous network in

which nodes will have different energy levels and 2) Cluster head (CH) selection scheme

which will only be performed on the few nodes of network having the highest current

energy to accomplish the ultimate goal of energy balancing in network, this analysis is

performed with in the cluster. Furthermore, for the spectral efficiency, we propose a

channel management scheme based on cognitive radio to allot the best available channel

having highest reliability in respect of the bit error rate (BER) using. Comprehensive results

exhibit the effectiveness in the performance of the proposed spectral and energy efficient

schemes and show better performance over other schemes.

Keywords: Channel management, clustering, cognitive Radio, energy management,

Internet-of-Things,

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Authors: Priya Bakshi, Payal Patial, Prabhat Thakur

Paper Title: Cluster Formation with Bacteria Optimization Method Using Cognitive Radio Based

Vehicular Ad-Hoc Network

Abstract: The growing demand of the radio spectrum is an important part in the multi-agent

intelligence management system of the vehicles. Cognitive radio is used for reducing the

restricted access to the wavelength of the spectrum and utilizing the radio spectrum is

dynamic allocation method. With the advent in the cognitive radio arrangement, the CR in

vehicular ad hoc networks allow the operator to sense and hop from one to another system

network in the desired frequency of the spectrum based on the environment of the cognitive

radio. In existing method implemented a cluster formation mechanism used for data

transmission one to another vehicle nodes. In this mechanism used CR-VANET network is

divided into subgroups or clusters and achieve accuracy rate among vehicles. In this work,

has implemented a cluster formation mechanism with Bacterial foraging optimization

algorithm method in Cognitive radio in VANETs. In planned technique a self-motivated

system network is established on the basis of clusters using BFOA network goals to achieve

better throughput in data transmission one node to another node with RSU (Road Side

Units). In the experimental result improves accuracy of the data transmission over the

network. In proposed research, vehicles and road side units are deployed in the network.

When there is loss of the data packets during the transmission in the network, then

32. optimized clustering phase has implemented. In addition, the selections of the cluster heads

are maintained the path and optimization (BFOA) phase implement to recover the path

losses and improve the network performance such as overhead, energy consumption, E2E

delay and Network Throughput and compared with existing method (Cluster-Formation).

Simulation tool used in this proposed work is MATLAB 2016a.

Keywords: Cognitive radio, Radio Spectrum, Vehicular ad-hoc network and Bacteria

Foraging Optimization algorithm.

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200-210

Authors: Neha Malgotra, Nitin Mittal, Prabhjot Singh

33.

Paper Title: A Hybrid Energy Efficient Reactive Multipath Routing for Wireless Sensor Network

Abstract: The efficiency of the wireless sensor network (WSN) can be evaluated by using

various factors such as lifetime of the network, number of dead nodes, number of alive

nodes, throughput of the network etc. All these factors are highly influenced by CH and

route formation process as the election of right node for CH and route is quite tedious task to

perform. Thus a large number of researches has been conducted in previous years. Few of

the previous approaches have been discussed in this study and it is analyzed that the

traditional HEEMP approach in quite effective to enhance the network lifetime but has a

weak data communication and transmission strategy. Thus a novel approach is developed in

this work by using the traditional HEEMP approach. The modification in the proposed work

is done in terms of data gathering and data transmission strategy. Only the relevant and

meaningful data packets are transmitted to the BS. The performance analysis of the

proposed and traditional HEEMP protocol is compared and proposed work is found to be

effective than the HEEMP protocol in terms of lifetime of the network.

Keywords:: Wireless Sensor Network, Data packets transmission, network lifetime, cluster

head, HEEMP, TEEN, CEED, and LEACH.

References:

1. M. Sajwan, D. Gosain and A. Sharma, "Hybrid energy-efficient multi-path routing for wireless sensor

networks", Computers & Electrical Engineering, vol. 67, pp. 96-113, 2018. Available:

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34.

Authors: Obaidullah Lodin, Nitin khajuria, Satyanand Vishwakarma, Gazia Manzoor

Paper Title: Modelling and Simulation of Wind Solar Hbrid System using Matlab/Simulink

ABSTRACT—this article is a simulation, designing and modeling of a hybrid power

generation system based on nonconventional (renewable) solar photovoltaic and wind

turbine energy reliable sources. The primary premeditated system is the solar electric

generator, consisting of six models and series connected to each other, based on predicted-

P&O and connected to a MPPT controller and DC/AC converter, system is associated with

PMSG (permanent magnet synchronous generator). The main purpose of this article is to

interconnect systems to generate maximum power for single auxiliary phase loading, as well

as the solar PV generator and systems of wind turbines for simulation with execution use of

Simulink / MATLAB. The results of this simulation indicate that the hybrid power system is

planned for stability, reliability, efficiency and model. Solar PV generator and wind turbine

from the use of a renewable energy source (for maximum voltage generation).The solar

photovoltaic module executable in MATLAB / Simulink captures five parameters, series

parameters and shunt resistance is an inverse photovoltaic saturation flow and an ideal

factor.

Keywords—MPPT algorithms, irradiance, Perturb-observe, wind power etc.

References:

1. B H Khan, “Non-Conventional Energy Resources,” Tata McGraw-Hill Pub.Co., 2009. 2. Gazia Manzoor, Kamalkant Sharma, Satyanand Vishwakarma”PSO-GSA based MPPT Algortihm for

Photovoltaic Sytems”, International Journal of Recent Technology and Engineering (IJRTE), ISSN:2277-

3878, vol.7, issue-6S4, April 2019 3. .H.Renaudineau,F.Donatantonio,G.Petrone, G.Spagnuolo, J. P. Martin, S.Pierfederici, J. Fontchastagner,“A

PSO-based global MPPT technique for distributed PV power generation,” IEEE Trans. Ind. Electron., vol.

62, no. 2, pp. 1047–1058, Feb. 2015 4. Vpiasha Sharma, Harpreet Kaur, Inderpreet Kaur,”Design and Implemetation of Multi Junction PV cell for

MPPT to improve the Transformation efficiency ” International Journal of Recent Technology and

Engineering (IJRTE), ISSN:2277-3878,vol.7, issue-6S4, April 2019 5. .K. Sundareswaran, V. Vigneshkumar, P. Sankar, S. P. Simon, P. S. R. Nayak, and S. Palani,

“Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV

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Transaction on smart grid, vol. 2, June 2011, pp.278 - 286.

8. .J. Ahmed and Z. Salam, “A critical evaluation on maximum power point tracking methods for partial shading in PV systems,” Renewable Sustain. Energy Rev., vol. 47, pp. 933–953, Jul. 2015.

9. Obaidullah Lodin , Inderpreet Kaur, Harpreet Kaur “Predictive- P&O Mppt Algorithm for Fast and

Reliable Tracking of Maximum Power Point in Solar Energy Systems”, International Journal of Recent Technology and Engineering (IJRTE), ISSN:2277-3878, Volume-7, Issue-6S4, April 2019

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energy, vol. 4, no. 2, April 2013,pp.402-413.

11. .F. Paz and M. Ordonez, “Zero oscillation and irradiance slope tracking for photovoltaic MPPT,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6138–6147, Nov. 2014.

12. Shefali, Harvinder Singh, Sachin Kumar, Inderpreet Kaur,” Designing and Imulation of Solar module in the

Islanded network of a solar PV microgrid”, International Journal of Recent Technology and Engineering (IJRTE), ISSN:2277-3878, Volume-7, Issue-6S4, April 2019

13. S. K. Kollimalla and M. K. Mishra, “Variable perturbation size adaptive P&O MPPT algorithm for sudden

changes in irradiance,” IEEE Trans. Sustain. Energy, vol. 5, no. 3, pp. 718–728, Jul. 2014.

218-224

Authors: Ankita Gupta, Ankit Srivastava, Rohit Anand & Paras Chawla

35.

Paper

Title:

Smart Vehicle Parking Monitoring System Using RFID

Abstrat:”With the vast growing influx of population in the developed, industrially and

technologically sound urban cities, an urgent need to make the cities smart is surmounted.

The cities are made smart utilizing data sharing, artificial intelligence, machine learning,

analytics, and thousands of RFID tags and sensors. One of the significant concerns of

today's smart cities is the growing need to manage the vehicles on-road as well as to create

sufficient and well-managed parking lots to prevent urban areas from traffic congestion.

This leads to a call for highly automated parking management system self-sufficient in

guiding the driver to an available parking space in the nearby area. In this paper, a real-time

prototype of the smart parking system (S.P system) based on Internet of Things (IoT) is

discussed. The proposed smart parking system works on an electronic device that collects

the parking availability status and assists drivers in finding and selecting the desired parking

space among the available parking spaces that effectively reduces the traffic problems and

mismanagement across the cities to a great extent.

Keywords:Smart Parking System, Parking Lot, Parking device, Internet-of-Things(IoT),

RFID Tags, Real Time Management, GUI Information Centre, Localised parking,

Centralised Parking, Smart Cities

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2787-2805.

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effects in the Shinjuku area-configuration, performance, and future improvement of system. In Pacific Rim

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01-503). Arizona. Dept. of Transportation. 7. Pala, Z., & Inanc, N. (2007, September). Smart parking applications using RFID technology. In 2007 1st

Annual RFID Eurasia (pp. 1-3). IEEE.

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Applications (pp. 65-70). IEEE.

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10. Reddy, P. D., Rao, A. R., & Ahmed, S. M. (2013). An Intelligent Parking Guidance and Information

System by using image processing technique. International Journal of Advanced Research in Computer and Communication Engineering, 2(10), 4044-4048.

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efficiency in wireless sensor networks", Computer Communications, vol. 36, no. 9, pp. 965-978, 2013.

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Networks in Home Automation", EURASIP Journal on Embedded Systems, vol. 2011, pp. 1-15, 2011.

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Kotb, A. O., Shen, Y. C., Zhu, X., & Huang, Y. (2016). iParker—A new smart car-parking system based on dynamic resource allocation and pricing. IEEE transactions on intelligent transportation systems, 17(9),

2637-2647.

36.

Authors: Simarpreet Kaur, Dr. Mohit Srivastava, Dr. Kamaljit Singh Bhatia

Paper

Title:

Simulation and Analysis of Telescopic Photonic Beam Combiner for

Stellar Interferometry in Labview

Abstract: The optical methods are the sensitive and subtle techniques of conducting optical

investigations without any physical contact. They are both accurate and fast at the same time

which provides an ease of having multiple observations. The need of optical interferometry

has been increased at a very quick pace due to its high range of applications, from surface

testing to locating of extra-solar planets in the universe. Optical Interferometry is a process

or technique of combining light from various telescopes for calculating the angular

resolution. The technique of optical interferometry helps astronomers for achieving a high

angular resolution, that is not possible with the conventional telescopes. Optical approaches

are the best approaches for non-contact evaluations, and these are accurate and fast. Due to

optical nature of the interferometry, this process is used in various fields, and today it has

become one of the important areas of research. Integrated Optics (IO) beam combiners are

an efficient and compact technology to combine light interferometrically collected by

multiple telescopes. IO beam combiner is based on the modal filtering properties of

waveguides or fibers, and it improves thermo-mechanical stability, because of the

characteristic rigidity of IO component substrate. Other advantages of this technology are

miniaturization and non-existence of alignment needs. Several IO beam combiners are being

proposed and tested nowadays. The purpose of this paper is to investigate different

techniques used to develop interferometric instrument from previous researches and to

simulate the technique suggested by Ermann in Labview.

Keywords:Beam Combiner, Condition Number, Integrated Optics Optical Interferometry,

Stellar Interferometer, and Visibility To Pixel Matrix.

References:

1. A. Saviauk, S. Minardi, F. Dreisow, S. Nolte, and T. Pertsch, “3D-integrated Optics Component for

Astronomical Spectro-Interferometry,” in Appl. Opt. vol. 52, no. 19, 2013,4556.

2. C. Schmid, “Stellar Interferometric Beam Combiners in the Context of Linear Optics Networks,” in

230-236

37.

Authors: Pallavi Choudekar, Sanjay Sinha, Divya Asija , Ruchira, Anwar Siddique

Paper

Title:

Transmission Congestion Management and Techno Economical Analysis using

Placement of TCSC

Abstract: Congestion management is a major problem in deregulated power system.

Congestion leads to increase in transmission line loading, increases losses and reducing

transmission efficiency. It also leads to increase in congestion cost and thus affects technical

as well as economical parameters. Research work is carried out under single line critical

contingency condition. Critical contingency is found out by overall performance index

(OPI). Optimal Power Flow (OPF) is carried out and objective is to maximize social

welfare. Thyristor controlled series compensator is used to improve power flow, thus it

reduces congestion and improves techno-economical parameters. Optimal location of

Thyristor controlled series compensator (TCSC) is found out by LMP difference method.

Index Terms: Critical Contingency, Optimal Power Flow, Locational Marginal Price.

References:

1. . I. Rashed, H. I. Shaheen, X. Z. Duan, and S. J. ChengEvolutionary optimization techniques for optimal

location and parameter setting of TCSC under single line contingency. vol. 205, no. 1. Appl. Math. Comput., (2008) 133–147

2. P. Sekhar and S. Mohanty.:Power system contingency ranking using Newton Raphson load flow

method.Annual IEEE India Conference, INDICON (2013)

3. S. Nagalakshmi and N. Kamaraj.:Comparison of computational intelligence algorithms for loadability

enhancement of restructured power system with FACTS devices. vol. 5. Swarm Evol. Comput. (2012). 17–27

4. 4. A. Elmitwally and A. Eladl.: Planning of multi-type FACTS devices in restructured power systems with

wind generation. vol. 77. Int. J. Electr. Power Energy Syst. (2016).33–42

5. C. Asir Rajan C., M. Surya Kalavathi, and S. Ranganathan.: Self-adaptive firefly algorithm based multi-

objectives for multi-type FACTS placement. vol. 10. no. 11. IET Gener. Transm. Distrib. (2016). 2576–2584

6. M. Saravanan, S. M. R. Slochanal, P. Venkatesh, and J. P. S. Abraham.: Application of particle swarm

optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. vol. 77. no. 3–4. Electr. Power Syst. Res. (2007) 276–283

7. F. B. Alhasawi and J. V. Milanovic.: Techno-Economic Contribution of FACTS Devices to the Operation

of Power Systems With High Level of Wind Power Integration. vol. 27, no. 3. IEEE Trans. Power Syst.

(2012). 1414–1421

8. A. Mishra and G. V. N. Kumar.: Congestion management of deregulated power systems by optimal setting

237-242

Proceedings of SPIE 9146, 2014, 914632.

3. E. Coscelli, et al., “ Analysis of the Modal Content into Large-Mode-Area Photonic Crystal Fibers under

Heat Load,” in IEEE Journal of Selected Topics in Quantum Electronics, vol. 22, no. 2, 2016, pp 323-330.

4. H. K.Hsiao and K.A.Winick, “An Integrated Infrared Optics Astronomical Beam Combiner for Stellar

Interferometry at 3-4 um,” in Optics Express, vol. 17, no. 21, 2009, pp 18489-18500.

5. J. D. Monnier, “Optical Interferometry in Astronomy,” Journal of Lightwave Technology, vol. 66, 2003, pp 789-857.

6. J. Tepper, L. Labadie, R. Diener, S. Minardi, J. Pott, R. Thomson, & S. Nolte, “Integrated Optics

Prototype Beam Combiner for Long Baseline Interferometry in the L and M bands,” in Astronomy & Astrophysics 602, 2017, p A66.

7. K. Perraut, and J.P. Berger, “Planar Integrated Optics and Astronomical Interferometer,” in Compt. Rend.

Acad. Sci. Ser. IV Phys. Astrophys, vol. 2, no. 1, 2001, pp 111-124.

8. M. Born and E. Wolf, Principles of Optics (Cambridge U. Press, 1999), Ch. 10.

9. M. Olivero and M. Svalbard, “ Direct UV Written Integrated Optical Beam Combiner for Stellar Interferometry,” in Journal of Lightwave Technology, vol. 25, no. 1, Jan. 2007, pp 367-371.

10. P. Benech and P. Kern, “On-chip Spectro-detection for Fully Integrated Coherent Beam Combiner,” in

Journal of Astrophysics, vol. 17, no. 3, 2009, pp 1976-87. 11. P. Kern and F. Malbet, “Single Mode Beam Combination for Interferometry,” in Aerospace Conference

JENAM , 2005, vol. 74, July. 2005, pp 5-6.

of Interline Power Flow Controller using Gravitational Search algorithm. vol. 118.J. Electr. Syst. Inf.

Technol. (2016).1–15.

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continuous power flow. vol. 19. no. 6. Sci. Iran. (2012.). 1683–1690,

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achieved by Bacterial Swarming Algorithm. in IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES. (2008)

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management using FACTS devices. vol. 90. no. 8. Electr. Eng. (Archiv fur Elektrotechnik). (2009).539–549

12. M. Basu.: Optimal power flow with FACTS devices using differential evolution..vol.30.no. 2. Int. J.

Electr. Power Energy Syst. (2008).150–156

13. E. Nanda Kumar and R. Dhanasekaran.: Optimal Power Flow with FACTS Controller Using Hybrid PSO.

vol.39. no. 4. Arab. J. Sci. Eng.. (2014), 3137–3146

14. R. Benabid, M. Boudour, and M. A. Abido.: Optimal location and setting of SVC and TCSC devices using

non-dominated sorting particle swarm optimization. vol. 79. no. 12. Electr. Power Syst. Res. (2009).1668–1677

15. Pallavi Choudekar, SK Sinha, Anwar Siddiqui: Optimal location of SVC for improvement in voltage

stability of a power system under normal and contingency condition. International Journal of System Assurance Engineering and Management.vol.8. Issue 2 (2017). 1312-1318.

38.

Authors: Shakshi Ghatwal, Himanshi Saini

Paper

Title:

Investigation on Applicability of Modulation Schemes for WDM High Speed Networks

using Hybrid Optical Amplifiers

Abstract: High speed networks face several challenges in order to meet desired Quality of

Service (QOS). In order to increase network speed with significant reduction in Bit Error

Rate (BER), new design techniques have to be deployed in high speed networks. In this

paper, basics of high speed networks along with Dense Wavelength Division Multiplexing

(DWDM) network issues and challenges have been discussed. Modulation schemes and

amplifier configurations are also summarized. Investigations on applicability of modulation

schemes for DWDM network architecture have been performed with various hybrid optical

amplifier configurations. The link configuration consists of 56 channels at speed of 15Gbps.

It is observed that Non Return to Zero (NRZ) scheme gives better performance in terms of

Quality-factor (Q-f) and BER. The network parameters are further improved using various

hybrid optical amplifier configurations. It is observed that NRZ scheme with Erbium Doped

Fiber Amplifier (EDFA) hybrid amplifier configuration has improved the quality factor of

system as compared to other hybrid amplifier configurations.

Keywords:: Amplifier, Bit Error Rate (BER), Modulation, Multiplexing, Quality-factor (Q-

f).

References:

1. Available: accessed on 1 May 2019 at 8:00 PM IST https://www.fs.com/the-advantages-and-

disadvantages-of-fiber-optic-transmission-aid-431.html

2. D. Sharma, Y. K. Prajapati, “Performance analysis of DWDM system for different modulation schemes

using variations in channel spacing”, J opt commun netw, vol. 37, no. 4, 2016, pp. 401-413

3. S. K. Gill, G. Kaur, “Performance evaluation of 32 channel DWDM system using dispersion

compensation unit at different bit rates”, International Research Journal Of Engineering And Technology,

vol. 03, no. 06, June 2016, pp. 2436-2441.

4. B.P. Patel, R. B. Patel, “Comparison of different modulation formats for 8 channel WDM optical network

at 40 GBPS datarate with non-linearity,” IJARET, vol. 5, no. 2, Feb. 2014, pp. 37-51.

5. N. V. Jaya, T. Kabilan, “Performance analysis of 48 channels dwdm system using edfa for long distance

communication,” GRD Journals, vol. 2, no. 3, Feb. 2017, pp. 39-47.

6. N. Rani, “Design and implementation of various advanced modulation format over 8-wdm bidirectional

243-248

39.

Authors: Jaspreet Kaur, Dr. Chandan Singh

Paper

Title:

Segmentation Of Brain Tissues From MRI Using Bilateral Filter Based Fuzzy C-Means

Clustering

Abstract: This paper represents a segmentation method that incorporates both local spatial

information and intensity information in an efficient fuzzy way. The newly introduced

segmentation method BWFCM is an abbreviation of Bilateral weighted fuzzy C-Means.

BWFCM uses the advantage of the bilateral filter in its objective function as a bilateral

kernel that replaced the spatial neighborhood term with Gaussian weighted Euclidean

distance mean of the intensity value of neighbor pixels. BWFCM preserves the damping

extent of adjacent pixels while removing the noise because of its averaging behavior. The

BWFCM segmentation method is perceived to be very focused on several state-of-the-art

methods on a range of images.Experiment analysis on simulated and real MR images show

that the proposed method BWFCM provides superior performance over the conventional

FCM method and several FCM based methods. The proposed method BWFCM has

weakened the impact of Rician noise and other artifact and gives more accurate and efficient

segmentation results.

Keywords: Fuzzy Clustering,Intensity information, MRI segmentation, Noise, Spatial

information.

References: 1. Brown RW, Haacke EM, Thompson MR, "Magnetic resonance imaging, physical principles, and sequence

design", John Willey Sons NY 1999. 2. V. Datta and S. Bhowmik, "A Survey on Clustering Based Image Segmentation," International Journal of

Advanced Research in Computer Engineering & Technology (IJARCET), vol. 1, no. 5, pp. 280--284, 2012.

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249-255

PON,” IJARIIT, vol. 3, no. 6, 2017, pp. 300-305

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optic communication with different modulation schemes and dispersion compensation fiber”, IJRET, vol.

03, no. 03, March 2014, pp. 287-290.

8. Y. J. Wen, J. Mo, Y. Wang, “Advanced data modulation techniques for WDM transmission”, IEEE

Commun. Mag., vol. 44, no. 8, Aug. 2006, pp. 58-65.

9. R. Mehra, V. Joshi, “Effect on Q factor of fixed bit pattern, and encoding techniques in intensity

modulated optical networks”. IJCA (0975 – 8887),vol. 106, no. 13, Nov. 2014, pp. 42-45

10. J. Singh, P. Gilawat, B. Shah, “Performance evaluation of 32×40 GBPS (1.28 TBPS) FSO link using RZ

and NRZ line codes,” IJCA (0975 – 8887), vol. 85, no 4, Jan. 2014, pp. 32-36.

11. S. Arya, D. Joseph, “A study performance of l- band optical communication system for NRZ and RZ

format,” Int J Phys Chem Math Sci, vol. 4, no. 1, 2015, pp. 9-13.

12. Payal, S. Kumar, D. Sharma, “Performance Analysis of NRZ and RZ Modulation Schemes in Optical

Fiber Link Using EDFA,” IJARCSSE, vol. 7, no. 8, Aug. 2017, pp. 161-168.

13. M. Sharma, N. Kaur, “Analysis of DWDM System Using Different Modulation and Compensation

Technique at Different Bit Rates,” International Journal of Exploring Emerging Trends in Engineering, vol. 02, no. 04, 2015, pp. 219-223.

14. A.A. Khadir, B. F. Dhahir, “Achieving optical fiber experiments using Optisystem”, IJCSMC, vol. 3, no.

6, 2014, pp. 42-53.

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Dec. 2014, pp. 1289-1292.

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optical fiber communication,” JASEI ,vol.1, no.2, 2014, pp. 83-91.

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bias field estimation and segmentation of MRI data,” IEEE Trans. Med. Imag., vol. 21, pp. 193–199,2002. 10. D. Zhang and S. Chen, “Robust image segmentation using FCM with spatial constraints based on new kernel-

induced distance measure", IEEE Trans. Syst., Man, Cybern., vol. 34, pp. 1907–1916, 2004.

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12. M. Gong, J. Ma, Z. Zhou “Change detection in synthetic aperture radar images based on image fusion and

fuzzy clustering,” IEEE Trans. Image Process, vol. 21, no. 4, pp. 2141-2151, 2012. 13. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” IEEE Intl. Conf. Comp. Vis., pp.

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medical image analysis,” IEEE Transactions on Medical Imaging, vol. 25, no. 11, pp. 1451–1461, 2006. 16. R.C.Gonzalez, R.E.Woods., “Image restoration and reconstruction”, in Digital Image Processing, 3rd ed. India:

Pearson Prentice Hall, (2011), pp.no.322-330.

17. A.C. Bovik, Z.Wang, Sheikh, E.P, H.R. Simoncelli, “Image quality assessment: from error visibility to

structural similarity,” IEEE Trans. Image Processing 13(2004) 600-612.

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40.

Authors: Shivani Gaba,Shifali Singla,Deepak Kumar

Paper

Title:

A Genetic Improved Quantum Cryptography Model to Optimize Network

Communication

Abstract— The communication becomes more critical when the network is having high

speed mobility and restricted coverage. WPAN is one such network defined for indoor

network and with smaller sensing limit. In this work, a quantum inspired encoded

communication is provided to improve the communication reliability and security. The work

model is defined for a randomly distributed and high mobility based WPAN network. At

first phase of this model, the node level characterization is applied under coverage, stability

and load parameters. Later on genetic model is applied to identify the most effective

communication pair. Finally, the quantum key based SHA is applied to perform the data

encoded. This encoded communication is performed over the network. The comparative

results shows that the work model has reduced the communication loss over the network.

Keywords:WPAN,Genetics,Quantum,Encoded,Cryptography

References: 1. S. Mandal et al., "Multi-photon implementation of three-stage quantum cryptography protocol," Information

Networking (ICOIN), 2013 International Conference on, Bangkok, 2013, pp. 6-11.

2. A. Porzio, "Quantum cryptography: Approaching communication security from a quantum perspective," Photonics Technologies, 2014 Fotonica AEIT Italian Conference on, Naples, 2014, pp. 1-4.

3. M. Niemiec and A. R. Pach, "Management of security in quantum cryptography," in IEEE Communications

Magazine, vol. 51, no. 8, pp. 36-41, August 2013. 4. Lei Zhou," A Simulation Platform for ZigBee-UMTS Hybrid Networks", IEEE COMMUNICATIONS

LETTERS 1089-7798/13@ 2013 IEEE

5. Adam Dahlstrom," Performance Analysis of Routing Protocols in Zigbee Non- Beacon Enabled WSNs", Internet of Things: RFIDs, WSNs and beyond 978-1-4673-3133-3/13 ©2013 IEEE

6. Meng-Shiuan Pan," Convergecast in ZigBee Tree-Based Wireless Sensor Networks", 2013 IEEE Wireless

Communications and Networking Conference (WCNC): NETWORKS 978-1-4673-5939-9/13 ©2013 IEEE 7. M. H. Shazly, E. S. Elmallah and J. Harms, "Location Uncertainty and Target Coverage in Wireless Sensor

Networks Deployment," 2013 IEEE International Conference on Distributed Computing in Sensor Systems,

Cambridge, MA, 2013, pp. 20-27. 8. Alexandru-Corneliu Olteanu," Enabling mobile devices for home automation using ZigBee", 2013 19th

International Conference on Control Systems and Computer Science 978-0-7695-4980-4/13 © 2013 IEEE

9. R. Rostom, B. Bakhache, H. Salami and A. Awad, "Quantum cryptography and chaos for the transmission of security keys in 802.11 networks," Mediterranean Electrotechnical Conference (MELECON), 2014 17th IEEE,

Beirut, 2014, pp. 350-356.

10. R. Khedikar and A. Kapur, "Energy effective target coverage WSNs," Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on, Ghaziabad, 2014, pp. 388-392.

11. Z. Lu and W. W. Li, "Approximation algorithms for maximum target coverage in directional sensor networks,"

Networking, Sensing and Control (ICNSC), 2014 IEEE 11th International Conference on, Miami, FL, 2014, pp. 155-160.

256-259

12. B. Diop, D. Diongue and O. Thiare, "A weight-based greedy algorithm for target coverage problem in wireless

sensor networks," Computer, Communications, and Control Technology (I4CT), 2014 International

Conference on, Langkawi, 2014, pp. 120-125.

13. A. Dahane, N. E. Berrached and A. Loukil, "Homogenous and secure weighted clustering algorithm for mobile wireless sensor networks," Control, Engineering & Information Technology (CEIT), 2015 3rd International

Conference on, Tlemcen, 2015, pp. 1-6.

14. P. Chaturvedi and A. K. Daniel, "An Energy Efficient Node Scheduling Protocol for Target Coverage in Wireless Sensor Networks," Communication Systems and Network Technologies (CSNT), 2015 Fifth

International Conference on, Gwalior, 2015, pp. 138-142.

15. X. Ren, W. Liang and W. Xu, "Quality-Aware Target Coverage in Energy Harvesting Sensor Networks," in IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1,

41.

Authors: Anupam Singh, Raghuraj Suryavanshi, Divakar Singh Yadav

Paper

Title:

Formal Development of Fault-Tolerant Majority Based Replica Control

Protocol Using Event-B

Abstract: In distributed environment, data availability and concurrency control both are

challenging issues. Data availability can be maintained by replicating data at several

locations or sites that will improve the availability but at the same time it is very challenging

task to maintain the consistency of it. In order to improve the performance of the system, it

is required to execute multiple transactions concurrently on several sites. Therefore, we need

to control these concurrent transactions for maintaining consistency of replica. Replica

control become more complex for the environment where messages are delayed due to

communication failure. In this paper, we develop formal model of fault-tolerant replica

control protocol Using Event-B. Formal methods are mathematical techniques through

which we can verify the correctness of model. Event-B is a formal method which is used to

develop the model in distributed environment.

Keywords: Formal Methods, Formal Verification, Event-B, Replication, Replica control

Protocol.

References

1. M. Ozsu and P. Valduriez: Principles of Distributed Database Systems.Pearson Education (Singapore) Pte.Ltd.

India 2004.

2. M. Singhal, N.G. Shivratri: Advanced Concepts in Operating Systems.Tata McGraw- Hill Book Company,

2012. 3. R. Suryavanshi and D.Yadav,Rigorous Design of Lazy Replication System Using Event-B, Communications in

Computer and Information Science ISSN: 1865-0929, Volume 0306, Springer, Verlag Germany 2012, pp

400-411. 4. Helal, A., Heddya, A. and Bhargava, B.: Replication Techniques in Distributed System. Kluwener Academic

Publishers (1997).

5. J. Gray, P. Helland, P. E. ONeil, and D. Shasha. The dangers of replication and a solution. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 173-182, Montreal, Canada, June 1996.

6. Kemme, B., Alonso, G.: A new approach to developing and implementing eager database replication protocols.

ACM Transaction Database System, 25(3), 2000, pp 333-379. 7. Paul Ammann, Sushil Jajodia, and Indrakshi Ray. Using formal methodsto reason about semantics-based

decompositions of transactions. InUmeshwar Dayal,Peter M. D. Gray, and Shojiro Nishio, editors, VLDB

Morgan Kaufmann, 1995, pp 218-227. 8. .D. Yadav and M. Butler. Application of Event B to global causal ordering for fault tolerant transactions. In

Proc. of Workshop on Rigorous Engineering of Fault Tolerant System, REFT05,Newcastle upon Tyne, 19

July 2005, pp 93-103. 9. Paul Ammann, Sushil Jajodia, and Indrakshi Ray. Applying formalmethods to semantic-based decomposition

of transactions. ACM Transaction on Database System., 22(2), 1997, pp 215-254.

10. Jean-Raymond Abrial and Dominique Cansell. Clickn prove: Interactiveproofs within set theory. In David A. Basin and Burkhart Wolff, editors, TPHOLs,volume 2758 of Lecture Notes in Computer Science, Springer,

2003, pp 124.

11. C Metayer, J R Abrial, and L Voison. Event-B language. RODIN deliverables ,http://rodin.cs.ncl.ac.uk/deliverables/D7.pdf, 2005.

12. M. Butler, J.-R. Abrial, and R. Banach, From Action Systems to Distributed Systems: The Renement

Approach. Tayloramp;Francis,2016, ch. Modelling and Hybrid Systems in Event-B and Rodin. 13. Michael Butler, Cli B. Jones, Alexander Romanovsky, and Elena Troubitsyna, editors. Rigorous Development

of Complex Fault-Tolerant Systems [FP6 IST-511599 RODIN project], volume 4157 of Lecture Notes in

Computer Science. Springer, 2006. 14. D. Yadav and M. Butler. Formal specifications and verification of message ordering properties in a broadcast

260-267

system using Event B. In Technical Report,School of Electronics and Computer Science, University of

Southampton, Southampton, UK, May 2007.

15. R. Suryavanshi, D. Yadav, Modeling of Multiversion ConcurrencyControl System Using Event-B in Federated

Conference on ComputeScience and Information systems (FedCSIS) 9-12 September,Poland,indexed and published by IEEE ISBN 978-83-60810-51-4, 2012, pp 1397-1401.

16. R. Banach, M. Butler, S. Qin, N. Verma, and H. Zhu,Core Hybrid Event-B I: Single Hybrid Event-B

machines, Science of Computer Programming, 2015. 17. E. Elsayed , G. El-Sharawy and E.Sharawy, Integration Of AutomaticTheorem Provers In Event-B

Patterns,International Journal of SoftwareEngineering amp; Applications (IJSEA), Vol.4, No.1, Jan. 2013.

18. D. Yadav and M. Butler. Formal specifications and verification of message ordering properties in a broadcast system using Event B. In Technical Report,School of Electronics and Computer Science, University of

Southampton, Southampton, UK, May 2007.

42.

Authors: Harsimarjeet Kaur, Parminder Singh

Paper

Title:

Text to Speech Synthesis System for Punjabi language Using Statistical Parametric

Speech Synthesis Technique

Abstract: Statistical Parametric Speech Synthesis has been most growing technique rather

than the traditional approaches that we are used to synthesizing the speech. The shortcoming

of traditional approaches will be overcome with latest statistical techniques. The main

advantages of SPSS from traditional synthesis technique are that it has more flexibility to

change the characteristics of voice and support more multiple languages i.e. multilingual,

has good coverage of acoustic space and robustness. It generates high quality of speech from

small training database. Deep Neural network and Hidden Morkov model are basic

statistical parametric speech synthesis techniques. Gaussian mixture model, sinusoidal

model are also under this categories. Features were extracted in two type spectral features

like spectral bandwidth, spectral centroid etc. and excitation features like F0 frequencies etc.

We are using 722 Punjabi phonemes. Using sound forge software we extracted the 200

wave file from 1 hour pre-recording wave file related to those phonemes. Each and every

phonemes feature was extracted and saved in database. We were extracting 28 features of

each phoneme. TTS text-to-speech system generates sounds or speech as a output when

provided the text of Punjabi language. There were already many TTS are developed on

different Indian languages. The system that we are trying to build is based only on Punjabi

language.

Keywords: SPSS, TTS, Phonemes, HMM.

References: 1. . F. Jalin and J. Jayakumari, “Text to speech synthesis system for Tamil using HMM,” in IEEE International

Conference on Circuits and Systems, ICCS 2017.

2. N. Adiga, B. K. Khonglah, and S. R. Mahadeva Prasanna, “Improved voicing decision using glottal activity features for statistical parametric speech synthesis,” Digit. Signal Process. A Rev. J., 2017.

3. E. Gerbier et al., “Deep Elman recurrent neural networks for statistical parametric speech synthesis,” Speech

Commun., 2017. 4. R. Kaur, R. K. Sharma, and P. Kumar, “Building a Text-to-Speech System For Punjabi Language,” IT-CSCP,

2016. 5. F. Araújo, J. Filho, and A. Klautau, “Genetic algorithm to estimate the parameters of Klatt and HLSyn

formant-based speech synthesizers,” BioSystems, 2016.

6. D. Mahanta, B. Sharma, P. Sarmah, S R Mahadeva Prasanna, “Text to Speech Synthesis System in Indian English” in IEEE Region 10 Conference (TENCON) — Proceedings of the International Conference, 2016.

7. D. Jurafsky and J. H. Martin, “Speech and Language Processing 18 BT - An Introduction to Natural Language

Processing, Computational Linguistics, and Speech Recognition,” in An Introdction to Natural Language Processing, Computational Lingustics, and Speech Recognition, 2009.

8. S. Lukose and S. S. Upadhya, “Text to speech synthesizer-formant synthesis,” in IEEE International

Conference on Nascent Technologies in Engineering, ICNTE 2017 - Proceedings, 2017. 9. G. Kaur and P. Singh “Formant Text To Speech Synthesis using Artificical Neural Network,” in IEEE Second

internal conference on advanced computational and communication paradigms,ICACCP2019.

268-272

Authors: Shradha Yadav, Shalley Bakshi, Manpreet Kaur

Paper Efficient routing in wireless sensor network by using water cycle algorithm to evaluating

43.

Title: the performance of density grid based clustering

Abstract: Wireless communication has an exceptional network known as a wireless

sensor network, which consists of many dedicated sensors. They have characteristics of

sensing capacity and have the capability to complete a common task. To increase the

performance of the network an energy efficient algorithm is needed which enhances the

network lifetime and make the system more energy efficient. In this paper, the density

grid based clustering is enhanced using the water cycle algorithm. The water cycle

algorithm is a nature-inspired optimization algorithm that is used to increase the

performance of the network which effects the lifetime of the network. The simulation is

done in the Matlab r2015a environment. The proposed methodology has increased the

performance of the network when compared with the existing algorithm ABC (artificial

bee colony) and its better improvement is shown in parameters like network lifetime,

computation time and cluster formed.

Keywords: WCA, WSN, optimization

Reference:

1. Baljinder Singh,"evaluating the performance of density grid based clustering using ABC technique for efficient

routing in WSNs" D.O.I:10.1109/CISS.2017.7926099,Conference:2017 ,51 annual conference on information

sciences and system(CISS) 2. Utpal Kumar Paul, Sudipta Chattopadhyay, “A Novel Grid based energy efficient Routing Algorithm for

Wireless Sensor Network”, International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), IEEE, pp 223-226, 2016.

3. Abdullah, Manal, et al. "Density Grid-Based Clustering for Wireless Sensors Networks." Procedia Computer

Science 65 (2015): 35-47. 4. Kumar Dilip "Performance analysis of energy efficient clustering protocols for maximizing lifetime of wireless

sensor networks." Wireless Sensor Systems, IET 4, No. 1, pp. 9-16, 2014.

5. Stefanos A. Nikolidakis, Dionisis Kandris, Dimitrios D. Vergados, and Christos Douligeris, (2013)“Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering,”

6. .Ji, Peng, Yupeng Li, Jingqi Jiang, and Tianbao Wang. "A Clustering Protocol for Data Aggregation in

Wireless Sensor Network." In Proceedings of the IEEE International Conference on Control Engineering and

Communication Technology, pp. 649-652, 2012.

7. Ashok Kumar and Vinod Kumar, (2011)“Energy Efficient Clustering and Cluster Head Rotation Scheme for

Wireless Sensor Networks,” (IJACSA) International Journal of Advanced Computer 8. Science and Applications, vol. 3, no. 5

9. Li, Nan, Shangru Li, and Xiaozhou Fang. "Adaptive data aggregation mechanism based on leach protocol." in

International Conference on Advanced Intelligence and Awareness Internet (AIAI 2010), pp. 131- 134, 2010 10. Liu XiaoLong, Li RongJun, YangPing, “A Bacterial Foraging Global Optimization Algorithm Based On the

Particle Swarm Optimization”, International Conference on Intelligent Computing and Intelligent Systems,

IEEE, pp. 21-27, December 2010. 11. Xiaorong Zhu, Lianfeng Shen, and Tak-Shing Peter Yum, (2009)"Hausdorff Clustering and Minimum Energy

Routing for Wireless Sensor Networks," IEEE Transactions on Vehicular Technology, vol. 58, no. 2.

12. Gao Yi, Sun Guiling, Li Weixiang, and Pan Yong, (2009)"Recluster- LEACH: A Recluster Control Based on Density for Wireless Sensor Network," International Conference on Power Electronics and Intelligent

Transportation System.

13. Dongkyun Kim, Hong-Jong Jeong, C. K. Toh, and Sutaek Oh, (2009)"Passive Duplicate Address-Detection Schemes for On- Demand Routing Protocols in Mobile Ad Hoc Networks," IEEE Transactions on Vehicular

Technology, vol. 58, no. 7.

14. Hu Junping, Jin Yuhui, and Dou Liang, (2008)“A Time-based Cluster-Head Selection Algorithm for LEACH,”

IEEE Symposium on Computers and Communications,

15. S. J. Baek and G. de Veciana, (2007) "Spatial Energy Balancing through Proactive Multipath Routing in

Wireless Multihop Networks," IEEE ACM Transactions Networking, vol. 15, no. 1, pp. 93-104, February 16. Uk-Pyo Han, Sang-Eon Park, Seung-Nam Kim, and Young-Jun Chung, (2006)“An Enhanced Cluster Based

Routing Algorithm for Wireless Sensor Networks,” IEEE Transactions on Dependable and Secure

Computing, vol. 3, no. 1 17. O. Younis and S. Fahmy, (2004)"HEED: A Hybrid, Energy- Efficient Distributed Clustering Approach for Ad

Hoc Sensor Networks," IEEETransactions Mobile Computing, vol. 3, no. 4, pp. 366-379, December.

18. Ali Sadollah,’’ water cycle algorithm: a detail standard code DOI: 10.1016/j.softx.2016.03.0

273-279

Authors: Jaswinder Kaur, Rajesh Khanna, Nitika Mittal

Paper

Title:

Rectangular Zigzag Microstrip Patch Antenna with Swastic shape DGS for WLAN,

C and Ku-Band Applications

44.

Abstract: A triple band microstrip-fed patch antenna is presented which contains the

radiating structure having rectangular zigzag shape patch and an altered ground structure

with a swastic shape design. This modified ground plane actually acts as a defected ground

structure (DGS). Both the modified ground plane and radiating patch are perfect electric

conductors. The patch is imprinted on a substrate named as Epoxy Glass FR-4 having

thickness 1.6 mm, relative permittivity 4.4, and loss tangent 0.0024. The designed

microstrip patch antenna (MPA) is able to generate three specific operating bands viz.

11.9–13.6 GHz, 5.71–5.82 GHz, 4.5-4.6 GHz with adequate bandwidth of 1.64 GHz, 110

MHz and 100 MHz and corresponding return loss of -32dB, -23dB, -14.3dB respectively

covering Wireless Local Area Network (WLAN), C-band and Ku-band applications. A

parametric study has been performed for the rectangular slots located in the patch.

Proposed MPA is simulated using Computer Simulation Technology Microwave Studio

Version 14.0 (CST MWS V14.0). Lastly, the fabrication of the proposed antenna with

optimized parameters has been accomplished and measured results for S-parameter

magnitude have been discussed.

Keywords: CST MWS V14.0, defected ground structure, direct broadcast satellite,

microstrip patch antenna, C-band, WLAN, zigzag

Reference: 1. Balanis CA. Antenna Theory: Analysis and Design. Third Edition. Wiley Interscience. 2005.

2. Sittironnarit T, Hwang HS, Sadler RA, Hayes GJ. Wide band/dual band package antenna for 5–6 GHz WLAN application. IEEE Trans. on Antennas and Propag. 2004;52(2):610-615.

3. Li L, Cheung SW, Yuk TI. Dual band antenna with compact radiator for 2.4/5.2/5.8 GHz WLAN applications.

IEEE Trans. on Antennas and Propag. 2012;60(12):5924- 5931. 4. Huang CY, Yu EZ. A slot-monopole antenna for dual-band WLAN applications. IEEE Antennas Wirel.

Propag. Lett. 2011;10:500–502.

5. Khalegh A. Dual band meander line antenna for wireless LAN communication. IEEE Trans. on Antennas and Propag. 2007;55(3):1404–1409.

6. Kaur J. Development of Dual Band Microstrip Patch Antenna for WLAN /MIMO/ WiMAX/AMSAT/WAVE

Applications. Microw. and Opt. Tech. Lett. 2014;56(8):1965-1970.

7. Kaur J, Khanna R, Kartikeyan MV. Optimization and Development of O-Shaped Triple band Microstrip Patch

Antenna for Wireless Communication Applications. IETE Journal of Research. 2014;60(2):95-105.

8. Patel JM, Patel SK, Thakkar FN. Comparative analysis of S-shaped multiband microstrip patch antenna. Int. J of Adv. Research in Electrical, Electronics and Instrumentation Engg. 2013;2(7):3273–3280.

9. Kaur J, Khanna R, Kartikeyan MV. Novel Dual Band Microstrip Monopole Antenna with Defected Ground

Structure for WLAN /IMT/Bluetooth/WiMAX Applications. Int. J of Microw. and Wirel. Tech. 2014;6(1):93-100.

10. Chitra RJ, Jayanthi K, Nagaraja V. Design of microstrip slot antenna for WiMAX Application. IEEE Int.

Conf. on Comm. and Signal Processing (ICCSP). 2013:645 – 649. 11. Chakraborty U, Chowdhury SK, Bhatacharjee AK. Frequency tuning and miniaturization of square microstrip

antenna embedded with ‘T’-shaped defected ground structure. Microw. and Opt. Tech. Lett. 2013;55(4):869–

872. 12. Chiang KH, Tam KW. Microstrip monopole antenna with enhanced bandwidth using defected ground

structure. IEEE Antennas and Wirel. Propag. Lett. 2008;7:532–535.

13. Ali T, Pathan S, Biradar RC. Multiband, frequency reconfigurable, and metama-terial antennas design techniques: Present and future research directions. Internet Tech Lett. 2018;1:e19.

14. Ali T, Saadh MAW, Pathan S and Biradar RC. A miniaturized circularly polarized coaxial fed superstrate slot

antenna for L-band application. Internet Tech Lett. 2018;1:e21.

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45.

Authors: Sapna Juneja, Abhinav Juneja, Rohit Anand and Paras Chawla

Paper

Title:

Mining aspects on the social network

Abstract: This paper proposes an effective concept of mining the feedback of product given

by the user. In return various solutions are suggested according to the ratings of the aspect

and its corresponding weightage. The satisfaction of user is determined by the help of user’s

rating and weight of the aspect determines the significance of each aspect in the user’s

review. These methodologies are thus, important and play a significant role for the

285-289

manufacturers and producers to improvise their product and eventually leading to rise in the

market value of that particular product. The methodology here extracts the aspects from the

feedbacks of users with the help of conditional probability and bootstrap technique. Also an

approach that is supervised and is called by the name, Naïve Bayes is used to classify aspect

ratings and the sentiment words are considered as properties or features.

Index Terms: Aspect, Aspect Extraction, Aspect Weight, Aspect Ratings, Core Term,

Naïve Bayes Conditional Probability.

Reference: 1. S Park, K Lee , JSong: Contrasting opposing views of news articles on contentious issues. Proceedings of the

49th annual meeting of the association for computational linguistics (ACL-2011),2011.

2. MVan Den Camp, A Bosch:The socialist network. Decis Support Syst, 2012,pp.761-69. 3. S K Li, Z Guan, LY Tang :Exploiting consumer reviews for product feature ranking. J Comput Sci

Technology, 2012, pp. 635-49.

4. C Lin, Y He, R Everson, S Ruger: Weakly supervised joint sentiment-topic detection from text,IEEE 2012,

pp.1134-45.

5. J Zhan, HT Loh, Y Liu: Gather customer concerns from online product reviews—a text summarization

approach. Expert Sysem 2009, pp.2107-15. 6. Y Dang, Y Zhang, H Chen, A lexicon-enhanced method for sentiment classification: an experiment on online

product reviews. IEEE, 2010, pp.46-53.

7. B Pang,L Lee: A sentiment education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the,42nd annual meeting on association for Computational Linguistics.

2004. 8. M Taboada, J Brooke, M Tofiloski, KVoll, M Stede: Lexicon-based methods for sentiment analysis.,2011,

pp.267-307.

9. P D Turney:Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp.417–24.

10. M Hu,B Liu:Mining and summarizing customer reviews, Proceedings of the Tenth ACM SIGKDD

international conference on knowledge discovery and data mining, New York, 2004, pp. 168–77. 11. B Liu: Sentiment analysis and opinion mining. Synth Lect Human Lang Technology1–67.CrossRef, 2012.

12. C Long, J Zhang, X Zhut:A review selection approach for accurate feature rating estimation. Proceedings of

Coling ,2010. 13. S Moghaddam ,M Ester: Opinion digger: an unsupervised opinion miner from unstructured product review,

Proceeding of the ACM conference on Information and knowledge management , 2010.

14. Li Chen, Feng Wang:Preference-based clustering reviews for augmenting e-commerce recommendation. Knowledge Based System, 2013, pp.44-59.

15. H Wang, Y Lu, C Zhai: Latent aspect rating analysis on review text data: a rating regression approach,

Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, 2010, pp.783-92.

16. K Ravi:A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge

Based System, 2015, pp.14-46. 17. A M Popescu, O Etzioni :Extracting product features and opinions from reviews, Proceedings of the conference

on Human Language Technology and Empirical Methods in Natural Language Processing.2005, pp. 339–46.

18. J Zhu, H Wang, B K Tsau, M Zhau:Multi-aspect opinion polling from textual reviews, Proceedings of ACM international conference on information and knowledge management,2009.

19. B Santorini: Part-of-speech tagging guidelines for the Penn Treebank Project, University of Pennsylvania,

School of Engineering and Applied Science, Dept. of Computer and Information Science, 1990. 20. Shivam Agarwal: Data Mining: Data mining concepts and techniques,International conference on machine

learning and research advancement, IEEE, 2013.

21. Xiuzhen Zhang, Shuliang Wang, Gao Cong,Alfredo Cuzzocrea:Social Big Data: Mining aspects, applications and beyond , Wiley, 2019.

46.

Authors: Haleema Sadiya, Harbinder Singh, Mamta Arora

Paper

Title:

Broad Band Propinquity Coupled Feed Line Micro-strip Blotch Antenna for 5G

Applications

Abstract— In this research based paper, as the need of capacity is increasing very

frequently so the demand of smart mobile phone and other wireless communication devices

are increasing. The only technology that would be able to fastly fulfill the increasing

communication capacity is 5G. For the establishment of communication between the

devices of wireless on the higher band of frequencies like mm wave (mili-meter) there is

much demand of such type of antennas that are lesser in size, not much expansive, compact

and also main easy to fabricate as well as simulate. So, according to all these requirements

the antennas that can fulfill all the requirements are only the patch antennas. The Micro-strip

290-296

blotch Antenna and the authentic viewpoint are shown with high gain millimeter-wave

antenna. The micro-strip blotch Antenna has high gain more prominent ground as of late.

The Antenna has a good return-loss, gain up-to 11.91dB and the directivity 12.62dB at

28GHz frequency with 6×5 micro-strip planar array configuration with Propinquity coupled

planar array Feeding method & line feed method procedure, it gives advantages over

traditional conventional Antennas and benefits for 5G applications. In this research work for

the implementation the designing and simulation work done on CST TOOL. This simulation

is basically performed for coup-led propinquity and line feed for 5G application in future

work together.

Keywords- Micro strip blotch Antenna (MPA), Return-loss, Feeding techniques etc.

References:

1. M Barrett, “Microwave Printed Circuits The Early Years”, IEEE Trans. Microwave Theory Tech., vo232, no.

19, Sept. 1987. 2, pp. 783-700.

2. H.Howe, “Microwave integrated Circuits –An Historical Prespective:, IEEE trans.. Microwave Theory Tech.,

vol. 132, no.9, Sept. 1987. 3, pp. 91-96

3. Y.B jung and H.A Diawuo “Wideband Propinquity coupled micro-strip linear array design for 5G

mobilecommunication”, Microwave Opt.Tecnol.Lettl,vol.59,no.12,2 018, pp, .996-002. 4. R .Z,W.Ling, T.Xiaobong, Z.Xinjing, “Study of micro-strip line inserted fed and two layer electromagnetically

coupled rectangular blotch Ant-ennas,” Microwave Conference Proceedings APMC, vol. 24, 2000.

5. V singh, M thakur, “Band-width Enhancement of Microstrip line inset fed Path Ant-enna,”IJERT, vol 1.3, August- 2018.

6. R. Verma, N. Vyas, R. Rana, V. Kaushik, A.K. Arya, ‘Design study of Microstrip Antenna with Various

Feeding Techniques: A Review, International Journal of Engineering Research & Technology, 3, 2014, pp. 619-622.

7. K.Mak, H.Lai and K.Luk. “A 5G Wideband Patch Ante-nna With Antisymmetric L-shaped Probe Feeds”.

IEEE Transactions on Ante-nnas and Propagation, 66(2), pp.957-961, 2018. 8. M. Sharawi, M. Ikram and A. Shamim, ”A Two Concentric Slot Loop Based Connected Array

MIMO Ante-nna System for 4G/5G Terminals”. IEEE Transactions on Ante-nnas and Propagation, 65(12),

2017, pp.6679-6686. 9. J. Ban G and J. Choi. “A SAR Reduced mm-Wave Beam-Steerable Array Ante-nna With Dual-Mode

Operation for Fully Metal-Covered 5G Cellular Handsets”. IEEE Ante-nnas and Wireless Propagation

Letters,17(6), 2018, pp.1118-1122.

10. L. Trinh, F. Ferrero, L. Lizzi,R. Staraj. and J. Ribero. “Reconfigurable Ante-nna for Future Spectrum

Reallocations in 5G communication’’ IEEE antennas and wireless Propogation Letters,15,2016, pp.1297-1300.

11. P. Dzagblete and Y. Jung, “Stacked Micro-strip Linear Array for Millimeter-Wave 5G baseband

Communication”. IEEE Antennas and Wireless Propagation Letters, 17(5), 2018, pp. 780-783,. 12. D. HA, J.Y-B. ‘Wideband proximity coupled microstrip linear array design for 5G mobile communication’

Microw Opt Technol Lett. 2017;59, pp. 2996–3002.

13. H. Abu, Y. Bae a.,(2018). broadband proximity-coupled microstrip planar antenna array for 5g cellular applications. ieee antennas and wireless propagation letters, 17(7), pp. 1286–1290.

47.

Authors: Saumya Srivastava & Tripti Sharma

Paper

Title:

Performance Improvement Strategies of Analog & Mixed Circuits

Abstract: In modern days technology becomes very advanced and it moves from analog to

digital domain. Everything depends on one and zero frame. In any case, the foundation of

any computerized circuit is constantly simple and without simple can't envision this world.

ADCs play an intermediate role between analog and digital circuits. Analog and mixed

circuits are basic piece of any gadget and there are bunches of works in analog domain for

researcher. This paper is extremely helpful for understanding the extent of analog and mixed

circuits, also give description about various smart techniques which help to improve the

performance of the device. Presently gadgets turn out to be little because of scaling and

circuits works on nano-scale process so area become comparably very small but on the

penalty of power dissipation which is important term for consideration. This paper

additionally talked about different way to deal with decrease control supply. It is thoroughly

legitimizes the way that Analog is exceptionally amazing area for researcher.

Keywords: Analog & Mixed signal, Bulk driven, Floating gate, Slew Rate, DTMOS, DC

297-301

48.

Authors: Ketan thakur, Dr. Tripti sharma

Paper

Title:

Area efficient high speed vedic multiplier

Abstract: Very large scale integration is a process of integrating hundreds of thousands of

transistors or devices into a single chip. VLSI can be categorized into two fields Frontend

and Backend. Digital VLSI design falls under the Frontend design. Multiplication is an

arithmetic operation important for the Digital Signal Processing (DSP) and for processors.

Multiplier is the main hardware block for the digital circuit. More than 70% of the

applications in a digital circuit are either addition or multiplication. As these operations

dominates most of the execution time so we need fast multipliers. The overall objective of a

good multiplier is to have high speed, low power consumption unit, less area. Vedic

multipliers are the fast multipliers and occupy less area. They are based on the Vedic

mathematics sutra "Urdhava-Triyakbhyam" . The paper contain a review of multipliers and

use of different adder structures.

302-306

gain

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(VLSICS) Vol.2, No.4, December 2011

Keywords-VLSI, DSP, Vedic, multiplier.

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10. Ms. G. R. Gokhale,et.al,"Design of Area and Delay Efficient Vedic Multiplier Using Carry Select

Adder",2015 International Conference on Information Processing (ICIP)Vishwakarma Institute of

Technology. Dec 16-19, 2015.

11. YogitaBansal,CharuMadhu,"Anovel high-speed approach for 16×16Vedic multiplication with compressor adders",Elsevier ComputersandElectricalEngineering49(2016), pp.39–49.

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49.

Authors: Afzal Hussain Shahid, M. P. Singh, Gunjan Kumar

Paper

Title:

Severity classification of Multiple Sclerosis disease: a rough set-based approach

Abstract: Multiple sclerosis (MS) is among the world’s most common neurologic disorder.

Severity classification of MS disease is necessary for treatment and medication dosage

decisions and to understand the disease progression. To the best of authors’ knowledge, this

is the first study for the severity classification of MS disease. In this study, Rough set (RS)

approach is applied to discern the three classes (mild, moderate, and severe) of the severity

of MS disease. Furthermore, the performance of the RS approach is compared with Machine

learning (ML) classifiers namely, random forest, K-nearest neighbour, and support vector

machine. The performance is evaluated on the dataset acquired from Multiple sclerosis

outcome assessments consortium (MSOAC), Arizona, US. The weighted average accuracy,

precision, recall, and specificity values for the RS approach are found to be 84.04%,

76.99%, 76.75%, and 83.84% respectively. However, among the ML classifiers, the

performance of random forest classifier is found best for which the weighted average

accuracy, precision, recall, and specificity values are 62.19 %, 52.65 %, 56.84 %, and 59.87

% respectively. The RS approach is found much superior to ML classifiers and may be used

for MS disease severity classification. This study may be helpful for the clinicians to assess

the severity of the MS patients and to take medication and dosage decisions.

Keywords: Multiple sclerosis, severity classification, rough sets, machine learning.

References: 1. Browne, P., et al., Atlas of multiple sclerosis 2013: a growing global problem with widespread inequity.

Neurology, 2014. 83(11): p. 1022-1024.

2. Federation, M.S.I., Atlas of MS 2013: Mapping multiple sclerosis around the world. Mult Scler Int Fed, 2013: p. 1-28.

3. Mackenzie, I., et al., Incidence and prevalence of multiple sclerosis in the UK 1990–2010: a descriptive study

in the General Practice Research Database. J Neurol Neurosurg Psychiatry, 2014. 85(1): p. 76-84. 4. Johnston Jr, R.B. and J.E. Joy, Multiple sclerosis: current status and strategies for the future. 2001: National

Academies Press. 5. Lublin, F.D., et al., Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology, 2014.

83(3): p. 278-286.

6. Whitaker, J., et al., Outcomes assessment in multiple sclerosis clinical trials: a critical analysis. Multiple

307-314

Sclerosis Journal, 1995. 1(1): p. 37-47.

7. LaRocca, N.G., et al., The MSOAC approach to developing performance outcomes to measure and monitor

multiple sclerosis disability. Multiple Sclerosis Journal, 2018. 24(11): p. 1469-1484.

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sclerosis. 2017, SAGE Publications Sage UK: London, England. 12. Lamers, I., et al., Perceived and actual arm performance in multiple sclerosis: relationship with clinical tests

according to hand dominance. Multiple Sclerosis Journal, 2013. 19(10): p. 1341-1348.

13. Drake, A., et al., Psychometrics and normative data for the Multiple Sclerosis Functional Composite: replacing the PASAT with the Symbol Digit Modalities Test. Multiple Sclerosis Journal, 2010. 16(2): p. 228-237.

14. Rudick, R., et al., Clinical outcomes assessment in multiple sclerosis. Annals of Neurology: Official Journal of

the American Neurological Association and the Child Neurology Society, 1996. 40(3): p. 469-479. 15. Balcer, L.J., et al., Validity of low-contrast letter acuity as a visual performance outcome measure for multiple

sclerosis. Multiple Sclerosis Journal, 2017. 23(5): p. 734-747.

16. Suraj, Z., An introduction to rough set theory and its applications. ICENCO, Cairo, Egypt, 2004. 3: p. 80.

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Computer Science, 2015. 47: p. 351-359.

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in Medical Imaging. 2009, Chapman and Hall/CRC. p. 61-102. 20. Wabnik, K., et al., Gene expression trends and protein features effectively complement each other in gene

function prediction. Bioinformatics, 2008. 25(3): p. 322-330.

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Media.

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p. 1-16. 25. Pawlak, Z., Rough set theory and its applications to data analysis. Cybernetics & Systems, 1998. 29(7): p. 661-

688.

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27. Ross, T.J., Fuzzy logic with engineering applications. Vol. 2. 2004: Wiley Online Library.

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33. Hvidsten, T.R., A tutorial-based guide to the ROSETTA system: A Rough Set Toolkit for Analysis of Data. 2010.

50.

Authors: Jasmeet Kaur, Gurpreet Singh

Paper

Title:

An Optimized Method of Module Selection During Software Development

Abstract: The field of Software Engineering comes into the existence to avoid the damage

caused by exploratory style of software development. Various steps specified during

software development leads to the successful completion of the software under

consideration. If the software is really not feasible then these steps will explain this in the

initial investigation without wasting efforts in terms of time and person months. In this

paper the focus is given on the Design and Implementation phases of the development

process. A new method has been proposed to prepare a valid sequence of execution of

modules to ease the work of implementation team. Convolution Neural Network has been

used to find the best sequence of execution based on various coupling and cohesion

parameters.

315-318

Keywords: Design, Implementation, Neural Network, Software Engineering.

References: 1. Yurinda, Software Engineeering. 2017.

2. G. Dougherty, Pattern recognition and classification: an introduction. Springer Science & Business Media,

2012. 3. A. Kumar and C. Ravikanth, “Personal authentication using finger knuckle surface,” IEEE Trans. Inf.

Forensics Secur., vol. 4, no. 1, pp. 98–110, 2009.

4. I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., vol. 3, no. Mar., pp. 1157–1182, 2003.

5. F. Penha, E. Miranda, M. Lucena, L. Lucena, F. Alencar, and C. S. Filho, “Actor ’ s social complexity : a

proposal for managing the iStar model,” 2018. 6. E. M. Guerra, S. M. Porto, J. Choma, and M. G. Quiles, “An approach for applying Test-Driven Development (

TDD ) in the development of randomized algorithms,”

7. D. F. Brito, M. P. Barcellos, and G. Santos, “Investigating measures for applying statistical process control in software organizations,” J. Softw. Eng. Res. Dev., vol. 6, 2018.

8. G. Singh and M. Sachan, “A Framework of Online Handwritten Gurmukhi Script Recognition,” vol. 8491, pp.

52–56, 2015. 9. S. Ramteke, “Automatic Segmentation of Content and Noncontent based Handwritten Marathi Text

Document,” pp. 404–408, 2016.

10. C. L. Huang and J. F. Dun, “A distributed PSO–SVM hybrid system with feature selection and parameter optimization,” Appl. Soft Comput., vol. 8, no. 4, pp. 1381–1391, 2008.

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by label-dependent feature weighting,” Pattern Recognit. Lett., vol. 33, no. 16, pp. 2232–2238, 2012. 12. A. Craig, O. Cloarec, E. Holmes, J. K. Nicholson, and J. C. Lindon, “Scaling and normalization effects in

NMR spectroscopic metabonomic data sets,” Anal. Chem., vol. 78, no. 7, pp. 2262–2267, 2006.

13. [ G. Singh and B. Singh, “Feature Based Method for Human Facial Emotion Detection using Optical Flow Based Analysis,” Int. J. Eng. Sci., vol. 4, no. 1, pp. 363–372, 2011.

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http://promise.site.uottawa.ca/SERepository/datasets-page.html. [Accessed: 07-Mar-2019]. 15. M. J. Lyons, “Automatic classification of single facial images,” IEEE Trans. Pattern Anal. Mach. Intell., vol.

21, no. 12, pp. 1357–1362, 1999.

16. G. Singh and M. K. Sachan, “Data capturing process for online Gurmukhi script recognition system,” in IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015, pp. 518–

521.

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Computing Research, IEEE ICCIC 2014, 2015.

51.

Authors: Satwinder Kaur, Garima Joshi, Renu Vig

Paper

Title:

Plant Disease Classification Using Deep Learning GoogleNet Model

Abstract: Plant diseases have been a major crisis that is disturbing the food production. So

there is a need to provide proper procedures for plant disease detection at its growing age

and also during harvesting stage. Timely disease detection can help the user to respond

instantly and sketch for some defensive actions. This detection can be carried out without

human intervention by using plant leaf images. Deep learning is progressively best for

image detection and classification. In this effort, a deep learning based GoogleNet

architecture is used for plant diseases detection. The model is trained using public database

of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82%

accuracy for 14 crop types making it capable of further deployment in a crop detection and

protection application.

Keywords: Deep learning, GoogleNet, Plant disease detection

References: 1. Yang, Xin, and Tingwei Guo. "Machine learning in plant disease research." European Journal of Biomedical

Research 3, no. 1 6-9.2017.

2. Mohanty, Sharada P., David P. Hughes, and Marcel Salathé. "Using deep learning for image-based plant disease detection." Frontiers in plant science 7:1419.2017.

3. Hughes, David, and Marcel Salathé. "An open access repository of images on plant health to enable the

development of mobile disease diagnostics." arXiv preprint arXiv: 1511.08060 2015.

4. Slodojevic, Srdjan, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, and Darko Stefanovic. "Deep

neural networks based recognition of plant diseases by leaf image classification." Computational intelligence

and neuroscience 2016. 5. Rehman, Tanzeel U., Md Sultan Mahmud, Young K. Chang, Jian Jin, and Jaemyung Shin. "Current and future

applications of statistical machine learning algorithms for agricultural machine vision systems." Computers

and Electronics in Agriculture 156: 585-605. 2019. 6. Zhang, Shanwen, Haoxiang Wang, Wenzhun Huang, and Zhuhong You. "Plant diseased leaf segmentation and

recognition by fusion of superpixel, K-means and PHOG." Optik 157: 866-872.2018.

7. Nagasubramanian, Koushik, Sarah Jones, Asheesh K. Singh, Arti Singh, Baskar Ganapathysubramanian, and Soumik Sarkar. "Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency

maps." arXiv preprint arXiv: 1804.08831 2018.

8. Johannes, Alexander, Artzai Picon, Aitor Alvarez-Gila, Jone Echazarra, Sergio Rodriguez-Vaamonde, Ana Díez Navajas, and Amaia Ortiz-Barredo. "Automatic plant disease diagnosis using mobile capture devices,

applied on a wheat use case." Computers and electronics in agriculture 138: 200-209.2017.

9. Ferentinos, Konstantinos P. "Deep learning models for plant disease detection and diagnosis." Computers and Electronics in Agriculture 145 : 311-318. 2018

10. Iqbal, Zahid, Muhammad Attique Khan, Muhammad Sharif, Jamal Hussain Shah, Muhammad Habib ur

Rehman, and Kashif Javed. "An automated detection and classification of citrus plant diseases using image

processing techniques: A review." Computers and electronics in agriculture 153: 12-32.2018.

11. Chaudhary, Piyush, Anand K. Chaudhari, A. N. Cheeran, and Sharda Godara. "Color transform based

approach for disease spot detection on plant leaf." International Journal of Computer Science and Telecommunications 3, no. 6: 65-70.2012.

12. Patil, Jayamala Kumar, and Raj Kumar. "Analysis of content based image retrieval for plant leaf diseases using

color, shape and texture features." Engineering in agriculture, environment and food 10, no. 2: 69-78.2017. 13. Sardogan, Melike, Adem Tuncer, and Yunus Ozen. "Plant Leaf Disease Detection and Classification Based on

CNN with LVQ Algorithm." In 2018 3rd International Conference on Computer Science and Engineering

(UBMK), pp. 382-385. IEEE, 2018. 14. Too, Edna Chebet, Li Yujian, Sam Njuki, and Liu Yingchun. "A comparative study of fine-tuning deep

learning models for plant disease identification." Computers and Electronics in Agriculture 2018.

15. Barbedo, Jayme Garcia Arnal. "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification." Computers and electronics in agriculture 153: 46-53.2018.

16. Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan,

Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with convolutions." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. 2015.

17. Erhan, Dumitru, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov. "Scalable object detection

using deep neural networks." In Proceedings of the IEEE conference on computer vision and pattern

recognition, pp. 2147-2154. 2014.

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52.

Authors: Ashima Kalra, Shakti Kumar, Sukhbir Singh Walia

Paper

Title:

ANN Model Identification: Parallel Big Bang Big Crunch Algorithm

Abstract: This paper proposes a modification to existing big bang big crunch optimization

algorithm that uses the concept of more than one population. In this the search begins with

all the populations independently in parallel and as the algorithm proceeds the local best of

the individual populations interact with global best to avoid local minima. In order to

validate the proposed approach the authors have identified two models one from control

field namely rapid battery charger and second a rating system for institutes of higher

learning and compared its results with simple BB-BC based approach. The author further

compared results of the proposed approach with the results of other recent soft computing

based algorithms for ANN model identification. The proposed algorithm outperformed all of

the other 7 algorithms in terms of MSE and convergence time.

Keywords: model identification, ANN (artificial neural network), big bang big crunch

(BB-BC) optimization, parallel big bang big crunch (PBB-BC) optimization Levenberg-

Marquardt algorithm (LM), error back propagation (EBP), Resilent prop (RPROP), particle

swarm optimization (PSO), ant colony optimization(ACO) and artificial bee colony(ABC) .

References:

1. Bishop Chris M., (1994) Neural Networks and their applications, Review of Scientific Instruments, Vol. 65,

No. 6, pp. 1803-1832. 2. Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Neural Network Design, ISBN:0-9717321-0-8.

3. Soroush A.R., Kamal-Abadi, NakhaiBahreininejad A, 2009 “Review on Applications of Artificial neural

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Applications to Control of Complex systems”, IEEE Transactions on Systems, Man and Cybernetics, Vol, 25,

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Computational Techniques In Engineering Oct 15-16, SLIET, Longowal Punjab pp 129-132.

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rapid battery charger for Ni-Cd batteries. HiPC (High Performance Computing). Workshop on Soft

Computing, Bangalore, pp. 9-14.

7. Khosla, A., Kumar, S. and Aggarwal,K. K. (2003 b). Fuzzy controller for rapid Nickel-Cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture. Proceedings of 22nd

International Conference of the North American Fuzzy Information Processing Society, Chicago, Illinois, USA, July 24–26, pp. 540–544.

8. Shakti Kumar, Savita Wadhawan and Neetika Ohri 2004 b, “ANDEng: An Artificial Neural Network Design

Automation Tool”. National Conference on Intelligent Systems and Networks, ISN-2004, Feb27-28, , Haryana Engineering College Jagadhri, Haryana, pp 26-29

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Machine learning, vol. 13, no.1, pp. 71-101. 14. Setiono, R., 1997, “Extracting rules from neural networks by pruning and hidden-unit splitting,” Neural

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numerical data IEEE Int’l Conf. on Fuzzy Systems, pp.131-136.

17. Eghbal G. Mansoori, M.J. Zolghadri and S.D. Katebi, (2008) “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE Transactions on Fuzzy Systems, Vol.16, No.4, pp.

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for extracting fuzzy classification rules from data,” IEEE Transactions on Fuzzy Systems, Vol.16, No.4, pp. 1061-1071.

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323-330

53

Authors: Shally Nagpal, Suneet Kumar, Suresh Chand Gupta

Paper Title: A New Approach for Modifying Blowfish Algorithm for IoT

Abstract: Due to rapid development of internet and web applications, the prominence and

the importance of the information exchange using the internet is growing. Communication

through internet faces data safety as an important issue. Data has to be safe when

communicating as slightly loss or danger to transmitted data can be responsible for

excessive harm to the society. For network safety encryption plays a vibrant part. Many

times it is little bit confusing to choose best encryption, as there are many cryptography

methods for securing the data during transmission. For many applications Blowfish is

currently assumed to be insecure. So it turns out to be essential to enhance this procedure

through addition of different levels of safety so that it can be used in several reliable

communication channels. Blowfish algorithm is modified in a way that it is platform

independent; however the present encryption schemes are restricted to platform dependent

proposal. This proposed modified blowfish algorithm supports text, images and media files.

Keywords: Network Security, Symmetric Block Cipher, Cell Automata, Internet of Things,

Entropy.

References:

1. Afaf M. Ali Al-Neaimi, Rehab F. Hassan, ”New Approach for Modifying Blowfish Algorithm by Using

Multiple Keys”, International Journal of Computer Science and Network Security, VOL.11 No.3, March

2011, pp.21-26. 2. .M. Anand Kumar and Dr.S.Karthikeyan, ”Investigating the Efficiency of Blowfish and Rejindael (AES)

Algorithms”, I. J. Computer Network and Information Security, 2012, 2, pp.22-2.

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54.

Authors: Arvind Kumar, Nidhi Garg, Gurpreet Kaur

Paper Title: An Emotion Recognition Based on Physiological Signals

Abstract: Emotion recognition is alluring considerable interest among the researchers.

Emotions are discovered by facial, speech, gesture, posture and physiological signals.

Physiological signals are a plausible mechanism to recognize emotion using human-computer

interaction. The objective of this paper is to put forth the recognition of emotions using

physiological signals. Various emotion elicitation protocols, feature extraction techniques,

classification methods that aim at recognizing emotions from physiological signals are

discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other

physiological signal for emotion detection. Working on basic as well as non-basic human

emotion and human-computer interface will make the system robust.

Keywords: Ayurveda, Emotion recognition system, Emotion elicitation protocols,

Physiological signals, Wrist Pulse Signal

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Authors: Deepti Aggarwal, Dr. Vikram Bali, Dr.Sonu Mittal

Paper Title: An Insight IntoMachine Learning Techniquesfor Predictive Analysis And Feature

Selection

55.

Abstract - Predictive analysis comprises a vast variety of statistical techniques like “machine

learning”, “predictive modelling” and “data mining” and uses current and historical statistics

to predict future outcomes. It is used in both business and educational domain with equal

applicability.This paper aims to give an overview of the top work done so far in this field. We

have briefed on classical as well as latest approaches (using“machine learning”) in predictive

analysis. Main aspects like feature selection and algorithm selection along with corresponding

application is explained. Some of the most quoted papers in this field along with their

objectives are listed in a table. This paper can give a good heads up to whoever wants to know

and use predictive analysis for his academic or business application.

Keywords:Classification, Clustering, Feature Selection, Machine Learning, Predictive

Analysis,Regression

References:

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Neurocomputing, Volume 300, 2018, Pages 70-79, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2017.11.077

2. Concepción Burgos, María L. Campanario, David de la Peña, Juan A. Lara, David Lizcano, María A. Martínez,

“Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout”, Computers & Electrical Engineering, Volume 66, 2018, Pages 541-556, ISSN 0045-7906,

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ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.05.115.

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342-349

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prediction in e-learning courses through the combination of machine learning techniques”, Computers &

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learning tools and techniques with Java implementations”, (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science.

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56

Authors: Kushnian Kour, Dr. Sandeep Singh Kang

Paper

Title:

Evaluation of Wireless Body Area Networks

Abstract: Remote body Area Network is the interconnection of different hubs that are

situated in or around the outside of the body which is equipped for remote correspondence.

Remote body Area Network includes different observing application condition, fighting,

farming, military and social insurance. The principle motivation behind WBAN is to

physiologically screen patient's fundamental signs and thus course the related information

towards a base station. The sensor hubs are normally light weight, minimal effort, low

power expending insight gadgets which are equipped for detecting, figuring, speaking with

one another remotely. This audit gives an unmistakable outline about the elements of

WBAN. The fundamental activity of conventions, transmitter and beneficiary of IEEE

802.15.6 are profoundly analyzed and examined in this work. The WBAN elements

include the sensor devices .These sensor devises are wearable devices. These sensor

devices are the detector devices such as motion sensors, ECG sensors etc. There are some

protocols which are within body protocols in WBAN. These protocols enhance the work of

the Wireless Body area network There are also some security aspects including

confidentiality, integrity in WBAN. There are various application areas where these

sensors can be used .This review paper gives snappy synopsis about the sensor plan,

WBAN architecture, within-body routing protocols, applications, control proficiency,

vitality preservation, correspondence conventions and security issues in WBAN.

Keywords—Wireless Body Area Networks (WBANs), Sensors, intra body and additional

body correspondence, detector devices.

References

1. Mark A. Hanson, Harry C. Powell Jr., AdamT. Barth, Kyle Ringgen berg, Benton H. Calhoun, James H.Aylor,and John Lach Body Area Sensor Networks: Challenges and

Opportunities , University of Virginia, IEEE Computer Society (2009)

2. Data Security And Privacy In Wireless Body Area Networks, Ming Li And Wenjing Lou, Worcester Polytechnic Institute Kui Ren, Illinois Institute Of Technology, IEEE Wireless Communications (

February 2010)

3. https://www.seminarsonly.com/computer%20science/wireless-body-area-network.php 4. https://www.techsparks.co.in/thesis-in-wireless-body-area-network/

5. https://www.researchgate.net/figure/Example-of-intra-body-and-extra-body-communication-in-a-

WBAN_fig24_45709672 6. https://www3.nd.edu/~dwang5/courses/spring19/ papers/bsn/BSNOverview.pdf

7. Javaid, N., Abbas, Z., Fareed, M.S., Khan, Z.A. and Alrajeh, N., 2013. M-ATTEMPT: A new energy-efficient

routing protocol for wireless body area sensor networks. Procedia Computer Science, 19, pp.224-231. 8. Ragesh, G. K., & Baskaran, K. (2012). An Overview of Applications , Standards and Challenges in Futuristic

Wireless Body Area Networks. Journal of Computer Science

9. Transmission-Rate-Adaption Assisted Energy-Efficient Resource Allocation With QoS Support in WBANs

350-356

10. Tang, Q.,Tummala, N., Gupta, S.K. and Schwiebert, L., 2005, June. TARA: thermal-aware routing algorithm for

implanted sensor networks.International Conference on Distributed Computing in Sensor Systems (pp. 206-

217). Springer Berlin Heidelberg

11. https://www.researchgate.net/publication/253418115_IEEE_802154_for_Wireless_Sensor_Networks_A_Tecnical_Overview

57.

Authors: Simerjeet Singh Bawa, Dr. Harpreet Singh

Paper Title: Factor Influencing the Formulation of Effective Marketing Strategies of Indian

Railways

Abstract: The growth of an organization depends on its marketing strategies and to create

successful marketing strategies, it is very important to be familiar with consumers. The

authors have tried to understand and analyze the marketing strategies preferred by Indian

railways. The sample of 50 respondents, who are working at various profiles have been taken

to evaluate the same.

Keywords: Indian Railways, Marketing Strategies, Passenger Safety.

Reference: 1. Adewale Gbolagade, Adesola M.A, Oyewale I.O, (2016), “Impactof Marketing Strategy on Business

Performance A Study of Selected Small and Medium Enterprises. In Oluyole Local Government, Ibadan,

Nigeria”, IOSR Journal of Business and Management, Vol.11, Issue.4, Jul.-Aug, 2016, pp. 59-66.

www.iosrjournals.org www.iosrjournals.org 2. CAG (2007), “Report of the Comptroller & Auditor General of India”, Union Government Railways, No.6.

3. Choudhary Atul, Bansal Sanjeev, Sharma Prashant, Prashaant Anu (2018), “An Impact of Recent Technological

Reforms in Indian Railways on its Revenue and Its Influence on the Passenger Satisfaction in Terms of Service”, International Journal of Innovative Technology and Exploring Engineering, Vol. 8, Issue. 2 pp. 90-95.

4. Kothari. R.K, Mehta, Sharma. A, (2007), “Indian Railways Marketing Management”, Ramesh Book Depot,

Jaipur. 5. Kotler Philip, Armstrong Gary, Saunders John, Wong Veronica. (1999), “Principles of Marketing”, Second

European Edition. Prentice Hall. London.

6. Michael Porter, Nov-Dec (1996), Harvard Business Review. 7. Ministry of Railways, Annual Reports, [2013 to 2016]

8. MOR, (1998), Status Paper on Indian Railways [New Delhi: Ministry of Railways]

9. Owomoyela S.K, Oyeniyi K.O and Ola O.S (2013). “Investigating the impact of marketing mix elements on consumer loyalty: An empirical study on Nigerian Breweries Plc. Interdisciplinary”, Journal of Contemporary

Research in Business, Vol.4 (11), pp. 485 –496.

10. Raghuram G, Niraja S. (2007), “Turnaround of Indian 11. Railways: increasing the axle loading – A case Study”, Indian Institute of Management, Ahmedabad.

12. Shim S, Eastlick M A, Lotz S (2004), Search-Purchase[S-P] Strategies of Multi-Channel Consumers: A

segmentation Scheme, J. Marketing Channel. 13. Slater. S.F, Hult. G.T. M & Olson. E.M. (2007), “On the importance of matching strategic behavior and target

market selection to business strategy in high-tech markets”, Journal of the Academy of Marketing Science, Vol.

35 No. 1, pp. 5–17.

357-362

58.

Authors: Dr. Sharad, Dr. Sandeep Singh Kang, Er. Deepshikha

Paper

Title:

Cluster Based Techniques LEACH and Modified LEACH Using Optimized

Technique EHO in WSN

Abstract— In the last few years, the Internet of Things (IoT) and the advance wireless

networks are becoming very prominent in various domains. Wireless Sensors are facing

problems of frequent energy loss which affects to the lifetime of the entire network. There are

number of researchers who are working on such energy losses which occur in the wireless

sensor nodes by using various approaches. One such method is Low- energy adaptive

clustering hierarchy (LEACH) and its number of methods. Despite of various methods of

LEACH, there is still immense scope of research as it is highly used in sensor nodes for

different scenarios. The emerging growth of energy aware wireless sensor networks for a

long time leads to various problems related to the lifetime of nodes in the wireless

environment. In our research paper, a new and performance aware approach named Elephant

Herd Optimization based Cluster Head Selection is devised and simulated so that the

optimization level can be achieved. The nature inspired soft computing approaches are

363--372

always beneficial for the use of optimization and reduction of various problems which can

occur during energy optimization and this is the main focus which is considered in this

research work. The main fundamental concept of the cluster head shuffling using EHO and

other methods of key exchange are simulated in Contiki-Cooja which is an open source

simulator for wireless sensor networks.

Keywords::Advance Wireless Networks, Energy aware Wireless Networks, Elephant Herd

Optimization, Energy Optimization.

Reference: 1. Feeney, L. and Nilsson , M. “Investigating the energy consumption of a wireless network interface in an ad hoc

networking environment”. IEEE INFOCOM 3:1548- 1557 vol.3, 2001

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ptimum sector distribution within a coverage area of a wireless communication system” U. Patent 6,418, 327,

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4. Chan, and Perrig, “Security and privacy in sensor network computer”, 36(10), pp.103-105, 2003 5. Gupta, Stebila, Fung, Shantz, Gura, and Eberle, , Augus “Speeding up Secure Web Transactions Using Elliptic

Curve Cryptograph In NDSS”, 2004

6. Sterbenz, Medhi, Monaco, Ramamurthy, Scoglio, Choi,B. Evans, J. Gruenbacher, Hui, Kaplow, and Minden,.GpENI: “Great Plains Environment for Network Innovation” Proposal).2008 ITTC technical report

ITTC-FY2009TR-0061349-01, The University of Kansas.

7. Roman, Zhou, and Lopez, “On the features and challenge of security and privacy in distributed internet of things,” Computer Networks, vol. 57, n 10, pp 2266–2279, 2013.

8. Jayashree, Arumugam, Anusha, and Hariny, A. (2006),“Apri On the accuracy of centroid based multilateration

procedure for location discovery in wireless sensor network In Wireless and Optical Communications Networks, IFIP International Conference on (p 6-pp). IEEE. 2006.

9. Uckelmann Harrison Michahelles “An architectural approach towards the future internet of things”, Springer

Berlin Heidelberg, pp 1-24, 2011. 10. Zhang, Liang, Lu, & Shen, “Sybil attacks and their defenses in the internet of things.” IEEE Internet of Things

Journal, (5), pp 372-383, 2014 1.

11. Wang, Deb Coelho,“Elephant herding optimization In Computational and Business Intelligence (ISCBI)”, 3rd International Symposium on Dec 7 (pp 1-5). IEEE, 2015.

12. Trappe, Howard, and Moore, “Low-energy security: Limits and opportunities in the internet of things”, IEEE

Security and Privacy, vol. 13, pp 14–21, 2015. 13. Said, Masu “Towards internet of things: Survey and future vision”. International Journal of Computer Network

pp.1-7, 2015.

14. Rehiman and Veni, “A secure authentication infrastructure for IOT enabled smart mobile devices - an initial prototype,” Indian Sc Techno vol. 9, pp 1-6 2016.

15. Li, Zheng, and Jin, “Secure and efficient data transmission in the Internet of Things,” Telecommunication

Systems, vol. 62, n 1, pp 111–122, 2016. 16. Mollah, Azad, and Vasilakos, “Secure data sharing and searching at the edge of cloud-assisted internet of

things,” IEEE Cloud Computing, vol. 4, pp 34–42, 2017.

59.

Authors: Harleen kaur, Gourav Bathla

Paper Title: Techniques of recommender system

Abstract –The term Recommender system is described as any organization that provides

personalized suggestions as a result and it effects the user in the individualized way to

favorable items from the large number of opinions. The voluminous inflation of the reachable

data online and also the number of users have lead to the information overload problem. To

overcome this problem the recommender system came into play as it is able to prioritize and

personalize the data. Recommendation systems have developed alongsidewith the

net.Recommender system has mainly three data filtering methodssuch as content based

filtering technique, collaborative based filtering technique and the hybrid approach to manage

the data overload problem and to recommends the items to the user the items they are

interested in from the dynamically generated data. This paper makes a comprehensive

introduction to the recommender system with its types, content based filtering , collaborative

filtering and the hybrid recommendation.

Keywords – Recommendation system,Collaborative filtering, Content based filtering, Hybrid

373-379

recommendation

References 1. Nehete, S.P. and Devane, S.R., 2018, August. Recommendation Systems: past, present and future. In 2018

Eleventh International Conference on Contemporary Computing (IC3) (pp. 1-7). IEEE.

2. Yang, X., Guo, Y., Liu, Y. and Steck, H., 2014. A survey of collaborative filtering based social recommender systems. Computer Communications, 41, pp.1-10.

3. Patel, A., Thakkar, A., Bhatt, N. and Prajapati, P., 2019. Survey and Evolution Study Focusing Comparative

Analysis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filtering Approach. In Information and Communication Technology for Intelligent Systems (pp. 155-163).

Springer, Singapore.

4. Yanxiang, L., Deke, G., Fei, C. and Honghui, C., 2013, January. User-based clustering with top-n recommendation on cold-start problem. In 2013 Third International Conference on Intelligent System Design

and Engineering Applications (pp. 1585-1589). IEEE.

5. Mustafa, N., Ibrahim, A.O., Ahmed, A. and Abdullah, A., 2017, January. Collaborative filtering: Techniques and applications. In 2017 International Conference on Communication, Control, Computing and Electronics

Engineering (ICCCCEE) (pp. 1-6). IEEE.

6. Thorat, P.B., Goudar, R.M. and Barve, S., 2015. Survey on collaborative filtering, content-based filtering and

hybrid recommendation system. International Journal of Computer Applications, 110(4), pp.31-36.

7. Ghodsad, P.R. and Chatur, P.N., 2018, August. Handling User Cold-Start Problem for Group Recommender

System Using Social Behaviour Wise Group Detection Method. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1-5). IEEE.

8. Ambulgekar, H.P., Pathak, M.K. and Kokare, M.B., 2019. A Survey on Collaborative Filtering: Tasks,

Approaches and Applications. In Proceedings of International Ethical Hacking Conference 2018 (pp. 289-300). Springer, Singapore.

9. Li, J., Zhang, K., Yang, X., Wei, P., Wang, J., Mitra, K. and Ranjan, R., 2019. Category Preferred Canopy–K-means based Collaborative Filtering algorithm. Future Generation Computer Systems, 93, pp.1046-1054.

10. Ortega, F., Hurtado, R., Bobadilla, J. and Bojorque, R., 2018. Recommendation to groups of users using the

singularities concept. IEEE Access, 6, pp.39745-39761. 11. Fletcher, K.K., 2017, June. A Method for Dealing with Data Sparsity and Cold-Start Limitations in Service

Recommendation Using Personalized sPreferences. In 2017 IEEE International Conference on Cognitive

Computing (ICCC) (pp. 72-79). IEEE. 12. Amatriain, X., Jaimes, A., Oliver, N. and Pujol, J.M., 2011. Data mining methods for recommender systems.

In Recommender systems handbook (pp. 39-71). Springer, Boston, MA.

13. Guo, L., Liang, J., Zhu, Y., Luo, Y., Sun, L. and Zheng, X., 2018. Collaborative filtering recommendation based on trust and emotion. Journal of Intelligent Information Systems, pp.1-23.

14. Schafer, J.B., Frankowski, D., Herlocker, J. and Sen, S., 2007. Collaborative filtering recommender systems.

In The adaptive web (pp. 291-324). Springer, Berlin, Heidelberg. 15. Katarya, R. and Verma, N., 2017, December. Automatically detection and recommendation in collaborative

groups. In 2017 International Conference on Intelligent Sustainable Systems (ICISS) (pp. 218-222). IEEE.

60

Authors: K GOKUL, RITU GARG

Paper Title: Multi-level QR code with Hill Climbed Encrypted Public level for secured Message

Transfer

Abstract: With the world turning into a worldwide town, the data sharing requires abnormal

state security with substantial verification of the data which ought not be undermined with the

time, hence providing food all the above necessities we propose another brisk reaction (QR)

code containing two stockpiling levels specifically open and private stockpiling levels, which

can go above and beyond in validating the inventiveness of the reports. The open stockpiling

level can be perused by any traditional QR code scanners while it's not the equivalent for the

private stockpiling level as its being produced with exchanging the dark modules with

finished examples of the first QR code consequently it tends to be perused by just explicit QR

scanners. The data's being put away in the private stockpiling level is being encoded sing q-

ary code substituted by mistake amendment systems along these lines expanding the QR

stockpiling limit while the open stockpiling level data's are being scrambled with the key

which will be sent to the private stockpiling level where it experiences all the procedure like

the private data's and will be moved by means of QR to the required gatherings. The size can

be expanded by the information's or with the finished example estimate.

Keywords: QR code, multiple Storage levels, Secured transfer, Extended hill climbing

algorithm.

380-384

References

1. Wu M, Liu B (2004) Data hiding in binary image for authenticationand annotation. IEEE Trans Multimedia 6(4):528–538

2. Yang H, Kot AC (2007) Pattern-based data hiding for binary imageauthentication by connectivity-preserving.

IEEE Trans Multimedia 9(3):475–486 3. Varna A, Rane S, Vetro A (2009) Data hiding in hard-copy textdocuments robust to print, scan and photocopy

operations. In:Proc. of IEEE International Conference on Acoustics, Speech andSignal Processing, pp 1397–

1400 4. Das S, Rane S, Vetro A Hiding information inside structured shapes. In: Proc. of IEEE International

Conference on AcousticsSpeech and Signal Processing, Mar 2010, pp 1782–1785

5. Kieseberg P, Leithner M, Mulazzani M, Munroe L, SchrittwieserS, Sinha M, Weippl E (2010) QR code security. In: Proc. of theInternational Conference on Advances in Mobile Computing andMultimedia, pp 430–

435

6. Jing Q, Vasilakos AV, Wan J, Lu J, Qiu D (2014) Security of the internet of things Perspectives and challenges. Wirel Netw20(8):2481–2501

7. Reed IS, Solomon G (1960) Polynomial codes over certain finitefields. J Soc Ind Appl Math 8(2):300–304

8. Wilds M, Chambers S Bar code authentication, Patent US2010/0 012 736 A1, Jan., 2010 9. Eldefrawy M, Alghathbar K, KhanMHardcopy document authentication based on public key encryption and 2D

barcodes. In: Proc. of International Symposium on Biometrics and Security Technologies, Mar. 2012, pp 77–81

10. Li CM, Hu P, Lau WC Authpaper: Protecting paper-based documents and credentials using authenticated 2d barcodes. In: 2015 IEEE International Conference on Communications (ICC). IEEE, 2015, pp 7400–7406

11. Yang H, Kot AC (2007) Pattern-based data hiding for binary image authentication by connectivity-preserving.

IEEE Trans Multimedia 9(3):475–486 12. Reed IS, Solomon G (1960) Polynomial codes over certain finitefields. J Soc Ind Appl Math 8(2):300–304

13. Villan R, Voloshynovskiy S, Koval O, Pun T (2006) Multilevel2-D bar codes: Toward high-capacity storage modules for multimedia security and management. IEEE Trans Inf Forensics Secur 1(4):405–420

61

Authors: Gurpreet Kaur, Vinay Bhatia, P. N. Hrisheekesha, R. S. Kaler

Paper

Title:

Bragg Grating Diffraction light at Different Wavelength and Incident Light

Abstract: The diffraction grating is an important device that makes use of the diffraction of

light to produce spectra. Diffraction is also fundamental in other applications such as x-ray

diffraction studies of crystals and holography. We proposed a design of Bragg grating

waveguide to investigate the behavior of diffraction of light at different incident angle and

wavelengths. Using finite difference time domain (FDTD) photonics simulation software the

performance of proposed waveguide is observed in term of output power, electric field,

diffraction efficiency (DE) and signal to noise ratio (SNR). It is found that the proposed

waveguide provides better diffracted light with electric field distribution with 1.969 v/m,

Diffraction efficiency 8%, and SNR (25.5 dB) at 1.55µm wavelength and 00 degrees of

incident angle.

Keywords:: optical fiber, Medical application, Bragg Grating Waveguide

References: 1. K. Venkatachalam, D. S. Kumar, and S. Robinson, “Investigation on 2D photonic crystal-based eight-

channel wavelength-division demultiplexer”, Photonic Network Communications, DOI: 10.1007/s11107-

016-0675-7. 2. S. Singh, “Investigation of wavelength division multiplexing ring network topology to enhance the system

capacity” Optik - International Journal for Light and Electron Optics, vol. 125, no. 21. pp. 6527–6529,

2014. 3. Nidhi, U.Tiwari, V. Mishra, A. kumar, Y. Singh, B. kumar, S.C. Jain, N. Singh, and P. Kapur, “A current

sensing technique based upon fiber Bragg grating”, Photonics - IIT Gawahati. vol. 39, no. 4. pp. 266, 2010.

4. N. Panwar, U. Tiwari, Nidhi, M. M. Khan, S. C. Jain, R. Garg and P. Kapur, “Long Period Fiber Grating Humidity Sensor with Gelatin/Cobalt Chloride coating”, is presented in International Conference on Fiber

Optics and Photonics, pp-W2B.4,OSA 2012,IIT Chennai.

5. Christopher Palmerk, Diffraction Grating Handbook, 5th Edition, Richardson Grating Laboratory 705 St. Paul Street, Rochester, New York. 2015

6. M. Dai, L. Ma, Yelong Xu, M. Lu, X. Liu, and Y. Chen, “Highly efficient and perfectly vertical chip-to

fiber dual-layer grating coupler”, Optik Express, vol. 23, no. 2, pp. 1691-8, 2015. 7. B. Yun, G. Hu, Y. Cui, “Third-order polymer waveguide Bragg grating array by using Conventional

contact lithography”, Optics Communications, vol. 330, no.1, pp. 113–116, 2014.

385-389

8. H. J. Wang, D. F. Kuang, X. D. Sun, and Z. L. Fang, “Period interaction on diffraction efficiency of blazed

transmission gratings”, Optik, vol. 121, no. 16, pp. 1511–1515, 2010.

9. J.C. Ibarra, M. O. -Gutie´rrez, A. Olivares-Pe´rez, G. ObregoPulido, M. Pe´rez-Corte, “Changes of the

diffraction efficiency due to emulsions thicknesses in holographic gratings”, Optical Materials, vol. 30, no. 2, pp. 255–259, 2007.

10. Yao Hu, L. Zeng, L. Li, “Method to mosaic gratings that relies on analysis of far-field intensity patterns in

two wavelengths”, Optics Communications, vol. 269, no. 2, pp. 285–290, 2007. 11. T. Harimoto, “Far-Field Pattern Analysis for an Array Grating Compressor” Jpn. J. Appl. Phys. vol. 43, no.

4A,pp.1362, 2004.

12. G. Kaur, R. S. Kaler, N. Kwatra, “On the optimization of fiber Bragg grating optical sensor using genetic algorithm to monitor the strain of civil structure with high sensitivity”, Optical Engineering, vol. 55, no.8,

pp. 087103-1 to 087103-6, 2016.

13. G.Kaur, R. S. Kaler, N. Kwatra, “Performance investigation of semiconductor optical amplifier and raman amplifier as an optical sensors”, Optoelectronics and Advanced Materials – Rapid Communications, vol. 9,

no. 9-10, pp. 1110 – 1113, 2015.

62.

Authors: Vinay Bhatia, Eti, Parveen Singla

Paper Title: VANET and FANET under the Impact of the Security Attack

Abstract - The flying ad hoc network (FANET) is a distributed type of network in which

nodes have the ability to join or leave the network at any time as and when needed. FANET

is the kind of ad-hoc network just like the Mobile ad-hoc network (MANET) and the

Vehicular ad-hoc network (VANET). In our previous work a monitor mode technique is

proposed for the detection of malicious nodes in Flying ad-hoc network (FANET). In this

research work, a secure architecture for FANET and VANET is designed for the detection

and isolation of malicious nodes in the network. The ad- hoc network is unstable when

malicious nodes are present in the network which create fake identities, so that security of

the network is to be reduced. A multiple copies of fake identities create by Sybil attack

which is harmful for the network. In this research work, the performance of the proposed

technique for the detection and isolation of malicious node of FANET is compared with

VANET. It is analyzed that FANET performs well in terms of all parameters like

throughput, packet loss and routing overhead as compare to VANET.

Keywords—Mobile ad-hoc network (MANET), Vehicular ad-hoc Network(VANET),

Flying ad-hoc network (FANET), Sybil attack, Malicious nodes , Unmanned aerial Vehicle

Network(UAV).

References:

1. V. Gatteschi, F. Lamberti, G. Paravati, A. Sanna, C. Demartini, A. Lisanti, and G. Venezia, ―New

frontiers of delivery services using drones: A prototype system exploiting a quadcopter for autonomous drug shipments,‖ in 39th IEEE Annual Computer Software and Applications Conference (COMPSAC), vol.

2, July 2015, pp. 920–927.

2. K. Mansfield, T. Eveleigh, T. H. Holler, and S. Sakami, 3. ―Unmanned aerial vehicle smart device ground control station cyber security threat model,‖ in IEEE

International Conference on Technologies for Homeland Security (HST), Nov 2013, pp. 722–728.

4. N. M. Roddy, R. d. O. Schmidt, and A. Pars, ―Exploring security vulnerabilities of unmanned aerial vehicles,‖ in IEEE/IFIP Network Operations and Management Symposium (NOMS), April 2016, pp. 993–

994.

5. W. Sad, A. L. Glass, N. B. Mandayam, and H. V. Poor, 6. ―Toward a consumer-centric grid: A behavioural perspective,‖ Proceedings of the IEEE, vol. 104, no. 4,

pp. 865–882, April 2016.

7. G. E. Rahil, A. Sanjeev, W. Sad, N. B. Mandayam, and H. V. Poor, ―Prospect theory for enhanced smart grid resilience using distributed energy storage,‖ in 54th Annual Allerton Conference on

Communication, Control, and Computing (Allerton), Sept 2016, pp. 248–255.

390-397

Authors: Parvinder Singh, Rajeshwar Singh

Paper

Title:

Multi-Metric Route Cost Based Routing In Wireless Multimedia Adhoc Networks

63.

Abstract: To improve the quality of life, bigger cities in the world addressing their

problems by adopting latest technology. IOT connect physical world to the internet and such

concept is adopted by bigger cities having large population growth rate. Wireless

multimedia sensor network helps to monitor various parts of a city without use of cables.

This network becomes the primary component of every smart city due to its surveillance and

monitoring capability In contrast with conventional sensor networks, multimedia sensor

nodes are equipped with cameras which acquire, process, compress and transmit information

about an event happing at some remote area in the form of an image or video. However, like

conventional WSNs this network is having multiple constrained and new metric needs to be

considered to improve network life time and Quality of service parameters. Multiple metric

based route cost function is defined and routing mechanism based upon this cost function is

proposed. Since each multimedia sensor node is battery operated and connected through low

power wireless links. This work helps to find an optimal path having minimum energy

consumption, maximum link reliability, minimum number of hops and minimum routing

table magnitude. A cost function considers constraints of wireless multimedia sensor

networks. It satisfies desirable network requirements and improves network life time

through efficient routing. The proposed algorithm is tested, simulated and compared with

previous routing algorithms.

Keywords: IEEE 802.15.4, Wireless multimedia sensor networks, Reliability, Storage

constraints, Hop-metric, Route cost function

References: 1. Tenager mekonnen, pawani porambage, erkki harjul and mika ylianttila, “Energy Consumption Analysis of

High Quality Multi-Tier Wireless Multimedia Sensor Network” IEEE Access, 2017, vol. 5.

2. R. Monika & R. Hemalatha and S. Radha, “Energy efficient surveillance system using WVSN with

reweighted sampling in modified fast Haar wavelet transform domain” Multimed Tools Appl (2018). 3. Joao Paulo,Just Peixoto and Daniel G. Costa, “ Wireless visual sensor networks for smart city applications:

A relevance-based approach for multiple sinks mobility” Future Generation Computer Systems 76 (2017) 51–

62. 4. G. Shahzad, H. Yang, A. W. Ahmad, and C. Lee, “Energy-efficient intelligent street lighting system using

traffic-adaptive control,” IEEE Sensors Journal, vol. 16, no. 13, 2016, pp. 5397–5405.

5. A. Kovacs, R. B ´ atai, B. Cs. Cs ´ aji, P. Dud ´ as, B. H ´ ay, G. Pedone, ´ T. Rev´ esz, and J. V ´ ancza, “Intelligent control for energy-positive street ´ lighting,” Energy, vol. 114, pp. 40–51, 2016

6. A. Lavric, V. Popa, and S. Sfichi, “Street lighting control system based on large-scale WSN: A step towards a

smart city,” in 2014 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2014, pp. 673–676.

7. M. Mahoor, F. R. Salmasi, and T. A. Najafabadi, “A hierarchical smart street lighting system with brute-force

energy optimization,” IEEE Sensors Journal, vol. 17, no. 9, 2017, pp. 2871–2879 8. F. Viani, A. Polo, P. Garofalo, N. Anselmi, M. Salucci, and E. Giarola, “Evolutionary optimization applied to

wireless smart lighting in energyefficient museums,” IEEE Sensors Journal, vol. 17, no. 5, 2017, pp. 1213–

1214. 9. F. Viani, A. Polo, F. Robol, E. Giarola, and A. Ferro, “Experimental validation of a wireless distributed

system for smart public lighting management,” in IEEE International Smart Cities Conference (ISC2), Trento,

Italy, September 12-15, 2016, pp. 678–683. 10. L. M. Oliveira and J. J. Rodrigues, “Wireless sensor networks: A survey on environmental monitoring.”

Journal of Communications, vol. 6, no. 2, 2011, pp. 143–151.

11. Balazs Cs.aji, Zsolt Kemeny, Gianfranco Pedone, Andr Kuti, J´ozsef Vancza “Wireless Multi-Sensor Networks for Smart Cities: A Prototype System with Statistical Data Analysis” IEEE sensors journal.

12. A. Monsalve, H.L. Vu, Q.B. Vo, “Optimal designs for IEEE 802.15.4 wireless sensor networks”, Wirel.

Commun. Mob. Comput. 13 (18) (2013) 1681–1692. 13. V. Vora, T. Brown, “High rate video streaming over 802.11n in dense wi-fi environments”, in: IEEE

Conference on Local Computer Networks, 2010, pp. 1054–1061.

14. Pan Zhaoqing, Zhang Yun, Kwong Sam, “Efficient motion and disparity estimation optimization for low complexity multiview video coding” IEEE Trans. Broadcast. 61 (2) , 2015, 166–176.

15. Mario Collottaa,Giovanni Paua,Daniel G. Costa “A fuzzy-based approach for energy-efficient Wi-Fi

communications in dense wireless multimedia sensor networks” Computer Networks 134 (2018) 127–139. 16. Hang Shen, Guangwei Bai, “Routing in wireless multimedia sensor networks: A survey and challenges

ahead”, Journal of Network and Computer Applications 71 (2016) 30–49.

17. Sarwesh P, N. Shekar V. Shet, K. Chandrasekaran “ETRT – Cross layer model for optimizing transmission range of nodes in low power wireless networks – An Internet of Things Perspective” Physical Communication

29 (2018) 307–318

18. Mario Collotta, Giovanni Pa , Daniel G. Costa “ A fuzzy-based approach for energy-efficient Wi-Fi comunications in dense wireless multimedia sensor networks” Computer Networks 134 (2018) 127–13.

398-401

19. Joao Paulo, Just Peixoto, Daniel G. Costa “Wireless visual sensor networks for smart city applications:A

relevance-based approach for multiple sinks mobility” Future Generation Computer Systems 76 (2017) 51–

62.

64.

Authors: Rekha Parihar, Ritu Garg

Paper

Title:

Wireless Energy Harvesting in Internet-of-Things Communication Systems with

Optimized Energy Efficiency using PSO

Abstract: IoT is an emerging technology having a wide range of application areas. IoT

applications are also affecting human lives. But these small devices are battery powered

which is major problem for IoT systems. Wireless energy transfer is a good solution for

such systems. Both information and energy can be transmitted together by wireless energy.

In this paper, time splitting-based relaying (TSR) protocol is used by relay node to harvest

the energy in IoT system. Here, dual-hop IoT system is considered for analysis. System with

three different Wi-Fi protocols is examined against the energy efficiency at the destination

node. All three protocols are analysed individually. Further, Particle Swarm Optimization

(PSO) technique is used to optimize the energy efficiency of the considered IoT system.

Keywords: Energy harvesting, Internet of things, Time splitting based relay protocol,

Wireless energy.

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65.

Authors: Amritpal Kaur, Balwinder S. Dhaliwal, Suman Pattnaik

Paper

Ttle:

Design of Hybrid Fractal Boundary Antenna for RF Energy Harvesting Applications

Abstract- The fractal antennas have multiband behavior and also have the capabilities of the

size reduction as compared to the other patch antennas. Ahybrid fractal boundary antenna

has been designed and simulated in this paper for radio frequency energy harvesting

(RFEH). The designed antenna has the multiband behavior, as it resonates at the two

frequencies. The multiband behavior of the antenna helps in harvesting the energy from

various frequencies band and improves the output of the circuit. This hybrid fractal

boundary antenna uses the microstrip feed line to improve the matching performance of the

antenna.

Keywords: Fractal antenna, Hybrid fractal boundary antenna, Radio Frequency (RF),

Energy Harvesting (EH).

References:

1. D. Y. Choi, S. Shrestha, J. J. Park, and S. K. Noh, “Design and performance of an efficient rectenna incorporating a fractal structure,” Int. J. Commun. Syst., no. 4, 2014, pp. 661-679.

2. M. Mrnka, P. Vasina, M. Kufa, V. Hebelka, and Z. Raida, “The RF Energy Harvesting Antennas Operating in

Commercially Deployed Frequency Bands: A Comparative Study,” Int. J. Antennas Propag., vol. 2016, pp. 1-11.

3. D. Mishra, S. De, S. Jana, S. Basagni, K. Chowdhury, and W. Heinzelman, “Smart RF energy harvesting

communications: Challenges and opportunities,” IEEE Commun. Mag., vol. 53, no. 4, 2015, pp. 70-78. 4. J. Jose, S. George, L. Bosco, J. Bhandari, F. Fernandes, and A. Kotrashetti, “A Review of RF Energy

411-413

Harvesting Systems in India,” in 2015 International Conference on Technologies for Sustainable

Development (ICTSD), 2015, pp. 1-4.

5. Q. Awais, Y. Jin, H. T. Chattha, M. Jamil, H. Qiang, and B. A. Khawaja, “A Compact Rectenna System With

High Conversion Efficiency for Wireless Energy Harvesting,” IEEE Access, vol. 6, 2018, pp. 35857-35866. 6. Y. Wang and S. Liu, “A New Modified Crown Square Fractal Antenna,” 2008 Int. Conf. Microw. Millim.

Wave Technol. Proceedings (ICMMT), vol. 1, 2008, pp. 400-402.

7. Y. Shi, Y. Fan, J. Jing, L. Yang, Y. Li, and M. Wang, “An efficient fractal rectenna for RF energy harvest at 2.45 GHz ISM band,” Int. J. RF Microw. Comput. Eng. , 2018, pp. 1-8.

8. V. V Reddy and N. V. S. N. Sarma, “Triband Circularly Polarized Koch Fractal Boundary Microstrip

Antenna,” IEEE Antennas Wirel. Propag. Lett., vol. 13, 2014, pp. 1057-1060. 9. D. Li, F. Zhang, Z. Zhao, L. Ma, and X. Li, “UWB Antenna Design Using Patch Antenna with Koch Fractal

Boundary,” in 2012 International Conference on Microwave and Millimeter Wave Technology (ICMMT),

2012, vol. 3, pp. 1-3. 10. N. Sharma, S. Kaur, and B. S. Dhaliwal, “A New Triple Band Hybrid Fractal Boundary Antenna,” in 2016

IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

(RTEICCT), 2016, pp. 874-878

66.

Authors: Mitali Desai, Rupa G. Mehta, Dipti P. Rana

Paper

Title:

An Empirical Analysis to Identify the Effect of Indexing on Influence Detection using

Graph Databases

Abstract: The data generated on social media platforms such as Twitter, Facebook,

LinkedIn etc. are highly connected. Such data can be efficiently stored and analyzed using

graph databases due to the inherent property of graphs to model connected data. To reduce

the time complexity of data retrieval from huge graph databases, various indexing

techniques are used. This paper presents an extensive empirical analysis on popular graph

databases i.e. Neo4j, ArangoDB and OrientDB; with an aim to measure the competencies

and effectiveness of primitive indexing techniques on query response time to identify the

influencing entities from Twitter data. The analysis demonstrates that Neo4j performs

efficient and stable for load, relation and property queries compare to other two databases

whereas the performance of OrientDB can be improved using primitive indexing.

Keywords: graph database, index, influence detection, query processing, Twitter

References

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International Conference on Cloud and Service Computing, 2011, pp. 336-341. 2. T. Petkova, “Why Graph Databases Make a Better Home for Interconnected Data than the Relational

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International Conference on Data Engineering Workshops, IEEE, 2012.

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Computing, Artificial Intelligence and Applications (IJSCAI), Vol. 5, No. 1, February 2016, pp. 33-39.

414-421

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No. 1, February 2008, pp.1-39.

15. R. K. Kaliyar, “Graph Databases: A survey”, in International Conference on Computing, Communication and

Automation (ICCCA), July 2015, pp. 785-790. 16. S. A. T. Mpinda, L. C. Ferreira, M. X. Ribeiro and M. T. P. Santos, “Evaluation of Graph Databases

Performance Through Indexing Techniques”, International Journal of Artificial Intelligence & Applications

(IJAIA) Vol. 6, No. 5, September 2015, pp. 87-98. 17. R. Angles, "A Comparison of Current Graph Database Models", in 28th International Conference on Data

Engineering Workshops, IEEE, 2012, pp. 171-177.

18. D. Fernandes, and J. Bernardino, “Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB”, in Proceedings of the 7th International Conference on Data Science, Technology and

Applications, Porto, Portugal, 26–28 July, 2018, pp. 373–380.

19. P. Jadhav and R. Oberoi, “Comparative Analysis of Different Graph Databases”, International Journal of Engineering Research and Technology, Vol. 3, No. 9, September 2014, pp. 820-824.

20. S. Jouili and V. Vansteenberghe, “An Empirical Comparison of Graph Databases” in SocialCom, IEEE, 2013,

pp. 708–715 21. D. S. Rawat and N. K. Kashyap, “Graph Database: A Complete GDBMS Survey”, International Journal for

Innovative Research in Science & Technology, Vol. 3, No. 12, May 2017, pp. 217-226.

22. A. Romano, “The Massive Data Dump Reveals How Trolls Disrupt and Destabilize Local and Global

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67.

Authors: Ashish Tiwari, Dr. Ritu Garg

Paper

Title:

Eagle Techniques In Cloud Computational Formulation

Abstract: The eagle expresses of cloud computing plays a pivotal role in the development

of technology. In this computing world everyone is active so the end users and providers use

various applications which are working as a broker for providing and managing the services.

The aim of the paper is to solve the problems in such a way that brokers will provide an

optimized solution for cloud service providers and the end users. The key role of allocating

the efficient resources by making the algorithm which works for the time and cost

optimization keeping in consideration of its quality of services and characteristics. These

both are affecting the performance of these techniques is a major drawback due to low

accuracy and large computational complexity of the algorithms. As per the scenario, the

approach of the research is based on the rough set theory (RST). It’s a strategy to found the

information revelation and handle the issues like number of parameters (Virtualization,

resource sharing, cloud standardization etc). The rough set theory is the new method in

cloud service selection so that the best services to provide for cloud users and efficient

service improvement for cloud providers.

Keywords: High-Performance computing, Cloud parameters, Cloud brokers, Mathematical

model, Cloud simulator.

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68

Authors: Simarjeet Kaur, Navdeep Kaur, Kamaljit Singh Bhatia

Paper

Title:

A Novel Prevention Mechanism for Replay Attack in Distance Vector - Hop Localization

Scheme

Abstract: Securing the process of node localization in Wireless Sensor Network (WSN) is

an important area of research. In this paper, we focus on securing Distance Vector (DV)-

Hop localization scheme from Replay Attack. The DV-Hop localization algorithm is a limit

free hop based reference Wireless Sensor Network architecture. It faces a couple of issues

like a route discovery mechanism and threat prevention issues. This paper focuses on the

establishment of a prevention mechanism for DV-Hop scheme against a Replay attack. The

proposed architecture uses an Artificial Bee Colony (ABC) and Neural Network in order to

prevent the attack. To prove the efficacy of the proposed scheme, the network scenario

without replay attack, with replay attack and with attack after prevention mechanism is

considered. The simulations are conducted for 200 iterations using MATLAB. The

evaluation is done on the basis of parameters like localization error and transmission loss.

Localization error of the proposed framework is similar to localization error without an

attack which is about 0.57, and the maximum localized error is about 0.92. The maximum

transmission loss without attack and after prevention is very close and varies by only 2 %,

which is very good. The experimental results reveal that the proposed technique successfully

prevents replay attack in WSN.

Keywords:DV-Hop, Localization Error, Node Localization, Replay Attack, Transmission

Loss, Wireless Sensor Network

References:

1. S. Biswas, R. Das, and P. Chatterjee, “Energy-efficient Connected Target Coverage in Multi-hop Wireless Sensor Networks” In Industry Interactive Innovations in Science, Engineering and Technology,2018, pp.411-

421, Springer, Singapore.

2. V. Mittal, S. Gupta, and T. Choudhury, “Comparative Analysis of Authentication and Access Control Protocols against Malicious attacks in Wireless Sensor Networks” In Smart Computing and

Informatics,2018, pp. 255-262, Springer, Singapore.

3. N. Jain, S. Madan and S. K. Malik, “A Charge System Search based DV Hop Algorithm for Wireless Sensor Networks” International Journal of Scientific Research in Computer Science, Engineering and Information

Technology, 2018, 3(1),pp.568-576.

4. O. Cheikhrouhou, G M Bhatti and R. Alroobaea, “A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks” Sensors, 2018, 18(5),pp. 1-14.

5. L. Cui, C. Xu, G. Li, Z. Ming, Y. Feng and N. Lu, “ A High Accurate Localization Algorithm with DV-Hop

and Differential Evolution for Wireless Sensor Network” Applied Soft Computing, 2018, 68, pp.39-52. 6. W. Zhao, S. Su and F. Shao, “ Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in

Anisotropic Wireless Sensor Networks” Wireless Personal Communications, 2018, 98(4), pp.3335-3353.

7. B. Peng and L. Li, “An Improved Localization Algorithm based on Genetic Algorithm in Wireless Sensor Networks”, Cognitive Neurodynamics, 2015, 9(2),pp. 249–256.

8. X. Chen and B. Zhang, “Improved DV-Hop node Localization Algorithm in Wireless Sensor Networks”

International Journal of Distributed Sensor Networks, 2012, pp.1-7. 9. Q. Qian, X. Shen and H. Chen, “An Improved Node Localization Algorithm based on DV-Hop for Wireless

Sensor Networks” Computer Science and Information Systems, 2011, 8(4), pp.953–972.

10. X. Chen and B. Zhang, “Improved DV-Hop Node Localization Algorithm in Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 2012, pp.1-7.

11. L. Gui, T. Val, A. Wei, and S. Taktak, “An adaptive range free localisation protocol in wireless sensor

networks”, International Journal of Ad Hoc and Ubiquitous Computing, 2014, 15(1–3),pp. 38–56. 12. F. Shahzad, T. R. Sheltami and E. M. Shakshukhi, “ DV-maxHop: A Fast and Accurate Range-Free

430-434

Localization Algorithm for Anisotropic Wireless Networks. IEEE Transactions on Mobile Computing, 2017,

16(9), pp. 2494-2505.

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Networks”, Wireless Networks, 2014, 20(4), pp. 681–694. 14. S. Kumar and D. K. Lobiyal, “An Advanced DV-Hop Localization Algorithm for Wireless Sensor

Networks. Wireless Personal Communications, 2013, 71(2), pp.1365–1385.

15. W. Ren and C. Zhao, “A Localization Algorithm based on SFLA and PSO for Wireless Sensor Network” Information Technology Journal, 2013, 12(3), pp.502–505.

16. M. Mehrabi, H. Taheri and P. Taghdiri, “An improved DV-Hop Localization Algorithm based on

Evolutionary Algorithms”, Telecommunication Systems, 2017, 64(4), pp.639–647. 17. G. Zhou, T. He, S. Krishnamurthy and J. A. Stankovic, “Models and Solutions for Radio Irregularity in

Wireless Sensor Networks”, ACM Transactions on Sensor Networks (TOSN), 2006, 2(2),pp. 221–262.

18. Z. Dengyi and L. Feng, “Improvement of DV-Hop localization algorithms in wireless sensor networks” In Int Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), IEEE,

2012,vol. 2, pp. 567–569.

69

Authors: Dr. Pooja Sahni , Dr. Sukhdeep Kaur, Jaswinder Singh, Ripple Sahni

Paper

Title:

Energy Aware Nature InspiredAlgorithm for better energy utilization for Wireless

Sensor Network

Abstract -Wireless sensor nodes are dead very early because of less battery power

availability. If any single node dead in the network then the workload shifted on the other

nodes. By this scenario battery consumption increase of other nodes from the regular

routine, and the whole sensor network down very soon. Every single node in a sensor

network interconnected with each other without the help of the radio waves technology.

Each sensor nodes associated with a batterythat provides sufficient power to complete the

whole tasks, like to sense data, receive data, transmit data,etc. Tasks are huge but the battery

lifetime is limited, this is a major problem in the sensor network. This paper represented a

newly develop Algorithmto better utilization of nodes battery power and make the sensor

network more stable by increasing the lifetime of the sensor nodes.EANIA results proved

that this approach is more energy aware and more secure.

Keywords::Cluster Head,Energy Efficient, Network Lifetime, Sensor Network.

Reference: 1. Heinzeleman, “energy efficient communication protocol for wireless microsensor network”, Hawaii

international conference on system science, 4 January 2000.

2. A. Bouyer, A. Hatamlou, M.Masdari, “A new approach for decreasing energy in wireless sensor networks

with hybrid LEACH protocol and fuzzy C-means algorithm”, International Journal of Communication Networks and Distributed Systems, pp. 400-12, 2015

3. A. Yektaparast, and A. Sarmast, “An improvement on LEACH protocol (cell-leach),” Advanced Comm. Tech.

(ICACT), pp. 992-996, Feb. 2012, 4. A. Manjeshwar; D.P. Agrawal, “TEEN: a routing protocol for enhanced efficiency in wireless sensor

networks”, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001,

Pages: 2009 – 2015, year 2001 5. G. Smaragdakis, I. Matta, and A. Bestavros, “SEP: A stable electionprotocol for clustered heterogeneous

wireless sensor networks,” 2nd international workshop sensor actor-network protocols appl.(SANPA),

Boston, MA, USA, pp.1-1, Aug. 2004. 6. Naranjo PG, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E., “P-SEP: a prolong stable election routing

algorithm for energy-limited heterogeneous fog-supported wireless sensor networks”, The Journal of

Supercomputing. 73(2):pp. 733-55, Feb 2017. 7. Vijayan K, Raaza A., “A novel cluster arrangement energy-efficient routing protocol for wireless sensor

networks”, Indian Journal of Science and Technology, Feb 2016

8. S.-H. Yang, “Principle of Wireless Sensor Networks”,Wireless Sensor Networks Signals and Communication Technology, Springer-Verlag London 2014.

9. Chandni et al. “Optimization through Bio-Inspired Algorithms in Wireless Sensor Network” Survey and

Future Directions, Volume 2, Spl. Issue 2 2015. 10. FakhrosadatFanian et al., “A Survey of Advanced LEACH-based Protocols”International Journal of Energy,

Information and Communications, Vol.7, Issue 1 (2016), pp.1-16

11. Vinay Kumaret al. “Energy Efficient Clustering Algorithms in Wireless Sensor Networks: A Survey” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011.

435-438

Authors: Jasjeet Singh, Harpreet Kaur

70

Paper

Title:

Congestion Management and Transmission Line Loss Reduction Using TCSC and

Market Split Based Approach

Abstract: Due to increasing power demand in a deregulated power system, the stability of

the power system may get affected and sometimes it may also cause congestion in the

transmission lines of power networks. It is a major issue for a deregulated power system and

its management provides a competition environment to different market players. In this

paper, market split based approach is used to tackle the problem of congestion which split

the system into zones. Locational Marginal Pricing (LMP) method is used to access the

prices at different buses. The objective is to minimize the congestion effect. DC optimal

power flow based system is used to solve such type of problem. TCSC (Thyristor-

Controlled Series Compensation), FACTS (Flexible Alternating Current Transmission

System) device is used to reduce the losses in a transmission system.Market splitting based

approach is effective to manage the prices at different buses and system stability is increased

by using TCSC. The whole work is carried out on IEEE 14 bus system.

Keywords: Locational marginal pricing (LPM), TCSC, DC optimal power flow, Congestion

management

Referenecs: 1. S.Meikandasivam, R. K. Nema, and S. K. Jain, “Selection of TCSC Parameters : Capacitor and Inductor,” no.

2, 2011, pp. 1–5.

2. M. Matcha et al., “Congestion management considering optimal placement of distributed generator in

deregulated power system networks,” IEEE Trans. Power Syst., vol 42, no. 1, 2010, pp. 1–8. 3. A. S. Siddiqui, “Zonal Congestion Management Based on Locational Marginal Price in Deregulated

Electricity Market,” , 2015 pp. 3–6.

4. N. Kirthika and S. Balamurugan, “Pass Through Congestion Management in Deregulated Power System Using Genetic Algorithm,” 2017.

5. A. K. R. K and S. P. Singh, “Congestion mitigation using UPFC,” vol. 8687, pp. 2433–2442, 2016.

6. F. Rahimi and A. Ipakchi, “Demand response as a market resource under the smart grid paradigm,” IEEE Trans. Smart Grid, vol. 1, no. 1, 2010, pp. 82–88.

7. A. J. Conejo, F. Milano, and R. García-Bertrand, “Congestion management ensuring voltage stability,” IEEE

Trans. Powe Syst., vol. 21, no. 1, 2006, pp. 357–364.

8. A. S. Siddiqui, R. Jain, M. Jamil, and C. P. Gupta, “LMP technique for locating series FACTS device (TCSC)

for social welfare benefits in deregulated electricity market,”India Int. Conf. Power Electron. IICPE, 2012.

9. a. Kumar, S. C. Srivastava, and S. N. Singh, “A Zonal Congestion Management Approach Using Real and Reactive Power Rescheduling,” IEEE Trans. Power Syst., vol. 19, no. 1, 2004, pp. 554–562.

10. S. Dutta and S. P. Singh, “Optimal Rescheduling of Generators for Congestion Management Based on Particle

Swarm Optimization,” IEEE Trans. Power Syst., vol. 23, no. 4, 2008, pp. 1560–1569. 11. K. Kaur, N. Kumar, S. Kumar, and K. Khatua, “Congestion management of transmission lines by FACTS

devices using Krill herd technique,” 2017 Innov. Power Adv. Comput. Technol. i-PACT 2017, vol. 2017–

Janua, no. 1, 2018, pp. 1–8. 12. M. Matcha and S. Kumari, “LMP Calculation wutg Distributed Loos using GA based DCOPF,” J. Electr.

Syst., vol. 8, no. 3, 2012, pp. 292–303.

13. M. Oloomi Buygi, H. M. Shanechi, G. Balzer, and M. Shahidehpour, “Transmission planning approaches in restructured power systems,” 2003 IEEE Bol. PowerTech - Conf. Proc., vol. 2, no. March, 2003, pp. 898–904.

14. K. Singh, V. K. Yadav, N. P. Padhy, and J. Sharma,“Congestion management considering optimal placement of distributed generator in deregulated power system networks,” Electr. Power Components Syst., vol. 42, no.

1, 2014, pp. 13–22.

15. M. Sarwar and A. S. Siddiqui, “Congestion Management in Deregulated Electricity Market Using Distributed Generation,” Ieee Indicon, no. 1, 2005, pp. 1–5.

16. R. Leou and J. Teng, “A Transmission Plan Considering Uncertainties Under a Deregulated Market,” vol. 6,

2011, pp. 33–37. 17. M. Afkousi-Paqaleh, A. Abbaspour-Tehranifard, M. Rashidinejad, and K. Lee, “Optimal placement and sizing

of distributed resources for congestion management considering cost/benefit analysis,” Power Energy Soc.

Gen. Meet. 2010 IEEE, 2010, pp. 1–7. 18. P. P. Kulkarni and N. D. Ghawghawe, “Optimal Placement and Parameter Setting of TCSC in Power

Transmission System toIncrease the Power Transfer Capability,” Int. Conf. Energy Syst. Appl., no. Icesa,

2015, pp. 735–739. 19. S.E. Gasim and J. Jasni," Power System Security Enhancement and Loss Reduction using the SMART Power

Flow Controller" ,IEEE Innovative smart grid Technologies - Asia (ISGT ASIA) ,2014,pp.307-311.

20. N. A. Belyaev ; N. V. Korovkin ; V. S. Chudny , "Reduction of active power loss in electric power system with optimal placement of FACTS device", 2015 IEEE NW Russia Young Researchers in Electrical and

Electronic Engineering Conference (EIConRusNW) , 2015, pp. 150-154

21. I. S. Latypov ; V. V. Sushkov, reduction of active power loss in overhead power transmission lines rated for 6-35kv, 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics), 2016,

439-446

22. G. Hocine ; L. Fatiha ; F.Z Gherbi ; A. Labiba "The intrest of SACTS to improve voltage and loss reduction in

the western Algerian network 2012”, 2015 4th International Conference on Electrical Engineering (ICEE) ,

2012,pp.1-6.Vol: 5 , Issue: 6,2014,pp: 2739 – 274

71

Authors: Afshan Hassan, Rajeev Sharma, Gurbaj Singh

Paper

Title:

Detection & Mitigation Of Selfish Node In Wireless Mesh Networks

Abstract: Wireless Mesh networks (WMN’s) are prone to a number of attacks & these

attacks compromise the security of these networks. Attaining security in these networks is a

challenging task. It is logical to consider that there are many types of scripts in the internet.

The virus can either be a key logger or somebody else's mischief. With this script we can

steal any information. Since the existence of virus cannot be ignored, therefore the authors

have tried to present their work on first detecting it and later on fixing it. With the help of

different protocols present in the Application Layer ,a hacker takes information out of the

script. The authors have used Covert Channel, which has been mentioned in many essays.

Now with the help of this channel, the information will go to all and it will not go to any of

the informatics. This research proposal envisions a methodology to first detect the selfish

node in the network & later on provides a technique for mitigation of the same.NS2

simulator has been used to simulate & analyze the performance of our proposed

methodology for Open Shortest Path First(OSPF) protocol in WMN’s.

Keyword: Wireless Mesh Networks(WMN’s), Distributed Denial Of Service (DDoS),

covert channel, Media Access Control (MAC), Open Shortest Path First(OSPF),Switch port

analyzer, Intrusion Detection Systems(IDS), Packet Delivery Ratio(PDR)

Referenecs: 1. Ali Mahmodi, Parisa Daneshjoo, Changez Delara,”Using Genetic Algorithm to Improve Bernoulli Naïve

Bayes Algorithm in Order to Detect DDoS Attacks in Cloud Computing Platform”2018

2. E. Cedex, “Protecting Wireless Mesh Networks through a Distributed Intrusion Prevention Framework,” ,

2015, pp. 1–6. 3. H. Al-Mefleh and O. Al-Kofahi, “Taking advantage of jamming in wireless networks: A survey,” Computer

Networks, vol. 99, 2016, pp. 99–124.

4. Y. Yu, Z. Ning, Q. Song, L. Guo, and H. Liu, “A Dynamic Cooperative Monitor Node Selection Algorithm in Wireless Mesh Networks,” 2015.

5. T. Sommestad and F. Sandström, “Information Computer"2014

6. Security & quot; Towards a framework for the potential cyber-terrorist threat to critical national infrastructure: An empirical test of the accuracy of an attack graph analysis tool,” Inf. Computer. Security, vol. 23, no. 5,

2015, pp. 516–531.

7. M.Baskar,T.Gnansekaran,J.Frank Vijay: Time Variant Predicate Based Traffic Approximation Algorithm for Efficient low Rate DDoS Attack TAGA Journal,2018 ,Vol 14

8. M. Mehic, J. Slachta, and M. Voznak, “Whispering through DDoS attack,” Perspect. Sci., vol. 7, 2016, pp.

95–100. 9. O. Cheikhrouhou, “Secure Group Communication in Wireless Sensor Networks: A survey,” J. Network.

Computing. Appl., vol. 61, 2016, pp. 115–132.

10. R. Upadhyay, Salman Khan,Herendra Tripathi,Uma Rathore Bhatt"Conference on Computing and Network Communications(CoCONet'15)",2015

11. D. Kaur and P. Singh, “Various OSI Layer Attacks and Countermeasure to Enhance the Performance of

WSNs during Wormhole Attack,” vol. 5, 2014, no. 1. 12. M. Ahmed, A. N. Mahmood, and J. Hu, “Journal of Network and Computer Applications A survey of network

anomaly detection techniques,” J. Network. Computing. Appl., vol. 60, 2016, pp. 19–31.

13. E. Technique and F. Preventing, “Enhanced Technique For Preventing and Isolating Distributed Denial of Service Attack in Wireless Mesh Networks,” pp. 1–38.

447-454

Authors: Abdul Khader Valli T, Monica Mittal

Paper

Title:

Analysis of fractional systems using Haar wavelet

72

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Abstract: Wavelets are relatively new tool and have quite been thriving domain in

mathematical research. Numerical solutions of differential and integral equations require

development of accurate and fast algorithms based on wavelets. This is more pertinent for

those problems having localized solutions, both in position and scale. Haar wavelet offers a

promising solution bases due to simple mathematical expressions and multi-resolution

properties. In this paper, A Haar wavelet based method to solve partial differential equations

(PDE) modeling fractional systems is presented. Operational approach is based on

representing various integro-differential mathematical operations in terms of matrices. In

this article, firstly introduction of Haar wavelet and different operational matrices used for

the analysis of fractional systems are presented. A modified computational technique is

explained to solve variety of partial differential equations modeling systems of fractional

order. This method achieves the solutions by solving Sylvester equation using MATLAB.

Demonstrations are provided with the help of two illustrative examples by suitable

comparisons with exact solutions.

Keyword: Fractional partial differential equations (FPDE), Haar wavelet, Fractional

calculus, Operational matrices, Sylvester equation.

Referenecs: 1. F. Liu, V. Anh, I. Turner, “Numerical solution of the space fractional Fokker–Planck equation,” J. Comput.

Appl. Math., 166 (2004), pp. 209-219.

2. S .A. EI-Wakil, A. Elhanbaly, M.A. Abdou, “Adomian decomposition method for solving fractional nonlineardifferential equations,” Appl. Math. Comput., 182 (2006), pp. 313-324.

3. S. Chen, F. Liu, “Finite difference approximations for the fractional Fokker–Planck equation,” Appl. Math.

Model., 33 (2009), pp. 256-273. 4. A. Calderon, B. Vinagre, “Fractional order control strategies for power electronic buckconverters,” Signal

Process., 86 (2006), pp. 2803-2819.

5. M. Tavazoei, M. Haeri, “Chaos control via a simple fractional-order controller,” Phys. Lett. A, 372 (2008), pp. 798-807.

6. Li Zhu, Qibin Fan, “Solving fractional nonlinear Fredholm integro–differentialequations by the second kind

Chebyshev wavelet,” Commun. Nonlinear Sci. Numer. Simul., 17 (2012), pp. 2333-2341. 7. Zaid M. Odibat, “A study on the convergence of variational iteration method,” Math. Comput. Model., 51

(2010), pp. 1181-1192.

8. I.L. EI-Kalla, “Convergence of the Adomian method applied to a class ofnonlinear integral equations,” Appl. Math. Comput., 21 (2008), pp. 372-376.

9. M.M. Hosseini, “Adomian decomposition method for solution of nonlinear differential algebraic equations,”

Appl. Math. Comput., 181 (2006), pp. 1737-1744. 10. Shaher Momani, Zaid Odibat, “Generalized differential transform method for solving a space and time-

fractional diffusion-wave equation,” Phys. Lett. A, 370 (2007), pp. 379-387.

11. Zaid Odibat, Shaher Momani, “Generalized differential transform method: application todifferential equations of fractional order,” Appl. Math. Comput., 197 (2008), pp. 467-477.

12. Abbas Saadatmandi, Mehdi Dehghan, “A new operational matrix for solving fractional-order

differentialequations,” Comput. Math. Appl., 59 (2010), pp. 1326-1336 13. Y. Zhang, “A finite difference method for fractional partial differential Equation,” Appl. Math. Lett., 215

(2009), pp. 524-529

14. Y.M. Chen, Y.B. Wu, et al., “Wavelet method for a class of fractional convection–diffusionequation with variable coefficients,” J. Comput. Sci., 1 (2010), pp. 146-149.

15. H. Jafari, S.A. Yousefi, “Application of Legendre wavelets for solving fractionaldifferential equations,”

Comput. Math. Appl., 62 (2011), pp. 1038-1045. 16. I. Podlubny, “Fractional Differential Equations,” Academic press (1999).

17. Khushboo Kumari , Monika Mittal, “State analysis of singular time-varying bilinear systems using non-

recursive Haar wavelet operational approach,” 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT-2017).

18. Zaid Odibat, Shaher Momani, “A generalized differential transform method for linear partial differential

equations of fractional order,” Appl. Math. Lett., 21 (2008), pp. 194-199. 19. Y.L. Li, N. Sun, “Numerical solution of fractional differential equations using thegeneralized block pulse

operational matrix,” Comput. Math. Appl., 62 (2011), pp. 1046-1054.

20. Ming xu Yi , Jun Huang , Jin xia Wei, “Block pulse operational matrix method for solving fractional partial differential equation,” Applied Mathematics and Computation, volume 221, 15 September 2013, Pages 121-

131.

21. M Garg, L Dewan, “Non-recursive Haar Connection Coefficients Based Approach for Linear Optimal Control,” Journal of Optimization Theory and Applications 153 (2) , 2012, 320-337.

22. M Garg, L Dewan, “A numerical method for linear ODEs using non-recursive Haar connection coefficients,”

International Journal of Computational science and Mathematics (IJCSM), 2010.

455-459

.

.

73.

Authors: Samarth Negi, Navneet Yadav, Rahul Rawat, Rishabh Singh

Paper

Title:

An Effective Technique for Determining Fish Freshness using Image Processing

Abstract: This paper presents an efficient image processing based algorithm to determine

the freshness of a fish. The determination is made after judging the condition of the eyes and

the gills of the sample. The method is applicable for most edible fishes and can be easily

reconfigured by changing the effect that the two determinants have on the final outcome.

The novelty of the method lies in the ability of the algorithm to easily adapt to new samples

which is a result of the universal applicability of the parameters selected. The method shows

highly accurate results when compared to the ground truth.

Keyword: Digital Image Processing, Image Segmentation, Fish Quality

Referenecs: 1. A. Danti, J. Y. Kulkarni and P. Hiremath, "An Image Processing Approach to Detect Lanes, Pot Holes and

Recognize Road Signs in Indian Roads," International Journal of Modeling and Optimization, vol. 2, 2012,

pp. 658-662.

2. N. Yadav, "DWT--SVD--WHT Watermarking Using Varying Strength Factor Derived From Means of the WHT Coefficients,” Arabian Journal for Science and Engineering, vol. 43(8), 2017, pp. 4131-4143.

3. M.T.-Salazar, L. E. C.-Suarez, D. R.-Marie, I. H. Pike et al. "Effect of fishmeal made from stale versus fresh

herring and of added crystalline biogenic amines on growth and survival of blue shrimp Litopenaeus stylirostris fed practical diets," Aquaculture. vol. 242(1-4), 2004, pp. 433-449.

4. S. V. Joshi, "To Study the Relationship between Indian Food Habits and Health for Healthy India," Imperial

Journal of Interdisciplinary Research (IJIR), vol. 3(3), 2017, pp. 1120-1122. 5. I. C. Navotas, C. N. V. Santos, E. J. M. Balderrama, F. E. B. Candido et al. "Fish identification and freshness

classification through image processing using artificial neural network," Journal of Engineering and Applied

Sciences, vol. 13(18), pp. 4912-4922. 6. A. Issac, M. K. Dutta, B. Sarkar and R. Burget, "An efficient image processing based method for gills

segmentation from a digital fish image," 2016 3rd International Conference on Signal Processing and

Integrated Networks (SPIN), Noida, 2016, pp. 645-649. 7. G. Xue, W. Yu, L. Yutong, Z. Qiang et al. "Construction of novel xanthine biosensor by using Zinc Oxide

(ZnO) by biotemplate method for detection of fish freshness," Analytical Methods. vol. 11(8), 2019,

10.1039/C8AY02554A.

8. A. Karagöz, "Fish Freshness Detection by Digital Image Processing, " Dokuz Eylül University ,Iżmir, Turkey,

2013, Available: http://acikerisim.deu.edu.tr

9. G. Ólafsdóttir, E. Martinsdottir, J. Oehlenschläger, P. Dalgaard, B. Jensen, et al. "Method to evaluate fish freshness in research and industry," Trends in Food Science & Technology, vol. 8(8), 1997, pp. 258-265.

10. Z. Chen, Y. Lin, X. Ma, L. Guo, , B. Qiu, G. Chen and Z. Lin, "Multicolor biosensor for fish freshness

assessment with the naked eye," Sensors and Actuators B: Chemical.vol. 252, 2017, pp. 201-208. 11. V. Lougovois, Freshness Quality and Spoilage of Chill-Stored Fish. ISBN 1-59454-408-5, 2005, pp. 35-36

12. J. J. Connell, Control of Fish Quality; 4th ed.; Fishing News Books Ltd.: Farnham, Surrey, 1995; pp 1-4, 37-

48, 135-164

460-464

Authors: Divya Thakur, Rajdeep Kaur

Paper

Title:

An Optimized CNN based Real World Anomaly Detection in Surveillance Videos

Abstract: Anomaly detection in automated surveillance video is an extremely monotonous

process for monitoring for crowded scenes and surveillance videos are capable to

incarcerate a mixture of sensible anomalies. An appropriate machine learning technique can

help to train the Anomaly Detection System (ADS) in identifying anomalous activities

during surveillance. To this end, we present an anomaly detection system that can be used as

a tool for anomaly detection in surveillance videos using the concept of artificial

intelligence. The main intention of the proposed anomaly detection system is to improve the

detection time and accuracy by using the concept of Convolutional Neural Network (CNN)

74

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as artificial intelligence technique. In this paper we present a CNN based Anomaly

Detection System (CNN-ADS), which is the combination of multiple layer of hidden unit

with the optimized MSER feature by using Genetic Algorithm (GA). Here CNN is used for

classifying the activity into normal and abnormal from the surveillance videos based on the

fitness function of GA which is used for the selection of optimal MSER feature sets.

Further, Self adaptive genetic algorithm (SAGA) is adopted to efficiently solve optimization

problems in the continuous search domain to select the best possible feature to segregate the

pattern of normal and abnormal activities. The main contribution of this research is

validation of proposed system for the large scale data and we introduce a new large-scale

dataset of 128 hours of videos. Dataset consists of 1900 long and untrimmed real-world

surveillance videos, with 13 sensible anomalies such as road accident, burglary, fighting,

robbery, etc. as well as normal activities. The experimental results of the planned system

show that our CNN-ADS for anomaly detection achieve essential improvement on anomaly

detection presentation as compared to the state-of-the-art approaches. The dataset is

available at: https://webpages.uncc.edu/cchen62/dataset.html. In this paper, to validate the

proposed ADS we provide the comparison of existing results of several recent deep learning

baselines on anomalous activity detection. The real-time ADS in surveillance video

sequences using SAGA based CNN with MSER feature extraction technique is implemented

using Image Processing Toolbox within Matlab Software.

Keyword: Anomaly Detection System (ADS), Convolutional Neural Network (CNN),

MSER Feature Extraction, Pattern recognition, Genetic Algorithm (GA).

Referenecs: 1. Sultani, Waqas, Chen Chen, and Mubarak Shah. "Real-world anomaly detection in surveillance videos."

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.

2. Hsu, Shih-Chung, et al. video-based abnormal human behavior detection for psychiatric patient monitoring." Advanced Image Technology (IWAIT), 2018 International Workshop on. IEEE, 2018.

3. Huang, Shaonian, Dongjun Huang, and Xinmin Zhou. "Learning Multimodal Deep Representations for

Crowd Anomaly Event Detection." Mathematical Problems in Engineering 2018 (2018). 4. Cheng, Kai-Wen, Yie-Tarng Chen, and Wen-Hsien Fang. "Gaussian process regression-based video anomaly

detection and localization with hierarchical feature representation." IEEE Transactions on Image Processing

24.12 (2015): 5288-5301 5. Cheng, Kai-Wen, Yie-Tarng Chen, and Wen-Hsien Fang. "Video anomaly detection and localization using

hierarchical feature representation and Gaussian process regression." Proceedings of the IEEE Conference on

Computer Vision and Pattern Recognition. 2015. 6. Agrawal, Vartika, and Satish Chandra. "Feature selection using Artificial Bee Colony algorithm for medical

image classification." Contemporary Computing (IC3), 2015 Eighth International Conference on. IEEE, 2015.

7. Subanya, B., and R. R. Rajalaxmi. "Feature selection using Artificial Bee Colony for cardiovascular disease classification." Electronics and Communication Systems (ICECS), 2014 International Conference on. IEEE,

2014.

8. Uijlings, Jasper RR, et al. "Realtime video classification using dense hof/hog." Proceedings of international conference on multimedia retrieval. ACM, 2014

9. Abu-Mouti, Fahad S., and Mohamed E. El-Hawary. "Overview of Artificial Bee Colony (ABC) algorithm

and its applications." Systems Conference (SysCon), 2012 IEEE International. IEEE, 2012. 10. Kharazmi, Pegah, et al. "Automated detection and segmentation of vascular structures of skin lesions seen in

Dermoscopy, with an application to basal cell carcinoma classification." IEEE journal of biomedical and health informatics 21.6 (2017): 1675-1684.

11. M. J. Khan, A. Zafar and K. S. Hong, “Comparison of brain areas for executed and imagined movements after

motor training: An fNIRS study,” 2017 10th International Conference on Human System Interactions (HSI), Ulsan, South Korea, 2017, pp. 125-130.

12. R. Majid Mehmood, R. Du and H. J. Lee, “Optimal Feature Selection and Deep Learning Ensembles Method

for Emotion Recognition from Human Brain EEG Sensors,” in IEEE Access, vol. 5, no. , pp. 14797-14806, 2017.

13. A Minz and C. Mahobiya, “MR Image Classification Using Adaboost for Brain Tumor Type,” 2017 IEEE 7th

International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 701-705. 14. H. Rao, P. V. Naganjaneyulu and K. S. Prasad, “Brain Tumor Detection and Segmentation Using Conditional

Random Field,” 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp.

807-810. 15. R. A. Jasmine and P. A. J. Rani, “A two phase segmentation algorithm for MRI brain tumor extraction,” 2016

International Conference on Control, Instrumentation, Communication and Computational Technologies

(ICCICCT), Kumaracoil, 2016, pp. 437-440. 16. M. Gupta, B. V. V. S. N. P. Rao and V. Rajagopalan, “Brain Tumor Detection in Conventional MR Images

Based on Statistical Texture and Morphological Features,” 2016 International Conference on Information

465-473

Technology (ICIT), Bhubaneswar, 2016, pp. 129-133

17. G. Singh and M. A. Ansari, “Efficient detection of brain tumor from MRIs using K-means segmentation and

normalized histogram,” 2016 1st India International Conference on Information Processing (IICIP), Delhi,

2016, pp. 1-6.

75

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Authors: Abhas Kanungo, Monika Mittal, Lillie Dewan

Paper

Title:

Optimal parameter tuning of MRPID controller for temperature control

Abstract: This paper presents the optimal tuning of gain parameters of Multi-resolution

PID controller for thermal system. Control of temperature in thermal system is very

important. MRPID controller utilizes the multi-resolution property of wavelet transform to

decompose the error signal in to different frequency components. Further different

coefficients of wavelet are used to generate the control signal. To generate the desired

control, optimal tuning is required. In this paper, optimal tuning of MRPID controller is

done by genetic algorithm (GA) & Particle swarm optimization (PSO). At the end,

performance comparison between these two techniques is done and concluded. Wavelet-

based MRPID controller is executed in MATLAB/Simulink@2015a.

Keyword: Thermal system, MRPID Controller, GA and PSO, MWT

Referenecs: 1. Jino, Y., Matsumoto, S., & Kamito, A. “Risk based Model Predictive Control with hybrid system structure

and its application to Thermal Power Plants”. 2006 SICE-ICASE International Joint Conference.doi:10.1109/sice.2006.315571

2. Emami-Naeini, A., Ebert, J. L., de Roover, D., Kosut, R. L., Dettori, M., Porter, L. M. L., & Ghosal,

S.”Modeling and control of distributed thermal systems”. IEEE Transactions on Control Systems Technology, 11(5), 668–683.doi:10.1109/tcst. 2003.816411.

3. Dong, Z., Su, Y., & Yan, X. “Temperature Control System of the Thermal Analyzer Based on Fuzzy PID

Controller”. 2009 Ninth International Conference on Hybrid Intelligent Systems.doi:10.1109/his.2009.123 4. Dong, Z., Su, Y., & Yan, X. “Temperature Control System of the Thermal Analyzer Based on Fuzzy PID

Controller”. Ninth International Conference on Hybrid Intelligent Systems.doi:10.1109/his.2009.123.

5. C. C. Hang, K. J. Aström, and W. K. Ho, “Refinements of the Ziegler– Nichols tuning formula”, Proc. Inst. Elect. Eng.—Control Theory Appl., vol. 138, no. 2, pp. 111–118,1991.

6. J. G. Ziegler and N. B. Nichols, “Optimal settings for automatic controllers, Trans. ASME”, vol. 64, no. 11,

pp. 759–768,1942. 7. Abhas Kanungo, Monika Mittal, Lillie Dewan , “Comparison of Haar and Daubechies Wavelet Denoising for

PID Controlled Thermal System”, Journal of Advanced Research in Dynamical and Control Systems,2018,

issue 9, pp 2405-2411. 8. Tang, K. S., Kim Fung Man, Guanrong Chen, & Kwong, S. “An optimal fuzzy PID controller”. IEEE

Transactions on Industrial Electronics, 48(4), 2001,757–765. doi:10.1109/41.937407. 9. Hari Om Bansal, Rajamayyoor Sharma, P. R. Shreeraman , “ PID Controller Tuning Techniques: A Review”,

Journal of Control Engineering and Technology (JCET) ,2012,Vol. 2 Iss. PP. 168-176.

10. Khan, M.A.S.K. and M.Azizur Rahman, "Implementation of wavelet-based controller for battery storage system of hybrid electric vehicles", IEEE Transactions on Industry Applications, 2011,Vol.47, No.5, pp.2241-

2249.

11. Das, P., Edavoor, P. J., Raveendran, S., Rathore, S., & Rahulkar, A. D. Design and implementation of PID controller based on orthogonal wavelet filter-banks in FPGA. 2017 7th International Symposium on

Embedded Computing and System Design (ISED). doi:10.1109/ised.2017.8303928

12. Parvez, Shahid and Zhiqiang Gao, "A wavelet-based multiresolution PID controller", IEEE Transactions on

Industry Applications, 2005, Vol.41, No.2, pp.537-543.

13. Vajpayee, Vineet, Siddhartha Mukhopadhyay and AkhilanandPatiTiwari, "A Multi resolution Wavelet based

Subspace Identification", An International Journal of IFAC ,2016, Vol.49, No.1, pp.247-253. 14. Dewan L and Garg M. , “A Numerical Method for Linear ODEs using

Non-recursive Haar Connection Coefficients”, International Journal of Computational and Applied

Mathematics (IJCAM) vol. 2 no.3b,pp 429-440,2012. 15. Khan, M.Abdesh SK and M.AzizurRahman , "A novel neuro-wavelet-based self-tuned wavelet controller for

IPM motor drives", IEEE Transactions on Industry Applications, 2010,Vol.46, No.3, pp.1194-1203.

16. Kanungo, A., Dewan, L., & Mittal, M.,” Performance improvement of a thermal system PID controller using Haar wavelet based denoising”. International Conference on Industrial Instrumentation and Control (ICIC).

2015, doi:10.1109/iic.2015.7150773.

17. Khan, M. A. S. K., & Rahman, M. A. “Implementation of a Wavelet-Based MRPID Controller for Benchmark Thermal System”. IEEE Transactions on Industrial Electronics, 57(12), 2010 ,4160–

4169.doi:10.1109/tie.2010.2044121

474-477

18. S. Parvez,, “Advanced control techniques for motion control problem”,2003, Ph.D. dissertation, Cleveland

State Univ., Cleveland.

19. Meena, D. C., & Devanshu, A. “Genetic algorithm tuned PID controller for process control”. 2017

International Conference on Inventive Systems and Control (ICISC). doi:10.1109/icisc.2017.8068639. 20. Seekuka, J., Rattanawaorahirunkul, R., Sansri, S., Sangsuriyan, S., & Prakonsant, A. “AGC using Particle

Swarm Optimization based PID controller design for two area power system.” International Computer

Science and Engineering Conference (ICSEC). doi:10.1109/icsec.2016.7859951. 21. Aranza, M. F., Kustija, J., Trisno, B., & Hakim, D. L. “Tunning PID controller using particle swarm

optimization algorithm on automatic voltage regulator system”. IOP Conference Series: Materials Science and

Engineering, 2016,128, 012038. doi:10.1088/1757-899x/128/1/012038

76

Authors: Jahangir Kamal, Dr. Meenu Dave

Paper

Title:

A Framework for Managing and Analyzing Big Data in Indian School Education System

with Reference to Jammu & Kashmir

Abstract: Big Data storm has reached all most in every sector whether be public or private

and is an important decision making factor for the administration or governing body of any

system or organization, because with the advancements in technologies, various public,

private, and social organizations are creating or exchanging a huge volume of data through

different sources in various formats. Big Data can be applied in various sectors of India,

wherein one of the essential sectors is education system where Big Data analytics is slowly

and steadily finding its place for betterment of the services being provided by this system.

India has one of the biggest school systems in the world which is spread over different states

of the country and one part of this system is functional in one of the northern states of India

known as Jammu and Kashmir (J&K). This study is based on Big Data management and

analysis in School Education System of J&K. In the area of School Education in J&K,

computers are finding an important place for data management as well as imparting learning

through digital means. There is a need of applying Big Data technologies to various aspects of

the School Education in J&K, as huge amount of data with variety and high frequency of

generation is available in the institutes under this system. This paper will analyze various

aspects of Big Data management and analysis system for School Education of J&K. This

paper will also highlight the current scenarios of School Education System of J&K in

handling Big Data such as sources of Big Data, Scope of Big Data analytics in School

Education System of J&K, opportunities of Big Data analytics, challenges and issues that can

be faced in this system, Applications of Big Data analytics in School Education of J&K, and

finally discussing the proposed architecture of Big Data management and analysis system for

School Education of J&K.

Keyword: Big Data, School Education, Analytics, Hadoop, HBase.

References:

1. , H. M. El-Bakry et al, “Enhancing Big Data Processing in Educational Systems,” Advances in Computers and

Technology for Education, 2014, pp.176-181.

2. Rao and K. Baglodi, “Role of Big Data in Education Sector: A Review,” International Journal of Advances in

Science Engineering and Technology, vol. 6, issue 1,spl. Issue 1, Feb. 2018. 3. F. Fayaz and S. Mehta, “Analysis of Education Sector-Study of Kerala and Jammu & Kashmir,” IOSR Journal

of Humanities and Social Science (IOSR-JHSS), vol. 23, issue 3, March 2018, pp. 44-51.

4. Y. Li and X. Zhai, “Review and Prospect of Modern Education Using Big Data,” Procedia Computer Science,

ELSEVIER, vol. 129, 2018, pp. 314-347.

5. Vatsala, R. Jadhav and Sathyaraj, “A Review of Big Data Analytics in Sector of Higher Education,”

International Journal of Engineering Research and Application, vol. 7, issue 6, part 2, June 2017, pp. 25-32. 6. Manohar, P. Gupta et al, “Utilizing Big Data Analytics to Improve Education,” Semantic Scholar, 2016.

Available:https://www.semanticscholar.org/paper/Utilizing-Big-Data-Analytics-to-Improve-Education-

Manohar-Gupta/1792463683531cd5db886972d96b92aea75db18e 7. P. Dar, “Top 7 Sectors where Data Science can transform India,” August 16, 2018. Available:

https://www.analyticsvidhya.com/blog/2018/08/top-7-sectors-where-data-science-can-transform-india-with-

free-datasets/ 8. Peterson, “Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and

Donors,” Handbook of statistical Analysis and Data Mining Applications, Academic Press ELSEVIER, United

Kingdom, 2nd ed., chap. 13, 2018, pp. 259-278. 9. J. Kohlhammer, D. Keim et al, “Solving Problems With Visual Analytics,” Procedia Computer Science,

ELSEVIER, vol.7, 2011, pp. 117-120.

10. L. Khanna, S. N. Singh, and M. Alam, “Educational Data mining and its Role in Determining Factors Affecting

478-493

Students Academic Performance: A Systematic Review,” In Proceedings of 1st India International Conference

on Information Processing (IICIP), IEEE, Aug. 2016, pp. 1-7.

11. U. Kumar, “Bringing Big Data to Your School’s Analytics,” Higher Education Marketing, September 3, 2013.

Available: http://www.highereducationmarketing.com/blog/bringingbigdataschoolsanalytics 12. Q. Liu, Y. Fu, G. Ni and J. Mei, "Big Data Management Performance Evaluation in Hadoop Ecosystem," In

Proceedings of 3rd International Conference on Big Data Computing and Communications (BIGCOM), IEEE,

2017, pp. 413-421. 13. W. T. Wu, W. W. Lin et al, “Energy-efficient hadoop for big data analytics and computing: A systematic

review and research insights,” Future Generation Computer Systems, ELSEVIER, vol. 86, sep. 2018, pp. 1351-

1367. 14. C.T. Yang et al, “Implementation of Data Transform Method into NoSQL Database for Healthcare,” In

proceedings of International Conference on parallel and Distributed Computing, Applications and

Technologies, IEEE, Dec. 2013, pp. 198-205. 15. N. V. Patil and T. Patel, “Apache Hadoop: Resourceful Big Data management,” International Journal of

Innovative Research in Science, Engineering and Technology, vol. 3, special issue 4, April 2014, pp. 201-209.

77

Authors: Anand Shanker Tewari, Aleesha S.J.

Paper

Title:

Generating Quality Items Recommendation by Fusing Content based and Collaborative

filtering

Abstract: Recommendation system has become an inevitable part of our life. It has already

spread its prominence in various fields like movies, music, news, article recommendations

etc. Due to the influence of social media, data is streaming from all over the Internet. Collect

the relevant information from chunks of data available has become much difficult.

Recommender systems guides in filtering data to get the relevant information. Commonly

used recommendation approaches are content based filtering and collaborative filtering. Each

approach has its own limitations. The hybrid approach combines the advantages of both the

approaches. In this paper, we have tried to enhance the quality of the items recommendation

system by fusing both content based and collaborative filtering uniquely. The experimental

results are compared with that of other traditional approach using precision and recall

evaluation measure. The comparison results show that our approach has 10% better precision

for top-10 recommendations than other established recommendation technique.

Keyword: E-commerce, Content based filtering, Collaborative filtering, Hybrid

Recommendation System.

References: 1. Boutemedjet, Sabri, and DjemelZiou. “A graphical model for context-aware visual content

recommendation.”IEEE Transactions on Multimedia 10.1 (2007): 52-62.

2. Pazzani, Michael J. “A framework for collaborative, content-based and demographic filtering.” Artificial

intelligence review13.5-6 (1999): 393-408. 3. Pazzani, Michael J., and Daniel Billsus. "Content-based recommendation systems." The adaptive web. Springer,

Berlin, Heidelberg, 2007. 325-341.

4. Herlocker, Jon, Joseph A. Konstan, and John Riedl. “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms.” Information retrieval 5.4 (2002): 287-310.

5. Salter, James, and Nick Antonopoulos. “Cinema Screen recommender agent: combining collaborative and

content-based filtering.” IEEE Intelligent Systems 21.1 (2006): 35-41. 6. Adomavicius, Gediminas, and Alexander Tuzhilin. “Toward the next generation of recommender systems: A

survey of the state-of-the-art and possible extensions.”IEEE Transactions on Knowledge & Data Engineering 6

(2005): 734-749. 7. Wei, Suyun, et al. "Item-based collaborative filtering recommendation algorithm combining item category with

interestingness measure." 2012 International Conference on Computer Science and Service System. IEEE,

2012. 8. Qin, Jie, Lei Cao, and Hui Peng. "Collaborative filtering recommendation algorithm based on weighted item

category." 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016..

9. Yi Mu, Nianhao Xiao, Ruichun Tang, Liang Luo, Xiaohan Yina. “An Efficient Similarity Measure for Collaborative Filtering.” Procedia computer science 147 (2019): 416-421

10. Gandhi, Sonali, and Monali Gandhi. "Hybrid Recommendation System with Collaborative Filtering and

Association Rule Mining Using Big Data." 2018 3rd International Conference for Convergence in Technology (I2CT). IEEE, 2018.

11. Tewari, Anand Shanker, and Kumari Priyanka. "Book recommendation system based on collaborative filtering

and association rule mining for college students." 2014 International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2014.

12. Kardan, Ahmad A., and Mahnaz Ebrahimi. “A novel approach to hybrid recommendation systems based on

association rules mining for content recommendation in asynchronous discussion groups.” Information

494-498

Sciences 219 (2013): 93-110

13. Puntheeranurak, Sutheera, and Thanut Chaiwitooanukool. "An Item-based collaborative filtering method using

Item-based hybrid similarity." 2011 IEEE 2nd International Conference on Software Engineering and Service

Science. IEEE, 2011. 14. Yang, S., Korayem, M., AlJadda, K., Grainger, T., & Natarajan, S. (2017). Combining content-based and

collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning

approach. Knowledge-Based Systems, 136, 37-45. 15. Zihayat, M., Ayanso, A., Zhao, X., Davoudi, H., & An, A. (2019). A utility-based news recommendation

system. Decision Support Systems, 117, 14-27.

16. Ochirbat, A., Shih, T. K., Chootong, C., Sommool, W., Gunarathne, W. K. T. M., Wang, H. H., & Ma, Z. H. (2018). Hybrid occupation recommendation for adolescents on interest, profile, and behavior. Telematics and

Informatics, 35(3), 534-550.

17. Lucas, Joel P., Nuno Luz, MaríA N. Moreno, Ricardo Anacleto, Ana Almeida Figueiredo, and Constantino Martins. "A hybrid recommendation approach for a tourism system." Expert Systems with Applications 40, no.

9 (2013): 3532-3550.

18. Wei, Shouxian, Xiaolin Zheng, Deren Chen, and Chaochao Chen. "A hybrid approach for movie recommendation via tags and ratings." Electronic Commerce Research and Applications18 (2016): 83-94.

19. Tewari, Anand Shanker, and Asim Gopal Barman. “Collaborative recommendation system using dynamic

content based filtering, association rule mining and opinion mining.” Int. J. Intell. Eng. Syst 10.5 (2017): 57-

66.

20. Tewari, Anand Shanker, Jyoti Prakash Singh, and Asim Gopal Barman. “Generating Top-N Items

Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance.” Procedia computer science 132 (2018): 1678-1684.

78

Authors: Nidhi Gaur, Anu Mehra, Shikha Bathla, Pradeep Kumar

Paper

Title:

Delay Efficient Vedic Multiplier for DSP

Abstract: Multipliers are very essential blocks in any arithmetic and logic unit, accumulators

and Digital signal processors. Due to the enlarging check on delay, design of faster

multipliers is desired. Amidst numerous multipliers, Vedic multipliers are favored for

their speed of operation. There are sixteen sutras in Vedic mathematics out of which four are

multiplication techniques. “URDHVA TIRYAKBHYAM” is the most efficient vedic

multiplication technique in terms of speed. In this paper we aim to develop a multiplier using

Ripple Carry Adder and parallel prefix adders which carry out the “URDHVA

TIRYAKBHYAM” sutra with improved speed of operation by providing the minimum

delay for the multiplication of numbers regardless of their bit sizes. A vast

majority of the engineering domain consists of ubiquitous technologies like DSP. As it is

one of the most rapid growing technologies of the 21st Century, it faces challenges and

improvisation at each step. Engineers are working diligently to improve the quality of

Digital Signal processors and major breakthroughs are being made at a very good

rate. Proposed multiplier could be applied for such DSP applications. Verilog

language has been used for the coding. Xilinx Vivado Tool is used for synthesis and Model

Sim 5.4 has been used for simulation.

Keyword: Urdhva Tiryakbhyam, Vedic Multiplier, Ripple Carry Adder, Parallel Prefix

Adder.

References: 1. Swami BharatikrishnaTirthaji Maharaja, “Vedic Mathematics’, MotilalBanarasidass Publishers,

1965.

2. Y Rana Lakshmanna, GVS Rao,” A Survey on Different Multiplier Techniques”; SSRG International Journal of Electronics ad Communication Engineering( SSRG- IJECE)- Volume 3, March 3, 2016.

3. Rakshith T R and RakshithSaligram, “Design of High speed Low power Multiplier using Reversible

logic: a vVedic Mathematical Approach”, International conference on Circuits, Power and Computing Technologies (ICCPCT-2013), ISBN: 978-1-4763-4922- 2/13,PP 775-781.

4. VaijyanathKunchigi, Subhash Kulkarni,” Simulation of Vedic Multipliers in DCT Applications.”

International Journal of Computer Applications (0975-8887) Volume 63-No. 16, February 2013. 5. Palladurai K. and K. Hariharan, “Implementation of Signed Vedic Multiplier Targeted at FPGA

architectures”, ARPN Journal of Engineering and Applied Science, Vol. 10, No. 5, March 2015.

499-501

Authors: Ashima Kalra, Shakti Kumar, Sukhbir Singh Walia

Paper ANN System Identification for Rapid Battery Charger using Parallel 3 Parent Genetic

79

Title: Algorithm

Abstract: Model identification is one of the main concerns in the field of system modeling.

The complete modeling of an ANN system using input output data consists of two processes:

architecture selection in which number of hidden layers and the number of neurons in each

hidden layer is to be decided. This is then followed by training of system by the given training

data. The problem here is formulated as search and minimization problem. This paper

presents the identification of ANN system for rapid Nickel Cadmium (Ni-Cd) batteries

charger by applying a new version of Genetic algorithm called as parallel 3 parent genetic

algorithm (P3PGA). It is a multi population based 3 parent genetic algorithm (3PGA) .The

proposed approach was implemented using MATLAB R2018A and was observed to be

computationally more efficient with minimum MSE. With increase in number of iterations,

system performance gets improved. We further compared results of the proposed algorithm

with the results of other recent soft computing based algorithms as well classical learning

based algorithms namely, big bang big crunch (BB-BC), parallel big bang big crunch (PBB-

BC), Levenberg-Marquardt algorithm (LM), error back propagation (EBP), Resilent prop

(RPROP), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial

bee colony (ABC) for ANN model identification. The proposed algorithm outperformed all of

the other 8 algorithms.

Keyword: Model identification, 3 parent genetic algorithm (3PGA), parallel 3 parent genetic

algorithm (P3PGA)

References: 1. P.J., "The Roots of Backpropagation", John Wiley k Sons, Inc., New York, 1994.

2. Sehgal et.al, “Minimization of Error in Training a Neural Network Using Gradient Descent Method”,

International Journal of Technical Research(IJTR), Vol 1, Issue 1, Mar-Apr 2012, pp 10-12.

3. Ahmad Hashim Hussein Aal-Yhia, and Ahmad Sharieh , “An Energy Backpropagation Algorithm”,

Proceedings of the World Congress on Engineering WCE 2007, Vol I, July 2007,

4. Byoung_Tak Zhang et.al., “Evolving Optimal Neural Networks Using Genetic Algorithms with Occam_s

Razor”, Complex Systems,7(3),199-220,1993

5. L. Yan-Peng, W. Ming-Guang, and Q. Ji-Xin, “Evolving neural networks using the hybrid of ant colony

optimization and bp algorithm,” in Advances in Neural Networks - 3rd International Symposium on Neural

Networks, ser. LNCS, vol. 3971. Springer-Verlag, 2006, pp. 714–722.

6. David J. Montana, “Neural Network Weight Selection Using Genetic Algorithms”, pp 1-17.

7. David J. Montana and Lawrence Davis, “Training Feedforward Neural Networks Using Genetic Algorithms",

Machine Learning, pp 762-767.

8. Alba E., Chicano J.F. (2004) Training Neural Networks with GA Hybrid Algorithms. In: Deb K. (eds)

Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. pp 852-863 Springer, Berlin, Heidelberg

9. Hiroaki Kitano, "Empirical Studies on the Speed of Convergence of Neural Network Training using Genetic

Algorithms", Machine Learning, AAAI-90 Proceedings, pp 789-795.

10. Jatinder N.D. Gupta, Randall S. Sexton, “Comparing backpropagation with a genetic algorithm for neural

network training”, Elsevier, Omega 27 (1999), pp 679-684.

11. Zhen-Guo Che, Tzu-An Chiang, and Zhen-Hua Che, "Feed forward Neural Networks Training: A Comparison

Between Genetic Algorithm and Back-propagation Learning Algorithm", International Journal of Innovative Computing, Information and Control, ICIC International 2011 ISSN 1349-4198, Volume 7, Number 10,

October 2011, pp. 5839- 5850.

12. V. Saishanmuga Raja and S.P. Rajagopalan, "A Comparative Analysis of Optimization Techniques for

Artificial Neural Network in Bio-Medical Applications", Journal of Computer Science 10 (1): pp 106-114,

Science Publications, 2014

13. Richa Mahajan, Gaganpreet Kaur, Guru Nanak Dev University, Amritsar.Neural Networks using Genetic

Algorithms, International Journal of Computer Applications (0975 – 8887), Volume 77– No.14, September

2013

14. Effect of the number of parents on the performance of multi-parent genetic algorithm, November 2016,

Conference: 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS), Seng Pan That Pann Phyu, Gun Srijuntongsiri

15. Shih-Hsin Chen, Min-Chih Chenb, Pei-Chann Chang, V. Mani, Multiple parents crossover operators: A new

approach removes the overlapping solutions for sequencing problems, Applied Mathematical Modelling,

Volume 37, Issue 5, 1 March 2013, 2737-2746.

16. Eiben A.E., Raué P.E., Ruttkay Z. (1994) Genetic algorithms with multi -parent recombination. In:

Davidor Y., Schwefel HP., Männer R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN

1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg

502-506

17. Paula Amato, Masahito Tachibana, Michelle Sparman, and Shoukhrat Mitalipov, “Three-Parent IVF: Gene

Replacement for the Prevention of Inherited Mitochondrial Diseases”, Fertility and Sterility, 101(1), 2014, pp.

31-35.

18. A Singh, S Kumar, A Singh, SS Walia , “Three-parent GA: A Global Optimization Algorithm”, Journal of

Multiple-Valued Logic & Soft Computing, Vol. 32, ,2019 ,pp 407-423.

19. Nargis Akhter, Amar Singh, Mr. Guljar Singh, Automatic Test Case Generation by using Parallel 3 Parent

Genetic Algorithm, International Journal for Research in Applied Science & Engineering Technology

(IJRASET), Volume 6 Issue VII, July 2018

20. A Singh, Shakti Kumar, Sukhbir Singh Walia, P3PGA: Multi-Population 3 Parent Genetic Algorithm and its

Application to Routing in WMNs, International Journal of Advanced Research in Computer Science, Volume 8,

No. 5, May – June 2017.

21. Ankita et al, Fuzzy system identification for Ni-Cd battery charger using genetic algorithm, International

Journal of Engineering Research & Technology (IJERT), Vol. 1, Issue 5, July – 2012

22. S Kumar, P Kaur and A Singh,” Fuzzy Model Identification: A Firefly Optimization Approach. International

Journal of Computer Applications 58(6):1-8, November 2012.

23. A Kalra, S Kumar, S S Walia, "ANN Model identification: Two Soft Computing Based Approaches",

International Journal of Research and Analytical Reviews, Vol. 4, issue 2, June 2017, pp 79-86.

24. A Kalra, S Kumar, S S Walia, ANN Model Identification: A BB-BC Optimization Algorithm Based Approach,

International Journal of Computer Sciences and Engineering, Vol.-6, Issue-12, Dec 2018, pp 264-271.

25. A Khosla, S Kumar, and K K Aggarwal,2002. Design and development of RFC-10: A fuzzy logic based rapid

battery charger for Nickel-Cadmium batteries. HiPC (High Performance Computing), Workshop on Soft

Computing, Bangalore, 9-14.

80

Authors: Shikha Bathla, Nidhi Gaur, Ayush Uniyal

Paper

Title:

Power analysis of digital circuits for VLSI applications

Abstract: In this modern world, power plays a very important role in designing of electronic

circuits. Portable devices like mobile phones, laptops require electronic circuits that consume

less power. Power dissipation causes invariably rise in temperature of electronic circuits. As

the temperature increases, the power gets dissipated more. MTCMOS (Multi-Threshold

Complementary Metal Oxide semiconductor) power gating is a design technique that reduces

power dissipation. It results in the prevention of sub-threshold leakage in standby mode and

high speed operation with low power consumption in active mode. In this paper, MTCMOS

based MUX is designed and is compared with CMOS MUX and PTL (Pass Transistor Logic)

based MUX. It has been concluded that MTCMOS based MUX consumes 16.56% and

15.19% less power than CMOS MUX and PTL MUX respectively. Results are simulated in

Mentor Graphics version 10.2.

Keyword: PTL, power gating, power dissipation, MTCMOS

References: 1. S. Shigematsu, S. Mutoh et al., "'I-V high-Speed MTCMOS CircuitScheme for Power-Down Application

Circuits", IEEE Journal of Solid-state Circuits, 1997

2. S. Mutoh, IEEE J. Solid State Circuits, vol. 31, pp. 1795-1802, 1996 3. S. M. Kang, Y. Leblebici, CMOS Digital Integrated Circuits: Analysisand Design, India:Tata McGraw-Hill,

2011.

4. N.abiallah Shiri Asmangerdi, Javad. Forounchi, Kuresh. Ghanbari, "Anew 8- Transistor Floating Full-Adder Circuit", IEEE Trans. 20th Iranian Conference on Electrical Engineering (ICEE2012), pp. 1405

1409, May, 2012.

5. Arvind Nigam, Raghvendra Singh,”Design and Analysis of Multi-Threshold CMOS 14T Full Adder using

180nm”, Intl J Engg Sci Adv Research 2016 , March;15-20, ISSN NO: 2395-0730

6. M.Geetha Priya, Dr.K.Baskaran, D.Krishnaveni “Leakage Power Reduction Techniques in Deep Submicron

Technologies for VLSI 7. Ch. Mohammad Arif , J. Syamuel John “Low Power 32-bit Improved Carry Select Adder based on

MTCMOS Technique”

507-511

Authors: Pooja, DavinderParkash, Harbinder Singh

Paper

Title:

Trapezoid Shape Patch Antenna for WLAN Application

81

Abstract: In this paper,CPW fed Trapezoid shape patch antenna is analyzed and investigated

for Wireless Local Area Network (WLAN) application. The proposed antenna is fabricated

on FR4 substrate having dimensions of 19mm ×21.2mm ×1.6mm. It resonates at 5.44 GHz

frequency with peak return loss of 25.8 dB. The parametric study of proposed antenna is

carried out to understand the effect of different values of ground plane on the impedance

bandwidth, return loss of the antenna andalso to optimize the antenna parameters. The CPW-

fed is used to enhance the bandwidth and to reduce the return loss of the antenna. The

importance of different design parameters like current distribution, S-parameter, gain, and

radiation pattern are studied. The results of the proposed antenna are useful for WLAN

Application.

Keyword: Bandwidth,CPW-feed, Patch Antenna, WLAN and Return loss.

References: 1. K. R. Carver and J. W. Mink, “Microstrip Antenna Technology,” IEEE Trans. Antennas Propag., vol. 29, no. 1,

1981, pp. 2–24.

2. N. Prema and A. Kumar, “Design of multiband microstrip patch antenna for C and X band,” Optik (Stuttg).,

vol. 127, no. 20, 2016, pp. 8812–8818.

3. S. Bisht, S. Saini, V. Prakash, and B. Nautiyal, “Study The Various Feeding Techniques of Microstrip Antenna Using Design and Simulation Using CST Microwave Studio,” Int. J. Emerg. Technol. Adv. Eng., vol. 4, no. 9,

2014, pp. 318–324.

4. R. K. Raj, M. Joseph, C. K. Aanandan, K. Vasudevan, and P. Mohanan, “A new compact microstrip-fed dual-band coplanar antenna for WLAN applications,” IEEE Trans. Antennas Propag., vol. 54, no. 12, 2006, pp.

3755–3762.

5. R. Vivek and S. Sreenath, “Coplanar Waveguide ( CPW ) -Fed Compact Dual Band Antenna for 2 . 5 / 5 . 7 GHz Applications,” vol. 74, no. July, 2018, pp. 51–59.

6. A. Dastranj and H. Abiri, “Bandwidth enhancement of printed E-shaped slot antennas fed by CPW and

microstrip line,” IEEE Trans. Antennas Propag., vol. 58, no. 4, 2010, pp. 1402–1407. 7. C.-S. Eun, J.-W. Kim, T.-H. Jung, H.-K. Ryu, J.-M. Woo, and D.-K. Lee, “Compact Multiband Microstrip

Antenna Using Inverted-L- and T-Shaped Parasitic Elements,” IEEE Antennas Wirel. Propag. Lett., vol. 12, pp.

1299–1302, 2013. 8. I. Wire and W. Communications, “Small Planar Monopole Antenna With a Shorted Parasitic,” vol. 52, no. 7,

2004, pp. 1903–1905.

9. L. Dang, Z. Y. Lei, Y. J. Xie, G. L. Ning, and J. Fan, “A compact microstrip slot triple-band antenna for

WLAN/WiMAX applications,” IEEE Antennas Wirel. Propag. Lett., vol. 9, 2010, pp. 1178–1181.

10. K. Srivastava, A. Kumar, B. K. Kanaujia, S. Dwari, and S. Kumar, “Multiband integrated wideband antenna for

bluetooth/WLAN applications,” AEU - Int. J. Electron. Commun., vol. 89, no. January, 2018, pp. 77–84. 11. S. S. Al-Bawri, M. F. Jamlos, P. J. Soh, S. A. Aljunid Syed Junid, M. A. Jamlos, and A. Narbudowicz,

“Multiband slot-loaded dipole antenna for WLAN and LTE-A applications,” IET Microwaves, Antennas

Propag., vol. 12, no. 1, 2017, pp. 63–68.

512-516

82

Authors: Dr. PankajBhambri, Vijay Kumar Sinha, Ms. MeenakshiJaiswal

Paper

Title:

Change in Iris Dimensions as a Potential Human Consciousness Level Indicator

Abstract: Detection of human consciousness is vital for validating a legal or official

statement whether in the form of written or verbal. A statement given under unconsciousness

state is considered null and void as the person is not fully awarded about the consequences of

the statement. Several factors affects human consciousness like effect of alcohol, drowsiness,

effect of anesthesia, sleeping pills, natural sleeping hours etc. The present available methods

for detection of level of consciousness works in a specific condition only. For instance

Alcohol Meter can detect the level of alcohol in the breath but not the effects sleeping pills or

partial conscious due to drowsiness. In this research we propose a new technique which can

detect all types of unconsciousness irrespective of reasons. We used the feature of iris light

sensitivity which dilates inversely to pupil. Consciousness and iris dilation is directly

proportional i.e. iris dilation increases with increase of consciousness and decreases with

decrease of consciousness. We tested this method on 66 volunteers with 99.83% accuracy for

0.03mm iris dilation threshold value unconscious under alcohol effect and under drossiness

conditions. Results shows that iris sensitivity decreases significantly during unconscious state,

for any reasons. This feature can be used for validation of contracts before signing legal

contracts.

517-525

Keyword: Consciousness, Iris Dilation, Pupil Dilation, Pupiliometer, ADHAR, Drowsiness,

consent, legal contract , Lux , Luminance

References: 1. Czajka "Pupil dynamics for iris liveness detection" IEEE Trans. Inf. Forensics Security vol. 10 Apr. 2015, pp.

726-735.

2. Czajka and A. Pacut, “Iris Recognition System Based on Zak-Gabor Wavelet Packets,” Journal of

Telecommunications and Information Technology, no. 4, 2010, pp. 10–18. 3. Czajka, “Database of iris printouts and its application: Development of liveness detection method for iris

recognition,” in Methods and Models in Automation and Robotics (MMAR), 2013 18th International Conference on, Aug 2013, pp. 28–33.

4. Adam Czajka, (2014), "Pupil dynamics for presentation attack detection in iris recognition," in International

Biometric Performance Conference. 5. Biometric Vulnerability Assessment Expert Group (BVAEG). [Online]. Available:

http://www.biometricsinstitute.org/pages/ biometric-vulnerability-assessment-expert-group-bvaeg.html

6. E. C. Lee and K. R. Park, (2010), "Fake iris detection based on 3D structure of iris pattern," International Journal of Imaging Systems and Technology, 2010, pp. 162-166.

7. E. Lee et al., Fake iris detection by using Purkinje image. In Proc. ICB, Hong Kong, China, 2006, pp. 397-403.

8. E. Lee, K. Park, and J. Kim , "Fake iris detection by using purkinje image," in Advances in Biometrics, ser. Lecture Notes in Computer Science, D. Zhang and A. Jain, Eds. Springer Berlin Heidelberg, 3832, 2005, pp.

397-403.

9. EuiChul Lee, Kang Ryoung Park, "Fake Iris detection Based on iris Pattern ", Wiley Periodicals Inc.(2010). 10. EuiChul Lee, Kang Ryoung Park, Jaihie Kim, "Fake Iris Detection by Using Purkinje Image “, Springer,

Advances in Biometrics, Vol 3832,2006, pp 397-403.

11. ISO/IEC JTC 1/SC 37 Text of Working Draft 30107-3, “Information Technology – Presentation Attack Detection – Part 3: Testing, reporting and classification of attacks,” February 28, 2014.

12. J. Galbally, J. Ortiz-Lopez, J. Fierrez and J. Ortega-Garcia, (2012), "Iris liveness detection based on quality

related features," Proc. ICB 2012, pp. 271-276, New Delhi, India, 2012. 13. J. Galbally, S. Marcel, and J. Fierrez, "Image quality assessment for fake biometric detection: Application to iris,

fingerprint, and face recognition," Image Processing, IEEE Transactions on, 23, 2, Feb 2014, pp. 710-724.

14. J. Galbally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia, “Iris liveness detection based on quality related features,” in Proc. 5th IAPR ICB, 2012, pp. 271–276.

15. Javier Galbally, SébastienMarcel,JulianFierrez, “Image Quality Assessment for Fake Biometric Detection:

Application to Iris, Fingerprint, and Face Recognition.” IEEE Trans. on Image Process, 23, February 2014 16. J. Galbally, R. Cappelli, A. Lumini, G. G. de Rivera, D. Maltoni,J. Fierrez, “An evaluation of direct and indirect

attacks using fake fingers generated from ISO templates,” Pattern Recognition. Lett., 31,8,2010, pp. 725–732.

17. J. Daugman, “Countermeasures against subterfuge,” in Biometrics: Personal Identication in Networked Society, Jain, Bolle, and Pankanti, Eds. Amsterdam: Kluwer, 1999, pp. 103–121.

18. L. Thalheim, J. Krissler, and P.-M. Ziegler, “Biometric Access Protection Devices and their Programs Put to the

Test,” Available online in c’t Magazine, No. 11/2002, p. 114. 19. M. Kohn and M. Clynes, “Color dynamics of the pupil,” Annals of New York Academy of Science, vol. 156, no.

2, 2006, pp. 931–950.

20. Pupil dynamics for iris liveness detection Adam Czajka, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 4, APRIL 2015

21. Trusted Biometrics under Spoofing Attacks (TABULA RASA). Project funded by the European Commission,

under the Seventh Framework Programme. [Online]. Available: http://www.tabularasa-euproject.org 22. T. Matsumoto, “Artificial fingers and irises: Importance of vulnerability analysis,” in Proceedings of the Seventh

International Biometrics Conference and Exhibition, 2004.

23. Sinha V.K., Gupta A.K , Khanna R. , “Detection of Fake Iris by using Frame Difference and Reflection Ratio”, I J C T A, 9(40) 2016, pp. 557-562

24. Sinha V.K., Gupta A., Mahajan M., “Detecting fake iris in iris bio-metric system”, Digital Investigation (2018),

https://doi.org/10.1016/j.diin.2018.03.002 25. Sinha V.K., Gupta A.K., "Enhancing Iris Security by Detection of Fake Iris". National Conference GyanJyoti-

National Conference MITE. - 2016. - P. 1-22.

Authors: Vijay Kumar Sinha, Gurmeet Kaur, Nisha Kumari

Paper

Title:

Detection of Involuntary Iris Scanning for Enhanced Biometric Security

Abstract: Although iris recognition system is considered as most robust, hard to counterfeit

and the most secure system of biometric authentication. However the existing system fails to

detect a forced authentication which might be misused by criminals to unlock the user's

account. In this paper we examine the conditions in which a real user is forcibly presented in

front of iris scanner on gun point to unlock the account. In this case a significant difference is

noted in the area of iris visibility with respect to user's normal iris area visibility. An

83

abnormal eye blink is also detected in forced condition. We successfully design and

developed an algorithm to detect such conditions to protect the users from criminals when a

user is forcibly presented to an iris scanner to unlock their account. A sample size of 65

volunteers are taken to record the iris authentication in both the conditions i.e. normal with

consent of user and forced under without user’s consent. The average size of iris is recorded

10.1 mm while it expands on 13.2 mm (average) in fear when iris is being scanned forcibly

by criminals. We conclude that a variation of 2 to 3 mm in iris exposure is a clear biomarker

to indicate some presence of criminal traces and take proactive measures to prevent losses.

Keyword: Area of Iris Visibility (AIV), Blink Rate Criminals, Fear Detection, Gun Point, Iris

Dilation, Kidnapped, Pupil Dilation, Purkinje Image.

References: 1. Czajka and A. Pacut, “Iris Recognition System Based on Zak-Gabor Wavelet Packets,” Journal of

Telecommunications and Information Technology, no. 4, 2010, pp. 10–18.

2. Czajka, “Database of iris printouts and its application: Development of liveness detection method for iris

recognition,” in Methods and Models in Automation and Robotics (MMAR), 2013 18th International

Conference on, Aug 2013, pp. 28–33. 3. Adam Czajka, (2014), "Pupil dynamics for presentation attack detection in iris recognition," in International

Biometric Performance Conference.

4. Biometric Vulnerability Assessment Expert Group (BVAEG). [Online]. Available: http://www.biometricsinstitute.org/pages/ biometric-vulnerability-assessment-expert-group-bvaeg.html

5. E. C. Lee and K. R. Park, (2010), "Fake iris detection based on 3D structure of iris pattern," International Journal

of Imaging Systems and Technology, 20, 2, pp. 162-166. 6. E. Lee et al. , (2006), Fake iris detection by using Purkinje image. In Proc. ICB, Hong Kong, China, pp. 397-403.

7. E. Lee, K. Park, and J. Kim , (2005), "Fake iris detection by using purkinje image," in Advances in Biometrics,

ser. Lecture Notes in Computer Science, D. Zhang and A. Jain, Eds. Springer Berlin Heidelberg, 3832, pp. 397-403.

8. Eui Chul Lee, Kang Ryoung Park, (2010), "Fake Iris detection Based on iris Pattern ", Wiley Periodicals Inc.

9. EuiChul Lee, Kang Ryoung Park, Jaihie Kim, (2006), "Fake Iris Detection by Using Purkinje Image “, Springer, Advances in Biometrics, Vol 3832, pp 397-403.

10. ISO/IEC JTC 1/SC 37 Text of Working Draft 30107-3, “Information Technology – Presentation Attack

Detection – Part 3: Testing, reporting and classification of attacks,” February 28, 2014.

11. J. Galbally, J. Ortiz-Lopez, J. Fierrez and J. Ortega-Garcia, (2012), "Iris liveness detection based on quality

related features," Proc. ICB 2012, New Delhi, India, 2012, pp. 271-276.

12. J. Galbally, S. Marcel, and J. Fierrez, (Feb 2014), "Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition," Image Processing, IEEE Transactions on, 23, 2, pp. 710-

724.

13. J. Galbally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia(2012), “Iris liveness detection based on quality related features,” in Proc. 5th IAPR ICB, pp. 271–276.

14. Javier Galbally, Sébastien Marcel,Julian Fierrez, (February 2014),“Image Quality Assessment for Fake

Biometric Detection: Application to Iris, Fingerprint, and Face Recognition.” IEEE Trans. on Image Process, 23.

15. J. Galbally, R. Cappelli, A. Lumini, G. G. de Rivera, D. Maltoni,J. Fierrez (2010), “An evaluation of direct and

indirect attacks using fake fingers generated from ISO templates,” Pattern Recognition. Lett., 31,8, pp. 725–732.

16. J. Daugman, “Countermeasures against subterfuge,” in Biometrics: Personal Identication in Networked Society,

Jain, Bolle, and Pankanti, Eds. Amsterdam: Kluwer, 1999, pp. 103–121. 17. L. Thalheim, J. Krissler, and P.-M. Ziegler, “Biometric Access Protection Devices and their Programs Put to the

Test,” Available online in c’t Magazine, No. 11/2002, p. 114.

18. M. Kohn and M. Clynes, “Color dynamics of the pupil,” Annals of New York Academy of Science, vol. 156, no.

2, 1969, pp. 931–950. Available online at Wiley Online Library (2006).

19. Pupil dynamics for iris liveness detection Adam Czajka, IEEE TRANSACTIONS ON INFORMATION

FORENSICS AND SECURITY, VOL. 11, NO. 4, APRIL 2015 20. Trusted Biometrics under Spoofing Attacks (TABULA RASA). Project funded by the European Commission,

under the Seventh Framework Programme. [Online]. Available: http://www.tabularasa-euproject.org

21. T. Matsumoto, “Artificial fingers and irises: Importance of vulnerability analysis,” in Proceedings of the Seventh International Biometrics Conference and Exhibition, 2004.

22. Sinha V.K., Gupta A.K , Khanna R. , “Detection of Fake Iris by using Frame Difference and Reflection Ratio”, I

J C T A, 9(40) 2016, pp. 557-562 23. Sinha V.K., Gupta A., Mahajan M., “Detecting fake iris in iris bio-metric system”, Digital Investigation (2018),

https://doi.org/10.1016/j.diin.2018.03.002 24. Sinha V.K., Gupta A.K., "Enhancing Iris Security by Detection of Fake Iris". National Conference Gyan Jyoti-

National Conference MITE. - 2016. - P. 1-22.

526-532

84

Authors: Lala Bhaskar, Pradeep Kumar, Kishore Naik Mude

Paper

Title:

Simulation Analysis of Wireless Power Transfer for Future Office Communication

Systems

Abstract: A Wireless Power Transfer system consists of a transmitter coil which is

inductively coupled with secondary coil and is popular for wireless charging of future office

communication system. Wireless power transfer is used in different applications ranging from

mobile chargers to charging stations. In this paper simulation of Wireless Power Transfer for

future office communication systems has been conducted over Maxwell 3d of Ansys

electromagnetic suite. The input frequency of primary coil is varied from 1kHz -120kHz with

respect to the change in resonant capacitance and observed that input frequency between

20kHz-30 kHz, the output power in secondary coil appears to be maximum at variable

distances between transmitter coil and receiver coil. There is an improvement of 72% seen in

the output power of secondary coil for 25kHz input frequency of primary coil as compared

with 40kHz input frequency. This model can be helpful to design future Office

Communication systems for charging the mobile phones, Laptops and to turn on the printer

wirelessly.

Keyword: Communication System, Laptop Charger, Mobile Charger, Receiver Coil,

Transmitter Coil, WPT(Wireless Power Transfer)

References: 1. M. G. L. Roes, J. L. Duarte, M. A. M. Hendrix and E. A. Lomonova, "Acoustic Energy Transfer: A Review,"

IEEE Trans. on Industrial Electronics, vol. 60, no. 1, Jan. 2013, pp. 242-248.

2. 2. Y. Hu, X. Zhang, J. Yang and Q. Jiang, "Transmitting electric energy through a metal wall by acoustic waves using piezoelectric transducers," IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 50,

no. 7, July 2003, pp. 773-781.

3. A. Sahai and D. Graham, "Optical wireless power transmission at long wavelengths," in Proc. International Conference on Space Optical Systems and Applications, Santa Monica, CA, 2011, pp. 164-170.

4. S. Sasaki, K. Tanaka and K. i. Maki, "Microwave Power Transmission Technologies for Solar Power

Satellites," in Proc. of the IEEE, vol. 101, no. 6, June 2013, pp. 1438-1447.

5. L. Summerer and O. Purcell, “Concepts for Wireless Energy Transmission via Laser,” 2008.

6. J. Dai and D. C. Ludois, "A Survey of Wireless Power Transfer and a Critical Comparison of Inductive and

Capacitive Coupling for Small Gap Applications," IEEE Transactions on Power Electronics, vol. 30, no. 11, Nov. 2015, pp. 6017-6029.

7. G. a. Covic and J. T. Boys, “Inductive Power Transfer,” Proc. IEEE, vol. 101, no. 6, 2013, pp. 1276–1289.

8. N. Tesla, “High frequency oscillators for lectro-therapeutic and other purposes,” in Proc. IEEE, vol. 87, no. 7, 1999, pp. 1282–1292.

9. S. Y. R. Hui, "Magnetic Resonance for Wireless Power Transfer [A Look Back]," IEEE Power Electronics

Magazine, vol. 3, no. 1, March 2016, pp. 14-31. 10. H. A. Wheeler, "Simple Inductance Formulas for Radio Coils," in Proceedings of the Institute of Radio

Engineers, vol. 16, no. 10, Oct. 1928, pp. 1398-1400.

11. H. A. Wheeler, "Inductance formulas for circular and square coils," in Proceedings of the IEEE, vol. 70, no. 12, Dec. 1982, pp. 1449-1450.

12. S. S. Mohan, M. del Mar Hershenson, S. P. Boyd and T. H. Lee, "Simple accurate expressions for planar spiral

inductances," in IEEE Journal of Solid-State Circuits, vol. 34, no. 10, Oct 1999, pp. 1419-1424. 13. Kunwar Aditya, Sheldon S. Williamson “Design Guidelines to Avoid Bifurcation in aSeries–Series

Compensated Inductive Power Transfer System IEEE Transactions on Industrial Electronics, vol.66, no.5, May

2019.

533-538

85

Authors: Sachin Majithia, Harjeet Singh, Astha Gupta, Neeraj Sharma

Paper

Title:

An Efficient Machine Learning Approach for Facial Expression Recognition

Abstract: Emotions play important role in human sentiments so broad studies are carried out

to explore the relation between human sentiments and machine interactions. This paper deals

with an automatic system which spontaneously identifies the facial emotion. Gradient

filtering and component analysis is used to extract feature vector and feature optimization is

taken place using swarm intelligence approach. Thus emotion recognition with optimized

feature extraction process is carried out with high accuracy rate and less error probabilities.

Finally the testing process is obtained for the classification of emotions and then performance

is measured in terms of false acceptance rate, false rejection rate, and accuracy.

539-546

Keyword: Facial emotion Detection, Feature Extraction, Feature Optimization, Gradient

Filtering.

References: 1. Dixit, B. A., &Gaikwad, A. N. (2015, June). Statistical moments based facial expression analysis. In Advance

Computing Conference (IACC), 2015 IEEE International (pp. 552-557). IEEE.

2. Chiranjeevi, P., Gopalakrishnan, V., &Moogi, P. (2015). Neutral face classification using personalized

appearance models for fast and robust emotion detection. IEEE Transactions on Image Processing, 24(9), 2701-2711.

3. Saeed, A., Al-Hamadi, A., Niese, R., &Elzobi, M. (2014). Frame-based facial expression recognition using geometrical features. Advances in Human-Computer Interaction, 2014, 4.

4. Liu, P., Han, S., Meng, Z., & Tong, Y. (2014). Facial expression recognition via a boosted deep belief network.

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1805-1812). 5. Kulkarni, P. U., Bharate, V. D., &Chaudhari, D. S. (2016, March). Human emotions recognition using adaptive

sublayer compensation and various feature extraction mechanisms. In Wireless Communications, Signal

Processing and Networking (WiSPNET), International Conference on (pp. 515-519). IEEE.Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3), 519-524.

6. Mathew, D., Kumar, D. S., & James, A. P. (2015, December). Facial emotion recognisingmemristive threshold

logic system. In Intelligent Computational Systems (RAICS), 2015 IEEE Recent Advances in (pp. 39-44). IEEE.

7. Said, S., Jemai, O., Zaied, M., & Amar, C. B. (2015, December). Wavelet networks for facial emotion

recognition. In Intelligent Systems Design and Applications (ISDA), 2015 15th International Conference on (pp. 295-300). IEEE.

8. Hmayda, M., Ejbali, R., &Zaied, M. (2015, December). Facial emotions recognition based on wavelet network.

In Intelligent Systems Design and Applications (ISDA), 2015 15th International Conference on (pp. 249-253). IEEE.

9. Chanthaphan, N., Uchimura, K., Satonaka, T., &Makioka, T. (2015, November). Facial emotion recognition

based on facial motion stream generated by Kinect. In Signal-Image Technology & Internet-Based Systems (SITIS), 2015 11th International Conference on (pp. 117-124). IEEE.

10. Moore, S., & Bowden, R. (2011). Local binary patterns for multi-view facial expression recognition. Computer

Vision and Image Understanding, 115(4), 541-558. 11. Barbu, T, Gabor Barbu, T. (2010). Gabor filter-based face recognition technique. Proceedings of the Romanian

Academy, 11(3), 277-283..

12. Chen, S., Sun, Y., & Yin, B. (2009, December). A novel hybrid approach based on sub-pattern technique and extended 2dpca for color face recognition. In Multimedia, 2009. ISM'09. 11th IEEE International Symposium

on (pp. 630-634). IEEE.

13. Thomas, M., Kambhamettu, C., & Kumar, S. (2008, November). Face recognition using a color subspace LDA approach. In Tools with Artificial Intelligence, 2008. ICTAI'08. 20th IEEE International Conference on (Vol. 1,

pp. 231-235). IEEE.

14. Belhumeur, P. N., Hespanha, J. P., &Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Yale University New Haven United States.

15. Draper, B. A., Baek, K., Bartlett, M. S., &Beveridge, J. R. (2003). Recognizing faces with PCA and

ICA. Computer vision and image understanding, 91(1-2), 115-137. 16. Yang, J., Zhang, D., Frangi, A. F., & Yang, J. Y. (2004). Two-dimensional PCA: a new approach to appearance-

based face representation and recognition. IEEE transactions on pattern analysis and machine

intelligence, 26(1), 131-137. 17. Torres, L., Reutter, J. Y., &Lorente, L. (1999). The importance of the color information in face recognition.

In Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on (Vol. 3, pp. 627-631).

IEEE. 18. Vasilescu, M. A. O., &Terzopoulos, D. (2002). Multilinear image analysis for facial recognition. In Pattern

Recognition, 2002. Proceedings. 16th International Conference on (Vol. 2, pp. 511-514). IEEE..

19. Dixit, B. A., &Gaikwad, A. N. (2015, June). Statistical moments based facial expression analysis. In Advance Computing Conference (IACC), 2015 IEEE International (pp. 552-557). IEEE.

20. Chiranjeevi, P., Gopalakrishnan, V., &Moogi, P. (2015). Neutral face classification using personalized

appearance models for fast and robust emotion detection. IEEE Transactions on Image Processing, 24(9), 2701-2711.

21. Saeed, A., Al-Hamadi, A., Niese, R., &Elzobi, M. (2014). Frame-based facial expression recognition using

geometrical features. Advances in Human-Computer Interaction, 2014, 4. 22. Liu, P., Han, S., Meng, Z., & Tong, Y. (2014). Facial expression recognition via a boosted deep belief network.

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1805-1812).

23. Sánchez, A., Ruiz, J. V., Moreno, A. B., Montemayor, A. S., Hernández, J., &Pantrigo, J. J. (2011). Differential optical flow applied to automatic facial expression recognition. Neurocomputing, 74(8), 1272-1282.

24. Chaudhar, M. B., &Deshmukh, R. R. (2016). Facial Expression Recognition and Analysis. Imperial Journal of Interdisciplinary Research, 2(3).

Authors: Rajeev Sharma,Sandeep singh kang

86

Paper

Title:

Challenges and Applications of Wireless Body Area Networks

Abstract: Modified low-power, ultra-slim, light in weight, intelligent devices are the result of

recent advances in technology. Wireless Body Area Network (WBAN) is a replacement

technology that can be used to incorporate these devices & thereby provide health monitoring

applications in healthcare. Further development of wireless communications in recent years

has led to the use of sensing element networks, which are low priced. These networks have a

wide variety of applications. Various technical problems in these application areas are being

resolved by researchers across the world. These sensing component networks play a

significant role in healthcare. These networks have deep roots in various sectors viz;

engineering, medicine& science & can show good performance even in harsh climatic

conditions. Therefore, this paper provides an associated degree of exposure for the analysis

and applications of wireless body area networks (WBAN’s), and body sensor networks

(BSN’s). Apart from it, it addresses a wide variety of challenges in these technologies.

Keyword: Wireless body area networks (WBANs), body sensor networks (BSNs), ultra-

wideband (IEEE 802.15.3) & ZigBee (IEEE 802.15.3), Sensor, personal digital assistant

(PDA)

References: 1. Ragesh, G. K., &Baskaran, K. “An Overview of Applications, Standards and Challenges in Futuristic Wireless

Body Area Networks”, Journal of Computer Science, (2012), 9(1), 180-186.

2. E. M. Staderini, ―UWB radars in medicine,IEEE Aerospace and Electronic Systems Magazine, vol. 17(1), pp. 13–18, 2002.

3. IEEE standard for information technology—telecommunications and information exchange between systems—

local and metropolitan area networks—specific requirements part 15.1: wireless medium access control (MAC) and physical layer (PHY) specifications for wireless personal area networks (WPANs),‖ IEEE Std 802.15.1TM,

2005.

4. IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—specific requirements part 15.3: wireless medium access control (MAC)

and physical layer (PHY) specifications for high rate wireless personal area networks (WPANs) amendment 1:

MAC sublayer,‖ IEEE Std 802.15.3b, 2006.

5. IEEE Standard for Information technology—telecommunications and information exchange between systems—

local and metropolitan area networks—specific requirements part15.4: wireless medium access control (MAC)

and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs),‖ IEEE STD 802.15.4TM, 2006.

6. S. U., Khan, P., Ullah, N., Saleem, S., Higgins, H., &Kwak, K. S. A Review of Wireless Body Area Networks

for Medical Applications.Sciences-New York, 1-7(2009). 7. C. Scott, G. Heath and J. Svoboda (2006, April) Preprint: ―Vibration monitoring of power

distributionpoles.‖[Online].http://www.inl.gov/technicalpublications/Documents/3493254.pdf.

8. Kyung Sup Kwak1, M. A. Ameen1, Daehan Kwak1, Cheolhyo Lee2, Hyungsoo Lee2, A Study on Proposed IEEE 802.15 WBAN MAC Protocols vol 978-1-4244-4522-6/09/$25.00 ©2009 IEEE

9. Y. Xiao, X. Shen, B. Sun, L. Cai, Security and privacy in RFID and applications in telemedicine, IEEE

Communications Magazine, 44(4) 64-72(2006). 10. ParneetDhillon, HarshSadawarti, “A Review Paper on Zigbee (IEEE 802.15.4) Standard”. International Journal

of Engineering Research & Technology (IJERT) IJERT ISSN: 2278-0181, Vol. 3 Issue 4, April – 2014.

11. IEEE Standard 802, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer(PHY) specifications for low-rate wireless personal area networks (WPANs), IEEE Std 802.15.4d™-2009, IEEE

Computer Society, 17 April 2009.

12. ArwaKurawar, AyushiKoul, Prof. Viki TukaramPatil, “Survey of Bluetooth and Applications”, International

Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 8, August

2014

13. Rongrong Zhang, Amiya Nayak, &Jihong Yu. “SleepScheduling in Energy Harvesting Wireless Body Area Networks”, IEEE Communications Magazine, February 2019, 95-101.

14. Yuan-Yao Shih, Member, IEEE, Pi-Cheng Hsiu, Senior Member, IEEE, and Ai-Chun Pang, Senior Member,

IEEE, “A Data Parasitizing Scheme for Effective Health Monitoring in Wireless Body Area Networks”, IEEE, 1536-1233 (c) 2018 ,1-14.

547-554

Authors:

Ramneet Kaur, Dr.Navdeep Kaur,Dr.Satwinder singh

Paper Enhanced Routing Protocol for VANET

87

Title:

Abstract: A class of networks called VANET(vehicular adhoc network) ,the extension of

MANETs(mobile adhoc network) are based on the principle of the formation of wireless

network for exchange of data and the creation of network is spontaneous in nature. The

mobility constraints, behavior of driver, high speed, limited coverage of wifi, hard delay

constraints leads to unique characteristics in VANETS. So the MANET routing protocols are

not suitable for VANET. Optimization of routing protocols becomes necessary to make it

suitable for VANET. In this paper , various optimized routing protocols are analyzed and

their optimization techniques are discussed. Parameters such as end to end delay and energy

spent are taken into consideration in order to show the improvement from the routing

protocols that are standard protocols. Then a method has been proposed to optimize the fine

tuned OLSR (optimized link state routing)protocol with the use of advanced genetic

algorithm to further improve the results and to make the protocol more efficient.

Keyword: Optimized link state routing (OlSR), particle swarm optimization (PSO), Quality

of service (QoS),Vehicular Adhoc Network (VANET).

References: 1. S. Ganguly, S. Das, "A Novel Ant Colony Optimization Algorithm for the Vehicle Routing Problem."

Swarm,Evolutionary and Memetic Computing, Springer, vol.8297, 2013, pp.401-412.

2. Isaac, J. Téllez, S. Zeadally, and J. S. Camara, "Security attacks and solutions for vehicular ad hoc networks." Communications, IET 4.7, 2010, pp.894-903.

3. Kochhar, Mandoria, "Performance study of VANET using ant based routing algorithms." Computing for Sustainable Global Development (INDIACom), 2nd International Conference, IEEE, New Delhi, 2015, pp.1803-1806.

4. Mane and S. Kulkarni, "QoS realization for routing protocol on VANETs using combinatorial optimization."Computing, Communications and Networking Technologies (ICCCNT), Fourth International Conference,IEEE,Tiruchengode, 2013,pp.1-5.

5. Y. Lin, Y. Chen and S. Lee, "Routing protocols in vehicular adhoc network:A survey and future perspectives." Journal of information science and engineering, vol.26, 2010, pp.913-932.

6. Guo, Zhong-Hua, and Hao-Shan Shi. "The Optimization of DSR Protocol of Ad Hoc Network with Constrained

Dynamic Query Localization Technique."2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007,pp.1585-1588.

7. Bandi, Anusha. "Parameters tuning of OLSR routing protocol with metaheuristic algorithm for VANET." Advance Computing Conference (IACC), 2015 IEEE International. IEEE, 2015,pp.1207-1212.

8. Rana, Hemant, P. Thulasiraman, and R. Thulasiram, "MAZACORNET: Mobility aware zone based ant colony optimization routing for VANET." Evolutionary Computation (CEC), IEEE, Cancun, 2013, pp.2948-2955.

9. Sumra, Ahmed, H. Hasbullah, and J. AbManan., "Attacks on Security Goals (Confidentiality, Integrity, Availability) in VANET: A Survey." Vehicular Ad-hoc Networks for Smart Cities, Springer, Singapore, vol.306, 2015, pp.51-61.

9. P. Stodola, J. Mazal, and M. Podhorec, "Improving the Ant Colony Optimization Algorithm for the Multi-

Depot Vehicle Routing Problem and Its Application." Modelling and Simulation for Autonomous Systems:FirstInternationalWorkshop,Springer,Rome,Vol.8906, 2014,pp.376-385.

10. S. Shah, M. Shiraj, R. Noor, "Unicast routing protocols for urban vehicular networks: review, taxonomy, and open research issues." Journal of Zhejiang University, vol.15, 2014, pp. 489-513.

11. Souza, A. B., Celestino, J., Xavier, F. A., Oliveira, F. D., Patel, A., & Latifi, M."Stable multicast trees based on

Ant Colony optimization for vehicular Ad Hoc networks." The International Conference on Information Networking 2013 (ICOIN). IEEE, 2013,pp.101-106.

12. Mavrovouniotis, Michalis, and S. Yang, "Dynamic vehicle routing: A memetic ant colony optimization approach." Automated Scheduling and Planning, Springer, Berlin Heidelberg, 2013, pp.283-301.

13. S. Kumar, A. Kumar, "Position Based Routing Protocols in VANET: A Survey." Wireless Personal Communications , Springer US, vol.83, 2015, pp. 1-26.

14. K. R. Jothi, A. Ebenezer, "Optimization and Quality-of-Service Protocols in VANETs: A Review." Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Springer India, vol.324, 2015, pp.275-284.

15. D. Gaikwad, Sudhakar, and M. Zaveri, "Vanet routing protocols and mobility models: A survey." Trends in Network and Communications, Springer, Berlin Heidelberg, vol.197, 2011, pp.334-342.

16. Baber, P. Wang, and C. Zou, "An economical, deployable and secure vehicular ad hoc network." In Military Communications Conference.IEEE,San Diego,CA, 2008,pp.1-7.

17 F. Ali, F. Sheikh, A. Ansari, "Comparative Analysis of VANET Routing Protocols: On Road Side Unit Placement Strategies." Wireless Personal Communications, Springer, 2015, pp.1-14.

555-560

18 M.Asgari, K.Jumari, M. Ismail, "Analysis of routing protocols in vehicular ad hoc network applications."

Software Engineering and Computer Systems, Springer, Berlin Heidelberg, vol.181, 2011,pp.384-397.

19 Safi, Seyed Mohammad, Ali Movaghar, and Misagh Mohammadizadeh. "A novel approach for avoiding

wormhole attacks in VANET." 2009 First Asian Himalayas International Conference on Internet. IEEE, 2009,pp.1-6.

88

Authors: Khushal Thakur, Bhasker Gupta, Balwinder Singh Sohi

Paper

Title:

BER analysis of Hybrid precoded Massive MIMO systems in Downlink with receiver

beamforming over mmWave Channels

Abstract: Next generation networks are required to deliver extremely high data rates in order

to enable mission critical services, massive IoT and enhanced mobile broadband. In the

pursuit of high data rates, significant research is focused on higher frequency bands.

mmWaves are the most promising carriers because of their associated bandwidth benefits.

However, on the other hand, mmWaves also bring along difficulties in link management as

the channel is totally different from the traditional systems. This paper evaluates the BER

performance of mmWave-massive MIMO systems with Hybrid precoding and receiver

beamforming. The availability of perfect CSI at both the transmitting and receiving ends of

the downlink is assumed. The results demonstrate that BER performance at low SNR region

remains almost exclusive of the system dimensions as long as CSI is available. It was

observed that almost 20 dB SNR is required to achieve error performance of 10-5. Zero

forcing and Wiener Filter precoder are also evaluated against each other with analog

precoding and receiver beamforming.

Keyword: mmWave, Massive MIMO, BER, Perfect CSI, Hybrid Precoding

References: 1. O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. Heath, “Spatially sparse precoding in millimeter wave

MIMO systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, Mar. 2014, pp. 1499–1513.

2. T. Rappaport et al., “Millimeter wave mobile communications for 5G cellular: It will work!” IEEE Access, vol. 1, May 2013, pp. 335–349.

3. S. Hur et al., “Millimeter wave beamforming for wireless backhaul and access in small cell networks,” IEEE

Trans. Commun., vol. 61, no. 10, Oct. 2013, pp. 4391–4403. 4. T. Nitsche et al., “IEEE 802.11ad: Directional 60 GHz communication for multi-Gigabit-per-second Wi-F,”

IEEE Commun. Mag., vol. 52, no. 12, Dec. 2014, pp. 132–141.

5. T. Baykas et al., “IEEE 802.15.3c: The first IEEE wireless standard for data rates over 1 Gb/s,” IEEE Commun. Mag., vol. 49, no. 7, Jul. 2011, pp. 114–121.

6. T. Parfait, Y. Kuang, and K. Jerry, “Performance analysis and comparison of ZF and MRT based downlink

massive MIMO systems,” in Proc. 6th Int. Conf. Ubiquitous Future Netw., Shanghai, China, 2014, pp. 383–388. 7. A. Kammoun, A. Muller, E. Bjornson, and M. Debbah, “Linear precoding based on polynomial expansion:

Large-scale multi-cell MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 861–875

8. V. P. Selvan, M. S. Iqbal, and H. S. Al-Raweshidy, “Performance analysis of linear precoding schemes for very large multi-user MIMO downlink system,” in Proc. 4th Int. Conf. Innov. Comput. Technol., Luton, U.K., 2014,

pp. 219–224.

9. E. Bjornson, L. Sanguinetti, J. Hoydis, and M. Debbah, “Optimal design of energy-efficient multi-user MIMO systems: Is massive MIMO the answer?” IEEE Trans. Wireless Commun., vol. 14, no. 3, Mar. 2015, pp. 3059–

3075

10. X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, “Linear pre-coding performance in measured very-large MIMO channels,” inProc. 2011 IEEE Veh. Technol. Conf., San Francisco, CA, USA, 2011, pp. 1–5.

11. J. Hoydis, S. Brink, and M. Debbah, “Massive MIMO in the UL/DL of cellular networks: How many antennas

do we need?” IEEE J. Sel. Areas Commun., vol. 31, no. 2, Feb. 2013, pp. 160–171. 12. A. Muller, A. Kammoun, E. Bjornson, and M. Debbah, “Linear precoding based on polynomial expansion:

Reducing complexity in massive MIMO,” EURASIP J. Wireless Commun. Netw., vol. 2016, 2016, Art. no. 63.

13. L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,” IEEE Wireless Commun. Lett., vol. 3, no. 6, Dec. 2014, pp. 653–656.

14. A. Alkhateeb, G. Leus and R. W. Heath, "Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave

Systems," in IEEE Transactions on Wireless Communications, vol. 14, no. 11, Nov. 2015, pp. 6481-6494 15. S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein and C. Studer, "Quantized Precoding for Massive MU-

MIMO," in IEEE Transactions on Communications, vol. 65, no. 11, Nov. 2017, pp. 4670-4684.

16. P. Kashyap and K. Thakur, "Improved ACE method for reducing PAPR in OFDM system," 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT),

Coimbatore, 2018, pp. 402-406.

17. S. Sharma and K. Thakur, "Carrier frequency offset in OFDM systems," 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 369-373.

561-566

18. S. Sharma and K. Thakur, "Improved CE-OFDM Using LDPC Codes for Frequency Offset Compensation,"

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, 2018, pp.

869-873

89

Authors: Bhisham Bhanot, Amit Jain

Paper

Title:

Load Balancing Applying Resources Based on Runtime Conditions in Cloud Computing

Abstract: With the fast advancement of the Internet, numerous sellers have begun to give

cloud administrations. An ever- increasing number of administrations can be gotten in the

cloud, and clients don't need to do activities on a nearby PC. All activities are figured on

the cloud. At the point when an enormous number of clients endeavor to access cloud

benefits, this frequently makes the server neglect to react. Deciding a technique by which to

give clients auspicious and exact reactions is a subject deserving of cutting edge examine. A

few examinations have been proposed to assess and to create calculations and load adjusting

approaches for cloud-based applications. It is hard for a server to manage the progression

of data created by the majority of the different endeavors endeavoring to get to it.

Unreasonable stream causes server over-load with a resulting loss of data. So the

primary target of this examination is to investigate the outstanding task at hand among

every single virtual machine and execute upgraded load adjusting the calculation to increase

the allotment of VM to cloudlet and limit the makespan time.

Keyword: cloud infrastructure, load balancing, makespan time.

References:

1. N. Ajith Singh, M. Hemalatha, “An approach on semi distributed load balancing algorithm for cloud

computing systems” International Journal of Computer Applications Vol-56 No.12 2012

2. Shanti Swaroop moharana, Rajadeepan d. Ramesh &DigamberPowar, “Analysis of load balancers in

cloudcomputing” International Journal of Computer Science and Engineering (IJCSE) ISSN 2278-9960 Vol. 2, Issue2, May 2013, 101-108.

3. D. Fernández-Baca: Allocating modules to processors in a distributed system, IEEE Transactions

onSoftware Engineering, Vol. 15, No. 11, pp. 1427-1436 (1989 4. Vibhore Tyagi, Tarun Kumar, “ORT Broker Policy: Reduce Cost and Response Time Using Throttled

LoadBalancing Algorithm” in Procedia Computer Science Volume 48, 2015, Pages 217-221

5. P.P. Geethu Gopinath, Shriram K. Vasudevan, “An In-depth Analysis and Study of Load BalancingTechniques in the Cloud Computing Environment” in Procedia Computer Science Volume 50, 2015,

Pages 427-432

6. S. Jain and A. K. Saxena, "A survey of load balancing challenges in a cloud environment," 2016International Conference System Modeling & Advancement in Research Trends (SMART), Moradabad,

2016, pp. 291-293

7. O. Kaneria and R. K. Banyal, "Analysis and improvement of load balancing in Cloud Computing," 2016International Conference on ICT in Business Industry & Government (ICTBIG), Indore, 2016, pp. 1-5

8. R. K. Jena, “Energy Efficient Task Scheduling in Cloud Environment” in Energy Procedia, Volume

141,December 2017, Pages 222-227 9. M. Lawanyashri, Dr. Balamurugan Balusamy, Dr. S. Subha, “Energy-aware Hybrid Fruitfly Optimizationfor

load balancing in cloud environments for EHR applications” in Informatics in Medicine Unlocked Volume8,

2017, Pages 42-50 10. Ahmed M. Manasrah, Tariq Smadi, Ammar ALmomani, “A Variable Service Broker Routing Policy for data

center selection in cloud analyst” in Journal of King Saud University - Computer and Information Sciences

Volume 29, Issue 3, July 2017, Pages 365-37 11. Obinna H. Ejimogu, SerenBaşaran, “A systematic mapping study on soft computing techniques to cloud

environment” in Procedia Computer Science Volume 120, 2017, Pages 31-38

12. Mousa Elrotub, Abdelouahed Gherbi, “Virtual Machine Classification-based Approach to Enhanced Workload Balancing for Cloud Computing Applications” in Procedia Computer Science Volume 130,

2018, Pages 683-688

13. Amanpreet Kaur, Bikrampal Kaur, Dheerendra Singh, " Meta-heuristic based framework for workflow load balancing in cloud environment " in International Journal of Information Technology, 2018, pp 1–7

14. Mohit Kumar, S. C. Sharma, “Dynamic load balancing algorithm for balancing the workload among

virtualmachine in cloud computing” in Procedia Computer Science Volume 115, 2017, Pages 322-329

567-571

Authors: P. Limameren Chang, Garima Saini

90

Paper

Title:

Different Substrate Materials For Designing A Passive UHF RFID Tag Antenna

Abstract: The Radio Frequency Identification (RFID) technology has been increasingly used

for various application such as tracking of products, smart cards, identification, item

management, security etc. In this paper, the performance parameter of the passive UHF RFID

tag antenna has been studied for four different substrate materials viz., FR4 epoxy, PET,

Rogers 4350, Taconic TLY materials. A simple meandered dipole antenna has been designed

using a T-match stub for impedance matching of the tag antenna with the attached RFID chip.

These different substrates are then designed separately, for the same antenna geometry. The

effect of using these substrates on RFID tag antenna parameters such as reflection coefficient,

antenna gain, VWSR, radiation pattern, impedance, ease of optimization level, read range,

and radiation efficiency are then observed.

Keyword: impedance matching, meander line antenna, tag antenna, UHF RFID

References: 1. ALN-9762 Short Inlay Technical Data Sheet, Alien Technology Co. USA. 2. Jasmin Grosinger,Walther Pachler,and Wolfgang Bosch, “Tag sizematters”, IEEE Microwave Magazine, Vol.

19, No.6, 2018, pp.101-111.

3. Y. He, X. Zhao, C. Zhang, and Z. Wang, “A fully Integrated Chip- ID tag used in chip information identification,” in Proc. IEEE Int.Conf. Radio Frequency Identification, 2012, pp. 172–176.

4. P. Bhartia, I. Bahl, R. Garg, and A. Ittipiboon, Microstrip Antenna Design Handbook. Boston, MA: Artech, 2000, pp. 759-761.

5. C. A. Balanis, Antenna Theory, Analysis and Design, Second Edition, New York, John Wiley &Sons Inc.,

1997,pp. 771-775. 6. A. Khan and R. Nema, “Analysis of five different dielectric substrates on microstrip patch antenna,” Int. J.

Comput. Appl., Vol. 55, No. 14, 2012, pp. 6-12.

7. Wee FwenHoon, Yew Been Seok, Mohamed Fareq Abdul Malek, Lee Yeng Seng,SitiZuraidah Ibrahim, “Radio Frequency Identification (RFID) tag antenna design at Ultra High Frequency (UHF) band”, Indian Journal of

Science and Technology, Vol 10, 2017, pp. 1-6.

8. Nanassy. A. I, “Dielectric measurement of moist wood in a sealed system.”, Wood Sci. Technol., Vol. 6, 1972, pp. 67-77.

9. A Guide to FR4: When Can You Use It and When Can You Not, Millennium Circuits Limited.

10. Diego Betancourt, and Joaquin Castan, “Printed antenna on flexible low-cost PETsubstrate for UHF applications”, Progress In Electromagnetics Research C, Vol. 38, 2013, pp. 129-140.

11. M. G. Faraj, K. Ibrahim, M. K. Ali, “PET as a plastic substrate for the flexible optoelectronic applications”,

Optoelectronics And Advanced Materials – Rapid Communications, Vol. 5, No. 8, 2011, pp. 879 – 882. 12. Nhan Ai Tran, Huy Nam Tran, Mau Chien Dangand Eric Fribourg-Blanc, “Copper thin film for RFID UHF

antenna on flexible substrate”, Advances In Natural Sciences: Nanoscience And Nanotechnology, Vol.1, 2010,

pp.1-6. 13. RO4000 Series Laminated Data Sheet, Rogers Corporation, pp. 1-4.

14. Rogers Material PCB- Rogers 4350, Rayming PCB & Assembly.

15. TLY Family of Low Loss Laminates, Taconic. 16. Olusola O. Olaode, W. Devereux Palmer, William T. Joines, “Characterization of meander dipole antennas

with a geometry-based, frequency- independent lumped element model”, IEEE Antennas And Wireless

Propagation Letters, Vol. 11, 2012, pp. 346-349. 17. A.Abattouy, M. Y. Douieb, M.A. Ennasar, O. EL Mrabet, K. Ameziane, “Design of a low cost meander line

RFID tag antenna using 3D printing technology”, International Conference on Multimedia Computing and

Systems, 2018, pp.1-3. 18. Gaetano Marrocco, “The art of UHF RFID antenna design: Impedance-matching and size-reduction

techniques”, IEEE Antennas and Propagation Magazine, Vol. 50, No. 1, 2008, pp. 66-79.

19. D. Prabhakar, CH. Balaswamy, M. AkhilPoorvika, M. Venkata Krishna Sai, M. Subba Naidu, “Flexible Microstrip Patch Antenna using Different Substrates for Bio- Medical Applications”,International Journal of

Innovative Technology and Exploring Engineering, Vol. 8, No. 7,2019pp.1350-1352.

20. Lingfei Mo and Chenyang Li, “Double loop inductive feed patch antenna design for antimetalUHF RFID tag”, International Journal of Antennas and Propagation, Vol. 2019, pp. 1-9.

21. Ying She, Tao Tang, Guang Jun Wen, Hao Ran Sun, “Ultra-High-Frequency Radio Frequency Identification

tag antenna applied for human body and water surfaces”, International Journal of RF and Microwave Computer Aided Engineering, Vol. 29, 2018, pp. 1-8.

572-580

Authors: Parul Sharma, Harpreet Kaur, Simran Uppal

Paper A novel Technique for Copy move forgery detection using Grey level co-occurrence

91

Title: matrix

Abstract: In this paper we have studied the GLCM approach as an improvement over SWT-

DCT method for feature extraction for CMFD. We have carefully studied the previously used

methods and also studied the SWT-DCT method for improvement in features. We have

proposed a method for the use of GLCM instead of SWT-DCT method for feature extraction

which will improve the results of CMFD method used in the base work framework.

Keyword: CMFD, GLC, SWT

References:

1. Islam, M., Shah, M., Khan, Z., Mahmood, T., & Khan, M. J. (2015, December). A new symmetric key encryption algorithm using images as secret keys. In 2015 13th International Conference on Frontiers of

Information Technology (FIT) (pp. 1-5). IEEE.

2. Kessler, G. C. (2004). An overview of steganography for the computer forensics examiner. Forensic science communications, 6(3), 1-27.

3. Almohammad, A., Hierons, R. M., & Ghinea, G. (2008, March). High capacity steganographic method based

upon JPEG. In 2008 Third International Conference on Availability, Reliability and Security (pp. 544-549). IEEE.

4. Al-Qershi, O. M., & Khoo, B. E. (2017). Comparison of matching methods for copy-move image forgery

detection. In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications (pp. 209-218). Springer, Singapore.

5. Mahmood, T., Nawaz, T., Ashraf, R., Shah, M., Khan, Z., Irtaza, A., & Mehmood, Z. (2015, December). A

survey on block based copy move image forgery detection techniques. In 2015 International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.

6. Muhammad, G., Al-Hammadi, M. H., Hussain, M., & Bebis, G. (2014). Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vision and Applications, 25(4), 985-995.

7. Li, Y. (2013). Image copy-move forgery detection based on polar cosine transform and approximate nearest

neighbor searching. Forensic science international, 224(1-3), 59-67. 8. Zhao, J., & Guo, J. (2013). Passive forensics for copy-move image forgery using a method based on DCT and

SVD. Forensic science international, 233(1-3), 158-166.

9. Lee, J. C., Chang, C. P., & Chen, W. K. (2015). Detection of copy–move image forgery using histogram of orientated gradients. Information Sciences, 321, 250-262.

10. Silva, E., Carvalho, T., Ferreira, A., & Rocha, A. (2015). Going deeper into copy-move forgery detection:

Exploring image telltales via multi-scale analysis and voting processes. Journal of Visual Communication and Image Representation, 29, 16-32.

11. Liu, G., Wang, J., Lian, S., & Wang, Z. (2011). A passive image authentication scheme for detecting region-

duplication forgery with rotation. Journal of Network and Computer Applications, 34(5), 1557-1565. 12. Uliyan, D. M., Jalab, H. A., Wahab, A. W. A., Shivakumara, P., & Sadeghi, S. (2016). A novel forged blurred

region detection system for image forensic applications. Expert Systems with Applications, 64, 1-10.

13. Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International journal of scientific and research publications, 3(5), 1.

14. Lee, J. C. (2015). Copy-move image forgery detection based on Gabor magnitude. Journal of Visual

Communication and Image Representation, 31, 320-334. 15. Li, G., Wu, Q., Tu, D., & Sun, S. (2007, July). A sorted neighborhood approach for detecting duplicated

regions in image forgeries based on DWT and SVD. In 2007 IEEE international conference on multimedia and

expo (pp. 1750-1753). IEEE. 16. Zimba, M., & Xingming, S. (2011). DWT-PCA(EVD) Based Copy-move Image Forgery

Detection. International Journal of Digital Content Technology and its Applications, 5(1).

17. Mahmood, T., Nawaz, T., Shah, M., Khan, Z., Ashraf, R., & Habib, H. A. (2016). Copy-move forgery detection technique based on DWT and Hu Moments. International Journal of Computer Science and Information

Security (IJCSIS), 14(5).

18. Coifman, R. R., & Donoho, D. L. (1995). Translation-invariant de-noising. In Wavelets

and statistics (pp. 125-150). Springer, New York, NY. 19. Starck, J. L., Fadili, J., & Murtagh, F. (2007). The undecimated wavelet decomposition and its

reconstruction. IEEE Transactions on Image Processing, 16(2), 297-309.

20. Muhammad, G., Hussain, M., & Bebis, G. (2012). Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digital investigation, 9(1), 49-57.

21. Mahmood, T., Mehmood, Z., Shah, M., & Khan, Z. (2018). An efficient forensic technique for exposing region

duplication forgery in digital images. Applied Intelligence, 48(7), 1791-1801. 22. Cao, Y., Gao, T., Fan, L., & Yang, Q. (2012). A robust detection algorithm for copy-move forgery in digital

images. Forensic science international, 214(1-3), 33-43.

23. Amerini, I., Uricchio, T., Ballan, L., & Caldelli, R. (2017, July). Localization of JPEG double compression through multi-domain convolutional neural networks. In 2017 IEEE Conference on computer vision and pattern

recognition workshops (CVPRW) (pp. 1865-1871). IEEE.

24. Chaplot, S., Patnaik, L. M., & Jagannathan, N. R. (2006). Classification of magnetic resonance brain images

581-586

using wavelets as input to support vector machine and neural network. Biomedical signal processing and

control, 1(1), 86-92.

25. Mahmood, T., Nawaz, T., Mehmood, Z., Khan, Z., Shah, M., & Ashraf, R. (2016, August). Forensic analysis of

copy-move forgery in digital images using the stationary wavelets. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH) (pp. 578-583). IEEE.

92

Authors: A.Deepa, Nitasha, NamrataChopra

Paper

Title:

Intensification of Lempel-Ziv-Welch Algorithm

Abstract: There is a necessity to reduce the consumption of exclusive resources. This is

achieved using data compression. The data compression is one well known technique which

can reduce the file size. A plethora of data compression algorithms are available which

provides compression in various ratios. LZW is one of the powerful widely used algorithms.

This paper attempts to propose and apply some enhancements to LZW, hence comes out with

an efficient lossless text compression scheme that can compress a given file at better

compression ratio. The paper proposes three approaches which practically enhances the

original algorithm. These approaches try to gain better compression ratio. In approach1, it

exploits the notion of using existing string code with odd code for a newly encounter string

which is reverse of existing. In approach2 it uses a choice of code length for the current

compression, so avoiding the problem of dictionary overflow. In approach3 it appends some

selective set of frequently encountered string patterns. So the intensified LZW method

provides better compression ratio with the inclusion of the above features.

Keyword: Algorithm, compression, decompression Intensification.

References: 1. G David Solomon, Data compression: The complete references book, Third edition, 2004.

2. Zhou Yan-li, Fan Xiao-ping, Improved LZW algorithm of lossless data compression for WSN, Conference proceedinmgs, 3rd International Conference on Computer Science and information Technology, 2010

3. M. Abu Alsheikh, S. Lin, D. Niyato, H.P. Tan, Rate-distortion balanced data compression for wireless sensor

networks, IEEE Sens. J., 16 (2016), pp. 5072-5083.

4. J.UthayakumarT.VengattaramanP.Dhavachelva, A survey on data compression techniques: From the

perspective of data quality, coding schemes, data type and applications, Journal of King Saud University - Computer and Information Sciences, 2018.

5. J. Abel, W. Teahan, Universal text preprocessing for data compression, IEEE Trans. Comput., 54 (2005), pp. 497-507.

6. Ezhilarasu P,Karthik Kumar P,LZW Lossless Text Data Compression Algorithm – A ReviewInternational Journal of Computer Science & Engineering Technology (IJCSET, Vol. 6 No. 11 Nov 2015.

7. H. Amri, A. Khalfallah, M. Gargouri, N. Nebhani, J.-C. Lapayre, M.-S. Bouhlel Medical image compression approach based on image resizing, digital watermarking and lossless compression, J. Signal Process. Syst., 87 (2017), pp. 203-214.

8. Simrandeep kaur, V.Sulochana Verma, Design and Implementation of LZW Data Compression Algorithm, International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.4, July 2012

9. Sawsan A. Abu Taleb Hossam M.J. Musafa Asma’a M. Khtoom Islah K. Gharaybih, Improving LZW Image Compression, European Journal of Scientific Research, Vol.44 No.3 (2010), pp.502-509.

10. Sawsan A. Abu Taleb , Hossam M.J. Musafa , Asma’a M. Khtoom Improving LZW Image

CompressionEuropean Journal of Scientific Research 1450-216X Vol.44 No.3 (2010), pp.502-509

11. Evon Abu-Taieh1, Issam AlHadid, A New Lossless Compression Algorithm, Modern Applied Science, Canadian Center of Science and Education, Vol. 12, No. 11, 2018.

12. Restu Maulunida*1, Achmad Solichin, Optimization of LZW Compression Algorithm With Modification of

Dictionary Formation , Indonesian Journal of Computing and Cybernetics Systems) Vol.12, No.1, January 2018, pp. 73~82.

587-591

Authors: Arti Tyagi,Cheshta Kashyap, Ashima Kalra

Paper

Title:

Optimal Sequence in General flow shop with Job block criteria using Fuzzy index

Technique

93

Abstract: Here, we are finding a novel methodology to solve a problem of scheduling of

general flow shop where proceeding time of job is indeterminate. The parameters required to

solve such problem was considered to be in triangular fuzzy number. The concept of job

block concept has been introduced to understand relative interference of one job with other.

The novelty of this method lies in the section that it will not convert the fuzzy processing time

into classical numbers to figure out the near optimal sequence of jobs. The method has been

made clearer by giving a numerical example to demonstrate the purposed technique.

Keyword: Triangular fuzzy number (TFN), Fuzzy processing time, Flow shop, Fuzzy

ranking, Location index etc.

References: 1. Johnson S.M. (1954), “Optimal two and three stage production schedule with set times included.” Naval

Research logistics quarterly, 1(1) 61-68.

2. Gupta J.N.D. & Dudek (1971), “Optimality criteria for flow shop schedule” AIIE transactions 3 (3) 199-205.

3. Maggu P.L. & Das G.(1977),“Equivalent job block in job scheduling”, Operation Research vol 14(4) pp 277-

281

4. Singh T.P. (1985),”On 2 x n flow shop problem involving job block, transportation time, arbitrary time and

break down machine times. “PAMS” Vol- XXI (1-2) 5. Mccahon S. and Lee E.S. (1990), “Job sequencing with fuzzy processing times,” Computer and mathematics

applications, Vol 19(7) pp. 294-301.

6. Ishibuchi H and Lee. K.H. (1996), “Formulation of fuzzy flow shop scheduling problem.” Proceedings of IEEE International Conference on Fuzzy System pp 199-205.

7. Hong T. and Chuang T. (1999), “New triangular Fuzzy Johnsons Algorithm.” Computer and Industrial Engg.

Vol 36(1) pp. 179-200. 8. Sayed Reza Hejari, SaeedEmami, Ali Akam (2009), “A Heuristic Algorithm for minimizing the expected make

span in two machine flow shops with fuzzy processing times.” Journal of Uncertain Systems, Vol 3(2) 114-122.

9. Yager R.R. (1981) “A procedure for ordering fuzzy subset of unit interval.” Information sciences, 24 pp. 143-161.

10. Ming Ma, Friedman, A. Kandel (1999). “A new fuzzy arithmetic,” Fuzzy Sets and Systems Vol 108 pp 83-90.

11. Singh T.P, Sunita and Praveen Ailawalia(2008), “Refer motion of Non-fuzzy Scheduling using concept of fuzzy Processing time under job – block”, International conference on Intelligent System & Network pp 322-

324.

12. T.P. Singh & Sunita (2009) “Fuzzy Flow Shop Problem on 2 Machines with single transportation facility: A

Heuristic Approach.” Aryabhatta J.Y. Maths& Info Vol (1-2) pp 38-46.

13. T.P. Singh & Sunita Gupta (2010), “An α-cut approach to Fuzzy Processing Time on 2 machine flow shop scheduling.” Aryabhatta Journal of Mathematics & Informatics,Vol 2 (1) pp 35-44.

14. T.P. Singh & Indira Vij (2007) “Minimize Rental Cost of machines in m stage scheduling with job-block

concept.” Reflections Das Era, J. of Mathematical Sciences Vol 2 (4) pp 359-370. 15. Menu, T.P. Singh & Deepak Gupta (2013) “A Heuristic Algorithm for General Weightage job scheduling

under uncertain environment.” Aryabhatta J. of Maths &Info Vol 5(2) pp 331-388.

16. T.P. Singh, Meenu Mittal & Gupta D. (2015) “Tardiness of Jobs and Satisfaction Level of Demand Maker in m stage Scheduling with Fuzzy due time.” Arybhatta J. of Maths & Info. Vol 7(2) pp 351-358.

17. Namita Aggarwal and Arti Tyagi (2017)“ optimal sequence in fuzzy flow shop scheduling with job – block

concept using location index and fuzziness index function technique” Aryabhatta Journal of Mathematics & Informatics Vol. 9, No. 1, ISSN : 0975-7139, pp: 21- 30.

18. Abbasbandy S. &Hajjari T. (2009). “A New Approach for Ranking of Trapezoidal Fuzzy Numbers.”

Computers and Mathematics with Applications, Vol 57 pp 413-419.

592-597

94

Authors: Dr. Charu Jain, Dr. Ved Mishra, Aarti Chugh

Paper

Title:

Palm Vein Technology for Biometrics

Abstract: The use and implementation of biometrics for identification and authentication has

become more important in the past decade. This is because there has been an increased risk

associated with textual passwords such as dictionary attacks, eavesdropping, shoulder surfing

etc. We have worked on Palm vein recognition for detecting palm veins in applications for

biometric security using near infrared absorption phenomena. The goal is to produce a

software prototype that is capable of identifying a person by the vein structures of the hand.

The images used for the same were taken from the CASIA-MS-PalmprintV1 database

collected by the Chinese Academy of Sciences' Institute of Automation (CASIA). After pre-

processing, LDR and DCT have been used for feature extraction and Euclidian Distance is

598-602

calculated for generating matching score. Acceptance/rejection is based on this matching

score. The efficiency obtained was 93.2% when compared with other systems.

Keyword: Biometrics, Near-Infrared Image, Palm Vein Recognition, Region of Interest

References: 1. Vishal U. Bhosale, Mr. Onkar S. Kale, Mr. Mahesh W. Pawar, Mr. Roshan R. Patil, Mr. Pritam S. Patil, Prof

Mrs. Sonali Madankar,” Palm Vein Extraction and Matching For Personal Identification”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727 Volume 16, Issue 2, Ver. IX

(Mar-Apr. 2014), PP 64-68.

2. Ajay Kumar, et al., "Personal Authentication Using Hand Vein Triangulation and Knuckle Shape.", IEEE Transaction on Image Processing. Vol.18, no.9, September 2009, pp2127- 2136.

3. Aycan Yuksel, Lale Akarun, Bulent Sankur, Jie Cao, "My Palm Vein: A Palm Vein-based Lowcost Mobile

Identification System For Wide Age Range", 17th International Conference on E-health Networking Application & Services 2015.

4. N. Miura, A.Nagasaka, T. Miyatake, "Feature extraction of finger-vein patterns based on repeated line tracking

and its application to Person Identification", Machine Vision and Applications, vol.15, no.4, 2004, pp.194-203. 5. Nisha Charaya, Dr. Priti Singh“International Journal of Pure and Applied Mathematics”, Volume 119, No. 16,

2018, pp. 2175-2185.

6. Jason Forté,”Development of a Near Infrared Hand Vein Imaging Device With Software Enhancement”, Presented at University of Cape Town, November 2014.

7. Jayanti Yusmah Sari, Chastine Fatichah, and Nanik Suciati “Local Line Binary Pattern For Feature Extraction

On Palm Vein Recognition”, Department of Informatics Engineering, Faculty of Information Technology, Institute Teknologi Sepuluh Nopember Surabaya Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia

8. Yingbo Zhou and Ajay Kumar, “Human Identification Using Palm-Vein Images”, IEEE Transaction on

Information Forensics and Security, December 2011, VOL.6, NO.4. 9. Sheetal, Ravi Prakash Goel, Kanwal Garg “Image Processing in Hand Vein Pattern Recognition System”,

Volume 4, Issue 6, June 2014, International Journal of Advanced Research in Computer Science and Software

Engineering 10. Dattatray V. Jadhav, Raghunath S. Holambe “Radon and discrete cosine transforms based feature extraction and

dimensionality reduction approach for face recognition”, Department of Instrumentation, SGGSIE & T,

Vishnupuri, Nanded (MS), Maharashtra 411037, India 11. Vijayta Chowdhary, Kamini Verma, Himanshu Monga “Human Identification Using Palm-Vein Images Using

Gabor Filter”, Volume 4, Issue 7, July 2014 ISSN: 2277 128X, International Journal of Advanced Research in

Computer Science and Software Engineering 12. Anisotropic Diffusion, “http://www.mathworks.com/ matlabcentral/fileexchange/14995-anisotropic-diffusion--

perona---malik-/content/anisodiff_PeronaMalik/ anisodiff2D.m.

13. Sudha Yadav, Charu Jain, Aarti Chugh, “Evaluation of Image Deblurring Techniques” IJCA, vol. 139, 2016.

14. Charu Jain, Aarti Chugh, Dr. Priti Singh, “An Offline Signature verification using Adaptive Resonance Theory”

IJCA, vol 94, issue 2, 2014, pp 8-11.

95

Authors: Mainka, Khushal Thakur, Kiran Jot Singh

Paper

Title:

Detection of Coverage Hole Nodes in Wireless Sensor Network using Artificial

Intelligence

Abstract: Adequate coverage of the sensing field in Wireless sensor networks (WSNs) is

critical to many applications. However, when one or more sensor nodes stop working due to

energy exhaustion or physical damage, the network may experience overlay vulnerability.

This can disrupt network connectivity and hinder performance. Therefore, it must be fixed

automatically. To resolve this problem, swarm inspired Artificial Bee Colony (ABC) scheme

in addition to the Artificial Neural Network (ANN) approach is used. The aim of ABC is to

optimize the shortest path by selecting an appropriate fitness function and then identify holes

using ANN. Before the detection of holes, ANN is trained as per the optimized properties of

nodes that are as per the genuine nodes and coverage hole repair properties. Therefore during

the testing process, ANN compares these properties with the stored properties and then

identify the hole repair node. From the experiment, it has been analyzed that the energy

consumption up to 23.88% is saved.

Keyword: WSN, coverage holes, mobility, ABC, ANN

References: 1. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor

networks. Journal of Network and Computer Applications, 35(2), 619-632.

603-606

2. Ahmed, N., Kanhere, S. S., & Jha, S. (2005). The holes problem in wireless sensor networks: a survey. ACM

SIGMOBILE Mobile Computing and Communications Review, 9(2), 4-18.

3. Tian, Y., Chang, X., Ou, Y., & Jiang, Y. (2019, April). Coverage Hole Detection Algorithm Based on Cooperative

Probability Coverage in Wireless Sensor Networks. In 2018 5th IEEE International Conference on Cloud

Computing and Intelligence Systems (CCIS) (pp. 835-840). IEEE.

4. Htun, A. M., Maw, M. S., & Sasase, I. (2014, September). Reduced complexity on mobile sensor deployment and

coverage hole healing by using adaptive threshold distance in hybrid wireless sensor networks. In 2014 IEEE 25th

Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC) (pp. 1547-1552). IEEE.

5. Tamboli, N., & Younis, M. (2010). Coverage-aware connectivity restoration in mobile sensor networks. Journal of

network and computer applications, 33(4), 363-374.

6. Wang, J., Ju, C., Gao, Y., Sangaiah, A. K., & Kim, G. J. (2018). A PSO based energy efficient coverage control

algorithm for wireless sensor networks. Comput. Mater. Contin, 56, 433-446.

7. Yi, L., Deng, X., Zou, Z., Ding, D., & Yang, L. T. (2018). Confident information coverage hole detection in

sensor networks for uranium tailing monitoring. Journal of Parallel and Distributed Computing, 118, 57-66.

8. Amgoth, T., & Jana, P. K. (2017). Coverage hole detection and restoration algorithm for wireless sensor

networks. Peer-to-Peer Networking and Applications, 10(1), 66-78.

9. Sahoo, P. K., & Liao, W. C. (2015). HORA: A distributed coverage hole repair algorithm for wireless sensor

networks. IEEE Transactions on Mobile Computing, 14(7), 1397-1410.

10. Zhao, L. H., Liu, W., Lei, H., Zhang, R., & Tan, Q. (2016). Detecting boundary nodes and coverage holes in

wireless sensor networks. Mobile Information Systems, 2016.

11. Cheng, C. T., Chi, K. T., & Lau, F. C. (2011). A delay-aware data collection network structure for wireless sensor

networks. IEEE sensors journal, 11(3), 699-710

12. He, Y. Q. (2017). Signal hole repair strategy based on sensor deployment density for mobile crowd

network. EURASIP Journal on Embedded Systems, 2017(1), 16.

13. Fan, X., Zhang, Z., Lin, X., & Wang, H. (2014, June). Coverage hole elimination based on sensor intelligent

redeployment in WSN. In The 4th Annual IEEE International Conference on Cyber Technology in Automation,

Control and Intelligent (pp. 336-339). IEEE.

14. Deng, L., Ma, X., Gu, J., & Li, Y. (2018). DETECTION AND REPAIR OF COVERAGE HOLES IN MOBILE

SENSOR NETWORKS USING SUB-VORONOI CELLS. International Journal of Robotics and

Automation, 33(6).

15. Mann, P. S., & Singh, S. (2019). Improved artificial bee colony metaheuristic for energy-efficient clustering in

wireless sensor networks. Artificial Intelligence Review, 51(3), 329-354.

16. Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018). A multiscale and multidepth convolutional neural

network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth

Observations and Remote Sensing, 11(3), 978-989.

17. Khalifa, B., Al Aghbari, Z., Khedr, A. M., & Abawajy, J. H. (2017). Coverage hole repair in WSNs using

cascaded neighbor intervention. IEEE Sensors Journal, 17(21), 7209-7216.

96

Authors: Himaja Sree, Dandabathula Kumudhini, Vuyyuru Sruthi Laya, Guttikonda Geetha

Paper

Title:

QSAR Analysis for For Drug Discovery

Abstract: Quantitative structure-activity relationship (QSAR), gives useful information for

drug design and medicinal chemistry. QSAR is a method used to anticipate the organic

reaction of a molecule by developing equations which use descriptors calculated from its

compounds. The molecular descriptors vary in complexity. A time consuming and expensive

process for pharmaceutical industries is drug discovery. An inspiration driving these QSAR

models is to help revive the revelation of molecular drug candidates through minimized test

work and to bring a drug to market faster. To obtain sorted features principal component

analysis is used. The biological activities of the test set are determined by training the neural

network using training set. By predicting the activities it can be known whether the drug is

close to the target or not.

Keyword: biological activity, descriptors, neural networks, Quantitative- structure activity

relationship.

References: 1. Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, and on Computational Methods in Drug Discovery 2. gor I. Baskin , Vladimir A. Palyulin , and Nikolai S. Zefirov on Neural Networks in

Building QSAR Models ,2008 Research Gate 3. Rachid Darnaga, BrahimMinaouia, MohamedFakir on QSAR models for prediction study of HIV protease

inhibitors using support vector machines, neural networks and multiple linear regression, Arabian Journal Of

Chemistry, Volume 10, Supplement 1, February 2017

607-610

4. Li Wen, Qing Li, Wei Li, Qiao Cai, and Yong-Ming Cai on A QSAR Study Based on SVM for the Compound of

Hydroxyl Benzoic Esters , BioInorganic Chemistry and Applications, Volume 2017, Article ID 4914272, 10 pages

5. Mohamed G. Malhat ; Hamdy M. Mousa ; Ashraf B. El-Sisi on Clustering of chemical data sets for drug

discovery ,2014 9th International Conference on Informatics and Systems 6. Breiman, L. Random forests. Machine Learning 2001, 45, 5−32.

7. Cortes, C.; Vapnik, V. N. Support-vector networks. Machine Learning 1995, 20, 273−297.

8. Svetnik, V.; Wang, T.; Tong, C.; Liaw, A.; Sheridan, R. P.; Song, Q. Boosting: an ensemble learning tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2005, 45, 786−799.

9. Bruce, C. L.; Melville, J. L.; Picket, S. D.; Hirst, J. D. Contemporary QSAR classifiers compared. J. Chem. Inf.

Model. 2007, 47, 219−227. 10. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: a classification

and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput.Sci. 2003, 43,

1947−1958. 11. Fernandez-Delgado, M.; Cernades, E.; Barro, S.; Amorim, D. A. Do we need hundreds of classifiers to solve real

world problems? J. Machine. Learning. Res. 2014, 15, 3133−3181.

12. Burden, F. R. Quantitative structure-activity relationship studies using Gaussian Processes. J. Chem.Inf. Comput. Sci. 2001, 41, 830− 835.

97

Authors: Sukhdeep Kaur, Rajesh Khanna, Pooja Sahni, Naveen Kumar

Paper

Title:

Design and Optimization of Microstrip Patch Antenna using Artificial Neural Networks

Abstract: In this paper a Neural Network model for the design of a Microstrip Patch

Antenna for an Ultra-wideband frequency range is presented. The reduced ground size is

used to enhance bandwidth in proposed design. The results obtained from the proposed

method are compared with the results of EM simulation software and are found to be in

good agreement. The advantage of the proposed method lies with the fact that the various

parameters required for the design of a Microstrip Patch Antenna at a particular frequency

of interest can be easily extracted without going into the rigorous time consuming, iterative

design procedures using a costly software package. In the paper staircase patch design is

considered for ultra-wideband matching of Antenna. The results obtained from artificial

neural network when compared with experimental and simulation results, found satisfactory

and also it is concluded that Radial Basis Function (RBF) network is more accurate and fast

for the proposed design.

Keyword: Neural network, microstrip patch antenna, staircase, radial basis function.

References: 1. Choudhury, B., Thomas, S., &Jha, R. M. (2015). Implementation of soft computing optimization techniques in

antenna engineering [antenna applications corner]. IEEE Antennas and Propagation Magazine, 57(6), 122-

131.

2. Singhal, M., & Saini, G. (2017). Optimization of antenna parameters using artificial neural network: A

review. Int. J. Comput. Trends Technol, 44, 64-73.

3. C. A. Balanis, Antenna Theory, John Wiley & Sons, Inc., 1997.

4. Manh, L. H., Mussetta, M., Grimaccia, F., Zich, R. E., &Pirinoli, P. (2014, April). Antenna optimization

based on Artificial Neural Network. In The 8th European Conference on Antennas and Propagation (EuCAP 2014) (pp. 3172-3175). IEEE.

5. Ozkaya, U., &Seyfi, L. (2015). Dimension optimization of microstrip patch antenna in X/Ku band via

artificial neural network. Procedia-Social and Behavioral Sciences, 195, 2520-2526.

6. Kaur, R., & Rattan, M. (2015). Optimization of the return loss of differentially fed microstrip patch antenna

using ANN and firefly algorithm. Wireless Personal Communications, 80(4), 1547-1556.

7. Guney, K., Sagiroglu, S., &Erler, M. (2002). Generalized neural method to determine resonant frequencies of

various microstrip antennas. International Journal of RF and Microwave Computer‐Aided Engineering:

Co‐sponsored by the Center for Advanced Manufacturing and Packaging of Microwave, Optical, and Digital

Electronics (CAMPmode) at the University of Colorado at Boulder, 12(1), 131-139..

8. Dhaliwal, B. S., &Pattnaik, S. S. (2016). Performance comparison of bio-inspired optimization algorithms for

Sierpinski gasket fractal antenna design. Neural Computing and Applications, 27(3), 585-592..

9. Mishra, R. K., &Patnaik, A. (1998). Neural network-based CAD model for the design of square-patch

antennas. IEEE Transactions on Antennas and propagation, 46(12), 1890-1891.

10. Devi, S., Panda, D. C., &Pattnaik, S. S. (2002, June). A novel method of using artificial neural networks to

calculate input impedance of circular microstrip antenna. In IEEE Antennas and Propagation Society

International Symposium (IEEE Cat. No. 02CH37313) (Vol. 3, pp. 462-465). IEEE.

11. Xiao, L. Y., Shao, W., Jin, F. L., & Wang, B. Z. (2018). Multiparameter modeling with ANN for antenna

design. IEEE Transactions on Antennas and Propagation, 66(7), 3718-3723.

611-616

12. Chetioui, M., Boudkhil, A., Benabdallah, N., &Benahmed, N. (2018, April). Design and optimization of SIW

patch antenna for Ku band applications using ANN algorithms. In 2018 4th International Conference on

Optimization and Applications (ICOA) (pp. 1-4). IEEE.

98

.

Authors: Pallavi Choudekar, Divya Asija, Upasana Gaur

Paper

Title:

Power Transmission Lines Congestion Control with Proper Placement of Smart Wire

Abstract: Congestion is severe problem that affects the power system security as it violates

the various operating limits of the power system so congestion management is an important

task for independent system operator. For managing congestion, smart wire module has

been used in series with transmission line. When smart wire is connected in series with most

congested line, there is improvement in voltage profile, reduction in transmission line

loading and losses. Transmission Congestion Distribution Factor (TCDF) is calculated to

know congestion in lines and congestion is managed with the help of smart wire module. It

is observed that value of TCDF also reduced when smart wire is connected. Work has been

carried out on IEEE 15 bus system on MATLAB.

Keywords:Smart wire, Congestion, TCDF (Transmission Congestion Distribution Factor),

PTCDF (Active Power Transmission Congestion Distribution Factor), QTCDF (Reactive

Power Transmission Congestion Distribution Factor).

References: 1 .Sananda Pal, Samarjit Sengupta, “Congestion Management of a Multi-bus Transmission System using

Distributed Smart Wires”, International conference on Control, Instrumentation, Energy and Communication (CIEC), 2014, pp.417-419.

2.Sananda Pal, A. Neogi, S. Biswas, M. Bandyopadhyay and S. Sengupta,” Loss Minimization and Congestion

Management of a Power Distribution Network through its Reconfiguration”, International Journal of Electrical,

Electronics ISSN No. (Online) : 2277-2626 and Computer Engineering 2(2): 95-99(2013) Special Edition for Best Papers of Michael Faraday IET India Summit-2013, pp. 95-98.

3.D. Venugopal, A. Jayalaxmi, “Congestion Management by Optimal Choice and Allocation of FACTS Controllers using Genetic Algorithm”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307,

Vol.4, pp. 72-73, July 2014.

4.S.N. Singh, A.K. David, “Congestion Management by Optimizing FACTS Device Location”, International

Conference on Electric Utility Deregulation and Restructuring and Power Technologies, 2000. Proceedings. DRPT

2000, pp. 23.

5.Frank Kreikebaum, Debrup Das, Yi Yang, Member, Frank Lambert, Prof. Deepak Divan, “Smart Wires A

Distributed, Low-Cost Solution for Con-trolling Power Flows and Monitoring Transmission Lines”, international conference on Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEEPES, pp. 2-4.

6. Deepak M. Divan, William E. Brumsickle, Robert S. Schneider, Bill Kranz, Randal W. Gascoigne, Dale T. Bradshaw, Michael R. Ingram, and Ian S. Grant, “ A Distributed Static Series Compensator System for Realizing

Active Power Flow Control on Existing Power Lines”, IEEE transactions on power delivery, vol. 22, pp. 642-644,

January 2007.

7.Jerry Melcher, “Distributed Series Reactance for Grid Power Flow Control”,IEEE PES Chapter Meeting pp. 3-5,

August 8, 2012.

8.Begovic,, Miroslav, “ Electrical Transmission System & Smart Grid,” Selected Entries from the Encyclopedia of

Sustainability Science and Technology Springer, 2013

9.D. M. Divan et al., "A Distributed Static Series Compensator System for Realizing Active Power Flow Control

on Existing Power Lines," in IEEE Transactions on Power Delivery, vol. 22, no. 1, Jan. 2007 pp. 642-649.

10.D. Divan, H. Johal, “Distributed FACTS A New Concept for Realizing Grid Power Flow Control,” IEEE

Transactions on Power Electronics, Vol. 22, Issue 6, Nov. 2007, pp. 2253 2260. S. soft, Power System Economics Piscataway: IEEE Press, 2002, pp.6-16.

11.Abhishek, Asija D., Choudekar P., Manganuri Y., “Series Smart Wire—Managing Load and Congestion in Transmission Line”, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory

617-621

99

Authors: Dinesh Arora, Hardeep Singh Saini, Harbinder Singh

Paper

Title:

Third party based security approach in vehicular ad-hoc networks

Abstract: Security is the key factor of consideration in the vehicular ad-hoc network

(VANET), which is prone to various security dangers. A VANET package gives information

on life’s essentials and provides security from detrimental external agencies. This paper

presents an outsider-based security approach which secures VANETs condition by verification

process, where marks are produced and conveyed to hubs and checked at the measure of any

transmission. In the suggested approach, the rise in mobility decreases the packet delivery

ratio and performance of proposed protocol is approximately 4% improved as compared to

other techniques. Moreover, the escalation in mobility increases the average delay and in case

proposed protocol is compared with the group based authentication then the improvement in

its performance is approximately 50%.Thus, the proposed approach is completely focused on

security and consequently secures the system..

Keywords:Ad-hoc network, end to end delay, nodes, packet delivery ratio (PDR), security,

VANETs security

References:

1. Qin B, Wu Q, Domingo-Ferrer J, Susilo W. Robust distributed privacy-preserving secure aggregation in

vehicular communication. Control and Cybernetics 2012;42(2):.277-296

2. Azogu IK, Ferreira MT, Larcom JA, Liu K. A new ati-jamming strategy for VANET metrics-directed

security defence in conference proceedings. IEEE Globecom Workshops; 2013, Atlanta, GA, USA,

pp.1344–1349.

3. DOI: 10.1109/GLOCOMW.2013.6825181

4. Dhurander SK, Obaidat M, Jaiswal A, Tiwari A, Tyagi A. Vehicular security through reputation and

plausibility checks. IEEE Systems Journal 2014;8 (2):384-394.

5. DOI. 10.1109/JSYST.2013.2245971

6. Petit J, Feiri M, Kargl F. Spoofed data detection in VANETs using dynamic thresholds”, IEEE Vehicular

Networking ;2011, Amsterdam, Netherlands, pp.25–32. 7. DOI: 10.1109/VNC.2011.6117120

8. Chakroun O, Cherkaoul S. Overhead-free congestion control and data dissemination for 802.11p VANETs.

Vehicular Communications 2014; 1 (3):123–133.

9. doi.org/10.1016/j.vehcom.2014.05.003

10. Pari NS, Jayapal S, Duraisamy S. A trust system in MANET with secure key authentication mechanism. Recent

Trends Information Technology (ICRTIT);2012, IEEE. Chennai, Tamil Nadu, India, pp.261–265. DOI:10.1109/ICRTIT.2012.6206818

11. Karger P, Frankel Y. Security and privacy threats to ITS. In Second World Congress on Intelligent Transport

Systems 2014; 5: 24522458.

12. Raiya R, Gandhi S. Survey of various security techniques in VANET. International Journal of Advanced

Research in Computer Science and Software Engineering 2014; 4 (6):431-433.

13. Dadali AS, Joshi, R. Survey on VANET protocols and security techniques. International Journal of Science and

Research 2015; 4 (6):1644-1648.

14. Ribagorda A, Gonzalez-Tablas AI., Ribagorda A. Overview of security issues in vehicular ad-hoc networks.

Handbook of Research on Mobility and Computing ; 2010, IGI Global, Hershey,USA.

15. Becker M, Gupta A, Marot M, Singh H. Improving clustering techniques in wireless sensor networks using

thinning process. Performance Evaluation of Computer and Communication Systems. Milestones and Future Challenges- Series Lecture Notes in Computer Science ; 2014, Springer, pp.203-214

622-628

and Applications. Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore, 2017.

12.Nibha Rani, Pallavi Choudekar, Divya Asija, P Vishnu Astick, “Congestion management of transmission line

using smart wire & TCSC with their economic feasibility”, 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017, pp 1-5.

13.P Vishnu Astick , Divya Asija , Pallavi Choudekar , Nibha Rani, “Transmission line efficiency enhancement with inclusion of smart wires and controllable network transformers”, th International Conference on Computing,

Communication and Networking Technologies (ICCCNT), 2017 ,pp 1-6.

16. Bitam S, Mellouk A, Zeadally S. HyBR: A hybrid bio-inspired bee swarm routing protocol for safety

applications in vehicular adhoc networks (VANETs). Journal of Systems Architecture 2013; 59 (10B):953–957.

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18. Chen L, Ng SL, Wang G. Threshold anonymous announcement in VANETs. IEEE Journal on Selected Areas in

Communications 2011; 29 (3):605–615.

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solutions. Vehicular Communications 2014;1(3):134-152.

21. doi.org/10.1016/j.vehcom.2014.05.004

22. Katal A,Wazid M, Goudar RH. A cluster based detection and prevention mechanism against novel datagram

chunk droppingattack 23. in MANET multimedia transmission. Information & Communication Technologies (ICT) ; 2013, IEEE.

Thuckalay, Tamil Nadu, India, pp.479–484.

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hoc networks. IEEE Transactions on Intelligent Transportation Systems 2013; 14 (1): 380- 387. DOI:10.1109/TITS.2012.2213595

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ad-hoc networks. Computers & Electrical Engineering. 2014 ; 40 (2) :517–529.

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32. DOI: 10.1109/TVT.2014.2335201

33. Chim TW, Yiu SM, Hui LCK, Victor OKL. VSPN: VANET-based secure and privacy-preserving

navigation. IEEE Transactions on Computers 2014; 63 (2): 510-524. DOI:10.1109/TC.2012.188

34. Song C, Liu M, Gong HG, Chen GH, Cao JN. Utilizing the dropped packets for data delivery in VANETs.

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35. Kaur, A., Arora, D.: Survey over VANET routing protocols for vehicle communication. Int. J. Electr.

Electron. Eng. 1, 1–6(2014)

36. Jain, P., Arora, D.: A survey on link connectivity of networks. Int. J. of Engineering Applied Sciences and Technology, 2016 Vol. 1, Issue 10, ISSN No. 2455-2143, Pages 156-162 Published Online August -

September2016.

37. P.Jain and D. Arora, "Fuzzification based intravehicular

communicationinVANETs,"2017InternationalConferenceonI- SMAC (IoT in Social, Mobile, Analytics

and Cloud) (I-SMAC), Palladam,2017,pp.223-228.doi:10.1109/I-SMAC.2017.8058344

100

Authors: Ruchika, Ravindra Kumar Purwar

Paper

Title:

Abnormality detection using LBP features and K-means labelling based feed-forward neural

network in video sequence

Abstract: Video surveillance is widely used in various domains like military, commercial and

consumer areas. One of the objectives in video surveillance is the detection of normal and

abnormal behavior. It has always been a challenge to accurately identify such events in any

real time video sequence. In this paper, abnormality detection method using Local Binary

Pattern and k-means labeling based feed-forward neural network has been proposed. The

performance of the proposed method has also been compared with four other techniques in

literature to show its worthiness. It can be seen in the experimental results that an accuracy of

up to 98% has been achieved for the proposed technique.

Keyword:NN, k-mean labeling, abnormality detection, video surveillance

References: 1. Singh, Sanjay, Sumeet Saurav, Chandra Shekhar, and Anil Vohra. "Prototyping an automated video

surveillance system using FPGAs." International Journal of Image, Graphics and Signal Processing 8, no. 8,2016, pp : 37

2. Ravanbakhsh, Mahdyar, Moin Nabi, Hossein Mousavi, Enver Sangineto, and Nicu Sebe. "Plug-and-play cnn

for crowd motion analysis: An application in abnormal event detection." In 2018 IEEE Winter Conference on

629-633

Applications of Computer Vision (WACV), pp. 1689-1698. IEEE, 2018.

3. Chong, Yong Shean, and Yong Haur Tay. "Abnormal event detection in videos using spatiotemporal

autoencoder." In International Symposium on Neural Networks,Springer, Cham, 2017, , pp. 189-196..

4. Yang, Michael Ying, Wentong Liao, Yanpeng Cao, and Bodo Rosenhahn. "Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process

Models." Photogrammetric Engineering & Remote Sensing 84, no. 4,2018, pp: 203-214.

5. Vu, Hung, Tu Dinh Nguyen, and Dinh Phung. "Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models." arXiv preprint arXiv:1805.01090 (2018).

6. Muhammad, Khan, Jamil Ahmad, Irfan Mehmood, Seungmin Rho, and Sung Wook Baik. "Convolutional

neural networks based fire detection in surveillance videos." IEEE Access 6,2018,pp: 18174-18183. 7. Hsu, Shih-Chung, Cheng-Hung Chuang, Chung-Lin Huang, Ren Teng, and Miao-Jian Lin. "A video-based

abnormal human behavior detection for psychiatric patient monitoring." In 2018 International Workshop on

Advanced Image Technology (IWAIT), IEEE, 2018, pp. 1-4. 8. Wang, Siqi, En Zhu, Jianping Yin, and Fatih Porikli. "Video anomaly detection and localization by local

motion based joint video representation and OCELM." Neurocomputing 277,2018,pp: 161-175.

9. Tay, Nian Chi, Tee Connie, Thian Song Ong, Kah Ong Michael Goh, and Pin Shen Teh. "A robust abnormal behavior detection method using convolutional neural network." In Computational Science and Technology,

Springer, Singapore, 2019,pp. 37-47..

10. Harjanto, Fredro, Zhiyong Wang, Shiyang Lu, Ah Chung Tsoi, and David Dagan Feng. "Investigating the

impact of frame rate towards robust human action recognition." Signal Processing124 (2016): 220-232.

11. http://cvrc.ece.utexas.edu/SDHA2010/Human\_Interaction.html

12. Gonzalez and Woods, Digital Image Processing, 4th edition, pearson, 2017. 13. Ojala T, Pietik¨ainen M, Harwood D. A comparative study of texture measures with classification based on

featured distributions. Pattern Recognition, 1996, 29(1) pp: 51−59

14. Ke-Chen, Song, et al. "Research and perspective on local binary pattern." Acta Automatica Sinica 39.6 2013,pp: 730-744.

15. Akosa, Josephine. "Predictive accuracy: A misleading performance measure for highly imbalanced data."

In Proceedings of the SAS Global Forum. 2017. 16. Taha, Abdel Aziz, and Allan Hanbury. "Metrics for evaluating 3D medical image segmentation: analysis,

selection, and tool." BMC medical imaging 15, no. 1 2015, pp: 29.

17. Wang, Xiaoyang, and Qiang Ji. "Hierarchical context modeling for video event recognition." IEEE transactions on pattern analysis and machine intelligence 39, no. 9,2016, pp: 1770-1782.

101

Authors: Ramandeep Kaur, Gagandeep, Parveen Kumar,Geetanjali Babbar

Paper

Title:

An Enhanced and Automatic Skin Cancer Detection using K-Mean AND PSO Technique

Abstract: Scientists have been trying to implement traditional methods around the world,

particularly in developing countries, to reduce the death rate of skin cancer in humans. The

scientific term is named as melanoma. But this effort always working hard as the system is

costly, the low availability of experts and the conventional telemedicine. There are three

types of skin cancer: basal cell cancer (BCC), squamous cell cancer, and melanoma. More

than 90% of human is affected by ultraviolet (UV) radiation exposed to the sun. In this

research, a skin cancer detection system (BCC) is designed in MATLAB. The images going

to different processes such as Pre processing, feature extraction and classification. In pre-

processing K-mean clustering is applied to determine the foreground and background of an

image, since some part of background appear in the image after K-mean. Therefore, to

resolve this problem Particle Swarm optimization (PSO) is applied. The segmented image

features are extracted using Speed Up Robust Features (SURF), this helps to enhance the

quality of the image. The Artificial neural network (ANN) is trained on the basis of these

extracted features. To determine the efficiency of the system, the images are tested and

performance parameters are measured. The detection accuracy determined by this model is

about 98.7 5 is obtained.

Keyword :Skin Cancer, K-mean, PSO, SURF, ANN

References: 1. N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, ... & A. Halpern, “Skin

lesion analysis toward melanoma detection,” A challenge at the 2017 international symposium on biomedical

imaging (isbi), hosted by the international skin imaging collaboration (isic). In 2018 IEEE 15th International Symposium on Biomedical Imaging, 2018, pp. 168-172.

634-639

2. A. C. Geller, R. R. Keske, S. Haneuse, J. A. Davine, K. M. Emmons, C. L. Daniel, ... & L. L. Robison, “Skin

cancer early detection practices among adult survivors of childhood cancer treated with radiation,” Journal of

Investigative Dermatology, 2019.

3. A. Mirbeik-Sabzevari, Li, S., Garay, E., Nguyen, H. T., Wang, H., & Tavassolian, N, “Synthetic ultra-high-

resolution millimeter-wave imaging for skin cancer detection,” IEEE Transactions on Biomedical Engineering,

2019, vol. 66, no. 1, pp.61-71.

4. A. Dascalu, & E. O. David, “Skin cancer detection by deep learning and sound analysis algorithms: A

prospective clinical study of an elementary dermoscope,” EBioMedicine, 2019.

5. A. F. Jerant, J. T. Johnson, C. Demastes Sheridan, & T. J. Caffrey, “Early detection and treatment of skin

cancer. American family physician,” 2000, vol. 62, no. 2.

6. R. J. Friedman, D. S. Rigel, & A. W. Kopf, “Early detection of malignant melanoma: the role of physician

examination and self‐examination of the skin,” CA: a cancer journal for clinicians, 1985, vol. 35, no.3, pp.130-

151.

7. M. A. H. Bhuiyan, I. Azad, & K. Uddin, “Image processing for skin cancer features extraction,” International

Journal of Scientific & Engineering Research, 2013, vol. 4, no. 2, pp.1-6.

8. J. Choi, J. Choo, H. Chung, D. G. Gweon, J., Park, H. J. Kim, ... & Oh, C. H, “Direct observation of

spectral differences between normal and basal cell carcinoma (BCC) tissues using confocal Raman

microscopy,” Biopolymers: Original Research on Biomolecules, 2005, vol. 77, no.5, pp. 264-272.

9. M. A. Sheha, M. S. Mabrouk, & A. Sharawy, “Automatic detection of melanoma skin cancer using texture

analysis,” International Journal of Computer Applications, , 2012.

10. A. Masood, & A. Ali Al-Jumaily, “Computer aided diagnostic support system for skin cancer: a review of

techniques and algorithms,” International journal of biomedical imaging, 2013.

11. P. Åberg, P. Geladi, I. Nicander, J. Hansson, U. Holmgren, & S. Ollmar, “Non‐invasive and microinvasive

electrical impedance spectra of skin cancer–a comparison between two techniques,” Skin research and technology, 2005, 11(4), 281-286.

12. M. A. Calin, S. V. Parasca, R. Savastru, M. R. Calin, & S. Dontu,, “Optical techniques for the noninvasive

diagnosis of skin cancer,” Journal of cancer research and clinical oncology, 2013, vol. 139, no.7, pp.1083-

1104.

13. P. Kharazmi, M. I. AlJasser, Lui, H., Wang, Z. J., & Lee, T. K, “Automated detection and segmentation of

vascular structures of skin lesions seen in Dermoscopy, with an application to basal cell carcinoma

classification,” IEEE journal of biomedical and health informatics,2016, vol.21, no. 6, pp.1675-1684.

102.

Authors: Puneet Kamal, Rajeev Sharma, Abhishek Gupta, Gaurav Kumar

Paper

Title:

Mitigating Gray Hole attack in Mobile AD HOC Network using Artificial Intelligence

Mechanism

Abstract: A mobile ad hoc network (MANET) is a combination of multiple mobile nodes,

which are interconnected by radio link. In MANET, sensor nodes are free to move, and each

node can act as a host or router. Routing is one of the most challenging tasks because nodes

move frequently. Therefore, in MANET, the routing protocol plays an important role in

selecting the best route to efficiently transmit data from the source node to the destination

node. In this paper, the best path with efficient Ad Hoc on Demand Distance Vector

(AODV) routing protocol is chosen as the routing mechanism. The properties of each node

are categorized using firefly algorithm. The Artificial Neural Network (ANN) is trained as

per these properties and hence in case if the gray hole node is detected within the route, it is

identified and the route between the source and the destination is changed. At last, to show

how effectively the proposed AODV with Firefly and ANN works is computed in terms of

performance parameters. The throughput and PDR is increased by 4.13 % and 3.15 %

compared to the network which is affected by gray hole attack. The energy up to 44.02 %

has been saved.

Keyword ::Mobile ad hoc network, gray hole attack, cuckoo search, support vector

machine, Ad Hoc On-Demand Distance Vector

References: 1. G Banerjee, S. “Detection/removal of cooperative black and gray hole attack in mobile ad-hoc networks”. In

Proceedings of the world congress on engineering and computer science , 2008 pp. 22-24.

2. Sen, J., Chandra, M. G., Harihara, S. G., Reddy, H., & Balamuralidhar, P. “A mechanism for detection of

gray hole attack in mobile Ad Hoc networks”. In 2007 6th International Conference on Information, Communications & Signal Processing, 2007, pp. 1-5. IEEE.

3. Vishnu, K., & Paul, A. J. . “Detection and removal of cooperative black/gray hole attack in mobile ad hoc

networks”. International Journal of Computer Applications, 1(22),2010, pp: 38-42.

4. Kanthe, A. M., Simunic, D., & Prasad, R.. “A Mechanism for Gray Hole Attack Detection in Mobile Ad–

hoc Networks”. International journal of computer applications, 53(16), 2012, pp: 23-30.

640-645

5. Jhaveri, R. H., Patel, S. J., & Jinwala, D. C, “DoS attacks in mobile ad hoc networks: A survey”. In 2012

second international conference on advanced computing & communication technologies , 2012 pp. 535-

541. IEEE.

6. Kumar, A., & Chawla, M. (2012). “Destination-based group Gray hole attack detection in MANET through

AODV”. International Journal of Computer Science Issues (IJCSI), 9(4), 292.

7. Jain, S., Jain, M., & Kandwal, H. “Advanced algorithm for detection and prevention of cooperative black

and gray hole attacks in mobile ad hoc networks”. International Journal of Computer Applications,

1(7),2010 , pp: 37-42.

8. Shalika, E., Bal, J. S., & Dhir, V. “A Review on Implementation of AODV Technique for Isolation of Gray

Hole Attack in MANET”, 2018.

9. Kumar, J., Kulkarni, M., & Gupta, D Effect of Black hole Attack on MANET routing protocols.

International Journal of Computer Network and Information Security, 5(5), 2013, pp: 64.

10. Gurung, S., & Chauhan, SA dynamic threshold based algorithm for improving security and performance of

AODV under black-hole attack in MANET. Wireless Networks, 25(4), 2019 pp:1685-1695.

11. Swapnil S. Bhalsagar, Manish D. Chawhan, Yogesh Suryawanshi, V. K. Taksande (2019) Performance

Evaluation of Routing Protocol under Black hole Attack In Manet And Suggested Security Enhancement

Mechanisms, Volume-8 Issue-5 March, 2019, pp 1-7.

103

Authors: Meenakshi Mittal, Dr S Veena Dhari

Paper

Title:

A Regression Model for Analysis of Bounce Rate Using Web Analytics

Abstract: Bounce rate is an effective parameter to measure the quality of any website.

Bounce rate refers to the percentage of visitors that leave a website (or “bounce” back to the

search results or referring website) after viewing only one page a website. High bounce rate

is bad as it depicts that the content on a site didn’t match what the visitor was looking for so

he left without viewing another page. Since bounce rate equates to visitors taking absolutely

no action on a website so this metric could be used as a measure of success .This paper

analyses the bounce rate of a website based on web analytics data. In this paper, analysis of

bounce rate will be based on performance of website. Data is collected using Google

Analytics tool. After applying preprocessing techniques to data an eleven step regression

model is built using the various attributes like Average Server Response Time, Average

Server Connection Time, Average Redirection Time, Average Page Download Time,

Average Domain Lookup time and Average Page Load Time. Mathematical equation is

constructed on the basis of outcome of result so that bounce rate can be analyzed and

predicted. Model is further refined after establishing the correlation between various

attributes. Correlation is established to improve the accuracy in analysis and prediction of

bounce rate. This regression model gives insight about the various parameters involved and

their effect on bounce rate. Qunatile Quantile plot is constructed to see if plausible data is

normally distributed. This complete experiment is done using R Studio

Keyword: Bounce Rate, Google Analytics, Regression model, Web Analytics, Website

Performance.

References: 1. H Singal, S. Kohli, “Trust Necessitated through Metrics: Estimating the Trustworthiness of Websites”

Elsevier Procedia Computer Science Volume 85, 2016, Pages 133-140.

2. Dr Veena Dhari, Meenakshi Garg “ Analytics For Content Based Sites –A Comparative Study “

International Journal of Science, Engineering and Technology Research (IJSETR) Volume 5, Issue 6, June 2016 Pages 2261-2264

3. Daniel Amo Filvà, María José Casany Guerrero , Marc Alier Forment, “Google analytics for time behavior

measurement in Moodle” 2014 9th Iberian Conference on Information Systems and Technologies (CISTI)

4. laza, Beatriz. “Google Analytics for measuring website performance. Tourism Management - TOURISM

MANAGE” 32. 477-481. 10.1016/j.tourman.2010.03.015.

5. Han Qin, Kit Riehle, Haozhen Zhao ” Using Google Analytics to Support Cybersecurity Forensics” 2017

IEEE International Conference on Big Data (Big Data), Dec. 2017,pp: 11-14.

6. https://www.searchenginejournal.com/10-reasons-website-can-high-bounce-rate/182260/#close accessed

on 15 May 2019

7. H Singal, S. Kohli, “ Conceptual Model For Obfuscated TRUST induced from Web Analytics data fro

content driven Websites” International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014.

646-649

8. Akiyuki Sekiguchia, KazuhikoTsuda “ Study on Web Analytics Utilizing Segmentation Knowledge in

Business to Business Manufacturer Site “ Elsevier Procedia Computer Science Volume 35, 2014, pp: 902-

909

9. Veena Dhari , Meenakshi Garg “A Novel Appraoch for Comparing Third Party Web Analytical Tools for

General Data Protection Regulation Policy“ International Journal of Computer Applications 181(42),

February 2019, pp: :10-12.

10. Berger P.D., Maurer R.E., Celli G.B. (2018) “ Introduction to Simple Regression. “ In: Experimental

Design. Springer, Cham

11. Naomi Altman. Martin Krzywinski “Simple linear regression“, Nature Methods volume12, 2015,

pp:999–1000

12. Oyerinde, O. D. , Chia, P. A. , “Predicting Students’ Academic Performances – A Learning Analytics

Approach using Multiple Linear Regression“, Vol. 157;No. 4; pp 37- 44

104

Authors: Praveen Barapatre, Dr. Jayantilal N. Patel

Paper

Title:

Development of Internet of Things (IoT) Based Smart Irrigation System for Sugarcane Crop

Abstract: This work focuses on the impact of climate change on agriculture water for

sugarcane crop of Gujarat region and alternatively, IoT (Internet of Things) technology to be

proposed for decision making and irrigating water requirement of the crop. Agriculture is a

major source of income for Indians and Agribusiness has a major effect on India's economy.

Sugarcane is an important crop utilized for bioenergy and sugar. It is one of the world's

major crops that for the most part develop in the tropic and subtropic areas. Climate and

atmosphere related occasions such as development condition of atmospheric CO2,

temperature, rainfall, and other extraordinary weather conditions are the key components for

sugarcane production around the world. So reasonable conditions and appropriate moisture

in beds of the crop can play a noteworthy job for crop production. Generally, irrigation of

sugarcane crop is done by conventional techniques in which stream flows from end to end.

The organization of the irrigation framework can be upgraded using automated watering

structure. In this paper automation of irrigation system using soil moisture sensors and

solenoid valves has been proposed. For implementing the system Arduino Uno and Esp8266

Node MCU microcontrollers have been proposed for gathering information from soil

moisture sensor, and operations of solenoid valves and water pump

Keyword: Automation, IoT, Irrigation, Soil Moisture Sensor, Sugarcane..

References: 1. T. Stambouli, J. M. Faci, and N. Zapata, “Water and energy management in an automated irrigation

district,” Agric. Water Manag., vol. 142, pp. 66–76, 2014.

2. Z. Feng, “Research on water-saving irrigation automatic control system based on Internet of things,” 2011

Int. Conf. Electr. Inf. Control Eng., pp. 2541–2544, 2011.

3. L. Zotarelli, M. D. Dukes, J. M. S. Scholberg, K. Femminella, and R. Muñoz-Carpena, “Irrigation

Scheduling for Green Bell Peppers Using Capacitance Soil Moisture Sensors,” J. Irrig. Drain. Eng., vol.

137, no. 2, pp. 73–81, 2011.

4. S. Sawant, S. S. Durbha, and J. Adinarayana, “Interoperable agro-meteorological observation and analysis

platform for precision agriculture : A case study in citrus crop water requirement estimation,” Comput.

Electron. Agric., vol. 138, pp. 175–187, 2017.

5. K. X. Soulis, D. Ph, and S. Elmaloglou, “Optimum Soil Water Content Sensors Placement in Drip

Irrigation Scheduling Systems : Concept of Time Stable Representative Positions,” J. Irrig. Drain Eng.,

2016, 142(11) 04016054, vol. 142, no. 11, pp. 1–9, 2016.

6. B. Khelifa and D. Amel, “Smart Irrigation Using Internet of Things,” 2015 Fourth Int. Conf. Futur. Gener.

Commun. Technol., no. Fgct, pp. 1–6, 2015.

7. K. Sakthivelu and D. Jalihal, “moisture scanner for efficient feasibility of soil Techno-commercial

feasibility of scheduling soil moisture moisture scanner scanner for for efficient efficient irrigation irrigation scheduling irrigation scheduling in in in in,” IFAC-PapersOnLine, vol. 49, no. 16, pp. 199–204,

2016.

8. R. C. Rm, “Automation in drip irrigation using IOT devices,” 2017 Fourth Int. Conf. Image Inf. Process.

Autom., pp. 323–327, 2017.

9. J. Casadesús, M. Mata, J. Marsal, and J. Girona, “A general algorithm for automated scheduling of drip

irrigation in tree crops,” Comput. Electron. Agric., vol. 83, pp. 11–20, 2012.

10. D. Zhao and Y.-R. Li, “Climate Change and Sugarcane Production: Potential Impact and Mitigation

650-654

Strategies,” Int. J. Agron., vol. 2015, pp. 1–10, 2015.

11. R. G. ALLEN, L. S. PEREIRA, D. RAES, and M. SMITH, “FAO Irrigation and Drainage Paper No. 56

Crop Evapotranspiration,” Guidel. Comput. Crop water Requir., vol. 13, no. 3, pp. 110–115, 1991.

105

Authors: Dr. Rinkesh Mittal, Agrima Kukkar, Dr. P. N. Hrisheekesha

Paper

Title:

An approach for optical communication with WDM based FSO over varying weather

conditions

Abstract: These days, Free Space Optics (FSO) has become a prominent mechanism due to

its economic nature, broad-bandwidth access technique. The effects of unpredictable

weather are difficult to deal. In order to draft the most effective system, Single beam and

multiple beam FSO systems are developed which can tackle the atmospheric weather

condition’s effects on signal. In this dissertation, a practical review on communication

channels (FSO) is presented. These channels are used in WDM. It was obtained that the

traditional Wavelength Division Multiplexing (WDM) based FSO system utilized the NRZ

encoding method which was less efficient and it was suitable for communication channel

which supports 16 transmitters and receivers only. This work is organized to achieve an

objective to perform some enhancements in traditional WDM system by replacing NRZ

encoding scheme with MDRZ encoding and also the channel size is increased to the 32.

Keyword: Free Space Optic, WDM, NRZ, Duobinary Return to Zero, Modified Duobinary

Return to Zero format.

References:

1. Nishu sahu and Jayesh c. Prajapti, “Optimization of WDM-FSO link using Multiple Beams under

different rain conditions”, Volume 4, Issue 5, May 2015 , IJARECE. 2. M. Boroon, S. Hitam, M. A. Mahdi, R. K. Z. Sahbudin and S. Seyedzadeh, "Performance of multi-

wavelength erbium doped fiber laser on free space optical medium," 2014 IEEE 5th International

Conference on Photonics (ICP), Kuala Lumpur, 2014, pp. 99-101. doi: 10.1109/ICP.2014.7002323

3. H. Henniger, O. Wilfert, “An Introduction to Free-space Optical Communications”, Radio engineering, vol.

19, no. 2, June 2010. 4. X. Cao, "An Integer Linear Programming Approach for Topology Design in OWC Networks," IEEE

Globecom Workshops, New Orleans, LA, 2008, pp.1-5.,doi: 10.1109/GLOCOMW.2008.ECP.6

5. Light pointe, “How to design a reliable FSO system”, Light pointe white paper series, 2009. Available at: https://www.nebula.wsimg.com

6. 6. S. Bloom, E. Korewaar, J. Schuster, H. Willebrand, “Understanding the performance of free-space

optics”, Journal of Optical Networking, Optical Society of America, vol. 2, no. 6, June 2003, pp. 178 – 200.

7. 7. C. P. Colvero, M. C. R. Cordeiro and J. P. von der Weid, "FSO systems: Rain, drizzle, fog and haze

attenuation at different optical windows propagation," SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference, Brazil, 2007, pp. 563-568. doi: 10.1109/IMOC.2007.4404328

8. Mustafa, Fatin & Supa'at, Abu & Charde, Nachi Mani, “Effect of Rain attenuations on free space optic

transmission in Kuala Lumpur”, (Proceeding of the International Conference on Advanced Science, Engineering and Information Technology), 2011, pp. 337 – 341.

9. Fadhil, H.A., Amphawan, A., Shamsuddin, H.A., Abd, T.H., Al-Khafaji, H.M., Aljunid, S.A., & Ahmed,

N.U., “Optimization of free space optics parameters: An optimum solution for bad weather conditions”. Optik, 124, 2013 pp.3969-3973.

10. Aditi and Preeti, "An effort to design a power efficient, long reach WDM- FSO system," 2014

International Conference on Signal Propagation and 11. Computer Technology (ICSPCT 2014), Ajmer, 2014, pp. 791-796. doi: 10.1109/ICSPCT.2014.6885013

12. Sharan, Lucky & G. Shanbhag, Akshay & Chaubey, Vinod, “Design and simulation of modified duobinary modulated 40 Gbps 32 channel DWDM optical link for improved non-linear performance”

Cogent Engineering. 3,2013.

13. Doi: 10.1080/23311916.2016.1256562. 14. “Fourth-Generation free space optics” available at: https://www.Electronicsforu.com.

15. P. Sathya and S. Robinson, "Impact of Modulation Techniques for Hybrid-WDM Based FSO," 2018

International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, 2018, pp. 1-6.

16. Ghazi, Alaan & Noori, Awab & Al-dawoodi, Aras & Aljunid, S & Syed Idrus, Syed Zulkarnain & Syed

Idrus, Zulkarnain. (2019). Design & Investigation of 10 x 10 Gbit/s MDM over Hybrid FSO link under Different Weather Conditions and Fiber to The Home.

655-660

106

Authors: Rahul Kumar Verma, R L Yadava

Paper

Title:

Analysis and design of DGS by inserting ofa active element PIN Diode in slot patch

Abstract: In this work, a rectangle is formed one side of substrate material i.e. FR-4/glass

epoxy, PIN diode and slot integrated on ground patch ,on the basis of reflection coefficient

S11, a re-configurability characteristics is found. Using the PIN diode the electrical length

of slot can be changed when the PIN diode is in ON / OFF position has more than one

resonant frequency. Patch antenna is compatible with characteristics use as dual band

antenna under ON condition of PIN diode in wireless communication, these frequencies are

6.45 Ghz and 9.7 GHz. OFF conditions 6.46GHz and 9.8GHz.all the parameters remains

same during the current distribution. A reconfigurable property of patch antenna is found.

Keyword: DGS, Electrical length, PIN Diode, reconfigurable, slot.

References: 1. Ó. Quevedo-Teruel, E. Pucci and E. Rajo-Iglesias, "Compact Loaded PIFA for Multifrequency

Applications," in IEEE Transactions on Antennas and Propagation, vol. 58, no. 3, pp. 656-664, March 2010.

2. J. Hu, Z. Hao and W. Hong, "Design of a Wideband Quad-Polarization Reconfigurable Patch Antenna Array

Using a Stacked Structure," in IEEE Transactions on Antennas and Propagation, vol. 65, no. 6, June 2017,

pp. 3014-3023.

3. K. M. Mak, H. W. Lai, K. M. Luk and K. L. Ho, "Polarization Reconfigurable Circular Patch Antenna With

a C-Shaped," in IEEE Transactions on Antennas and Propagation, vol. 65, no. 3, March 2017, pp. 1388-1392.

4. H. W. Liu, Z. F. Li, and X. W. Sun, “A novel fractal defected ground structure and its application to the low-

pass filter,” Microwave and Optical TechnologyLetters, vol. 39, no. 6, 2003, pp. 453–456.

5. Jong-Sik Lim, Chul-Soo Kim, Jun-Seok Park, Dal Ahn and Sangwook Nam, "Design of 10 dB 90/spl deg/

branch line coupler using microstrip line with defected ground structure," in Electronics Letters, vol. 36, no. 21, 12 Oct. 2000, pp. 1784-1785.

6. J.-S. Lim, S.-W. Lee, C.-S. Kim, J.-S. Park, D. Ahn, and S. Nam, “A 4.1 unequal Wilkinson power divider,”

IEEE Microwave and Wireless Components Letters, vol. 11, no. 3, 2001, pp. 124–126.

7. A. K. Gautam and B. Kr Kanaujia, “A novel dual-band asymmetric slit with defected ground structure

microstrip antenna for Circular Polarization operation,” Microwave and Optical Technology Letters, vol. 55,

no. 6, 2013, pp. 1198–1201.

8. J. -. Wu, H. -. Hsiao, J. -. Lu and S. -. Chang, "Dual broadband design of rectangular slot antenna for 2.4 and

5 GHz wireless communication," in Electronics Letters, vol. 40, no. 23, 11 Nov. 2004, pp. 1461-1463 .

661-663

107

Authors: Anshu Soni and Virender Ranga

Paper

Title:

API Features Individualizing Comparison of Web Services: REST and SOAP

Abstract: Web Services are combination of open protocols and standards to allow

communication between client and server. It provides an interoperability between

contrasting applications. Representational state Transfer (REST) and Simple Object Access

Protocol (SOAP) are the two main popular used web services now-a-days. REST is an

architectural style based, whereas SOAP is a underlying protocol. Both services are used to

handle the communication on the world wide web (www). Both services have some

advantages and drawbacks and it is the decision of web developer to decide which service is

best to use according to its requirements. The aim of this research work is to design a REST

API and SOAP API by JAX-RS and JAX-WS, respectively and gives a comparative

analysis of Application Programming Interface (API) features (in terms of response time,

memory usage, execution speed and so on) of these services by using API testing tool like

Postman. This gives insight view of which service is better to use as per requirements. The

result of experiments shows that the response time of SOAP is approximate takes 4ms to

7ms more than REST. It has been observed that as number of API increases, SOAP takes

approximate 1MB to 2MB more memory usage than REST.

Keyword:Architectural style, JAX-RS, JAX-WS, Jersey, Postman, Protocol, REST,

SOAP, Tomcat, Web service..

References:

664-671

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International Conference on Computer Science and Service System (CSSS), Nanjing,2011, pp. 3912-3915.

2. R.T.Fielding, "Architectural Styles and the Design of Network-based Software Architectures

DISSERTATION", 2000.

3. P.Adamczyk, P.H. Smith, R.E.Johnson, and Munawar Hafiz,"REST and Web Services: In Theory and in

Practice", DOI 10.1007/978-1-4419-8303-9\_2, Springer Science+Business Media, 2011.

4. Mark Massé,"Designing Consistent RESTful Web Services Interface",Published by O’Reilly Media,ISBN:

978-1-449-31050-9.

5. C.H.Kao, C.C.Lin and J.Chen, "Performance Testing Framework for REST-basedWeb Applications", 13th

International Conference on Quality Software,2013 IEEE, DOI 10.1109/QSIC.2013.32, 2013.

6. F.Haupt, F.Leymann, A.Scherer and K.Vukojevic-Haupt,"A Framework for the Structural Analysis of

REST APIs", IEEE International Conference on Software Architecture,DOI 10.1109/ICSA.2017.40, 2017.

7. Munonye K* and Martinek P**, "Performance Analysis of the Microsoft .Net- and Java-Based

Implementation of REST Web Services", IEEE 16th International Symposium on Intelligent Systems and

Informatics, Subotica, Serbia, September 13-15, 2018.

8. R.Sinha, M.Khatkar and S.Chand Gupta, "Design & Development of a REST based Web Service Platform

for Applications Integration on Cloud", IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 7, September 2014.

9. P.Giessler, M.Gebhart, D.Sarancin, R.Steinegger and S.Abeck "Best Practices for the Design of RESTFul

Web Services," Tenth International Conference on Software Engineering Advances, Barcelona, 2015.

10. D.Qiu, B.Li and H.Leung,"Understanding the API usage in Java",Information and Software Technology,

Elsevier, 2016, pp 81-100.

11. F.Halili and E.Ramadani, "Web Services: A Comparison of Soap and Rest Services", Canadian Center of

Science and Education, Modern Applied Science; Vol. 12, No. 3; 2018.

12. S.Mumbaikar and Puja Padiya,"Web Services Based On SOAP and REST Principles", International Journal

of Scientific and Research Publications, Volume 3, Issue 5, May 2013.

13. A.Navarro, A.d.Silva, "A metamodel-based definition of a conversion mechanism between SOAP and

RESTful web services", Computer Standards & Interfaces, pp.49-70,2016.

14. M.Govindaraju, A.Slominski, K.Chiu, P.Liu, R.v.Engelen and M.J.Lewis,"Toward Characterizing the

Performance of SOAP Toolkits", Fifth IEEE/ACM International Workshop on Grid Computing, Pittsburgh,

PA, 2004, pp. 365-372.

15. S.Malik and S.Malik,"A Comparison of RESTful vs. SOAP Web Services in Actuator Networks", 2017

Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, 2017, pp. 753-755.

16. A.Neumann, N.Laranjeiro and J.Bernardino, "An Analysis of Public REST Web Service APIs", IEEE

Transactions on Services Computing,2018.

17. J.Tihomirovs and J.Grabis,"Comparison of SOAP and REST Based Web Services Using Software

Evaluation Metrics",Information Technology and Management Science,2016,pp. 92-97.

18. J.Bian, Y.Cheng and J.Kuang, "RESEARCH ON WEBSERVICE TECHNOLOGY BASED ON OMS

PLATFORM",2010 International Conference on Advanced Intelligence and Awarenss Internet (AIAI

2010), Beijing, China, 2010, pp. 162-165.

19. C.Kiama and L.Muchemi,"Comparative Study of REST and SOAP: Case of Registrar of Political Parties’

Kenya", Trends in Distributed Computing, 2014, pp.105-116.

20. A.Dudhe and S.S. Sherekar, "Performance Analysis of SOAP and RESTful Mobile Web Services in

Cloud Environment", International Journal of Computer Applications (0975 – 8887) Second National

Conference on Recent Trends in Information Security, GHRCE, Nagpur, India, Jan-2014.

21. V.Kumari, "Web Services Protocol: SOAP vs REST",International Journal of Advanced Research in

Computer Engineering & Technology (IJARCET) Volume 4 Issue 5, May 2015, pp.2467-2469.

22. K.Wagh and R.Thool, "A Comparative Study of SOAP Vs REST Web Services Provisioning Techniques

for Mobile Host", Journal of Information Engineering and Applications,Vol 2, No.5, 2012,pp 12-16.

23. G. Sambasivam , J. Amudhavel, T. Vengattaraman and P. Dhavachelvan,"An QoS based multifaceted

matchmaking framework for web services discovery",Future Computing and Informatics Journal Volume

3, Issue 2, pp.371-383, December 2018.

24. R. Padmanaban, M. Thirumaran, P. Anitha and A. Moshika," Computability evaluation of RESTful API

using Primitive Recursive Function", Journal of King Saud University–Computer and Information

Sciences, accepted (article in press), 2018.

25. M.Z.Gashti, "Investigating Soap And Xml Technologies In Web Service", InternationalJournal on Soft

Computing (IJSC) Vol.3, No.4, November 2012.

26. jersey: https://jersey.github.io/, online Accessed on 5-oct-2018.

27. JAX-WS: https://javaee.github.io/metro-jax-ws/, online Accessed on 15-oct-2018.

28. Postman: https://www.getpostman.com/, online accessed on 20-nov-2018.

Authors: Gajanan Choudhari, Rajesh Mehra

Paper

Title:

Iris Recognition using Convolutional Neural Network Design

108

Abstract: Iris trait has gained the attention of many researchers recently as it consists of

unique and highly random patterns. Many methods have been proposed for feature

extraction and classification for iris trait but suffer from poor generalization ability. In this

paper, a scratch convolutional neural network is designed in order to extract the iris features

and softmax classifier is used for multiclass classification. The various optimization

techniques with backpropagation algorithm are used for weight updating. The results show

that the Convolutional Neural Network based feature extraction has proven to provide good

generalization ability with improved recognition rate. The effect of various optimization

techniques for generalization ability is also observed. The method is tested on IITD and

CASIA-Iris-V3 database. The recognition rates obtained are comparable with state of art

methods.

Keyword:Bio-metric, Deep Learning, Iris Recognition, Softmax Classifier, Adam, SGD

with Moment, RMSprop, Convolutional Neural Network..

References: 1. Chengcheng Li , Weidong Zhou , Shasha Yuan, “Iris Recognition based on a Novel Variation of Local

Binary Pattern,” The Visual Computer, Springer, Vol.31, No.10, pp. 1419–1429, 2015.

2. Tze Weng Ng, Thien Lang Tay, Siak Wang Khor, “ Iris Recognition Using Rapid Haar Wavelet

Decomposition,” IEEE, International Conference on Signal Processing Systems (ICSPS) , pp. 820–823, 2010.

3. K. Roy, P. Bhattacharya, and C. Y. Suen, “Iris Recognition using Shape-Guided Approach and Game

theory,” Pattern Analysis and Applications ,Springer,Vol.14, No.4, pp. 329–348, 2011.

4. J. G. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,”

IEEE transactions on pattern analysis and machine intelligence , Vol. 15, no. 11, 1993..

5. J. Daugman, “The importance of being random : statistical principles of iris recognition,” Pattern

recognition, Vol. 36, pp. 279–291, 2003..

6. Saiyed Umer, Bibhas Chandra Dhara, Bhabatosh Chanda “Texture Code Matrix-based Multi-Instance Iris

Recognition,” Pattern Analysis and Application,Springer, Vol.19, No.1, pp. 283-295, 2016.

7. Mahmoud Elgamal, Nasser Al-Biqami, “An Efficient Feature Extraction Method for Iris Recognition Based

on Wavelet Transformation,” International Journal of Computer and Information Technology ,Vol. 02, No. 03, pp. 521–527, 2013.

8. Shervin Minaee, AmirAli Abdolrashidi, and Yao Wang, “Iris Recognition Using Scattering Transform And

Textural Features ",IEEE Signal Processing and Signal Processing Education Workshop, pp. 37–42, 2015.

9. Rathgeb, C and Wagner, J and Busch, C, “SIFT ‑ based iris recognition revisited : prerequisites ,

advantages and improvements,” Pattern Anal. Appl., no. pp. 1-18, 2018.

10. Abhiram M.H, Chetan Sadhu, K. Manikantan, S. Ramachandran” Novel DCT based feature extraction for

enhanced iris recognition”, IEEE International Conference on Communication, Information & Computing Technology (ICCICT), pp. 1-6, 2012.

11. Alaa S. Al-Waisy, Rami Qahwaji, Stanley Ipson, Shumoos Al-Fahdawi,“ A fast and accurate iris

localization technique for healthcare security system” IEEE International Conference on Computer and

Information Technology; Ubiquitous Computing and Communications,pp.1028-1034,2015.

12. A. S. Al-waisy, R. Qahwaji, S. Ipson, and S. Al-fahdawi, “A Multimodal Biometric System for Personal

Identification Based on Deep Learning Approaches,” pp. 163–168, 2017.

13. Itamar Arel, Derek C Rose, Thomas P, Karnowski,” Deep machine learning-a new frontier in artificial

intelligence research”, IEEE computational intelligence magazine, Vol. 5, No.4, pp. 13-18, 2010.

14. David Menotti , Allan Pinto , William Robson Schwartz , Helio Pedrini , Alexandre Xavier Falcao ,

Anderson Rocha ,”Deep representations for iris, face, and fingerprint spoofing detection”, IEEE

Transactions on Information Forensics and Security,Vol.10, No.4, pp. 864-879, 2015.

15. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov,”Dropout: a

simple way to prevent neural networks from overfitting”, The Journal of Machine Learning Research,Vol.

15,No. 01, pp.1929-1958, 2014.

16. Rui Zeng, Jiasong Wu, Zhuhong Shao, Lotfi Senhadji and Huazhong Shu, “Quaternion softmax classifier,”

IET Electronics Letters, Vol. 50, No. 25, pp-1929-1931, 2014.

17. Diederik P.Kingma, Jimmy Li Ba, “Adam: A method for stochastic optimisation”, International Conference

on Leraning representation ,pp. 1-15, 2015.

18. M. G Alaslani and L. A. Elrefaei, “Convolutional Neural Network Based Feature Extraction for IRIS

Recognition,” International Journal of Computer Science Informatics Technology, Vol. 10, No. 2, pp. 65–

78, 2018.

19. Tze Weng Ng, Thien Lang Tay, Siak Wang Khor, “ Iris Recognition Using Rapid Haar Wavelet

Decomposition,” IEEE, International Conference on Signal Processing Systems (ICSPS) ,2010, pp. 820–823.

20. Ajay Kumar , Arun Passi ,“Comparison and combination of iris matchers for reliable personal

authentication”, Pattern recognition, Elsevier,Vol.43,No.3,pp-1016-1026, 2010.

672-678

21. M. Vatsa, S. Member, R. Singh, S. Member, and A. Noore, “Improving Iris Recognition Performance

Using Segmentation , Quality Enhancement , Match Score Fusion , and Indexing,” IEEE Transactions on

Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 38, No. 4, pp. 1021–1035, 2008.

109

Authors: Navrattan Parmar and Virender Ranga

Paper

Title:

Performance Analysis of WebRTC and SIP for Video Conferencing

Abstract: With the advancement in communication and development of technologies like

VoIP and Video Conferencing, Web Real-Time Communication (WebRTC) is developed to

communicate without plugins and stream the videos on a real time. It was initially

developed by Web Consortium(W3C) and Internet Engineering Task Force (IETF). It

allows to transfer videos and audios between different browsers. This research paper,

analyse the parameters during the call in different browsers and conditions (number of end

points). The concept of WebRTC is inspired from Session Initiation Protocol(SIP). It helps

in the establishment of sessions and maintain it. It also supports data and message

transmissions. It also works on remote location and different network transmission

protocols. It also allows peer to peer communication. In this research work, we examine the

behaviour of WebRTC and SIP during the call from different browsers. We examine the

different parameters like packets sent, jitter, VO-Width and bandwidth during the call and

call supported on cloud during our experimental work.

Keyword: WebRTC , SIP , SDP , UDP , Codec , VoIP , Session Management , Internet

Engineering Task Force (IETF), TLS, Channel Bitrate, Inter-Process Communication (IPC).

References: 1. Hussain, Md.I, Internet of Things: Challenges and Research Opportunities, published in International

conference on dependable systems and networks workshops 35, 123-126.

2. Mizukusa ,T., Nagano, T. , Shimizu,,Y,Sakata,K. & Kato,K.(2009), Development of Feed-Forward Design

System for Rapid SiP Design, IEEE International Conference on 3D System Integration, San Francisco,

CA,1-4.

3. Hoffstadt,D. Monhof,S. & Rathgeb,E.(2012), SIP Trace Recorder: Monitor and Analysis Tool for threats in

SIP-based networks, 8th International Wireless Communications and Mobile Computing Conference

(IWCMC), Limassol, 631-635.

4. Hlavacs,H.,Hummel,K.A., Hess,A. & Nussbaumer,M. (2008), Babel-SIP: Self-learning SIP Message

Adaptation for Increasing SIP-Compatibility, IEEE INFOCOM Workshops 2008, Phoenix, AZ, 1-6.

5. Vavas,D.V., Hokelek,I. & Gunsel,B.(2018), On modeling of priority-based SIP request scheduling,

Simulation Modelling Practice and Theory, vol 80, 128–144.

6. Yildiz C., Kurt,A.B, Ceritli,T.Y., Sankur,B & Cemgil,A.T.(2018), A real-time SIP network simulation and

monitoring system", SoftwareX, vol.8 ,21–25.

7. Zhou,J., Li,J., Xia,Y.B., Cai,B., & Ying,C.(2008), SIP Network Discovery by Using SIP Message Probing,

IEEE Network Operations and Management Symposium, Salvador, Bahia, 791-794.

8. Zhiguo,G., Zhe,X.,Wei,X., Zhiyong,L., & Bo,Y.(2009), SIP Offload Engine for Accelerating J2EE Based

SIP Application Server, International Conference on Communication Software and Networks, Macau, 749-

753.

9. Bansal,A., Kulkarni,P., & Ais,A.R.(2013), Effectiveness of SIP Messages on SIP Server, IEEE Conference

on Information & Communication Technologies, Thuckalay, Tamil Nadu, India, 616-621.

10. Beltran,V. & Bertin,E (2015), Unified communications as a service andWebRTC: An identity-centric

perspective, Computer Communications, 73–82.

11. Rosas,A.S. & Martínez,J.L.A.(2016), " Videoconference System Based on WebRTC With Access to the

PSTN, Electronic Notes in Theoretical Computer Science, vol 329,105–121.

12. Daldal,B., Bilgin,I., Basaran,D. & Metin,S.(2016), Using Web Services For WebRTC Signaling

Interoperability, IEEE/IFIP Network Operations and Management Symposium, Istanbul, 780-783.

13. .Montazerolghaem, A., Moghaddam,M.H.Y., & Leon-Garcia,A.(2018), OpenSIP: Toward Software-Defined

SIP Networking , in IEEE Transactions on Network and Service Management, vol. 15,no.1, 184-199.

14. .Moor,K.D., Arndt,S. & Ammar,D.(2017), Exploring diverse measures for evaluating QoE in the context of

WebRTC, Ninth International Conference on Quality of Multimedia Experience (QoMEX), Erfurt,, 1-3.

15. Xue,H. & Zhang,Y. (2016) A WebRTC-Based Video Conferencing System with Screen Sharing, 2nd IEEE

International Conference on Computer and Communications (ICCC), Chengdu, 485-489.

16. Gouaillard,A. & Roux,L.(2017), Real-Time Communication Testing Evolution with WebRTC 1.0,

Principles, Systems and Applications of IP Telecommunications (IPTComm), Chicago, IL, 1-8.

679-686

17. Jian.C. & Lin,Z.(2015), Research and Implementation of WebRTC Signaling via WebSocket-based for Real-

time Multimedia Communications, ,5th International Conference on Computer Sciences and Automation

Engineering ICCSAE, 374-380.

18. Edan,N.M., Al-Sherbaz,A. & Turner,S.(2017), Design and Evaluation of Browser-to-Browser Video

Conferencing in WebRTC",Global Information Infrastructure and Networking Symposium (GIIS), St. Pierre,

75-78.

19. Haensge,K., & Maruschke,M.(2015), QoS-based WebRTC Access to an EPS Network Infrastructure", 18th

International Conference on Intelligence in Next Generation Networks, Paris, 9-15.

20. Kim,W., Jang,H., Choi,G., Hwang,I. & C. Youn,(2016), A WebRTC based live streaming service

platform with dynamic resource provisioning in cloud, IEEE Region 10 Conference (TENCON), Singapore,

2424-2427.

21. Zubair,M., Kong,X., Jamshed,I., & Ali,M.(2014), Integrating SIP with F-HMIPv6 to Enhance End-to-End

QoS in Next Generation Networks, Advances in Intelligent Systems and Computing 240,Springer International Publishing Switzerland.

22. Yan,S, Guo,Y. Y.Chen, & Xie,F.(2019), Predicting Freezing of WebRTC Videos in WiFi Networks, ICST

Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Published by

Springer Nature Switzerland AG 2019, 292–30.

23. Rodríguez,P., Cerviño,J., Trajkovska,I. & Salvachúa,J(2019),"Advanced VideoConferencing Services Based

on WebRTC",Conference: IADIS Multi Conference on Computer Science and Information Systems.

24. Nayyef, Z.T., Amer, S.F. & Hussain,Z.(2018),Peer to Peer Multimedia Real-Time Communication System

based on WebRTC Technology, International Journal of Engineering & Technology, 125-130.

25. Zafran, M.R.M., Gunathunga, L.G.K.M. , Rangadhari, M.I.T, Gunarathne, M.D.D.J, Kuragala K.R.S.C.B,

Dhammearatchi, M.D.(2016), Real Time Information and Communication Center based on webRTC,

International Journal of Scientific and Research Publications, Volume 6, Issue 4, 644-649.

110

Authors: Vikas Kumar, Jagjit Singh, Arvind Kumar

Paper

Title:

Non Volatile Low Power Wake up Radio Transceiver for Wireless Sensor Network

Abstract: Wireless sensor nodes consume lots of energy during communication but huge

power consumption has been observed during active listening in idle mode as source nodes

can start data transmission at any time. Power saving can be achieved by establishing

synchronization among end nodes. Many rendezvous solutions are available and out of

which wake up receiver found extremely adroit. A non volatile wake up transceiver has been

proposed in the present paper that works on the basis of ID matching. State of art using 4GB

of memory to remember states of sensor nodes while proposed technique used only 60 bits

of memory with very less false alarm probability. Power consumption for proposed model is

only 59.47 nW. Hence this model is quite effective in terms of power consumption and

memory usage as compared to trailing models.

Keyword: ID matching, low power, wake-up receiver, wireless sensor network.

References: 1. Said, O.,” Performance evaluation of WSN management system for QOS gurantee,” EURASIP Journal on

Wireless Communications and Networking,2015, pp. 1-18

2. Pflaum,R., Weigel,R., and Koelpin, A., “Ultra-low-power sensor node with wake-up-functionality for smart-

sensor-applications," 2018 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), Anaheim, CA, 2018, pp. 107-110.

3. Lee, D.,” Fast notification architecture for wireless sensor network,” International journal of electronics,

Vol100, No.3, 2013, pp 371-383. http://dx.doi.org/10.1080/00207217.2012.713012

4. Pughat, A. and Sharma, V., “A review on stochastic approach for dynamic power management in wireless

sensor network’” Springer, Human-centric Computing and Information Sciences, 2015, pp. 1-14.

5. Popovici, E., Magno, M., and Marinkovic, S.,” ‘Power Management Techniques for Wireless Sensor

Networks.” Advances in Sensors and Interfaces (IWASI), 2013 pp. 194-198

6. Hutu, F., Khoumeri, A., Villemaud, G., and Gorce , J.,”A new wake-up radio architecture for wireless sensor

network” EURASIP Journal on Wireless Communications and Networking, 2013, pp.1-13

7. Berder, O., and Sentieys, O.,”Power optimized hardware/software framework for wireless motes,” In Proc.

of the Workshop on Ultra-Low Power Sensor Networks, Co-located with ARCS,2010, pp. 229–233

8. Wardlaw, J., Karaman, I., and Karsilayan, A.,” Low-Power Circuits and Energy Harvesting for Structural

Health Monitoring of Bridges,” IEEE Sensors Journal, vol. 13, No. 2, 2013, pp.709-722

9. Dikovic, A., Sisul, G., and Modlic, B.,” A Low Cost Platform For Sensor Network Applications and

educational purposes,” Radio engineering, Vol. 20, No. 4, 2011, pp. 758-765

10. Magno, M., Marinkovic, S., Brunelli, D., and Popovici, E.,”Smart Power Unit with Ultra Low Power Radio

Trigger Capabilities for Wireless Sensor Networks,” DATE '12 Proceedings of the Conference on Design,

687-691

Automation and Test in Europe, 2012, pp. 75-80

11. Seyed, N., Mazloum, and Edfors, O., ”Performance Analysis and Energy Optimization of Wake-Up Receiver

Schemes for Wireless Low-Power Applications,” IEEE Transaction on wireless communication, 2014, pp.

7050-7061.

12. Magno, M., and Benini, L.,”An Ultra Low Power High Sensitivity Wake-Up,” IEEE Transactions on

Industrial Informatics, 2014, pp. 92-99.

13. Stecklina, O., Kornemann, S., and Methfessel, M.,”A secure wake-up scheme for low power wireless sensor

nodes,” Collaboration Technologies and Systems (CTS) IEEE, 2014, pp.279-286, ISBN 978-1-4799-5157-4.

14. Vodel, M., Lippmann, M., and Hardt, W.,” Energy-Efficient Communication with Wake-Up Receiver

Technologies and an Optimised Protocol Stack,” International Conference on Advances in ICT for Emerging

Regions, 2013, pp.177-184.

15. Zhang, D., Wang, X., Song, X., Zhang, T., and Zhu Y. ,”A new clustering routing method based on PECE

for WSN,” EURASIP Journal on Wireless Communications and Networking, 2015, pp. 1-13, DOI: 10.1186/s13638-015-0399-x.

16. Ali Shah, M., Abbas, G., Basit Dogar, A. and Halim, Z. ,” Scaling hierarchical clustering,” Springer

Complex Adapt System Model, 2015, pp. 1-23, DOI: 10.1186/s40294-015-0011-6.

17. Mathews, J., Barnes, M. and Arvind, D.,”Low Power Free Space,” Optical Communication in Wireless

Sensor Networks, Euro micro Conference on Digital System Design, 2009, pp. 849-856,

DOI:10.1109/DSD.2009.234

18. Mathews, J., Barnes, M., Young, A., and Arvind, D.,” Low Power Wake-Up in Wireless Sensor Networks

using Free Space Optical Communications,” International Conference on Sensor Technologies and

Applications, 2010 pp. 257-261.

19. Deng, B., Li, W., Huang, G., Liu, S., and Zhang, Q.,” High-accuracy and low-cost localisation scheme for

wireless sensor networks,” International Journal of Electronics, Vol. 99, No. 4, April 2012, pp 455–476,

DOI: 10.1080/00207217.2011.609969

20. Unterassinger, H., Dielachery, M., Flatschery, M., Grubery, S., and Kowalczyky, G.,”A Power Management

Unit for Ultra-Low Power Wireless Sensor Networks,” IEEE Africon, 2011 pp. 1-6.

21. Tang S., Yomo H., Kondo Y., and Obana S. (2012), “ Wake-up receiver for radio-on-demand wireless

LANs,“ EURASIP Journal on Wireless Communications and Networking, 2011, pp.1-13, DOI:

10.1186/1687-1499-2012-42.

22. Jurdak, R., Ruzzelli, A., and Hare, G., “Multi-hop RFID Wake-up Radio: Design Evaluation and Energy

Tradeoffs,” IEEE conference on Computer Communications and Networks, 2011, pp.1-8, DOI: 10.1109/ICCCN.2008.ECP.124.

23. Gamm, G., Sippel, M., Kostic M., and Reind, L.,”Low Power Wake-up Receiver for Wireless Sensor

Nodes,” IEEE International Conference on Intelligent Sensors, Sensor Networks and Information

Processing, 2010, pp.121-126

24. Rosello, V., and Riesgo, T. ,”Ultra Low Power FPGA-Based Architecture for Wake-up Radio in Wireless

Sensor Networks,” Annual Conference on IEEE Industrial Electronics Society, 2011, pp. 3826-3831.

25. Uraiby, A., Yoshigoe, K., Seker, R., and Babiceanu, R. ,”FPGA Implementation of Low-profile Wake-up

Radio Receiver for Wireless Sensor Networks,” IEEE conference on consumer electronic, 2012, pp. 20-24

26. Vodel, M., Caspar, M., and Hardt, W. ,”Wake-Up-Receiver Concepts -Capabilities and Limitations,”

Journal of networks, vol. 7, no. 1, 2012, pp 126-134.

27. Shuangming Y., Peng F., and Nanjian W. ,” A low power non Volatile LR- WPAN baseband processor with

wake up identification receiver ,” Communication system design, China Communications, 2016, pp 33-46.

28. Kaushik, K., Mishra, D., Swades De, Chowdhury, K., and Heinzelman, W.,” Low cost Wake-up Receiver for

RF Energy harvesting Wireless Sensor Networks,” IEEE sensor, 2016 pp. 1-9, 2DOI: 10.1109/JSEN.2016.2574798

29. Divyabharathi, R., Scholar, P., Hakeem, C., and Mian, A.,” Design and simulation of zigbee transmitter

using verilog,” International Conference on Information Communication and Embedded Systems, 2013,

pp.882-888.

111

Authors: NavleenKaur, Munish Rattan, Chahat Jain

Paper

Title:

Design of t slot shaped microstrip patch antenna for s band applications

Abstract: In this paper, the T slot shaped micro strip patch antenna is designed on FR4

substrate. The length and width of proposed antenna is 32.5mm and 25.2mm. The T slot

shaped antenna is simulated on IE3D software for s band application.This simulated antenna

operates on 3.379 GHz with Return Loss -23.66s. Finally, the simulated and base/ previous

results are compared.

Keyword: Microstrip, Patch, Antenna, T Slot.

References:

692-695

1. A.Q. Khan, M. Riaz, A. Bilal, “Various types of antenna with respect to their applications: A review”

International Journal of Multidisciplinary Science and Engineering, vol.7, no.3, March 2016.

2. A. Mehta, “Microstrip Antenna” International Journal of Scientific & Technology Research, vol. 4, issue.3,

March 2015.

3. P. A. Ambresh, P. A. Hadalgi, P. V. Hunagund, “Effects of Slots on Microstrip Patch Antenna

characteristics” International Conference on Computer and Electrical Technology, 978-1-4244-9394-1/11,

March 2011.

4. Y. L. Kuo, K. L. Wong, “Printed Dual T Monopole Antenna for 2.4/5.2 GHz Dual Band WLAN

Application” IEEE Transaction on Antenna and propagation, vol.51, no. 9,September 2003.

5. A. Kumar, S. Singh, “Design and Analysis of T shaped Microstrip Patch Antenna for the 4G System”

Global Journal of Computer Science and Technology Network Web &Security, vol.13, issue 8, 2013.

6. A. Goyal, M. R. Tripathi, S. A.Zaidi “Design & Simulation of Inverted T Shaped Antenna for X Band

Application” International Journal of Computer & Technology, vol.4, no. 6, November 2014.

7. M. N. Moghadsai, R. A. Sadeghzadeh, T.Sdghi, T. Aribi, B.S.Birdhi, “UWB CPW Fed Fractal Patch

Antenna with band notched function employing folded T shaped element” IEEE antenna & propagation letters,vol. 12, 2013.

8. H. Saini, A. Kaur, A. Thakur et al. “Compact Multiple Ground slotted patch antenna for X band

Applications” IEEE 2nd international Conference on Recent Advances in Engineering and Computational,

pp. 1-6, 2015.

9. Y.W.Zhang, S. Lin, Y.W. Zhang et al. “Simulation Design of a Broadband Dual-Polarized Minkowski

Fractal Antenna Fractal Microstrip Antenna for S-Band”IEEEInternatinal of Symposium on Antenna

&Propgation USNC/URSI National Radio Science Meeting, 2018.

10. P. Bhattacharjee, V. Hanumante, S. Roy, “Design U slot Patch Antenna for Wireless LAN at 2.45GHz” 9th

International Conference Microwave Antenna Propagation and Remote Sensing, Dec. 2013

Authors: Shipra, B. P. Garg

Paper

Title:

Effect of Heat Source/Sink on Free Connective MHD Flow past an Exponentially Accelerated

Infinite Plate with Mass Diffusion and Chemical Reaction

112 Abstract: The effects of heat source/sink and chemical reaction with mass diffusion on free

convective incompressible viscous fluid flow past an accelerated vertical plate with

magnetic field has been investigated. Laplace transformation method has been applied to

solve the system of linear partial differential equations. The result is presented in form of

complementary error function and exponential function. The effect of non dimensional

parameters such as Schmidt number (Sc), Accelerated parameter (a), Chemical reaction

parameter (K), Prandtl number (Pr), Magnetic field parameter (M), Mass Grashof number

(Gm), Heat source/sink parameter (H), Thermal Grashof number (Gr) on temperature,

concentration, velocity has been discussed with graphs.

Keyword:Free-convection, Mass transfer, MHD, Heat Source/Sink, Chemical Reaction..

References: 1. G. G. Stokes, On the Effect of the Internal Fraction of Fluids on the Motion of a Pendulum Trans. Camb.

Phil. Soc. 9, 1851, pp. 8-106.

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and Mass Transfer, 34, 1998, pp.107-109.

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Characteristics in an Unsteady Upward Motion of an Isothermal Plate. J. Appl. Mech. Tech. Phys., 42, 2001,

pp. 665-671.

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impulsively started infinite vertical plate with variable temperature or constant heat flux. Astrophysics and

Space Science, 100, 1984, pp. 159-164.

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Astrophysics and Space science, 98,2000, pp. 245-258.

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with variable temperature and mass diffusion. Int. J. of Engg. Annals. of Faculty Engineering Hunedoara .Tom IX, Fascicule 2, 2008, pp. 137-140.

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with variable heat and mass transfer. International Journal of Advance and Innovative Research, 6(1), 2019.

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Authors: Navjot Kaur, Manish Mahajan, Rajeev Sharma

Paper

Title:

An Enhanced LEACH in Wireless Sensor Network

113

Abstract: Wireless sensor networks (WSNs) consist of self-governing sensors that sense as

well as monitor the area in which these nodes are deployed and distribute this information

in a distributed manner. Presently, the WSN with long life and minimum energy

consumption are in demand. To overcome this problem, Low Energy Adaptive Clustering

Hierarchy (LEACH) is presented with the addition of Cuckoo Search (CS) and Support

Vector Machine (SVM) concept. The problem of LEACH protocol like which node is

considered as Cluster Head (CH) is overcome by CS. On the basis of healthy function, the

nodes property such as energy consumed by each node is categorized. Those nodes that

have higher energy compared to the defined function are put in one category and remaining

in another category. These two categories of nodes are provided as an input to SVM and

train the system. Therefore, the best node having the highest energy is considered as CH

and hence enhanced the lifetime by saving the energy upto 21.86 %.

Keyword:WSN, LEACH, CS, SVM, CH.

References: 1. Zhang, W. H., Li, L. Y., ZHANG, L. M., & WANG, X. Z. (2008). Energy consumption balance

improvement of LEACH of WSN. Chinese Journal of Sensors and Actuators, 11, 1918-1922.

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Authors: Dr. Shivinder Nijjer, Jaskirat Singh, Dr. Sahil Raj

Paper

Title:

Developing HRIS for Predictive Attrition and Retention management of Indian IT Engineers-

Using ANN, ANOVA and SmartPLS

114

Abstract: Growth of IT sector in India (Heeks, 2015) is phenomenal, however, employee

turnover has been a persistent issue in IT sector (Yiu & Saner, 2008). Voluntarily turnover

among employees has been attributed to dissatisfaction with organizational factors and

individual characteristics (Elkjaer & Filmer, 2015). Therefore, this research examines how

to retain employees in IT firms, by focusing on the Job attitudes, theory of individual

differences and theory of planned behaviour. It also explores which individual

characteristics contribute to employee turnover intent, as a consequence of their negative job

attitudes. The techniques of Artificial Neural Networks, Two-way ANOVA and PLS testing

have been utilised. The analysis confirms the proposition that individual differences have an

effect on job attitudes, which ultimately affect the turnover intention.

Keyword:Predictive Analytics, ANN, HRM, Attrition, Retention, IT industry, Software

Engineers

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organisation fit and behavioural outcomes. Journal of Vocational Behavior , 389-399.

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341-367.

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Generalized Self-Efficacy, Locus of Control, and Emotional Stability—With Job Satisfaction and Job

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43. Cascio, W. F. (2014). Leveraging employer branding, performance management and human resource

development to enhance employee retention . Human Resource Development International , 121-128.

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Authors: Dr. Monika Gupta Vashisht, Dr. Bhawna, Ms. Ashima Kalra

Paper

Title:

Students Expectations Using Opinion Mining Clustering Approach

115

Abstract: Students and their parents have become more and more aware of the importance

of gaining higher education in India. Government of India, as well as state governments, has

been framing various policies to promote higher education in various fields such as

engineering, management, and hotel management, medical and allied disciplines. An

attempt has been made to analyze the expectations of students pursuing higher education in

the state of Punjab and Haryana in India. For this, Clustering approach has been used.

Students studying in selected engineering colleges have been approached. Two clusters have

been evolved: Career-Oriented Students and Society Conscious Students. This research

gives further directions for the future as the same can be conducted in other institutes and in

other cities, states, and countries too

Keyword:Students, Higher Education, Engineering, Career, Social, Fun, Clustering

Analysis.

References: 1. A. Dutt, M.A. Ismail and T. Herawan, “A systematic review on educational data mining,” IEEE Access,

ResearchGate, vol. 5, pp. 15991–16005, 2017.

2. S. Kausar, X. Huahu, I. Hussain , Z. Wenhao, and M. Zahid, “Integration of data mining clustering

approach in the personalized E-Learning system,” IEEE Access, vol. 6, pp. 72724–34, 2018.

3. I. Majeed, “Current State of Art of Academic Data Mining and Future Vision,” Indian Journal of

Computer Science and Engineering, vol. 9(2), pp. 49-56, April-May 2018.

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expectations and perceptions of assessment quality,” Cogent Education, vol. 5, pp. 1-16, 2018.

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students in Hong Kong,” Developments in Business Simulations and Experiential Learning, vol. 32, pp.

373-80, 2005.

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Journal of Computer Science and Application, pp. 140-144, 2010.

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relationship with their academic performance”, International Journal of Business and Management, Vol.

5, No. 4, pp. 80-88, April 2010.

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4(12), pp. 93-97, 2018.

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students academic performance: A systematic review,” International Journal of Innovations & Advancement in Computer Science, vol. 7(3), pp. 66-70, March 2018.

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and Literature, IEEE Access, ResearchGate, vol. 6(5), pp. 23-30, May 2018.

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116

Authors: Navpreet Kaur Samra, Ramanpreet Kaur

Paper

Title:

A Fuzzy based approach in Wireless Body Area Network for Controlling Congestion

Abstract: With the wireless communication, the ways of communication in present era of

technology has changed which helps in fastest and efficient way of communication in each

and every domain. In the field of medical science, to sense the human body activities such

as heartbeat, blood pressure and other activities performed by internal body parts of the

human, Wireless Sensor Network is employed. Then this sensed data is transmitted to the

centralized server. The information that is collected is made to transfer to the destination

through a dedicated route created by routing protocols in form of data packets. Thus, the

network sometimes faces the issue of congestion due to increased data traffic to the nodes.

The present paper defines an enhanced congestion handling concept for Wireless Body Area

Network. For this purpose, the cost function of the nodes is evaluated on the basis of major

factors such as distance, residual energy and delay. Additionally, by applying the Fuzzy

Inference System, the congestion control model is executed. It also improves the routing

strategy by introducing the firefly algorithm based forward-looking node selection approach.

For evaluation, the proposed work is simulated in MATLAB and compared with the

traditional congestion technique. The simulation results show that the lifetime of the

network increases by 30%. The efficiency of packet received at sink improves by 18%. Path

loss in the present study is 33% less as compared to traditional approach. And, also

consumes near about 8% less energy.

Keywords: Congestion Control, Firefly Algorithm, Forwarder node selection, Fuzzy

Inference System, WBAN.

References: 1. A. Kumar, C. V.Raj, “On designing lightweight QoS routing protocol for delay-sensitive wireless body

area networks”, IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 740-744.

2. J. Anand, D.Sethi, “Comparative analysis of energy efficient routing in WBAN,” IEEE International

Conference on Computational Intelligence & Communication Technology (CICT), 2017, pp. 1-6.

3. M. Roy, C. Chowdhury, N. Aslam, “Designing an energy efficient WBAN routing protocol,” IEEE

International Conference on Communication Systems and Networks (COMSNETS), 2017, pp. 298-305.

4. O. Smail, A.Kerrar, Y. Zetili, B. Cousin, “ESR: Energy aware and stable routing protocol for WBAN

networks,” IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), 2016, pp. 452-457.

5. Rakhee, M. B. Srinivas, “Cluster Based Energy Efficient Routing Protocol Using ANT Colony

Optimization and Breadth First Search,” ELSEVIERin Procedia Computer Science, vol. 89,2016, pp. 124-

133.

6. S.Ahmeda, N.Javaida, S.Yousaf, A.Ahmad, M.M.Sandhu, M.Imran, Z.A.Khand, N.Alrajeh, “Co-LAEEBA:

Cooperative link aware and energy efficient protocol for wireless body area networks,” ELSEVIERin

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7. L. Liang, Y. Ge, G. Feng, W. Ni, A. A. P. Wai, “A low overhead tree-based energy-efficient routing

scheme for multi-hop wireless body area networks,” ELSEVIER in Computer Networks, vol. 70, 2014,pp.

45-58.

8. I. Karthiga, S. Sankar, P. Dhivahar, “A study on routing protocols in wireless body area networks and its

suitability for m-Health applications,”IEEE International conference on Communications and Signal Processing (ICCSP), 2015,pp. 1064–1069.

9. N. K. Samra, R. Kaur, B. P. Kaur, “Congestion Control in WBAN-A Review”,5thInternational conference

oncomputing for sustainable global development,INDIACOM-2018,pp.

10. N. K. Samra, R. Kaur, B. P. Kaur, “A Novel Approach for Energy Efficiencyand Congestion Control in

WBAN” 6th International conference on computing for sustainable global development,INDIACOM-

721-725

2019,pp.

11. H. B. Elhadj, J. Elias, L. Chaari, L.Kamoun, “A Priority based Cross Layer Routing Protocol for healthcare

applications,” ELSEVIER in Ad Hoc. Networks, vol. 42, 2016, pp. 1-18.

12. N. Kaur, S. Singh, “Optimized cost effective and energy efficient routing protocol for wireless body area

networks,” ELSEVIER in Ad Hoc Networks, vol. 61, June 2017, pp. 65-84.

117

Authors: Pardeep Singh Tiwana, Harjot Singh Tiwana, Rajeev Sharma, Astha Gupta

Paper

Title:

An Scrutiny of Run-time Ramification for 5-Proviso Busy Beaver Proving Empirical

Composition

Abstract: The major aim of this paper is to undertake an experimental investigation for

analyze the fluctuation between the descriptional (program-size) and computational time

complexity. The investigation proceeds by systematic and exhaustive study for analysis of

run-time complexity for 5-state Busy Beaver function using an experimental setup. To carry

out experiment, TM simulator for Busy Beaver function will be tested for different N-values

on different machines with different configurations and different platforms to calculate the

run-time complexity. This study revealed that whether the Busy Beaver function is machine

dependent. It also report that the average run-time of Busy Beaver function surely increases

as the number of states.

Keyword:Busy Beaver function, Computational complexity, Program-size complexity.

References: 1. Joost J. Joosten, Fernando Soler-Toscano and Hector Zenil “Program-size versus Time complexity

Slowdown and speed-up phenomena in the micro-cosmos of small Turing machines,” 16 April 2011.

2. Francisco B. Pereira, Penousal Machado, Ernesto Costa, Amílcar Cardoso, “Busy Beaver – An

Evolutionary Approach”

3. Qiang Gao and Xu Xinhe “The analysis and research on computational complexity,” Control and Decision

Conference, The 26th Chinese, IEEE 2014.

4. Claus Diem, “On the complexity of some computational problems in the Turing model,” Preprint,

November 18, 2013.

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Problems in Communication and Computation, Springer, pp. 108–112,1987.

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362, 1 June 2006.

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326, 18 May 2004.

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10. Woods Damien, and Turlough Neary, “The complexity of small universal Turing machines: A survey,”

Theoretical Computer Science 410.4, 2009.

11. Francisco B Pereira, et al. "Graph based crossover–a case study with the busy beaver problem,"

Proceedings of the 1999 Genetic and Evolutionary Computation Conference, 1999.

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the How," Electronic Notes in Theoretical Computer Science 270.1, 2011.

13. Thomas Worsch, "Parallel Turing machines with one head control units and cellular automata," Theoretical

computer science 217.1,1999.

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Problem," ICGA, 1993.

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Transactions on Electronic Computers, vol. EC-15, pp. 802-803, October 1966.

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Science Conference Washington, DC, p. 27, ACM, February 18-20, 1975.

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problem,” Winter, 1998.

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Recreations Dept., Scientific American 251, No. 2, Aug, 1984.

21. Penousal Machado, Francisco B. Pereira, Amílcar Cardoso , Ernesto Costa, “ Busy Beaver – The Influence

726-732

of Representation”.

118

Authors: Chetanjot Kaur, Narwant Singh Grewal

Paper

Title:

Antenna Systems for Base Station Applications

Abstract: Base Station is the primary unit of any mobile communication system. An

antenna is the most important part of the Base Station as it is responsible for exchange of all

the electrical signals and electromagnetic waves radiations. From the last two decades there

is huge advancement in the mobile communications and so as in the antennas for the base

stations. This advancement gives rise to new designs of antennas with different

specifications for different applications. In this paper, we discussed different geometries,

designed earlier for the base station applications. The structures and respective results of

antennas are discussed in the paper.

Keyword: Base Station, Dipole Antenna, Dual Polarization, Patch antennas, Slot antennas.

References: 1. T.W. Chiou and K.L. Wong, “Broad-Band Dual-Polarized Single Microstrip Patch Antenna with High

Isolation and Low Cross Polarization,” IEEE Transactions on Antennas and Propagation, vol.50, no. 3, 2002, pp. 399-401.

2. R. Lian, Z. Wang, Y. Yin, et al., “Design of a Low-Profile Dual-Polarized Stepped Slot Antenna Array for

Base Station,” DOI 10.1109/LAWP.2015.2446193, IEEE Antennas and Wireless Propagation Letters, 2015. 3. H. Huang, Y. Liu and S. Gong, “A Dual-Broadband, Dual-Polarized Base Station Antenna for 2G/3G/4G

Applications,” IEEE Antennas and Wireless Propagation Letters, vol.16, 2017, pp. 1111-1114.

4. Y. He, C. Li and J. Yang, “A Low-Profile Dual-Polarized Stacked Patch Antenna for Micro-Base-Station Applications,” IEEE MTT-S International Wireless Symposium (IWS), 2018

5. K. Moradi1 and S. Nikmehr, “A Dual Band Dual Polarized Microstrip Array Antenna for Base

Stations,”Progress In Electromagnetics Research, , 2012Vol. 123, 527{541. 6. Y.Gao, R. Ma and Y. Wang et.al, “Stacked Patch Antenna with Dual-Polarization and Low Mutual

Coupling for Massive MIMO,” IEEE Transactions on Antennas and Propagation, vol.64, no 10, , 2016. pp.

4544-4549

7. A. A. Serra, P. Nepa, G. Manara, G. Tribellini, and S. Cioci, “A Wide-Band Dual-Polarized Stacked Patch

Antenna,”IEEE Antennas and Wireless Propagation Letters VOL. 6, 2007

8. X. L. Jiang, Z. J. Zhang, Y. Li, and Z. H. Feng, “A wideband dual-polarized slot antenna,” IEEE Antennas Wireless Propagation Letters, vol. 12, Jul. 2013, pp. 1010-1013.

9. R.V.S. Krishna and R. Kumar, “A Dual-Polarized Square Ring Slot Antenna for UWB, Imaging and Radar Applications,” IEEE Antennas Wireless Propagation Letters, vol.15, 2016, pp. 195-198.

10. R.Kumar, R. K. Khokle, and R. V. S. R. Krishna, “A Horizontally Polarized Rectangular Stepped Slot

Antenna for Ultra- Wide Bandwidth with Boresight Radiation Patterns,”IEEE Transactions on Antennas and Propagation, vol. 62, NO. 7, JULY 2014.

11. W. Li, Z. Xia, B. You, Y. Liu and Q. Liu, “Dual-Polarized H-Shaped Printed Slot Antenna,”DOI

10.1109/LAWP.2016.2646805, IEEE Antennas and Wireless Propagation Letters, 2016. 12. Y. Liu and Z. Tu, “Compact Differential Band-Notched Stepped-Slot UWB-MIMO Antenna with

Common-Mode Suppression,” IEEE Antennas Wireless Propagation Letters, DOI

10.1109/LAWP.2016.2592179. 13. Polarized Magneto-Electric Dipole Antenna with Simple Feeds,” IEEE Antennas and Wireless Propagation

Letters, vol.8, pp. 60-63, 2009.

14. X.Gao, H. W. Lai, K. Kan So, et al., “Dual-Polarized Antenna Element for LTE Applications”, IEEE International Workshop on Electromagnetics, Applications and Student Innovation Competition, DOI

10.1109/iWEM.2013.6888800, August 2013.

15. Y. Liu, H. Yi, F. W. Wang, and S. X. Gong, “A novel miniaturized broadband dual-polarized dipole antennas for base station,” IEEE Antennas Wireless Propagation Letters, vol. 12, Oct. 2013. pp. 1335-1338.

16. W. Di, Y. Yingzeng, G.Minjun and S. Renqiang, “Wideband Dipole Antenna for 3G Base Stations,” IEEE

International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications Proceedings, 2005.

17. H.Huang, Y. Liu and S. Gong, “A Broadband Dual-Polarized Base Station Antenna with Anti-Interference

Capability,”IEEE Antennas and Wireless Propagation Letters, vol.16, 2017. 18. H. Huang, Y. Liu and S. Gong, “A Broadband Dual-Polarized Base Station Antenna with Sturdy

Construction,” IEEE Antennas and Wireless Propagation Letters, 2017,vol. 16, pp. 665-668.

19. G. Zhang, L. Sun and B. Sun, “A Wideband Dual-Polarized Antenna Using Planar Quasi-Open-Sleeve Dipoles for Base Station Applications,” DOI 10.1155/2015/164392, International Journal of Antennas and

Propagation, 2015.

20. X. L. Jiang, Z. J. Zhang and Y. Li et.al, ”A low-cost dual-polarized array antenna etched on a single substrate,”

733-736

IEEE Antennas Wireless Propagation Letters, vol.12, Mar. 2013,pp.265-268.

21. Y.Cui, R.Li, and H. Fu, “A Broadband Dual-Polarized Planar Antenna for 2G/3G/LTE Base Stations,”IEEE

Transactions on Antennas and Propagation, vol.62.no.9, September 2014.

22. Y. Cui , L. Wu and R. Li, “Bandwidth Enhancement of a Broadband Dual-Polarized Antenna for 2G/3G/4G and IMT Base Stations,”IEEE Transactions on Antennas and Propagation,vol,66.no.12, December,2018

23. Q. Zhang and Y.Gao, “A Compact Broadband Dual-Polarized Antenna Array for Base Stations,” IEEE

Antennas and Wireless Propagation Letters, vol.17, no. 6, June 2018. 24. H. Zhai, Lei Xi, Y. Zang, and Long Li, “A Low Profile Dual-polarized High Isolation MIMO Antenna

25. Arrays for Wideband Base Station Applications,”IEEE Transactions on Antennas and Propagation, DOI

10.1109/TAP.2017.2776346. 26. H. Zhai, J. Zhang, Y. Zang, Q. Gao and C. Liang, “An LTE Base-Station Magneto-electric DipoleAntenna

with Anti-Interference Characteristics and Its MIMO System Application,”IEEE Antennas and Wireless

Propagation Letters, vol.14, 2015. 27. J.N. Lee, K.C. Lee and P.J. Song, “The Design of a Dual-Polarized Small Base Station Antenna with High

Isolation Having a Metallic Cube,”,”IEEE Transactions on Antennas and Propagation, vol.63, no.2,

February 2015. 28. R. Wu and Q. Chu, “Resonator-Loaded Broadband Antennafor LTE700/GSM850/GSM900 Base

Stations,”,”IEEE Antennas and Wireless Propagation Letters, vol.16, 2017.

29. Y. He, W. Tian and L. Zhang, “A Novel Dual-Broadband Dual-Polarized Electrical Down-tilt Base Station

Antenna for 2G/3G Applications,” IEEE Access, vol.5, June 2017.

30. M. M. Fadoul, T. A. Rahman, and A. Moradikordalivand, “Novel Planar Antenna for Long Term Evolution

(LTE),” International Journal of Information and Electronics Engineering, Vol. 4, No. 1, January 2014. 31. A. Elsherbini, J. Wu and K. Sarabandi, “Dual Polarized Wideband Directional Coupled Sectorial Loop

Antennas for Radar and Mobile Base Stations Applications,”IEEE Transactions on Antennas and

Propagation,DOI 10.1109/TAP.2015.2392773. 32. Y. Cui, X. Gao, H. Z. Fu, Q. Xin Chu, and R. Li, “Broadband Dual-Polarized Dual-Dipole Planar

Antennas,” IEEE Antennas and Propagation Magazine, pp. 77-87, December 2017.

33. . SEbadi, N.Amiri and L. Forooraghi, “ A Low Side lobe level Non-equispaced Microstrip Array Antenna Design for BTS Application Using Genetic Algorithm,”IEEE International Symposium on Microwave,

Antenna, Propagation and EMC Technologies for Wireless Communications Proceedings ,2005.

119.

Authors: Prabhjot Kaur, Praveen Kumar Khosla

Paper

Title:

Recent Automated Glaucoma Detection Techniques using Color Fundus Images

Abstract: One of the areas in which C-DAC, Mohali is actively engaged, is development of

AI powered fundus imaging system providing insight into several severe eye diseases.

Glaucoma, one of the most hazardous ocular disease, continues to affect and burden a large

section of our population. Neuropathy of optic nerve cells is the prime cause of glaucoma and

is the second leading cause of blindness worldwide. It doesn’t manifest itself and is often

termed as the silent thief of eye sight. The damage caused by glaucoma is irreversible.

Therefore, it is imperative to detect glaucoma at an early stage. The medical literature related

to glaucoma indicates that glaucoma detection is a complex process and depends on

combination of several parameters. The conventional methods of hand-crafted feature

extraction are tedious, time consuming and require human intervention. Even though many

such systems have recently shown promising results, but these systems require extensive

feature engineering and have limited representation power owing to varied morphology of the

optic nerve head. Most of the proposed systems have targeted the parameter cup to disc ratio

(CDR) for detection of glaucoma, but that may not be the best approach for building efficient,

robust and accurate automated system for glaucoma diagnosis. This paper advocates the use

of hybrid approach of manual feature crafting with deep learning. It holds promise of

improving the accuracy of glaucoma diagnosis through the automated techniques. It is

further proposed that if diagnosis based on CDR remains inconclusive other methods of

diagnosis should be adopted to come to a certain conclusion.

Keywords: CDR, CNN, Deep Learning NN, Feature Extraction, Glaucoma, Fundus, ISNT

rule, Transfer Learning

References:

737-742

1. Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence of glaucoma

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Ophthalmology, vol. 121, no. 11, pp. 2081–2090, 2014.

2. American Optometric Association, “Glaucoma” https://www.aoa.org/patients-and-public/eye-and-vision-problems/glossary-of-eye-and-vision-conditions/glaucoma, Access Date: 1 May 2019

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Head in Glaucoma”. Optometry and Vision Science, 85(6), E425–E435. 4. 4. Jonas JB, Budde WM, “Diagnosis and Pathogenesis of Glaucomatous Optic Neuropathy: Morphological

Aspects”, Prog Retin Eye Res, 2000;19:1–40

5. 5. American Academy of Opthalmology, “Clinical Evaluation of the Optic Nerve Head”, https://www.aao.org/bcscsnippetdetail.aspx?id=ee28fa7a-e1f7-4495-99a4-7828a800fcd2, Access Date: 3

May 2019

6. 6. Naida Jakirlic ,”Optic Nerve Evaluation in Glaucoma‘, California Optometric Association”, 2016 7. 7. Downs, J. C., Roberts, M. D., & BURGOYNE, C. F. (2008). “Mechanical Environment of the Optic

Nerve Head in Glaucoma”. Optometry and Vision Science, 85(6), E425–E435.

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15. Hana L. Takusagawa, Kaidi Wang, Teresa C. Chen, “The ISNT Rule: How Often Does It Apply to Disc Photos and Retinal Nerve Fiber Layer Measurements in the Normal Population”, Am J Ophthalmol, 2018.

16. 14. W. Ruengkitpinyo ; W. Kongprawechnon ; T. Kondo ; P. Bunnun ; H. Kaneko , “Glaucoma screening

using rim width based on ISNT rule”, IEEE 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

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27. 25. James G Fujimoto, Costas Pitris, Stephen A Boppart, “Optical Coherence Tomography: An Emerging

Technology for Biomedical Imaging and Optical Biopsy”, PMC, 2000. 28. 26. Tehmina Khalil ; Muhammad Usman Akram ; Samina Khalid ; Amina Jameel, “An overview of

automated glaucoma detection”, IEEE 2017 Computing Conference.

29. 27. Michael Abràmoff, Christine N. Kay, Chapter 6 Image Processing, Book: Retina, Elsevier, 2013, doi: 10.1016/B978-1-4557-0737-9.00006-0.

30. 28. Hossein Nazari Khanamiri, Austin Nakatsuka, and Jaafar El-Annan, “Smartphone Fundus Photography”,

PubMed, 2017 31. 29. U. Raghavendra, Sulatha V. Bhandary, Anjan Gudigar, U. Rajendra Acharya, “Novel expert system for

glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images”, Bio

cybernetics and Biomedical Engineering, Volume 38, Issue 1, 2018, pp:170-180 . 32. 30. S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical

Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health

Inform. 21(3), (2017) pp: 803–813.

33. 31. U Rajendra Acharya, Shreya Bat, Joel EW Koh, Sulatha V Bhandary, Hojjat Adeli, A novel algorithm, to

detect glaucoma risk using texton and local configuration pattern features extracted from fundus images, Computers in Biology and Medicine, vol. 88 (2017),pp: 72-83.

34. 32. Julian Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra, “Glaucoma detection using entropy

sampling and ensemble learning for automatic optic cup and disc segmentation”, Computerized Medical

Imaging and Graphics, Elsevier, Volume 55, January 2017, pp: 28-41. 35. 33. Simonthomas, S., N. Thulasi, and P. Asharaf. "Automated diagnosis of glaucoma using Haralick texture

features." In Information Communication and Embedded Systems (ICICES), 2014 International

Conference on, pp. 1-6. IEEE, 2014. 36. 34. Annu, N., and Judith Justin. "Automated classification of glaucoma images by wavelet energy features."

International Journal of Engineering and Technology 5, no. 2 (2013),pp: 1716-1721.

37. 35. Abhishek Pal, Manav Rajiv Moorthy , A. Shahina , “G-EYENET: a convolutional autoencoding classifier framework for the detection of glaucoma from retinal fundus images”, 25th IEEE International Conference

on Image Processing (ICIP), 2018.

38. 36. U Raghavendra, Hamido Fujita, Sulatha V Bhandary, Anjan Gudigar, Jen Hong Tan, U Rajendra Acharya, “Deep Convolution Neural Network for Accurate Diagnosis of Glaucoma Using Digital Fundus

Images”, Computerized Medical Imaging and Graphics, ScienceDirect, Elsevier, Volume 55, January 2017,

pp: 28-41 39. 37. Xiangyu Chen, Yanwu Xu, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu, “Glaucoma Detection

based on Deep Convolutional Neural Network”, 37th Annual International Conference of the IEEE

Engineering in Medicine and Biology Society (EMBC), 2015.

40. 38. Juan J. Gómez-Valverde et al., “Automatic glaucoma classification using color fundus images based on

convolutional neural networks and transfer learning”, Biomedical Optics Express, Vol. 10, No. 2 | 1 Feb

2019. 41. 39. Alan Carlos de Moura Lima, Lucas Bezerra Maia, Roberto Matheus Pinheiro Pereira, Geraldo Braz

J´unior, Joao Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva, “Glaucoma Diagnosis over Eye

Fundus Image through Deep Features”, 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 2018.

42. 40. Annan Li , Yunhong Wang , Jun Cheng , Jiang Liu, “Combining Multiple Deep Features for Glaucoma

Classification”, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 43. 41. Yidong Chai, Hongyan Liu, JieXu, “Glaucoma diagnosis based on both hidden features and domain

knowledge through deep learning models”, Knowledge-Based Systems, ScienceDirect, Volume 161, 1

December 2018, 147-156. 44. 42. D. J Hemanth, J. Anitha, L Hoang Son, M Mittal “Diabetic Retinopathy Diagnosis from Retinal Images

using Modified Hopfield Neural Network”, Journal of Medical Systems, 42(2018),pp:247.

45. 43. M Mittal, A Verma, I Kaur, B Kaur, M Sharma, L M Goyal, S Roy & T-H Kim, “An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis”, IEEE Access, Vol. 7(2019),

pp:33240-33255.

46. 44. Kaur B., Sharma M., Mittal M., Verma A., Goyal L. M., Hemanth D. J, “An improved salient object

detection algorithm combining background and foreground connectivity for brain image analysis”,

Computers and Electrical Engineering, vol. 71(2018), pp:692-703.

47. 45. M. Mittal, L M Goyal, S Kaur, I Kaur, A Verma, D. J Hemanth, “Deep learning based enhanced tumor segmentation approach for MR brain images”, Applied Soft Computing, Vol 78 (2019), pp:346-354.

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using Modified Hopfield Neural Network”, Journal of Medical Systems, 42(2018),pp:247. 49. 43. M Mittal, A Verma, I Kaur, B Kaur, M Sharma, L M Goyal, S Roy & T-H Kim, “An Efficient Edge

Detection Approach to Provide Better Edge Connectivity for Image Analysis”, IEEE Access, Vol. 7(2019),

pp:33240-33255.

Authors: Navneet Kaur, Surbhi Sharma, Jaswinder Kaur

Paper

Title:

Concave Shape Microstrip Patch Antenna using SRR for 5G Applications

120

.

.

Abstract: A single band microstrip-fed patch antenna is presented which contains the

radiating structure having concave shape slots and split ring resonator loaded in the partial

ground plane. This partial ground plane has been used to enhance the bandwidth of proposed

antenna. Both the partial ground plane and radiating patch are perfect electric conductors. The

patch is imprinted on a substrate named as Epoxy Glass FR-4 having thickness 1.6 mm,

relative permittivity 4.4, and loss tangent 0.0024. The designed concave shape microstrip patch

antenna (MPA) is resonate at single frequency band from 3.4-3.8 GHz with 400 MHz

bandwidth and corresponding return loss of -25dB. A parametric study has been performed for

the concave shape slots located in the patch. Proposed MPA is simulated using Computer

Simulation Technology Microwave Studio Version 14.0 (CST MWS V14.0). Furthermore, the

radiation performance of antenna in terms of gain and radiation efficiency has been analyzed .

The proposed antenna is having a peak gain of 3.2 dB and radiation efficiency of 94%.

Keywords: CST MWS V14.0, partial ground plane, fifth generation, microstrip patch

antenna, SRR.

References: 1. A. Osseiran et al., "Scenarios for 5G mobile and wireless communications: the vision of the METIS project,"

in IEEE Communications Magazine, vol. 52, no. 5, May 2014, pp. 26-35. 2. Nokia white paper. 2017. 5G deployment below 6 GHZ. https://resources.ext.nokia.com/asset/201315.(accessed

August 2017). 3. O. M. Haraz, A. Elboushi, S. A. Alshebeili and A. Sebak, "Dense Dielectric Patch Array Antenna With Improved

Radiation Characteristics Using EBG Ground Structure and Dielectric Superstrate for Future 5G Cellular

Networks," in IEEE Access, vol. 2,2014, pp.909-913. 4. K. Mak, K. So, H. Lai and K. Luk, "A Magnetoelectric Dipole Leaky-Wave Antenna for Millimeter-Wave

Application," in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12,Dec.2017, pp. 6395-6402.

5. K. M. Mak, H. W. Lai, K. M. Luk and C. H. Chan, "Circularly Polarized PatchAntenna for Future 5G Mobile Phones," in IEEE Access, vol. 2, pp. 1521-1529, 2014.

6. Alieldin, Ahmed, Yi Huang, Stephen J. Boyes, Manoj Stanley, Sumin David Joseph, Qiang Hua, and Dajun Lei.

"A Triple-Band Dual-Polarized Indoor Base Station Antenna for 2G, 3G, 4G and Sub-6 GHz 5G Applications." IEEE Access 6 (2018): 49209-49216.

7. Sarkar, Debdeep, and Kumar Vaibhav Srivastava. "Four Element Dual-band Sub-6 GHz 5G MIMO Antenna

Using SRR-loaded Slot-Loops." In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical,

Electronics and Computer Engineering (UPCON), pp. 1-5. IEEE, 2018.

8. Vaidya, Avinash R., Rajiv K. Gupta, Sanjeev K. Mishra, and Jayanta Mukherjee. "Right-hand/left-hand

circularly polarized high-gain antennas using partially reflective surfaces." IEEE antennas and wireless propagation letters, vol. 13, 2014, pp. 431-434.

9. Pandit, Soumen, Akhilesh Mohan, and Priyadip Ray. "A low-profile high-gain substrate-integrated waveguide-

slot antenna with suppressed cross polarization using metamaterial." IEEE Antennas and Wireless Propagation Letters,vol. 16,2017, pp.1614-1617.

10. Singh, Amit K., Mahesh P. Abegaonkar, and Shiban K. Koul. "High-gain and high-aperture-efficiency cavity

resonator antenna using metamaterial superstrate." IEEE Antennas and Wireless Propagation Letters , vol.16, 2017, pp. 2388-2391.

11. Balanis CA. Antenna Theory: Analysis and Design. Third Edition. Wiley Interscience. 2005.

743-746

Authors: Dr. Monika Gupta Vashisht, Ms. Ashima Kalra, Dr. Bhawna

Paper

Title:

Determining Factors Influencing Faculty Feedback Using Data Mining Technique

Abstract: Faculty is a major stakeholder in an education institute. Quality of faculty reflects

the quality of youth, the future nation builders. Based on the vision of honorable management,

an attempt has been made to gather the feedback of faculty reflecting their current state of

work. Data have been collected initially via open-ended questionnaire. Faculties serving

renowned institutes in the states of Punjab and Haryana have been approached to gather

information. Attributes have been identified from the literature review and using an appropriate

measurement scale. Only the willing respondents and their responses have been taken into

consideration. The responses gathered using 5-point Likert-Scale was then factor analyzed.

The attributes converged mainly on six dimensions- recognition, sense of belongingness,

working environment, basic need fulfillment, self-respect, and contribution to society. These

dimensions have been discussed keeping in view the current scenario in the education sector.

121.

.

Keywords: Faculty, Higher Education, Factor Analysis and Satisfaction

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122.

Authors: Dr. Sunayna Khurana, Dipti Jain

Paper

Title:

Applying and Extending UTAUT2 Model of Adoption of New Technology in the context of

M-Shopping Fashion Apps

Abstract: The drive of this work is to recognize the factors that affect the adoption of m-

shopping fashion apps from the consumer perspective in Delhi NCR by extending UTAUT2

model with Post-purchase behavior with the aim to find out the consumers experience and

satisfaction level after adopting new technology (in this case mobile shopping application for

fashion).The variables identified including new variable, i.e. perceived risk and post-purchase

behaviour, were tested using structural equational modelling. Data collection was done using

the structured online survey on the sample of 557 mobile app users in Delhi-NCR on the young

Indian mobile users of age bracket (18-25) years. The outcomes of the work revealed that

except effort expectancy and social influence, all the remaining factors used in the proposed

model significantly influence the formation of behavioral intention of young mobile users to

embrace mobile based fashion shopping apps. Also, the results of the work revealed that

consumers actual purchase significantly affect their post-purchase behaviour. Hence, validates

the proposed model extended till post adoption. This is the first work in Delhi NCR on the

specific mobile shopping application category particularly fashion apps using extended

UTAUT2 model.

Keywords: Mobile Fashion Applications, Mobile Shopping, Utaut2, Technology Adoption,

India

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752-759

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Authors: Gurmeet Singh and Manish Mahajan

Paper

Title:

A Green Computing Supportive Allocation Scheme Utilizing Genetic Algorithm and

Support Vector Machine

Abstract: Green Computing leads to energy-aware computation. When a Physical Machine

gets a job from user, it intends to complete it at any cost. Virtual Machine (VM) helps to attain

maximum completion ratio. The Host to VM ratio increases with the increase in the workload

over the system. The allocation policy of VM has ambiguities with leads to an overloaded

Physical Machine (PM). This paper aims to reduce the overhead of the PMs. For the allocation,

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Modified Best Fit Decreasing (MBFD) algorithm is used to check the resources availability.

For the allocation, Modified Best Fit Decreasing (MBFD) algorithm is used to check the

resources availability. Genetic Algorithm (GA) has been used to optimize the MBFD

performance by fitness function. For the cross-validation Polynomial Support Vector Machine

(P-SVM) is used. It has been utilized for training and classification and accordingly,

parameters, viz. (Service Level Agreement) SLA and Job Completion Ratio (JCR) are

evaluated. A comparative analysis has been drawn in this article to depict the

research work effectiveness and an improvement of 70% is perceived.

Keywords: Green Computing, VM Allocation, MBFD, GA, SLAV, JCR

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Authors: Anjana Sharma, Amarjeet Kaur, Shikha Saxena and Prashant Singh

Paper

Title:

A Novel Signal Scrambling Technique for PAPR Reduction in OFDM Systems

Abstract: OFDM forms the basis of the upcoming next generation technologies so as to

achieve higher data rates within a given bandwidth effectively. One of the major issues

associated with OFDM is Peak to Average Power Ratio (PAPR) which needs to be minimized

to get an efficient performance. The random variation in the signal amplitude of the OFDM

signal leads to additional interference in the system and hence affecting the performance of

HPA in non-linear region. In this paper, we propose a technique for the reduction of PAPR in

OFDM systems with some increased complexity which works for any modulation type and any

number of subcarriers. The simulation results show performance improvement with respect to

the existing signal scrambling techniques.

Keywords: Orthogonal frequency division multiplexing (OFDM), Peak to Average Power

Ratio (PAPR), High Power Amplifier (HPA), Complimentary Cumulative Distribution

Function (CCDF).

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Jenerio: Cultura Médica, 2006, 1–12.

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10. Peter E. Libre, MD, Norwalk, Conn. “Revisiting the Importance of Disc Size, Review of Ophthalmology”, 2004.

11. Spaeth GL, Lopes JF, Junk AK, et al., “Systems for staging the amount of optic nerve damage in glaucoma: A critical review and new material”, Surv Ophthalmol, 2006; 51:293–315.

12. Fingeret M, Medeiros FA, Susanna Jr R, Weinreb RN, “Five rules to evaluate the optic disc and retinal nerve fiber

layer for glaucoma”, Optometry, 2005;76:661–8. 13. Linda Yi-Chieh Poon, David Solá-Del Valle, Angela V. Turalba,

Iryna A. Falkenstein, Michael Horsley, Julie H. Kim, Brian J. Song,

Hana L. Takusagawa, Kaidi Wang, Teresa C. Chen, “The ISNT Rule: How Often Does It Apply to Disc Photos and Retinal Nerve Fiber Layer Measurements in the Normal Population”, Am J Ophthalmol, 2018.

14. W. Ruengkitpinyo ; W. Kongprawechnon ; T. Kondo ; P. Bunnun ; H. Kaneko , “Glaucoma screening using rim

width based on ISNT rule”, IEEE 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

15. Andrew J. Tatham; Robert N. Weinreb; Linda M. Zangwill; Jeffrey M. Liebmann; Christopher A. Girkin; Felipe

A. Medeiros,”The Relationship Between Cup-to-Disc Ratio and Estimated Number of Retinal Ganglion Cells”, Investigative ophthalmology and visual science, 2013.

16. Jonas JB, Gusek GC, Naumann GO, “Optic disc, cup and neuroretinal rim size, configuration and correlations in

normal eyes”, Invest Ophthalmol VisSci, 1988;29:1151–8. 17. Hayreh SS, “Ischemic Optic Neuropathies”, Berlin Heidelberg: Springer-Verlag, 2011.

18. Bourne RR, “The optic nerve head in glaucoma”, Community Eye Health, 2006;19:44–5.

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125.

Authors: Deepti Sharma, Deepshikha Aggarwal, Disha Grover

Paper

Title:

Academic Performance Analysis of Information Technology Students in Higher Education

Institutions

Abstract: This research is conducted to analyse the factors that may affect the academic

performance of students in the MCA (Masters in Computer Application) course in Delhi, India.

MCA is a three year post graduate programme in Computer Application. This work will help to

better understand the factors that commonly affect the performance of students in academics

and also will contribute to the pedagogy development of educational institutes. The seven

factors that have been considered for the analysis are the Faculty, time management, interest of

students, placements, difficulty of course, sources of study and extra efforts by students. The

hypothesis has been developed to establish the relationship between the independent variables

which are the seven factors and the dependent variable which is the academic performance.

The research is conducted on the data collected from the students through questionnaire and we

have chosen to use convenience sampling to conduct this study. The data thus collected is

tested using the multiple linear regression model as multiple factors have been considered. The

result of the analysis indicate that the Faculty, time management, interest of students,

placements, difficulty of course, sources of study and extra efforts by students have a positive

effect on the academic performance of the students. Measuring the academic performance of

the student is a difficult task as it cannot be measured quantitatively. In most of the cases the

student performances are also affected by various environmental, socio-economic and

psychological factors. These factors also need to be considered while assessing the academic

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performance of the students. Still the factors incorporated in this study give a considerable

result regarding their influence on the academic performance of the students. The results of the

research can be useful for various academic institutions to formulate the educational pedagogy

that can accommodate the most influential factors for better academic performance of the

students.

Keywords: Data analysis, Regression, Student performance Analysis, Higher education,

Technical education, Data Science

References: 1. S. N. K. Irfan Mushtaq, “Factors Affecting Students’ Academic,” Global Journal of Management and Business

Research, vol. 12, no. 9, June 2012 pp. 16-22

2. N S. Nisha Arora, “Factors Affecting the Academic Performance of College Students,” i-manager’s Journal of Educational Technology, 2017 pp. 47-53.

3. G M. Hiluf Reda, “Investigating the Causes of Students’ Less Academic Performance in Engineering College

of Debre Berhan University,” American Journal of Theoretical and Applied Statistics, vol. 7, no. 3, 2018 pp. 126-131.

4. 4. S. R. N. Syed Tahir Hijazi, “Factors Affecting Students’ Performance,” Bangladesh e-Journal of Sociology,

vol. 3, no. 1, 2006. 5. S. N. K. Irfan Mushtaq, “Factors Affecting Students’ Academic,” Global Journal of Management and Business

Research, vol. 12, no. 9, 2012.

6. S. K. Kochhar, Educational and Vocational Guidance in Secondary Schools, New Delhi: Sterling Publishers Private Limited, 2000.

7. A. AL-Mutairi, Factors affecting business students’ performance in Arab Open University: The case of Kuwait, vol. 6, 2011, pp. 146-155.

8. A H. Ch., “Effect of Guidance Services on Study Attitudes, Study Habits and Academic Achievement of

Secondary School Students,” Bulletin of Education & Research , vol. 28, no. 1, 2006, pp. 35-54. 9. C. M. J. S. P. H. a. H. M. Javed Hussain, “Ethnic minority graduate entrepreneurs in the UK,” vol. 21, no. 6,

2007, pp. 455-463.

10. J. F. K. J. M. M. S. G. R. Eric A. Hanushek, “Does Peer Ability Affect Student Achievement,” Journal of Applied Econometrics, vol. 18 , no. 5, 2003, pp. 527-544.

11. S. D. S.P Rao, “Peer instruction improves performance on quizzes,” Advances in Physiology Education, vol.

24, no. 1, 2000, pp. 51-55.

12. D karemera, “The Effects of academic environment and background characteristics on students' satisfaction and

performance: The Case of South Carolina State University's School of Business,” College Student Journal, vol. 37, no. 2, 2003, pp. 298-311.

126.

Authors: Deepak Dhadwal, Vinay Bhatia, PN Hrisheekesha

Paper

Title:

Method & Implementation of Fault Detection & Prevention Attack in WSN

Abstract: In WSNs, a major problem is the assaults on nodes or more sinks. In any case, this

information rate is obliged by the accessible vitality at every hub just as connection limit. After

sending, some sensor hubs may obstruct the measure of information that land at a sink due to

their low vitality reaping rate. In this work, the fundamental objective is to recognize and

detect black hole attack in WSN. These assaults may decrease the exhibition of framework. In

this work, it gives deficiency dealing with in system and can improve execution. Likewise

stream can improve by utilization of advancement calculation in the system. The proposed

framework improves vitality just as stream of framework. All recreations are done in

simulation tool. The proposed system is executed with MATLAB. The work has increased the

maximum flow of information to 30% with increase in degree of nodes

Keywords: Routing in WSN ,Max Flow,Tabu search,Routing in WSN

References: 1. D. Linden and T. B. Reddy, 2002,Handbook of Batteries. McGraw-Hill Professional: New York.

2. S. Davis, 2004, Basics of Design: Battery Power Management. Supplement to Electronic Design.

3. Y. Cheng, D.P. Agrawal, 2006, “An improved key distribution mechanism for large-scale hierarchical

wireless sensor networks”, Elsevier Ad Hoc Networks, pp.35-48. 4. Wenqing Cheng, Zhiqiang Xiong, Wei Liu, 2006, “Hybrid Solution: A FEC Algorithm for Fault Tolerant

Routing in Sensor Networks”, IEEE International Conference on Communications and Networks, China,

pp.0463-0467. 5. Zhiqiang Xiong, Zongkai Yang, Wei Liu, Zhen Feng, 2006, “A Lightweight FEC Algorithm for Fault Tolerant

Routing in Wireless Sensor Networks”, IEEE International Conference on Wireless Communications,

Networking and Mobile Computing, pp 1-4. 6. S. Ozdemira, Y. Xiao, 2008, “Secure data aggregation in wireless sensor networks: A comprehensive

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overview”, Elsevier Computer Networks, pp. 202-2037.

7. L.T Nguyen, X.Defago, R.Beuran, Y.Shinoda, 2008, “An Energy Efficient Routing Scheme for Mobile

Wireless Sensor Networks”, IEEE International Symposium on Wireless Communication Systems, pp. 568-

572. 8. W.H Liao, H-H Wang, 2008, “An asynchronous MAC protocol for wireless sensor networks”, Elsevier

Journal of Network and Computer Applications, pp. 807–820.

9. J. Zhua, K.L Hunga, B. Bensaoua, F.N Abdesselam, 2008, “Rate-lifetime tradeoff for reliable communication in wireless sensor networks”, Elsevier Computer Networks, pp. 25-43.

10. Lan Tien Nguyen, Xavier Defago, 2008, “An Energy Efficient Routing Scheme for Mobile Wireless Sensor

Networks”, IEEE International Symposium on Wireless Communications Systems, pp.568-572. 11. Tsai-Wei Wu and Hung-Yun Hsieh, 2008, “Interworking Wireless Mesh Networks: Performance

Characterization and Perspectives”, IEEE Global Telecommunications Conference, pp.4846-4851.

12. Zhang Lili, Wang Huibin, Xu Lizhong, 2009, “Fault Tolerance and Transmission Delay In Wireless Mesh Networks”, IEEE International Conference on Networks Security, Wireless Communications and Trusted

Computing, pp. 193-196.

13. Chuang Wang, Taiming Feng, Jinsook Kim, 2009, “Catching Packet Droppers and Modifiers in Wireless Sensor Networks”, IEEE Social Conference on Sensors, Mesh and Ad hoc Communication and

Networks,pp.2908-2916.

14. Anna Abbagnale, Emanuele Cipollone, 2009, “A case study for evaluating IEEE 802.15.4 wireless sensor

network formation with mobile sinks”, IEEE International Conference on Communications, pp. 3435-3439.

15. Che-Aron, Z., Al-Khateeb, 2010, “An Enhancement of Fault-Tolerant Routing Protocol for Wireless Sensor

Network”, International Conference on Computer and CommunicatioEngineering (ICCCE), pp.6235-6240. 16. Dario Bruneo and Marco Scarpa, 2010, “Adaptive Swarm Intelligence Routing Algorithms for WSN in a

Changing Environment”, IEEE Sensors Conference, pp.1813-1818.

17. Preetam Ghosh, Michael Mayo, Vijender Chaitankar, 2011, “Principles of Genomic Robustness Inspire Fault-Tolerant WSN Topologies: a Network Science Based Case Study”, Seventh IEEE International Workshop on

Sensor Networks and Systems for Pervasive Computing, pp.160-165.

18. Z Jun, C. Xiang-guang, 2011, “The application of multi-path fault tolerant algorithm in WSN nodes”, IEEE International Conferences on Artificial Intelligence, Management Science & Electronic Commerce, pp. 7323-

7326.

19. L. Karim, N. Nasser, 2012, “Reliable location-aware routing protocol for mobile wireless sensor network”, IET Communication, Vol. 6, Iss. 14, pp. 2149–2158.

20. K.Akkaya, I. F. Senturk, S.Vemulapalli, 2013, “Handling large-scale node failures in mobile sensor/robot

networks”, Elsevier Journal of Network and Computer Applications, pp.195-210.

127

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Authors: Sanjive Tyagi, Rakesh Kumar Dwivedi, Ashendra Kumar Saxena

Paper

Title:

High Capacity Steganography Protected using Shamir’s threshold scheme and

Permutation Framework

Abstract: This paper presents a framework that conceal large volume of secret information

using distributed file system that permit implantation of decomposed secret images across

multiple-cover images. The strong security is imposed utilizing Shamir’s threshold scheme and

permutation generator framework. Three layers of security is being applied to protect the secret

information, in first, secret image is decomposed into equal size of smaller sub-images and

generate a framework of permutations from an integer for distributing and reassembling the

circulated broken secret sub images among the intended participants. At the time of embedding

purpose of permutation generator is to arrange the sub-images in unknown order for outsider.

During the discloser stage only inverse of permutation can rearrange the distributed sub-images

to reassemble into original image by authorized contributors. In second, Shamir’s threshold

scheme is designed for authentication of shared associated stego-cover images before starting

the extraction process. This process provides an extremely secured construction of shared

secret information. In third, image is divided in 2x2 blocks of pixels and traverses it in zig-zag

manner; the pixel value difference is computed for all Red, Green, and Blue (RGB)

components between non-overlapping pixels of selected diagonal path with in targeted block.

Secret bits are concealed inside RGB color pixels of cover image by utilizing proposed novel

pixel-value differencing (PVD) scheme, furthermore varying embedding capacity may be

obtained by controlling the selection of number of 2x2 block. Exploratory result displays that

the proposed approach provides productive algorithms in term multilayer unbreakable security

and higher payload of embedded information.

Keywords: Secret sharing, Distributed steganography, Pixel value differencing,

Cryptography.

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References: 1. S. Tyagi, R. K. Dwivedi, and A. K. Saxena, "A Novel PDF Steganography Optimized Using Segmentation

Technique," International Journal of Information Technology, https://doi.org/10.1007/s41870-019-00309-7,

Springer, 2019, pp 1-9. 2. D.-C. Wu and W.-H. Tsai, “A steganographic method for images by pixel-value differencing,” Pattern

Recognition Letters, vol. 24(9-10),2003, pp. 1613–1626.

3. C.-C. Chang and R.-J. Hwang, “A New Scheme to Protect Confidential Images,” Journal of Interconnection Networks, Vol. 5, no. 3, 2004, pp. 221-232.

4. C. H. Yang and C. Y. Weng, “A steganographic method for digital images by multi-pixel differencing,” in

Proceedings of International Computer Symposium Taipei, Taiwan, 2006, pp. 831–836,. 5. K.-H. Jung, K.-J. Ha, and K.-Y. Yoo, “Image data hiding method based on multi-pixel differencing and LSB

substitution methods,” in Proceedings of International Conference on Convergence and Hybrid Information

Technology (ICHIT 08), 2008, pp. 355–358. 6. J.-C. Liu and M.-H. Shih, “Generalizations of pixel-value differencing steganography for data hiding in images,”

Fundament- Informaticae, vol. 83, no. 3, 2008, pp. 319–335.

7. X. Liao, Q.-Y. Wen, and J. Zhang, “A Steganographic Method for Digital Images with Four-Pixel Differencing and Modified LSB Substitution,” Journal of Visual Communication and Image Representation, vol. 22, no. 1,

2011, pp. 1–8.

8. C.-H. Yang, C.-Y. Weng, H.-K. Tso, and S.-J. Wang, “A Data Hiding Scheme using the Varieties of Pixel-Value Differencing in Multimedia Images,” Journal of Systems and Software, vol. 84, no. 4, 2011, pp. 669–678.

9. K.-C. Chang, P.S. Huang, T.-M. Tu, and C.-P. Chang “Adaptive Image Steganographic Scheme Based on Tri-

Way Pixel-Value Differencing,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 07), 2007, pp. 1165–1170.

10. X. Liao, Q.Y. Wen, S. Shi, “Distributed Steganography,” in Proceedings of Seventh International Conference on

Intelligent Information Hiding and Multimedia Signal Processing, 2011, pp. 153-156. 11. Fendi, A. Wibisurya, and Faisal, “Distributed Steganography using Five Pixel Pair Differencing and Modulus

Function,” in Proceedings of International Conference on Computer Science and Computational Intelligence,

ICCSCI, Bali, Indonesia,116 (2017), 2017, pp. 334–341. 12. A.K. Gulve, M.S. Joshi, “An Image Steganography Algorithm with Five Pixel Pair Differencing and Gray Code

Conversion,” International Journal of Image, Graphics and Signal Processing, Vol. 6(3), 2014, pp. 12-20.

13. C.-C. Thien, J.-C. Lin, “Secret Image Sharing,” Computers & Graphics, Vol. (26), 2002, pp. 765-770,. 14. R. Koikara, D.J. Deka, M. Gogoi, and R. Das, “A Novel Distributed Image Steganography Method Based on

Block-DCT,” Advanced Computer and Communication Engineering Technology, Springer, 2015 pp. 423-435.

15. S. Hemalatha, U. D. Acharya, A. Renuka, and P.R. Kamath, “A Secure Image Steganography Technique to Hide Multiple Secret Images”, in Proceedings of the Fourth International Conference on Networks and

Communications, NetCom, Vol. 131, Springer, 2012, pp. 613-620, .

16. S. Tyagi, A. K. Saxena, and S. Garg, “Secured High Capacity Steganography using Distribution Technique with Validity and Reliability”, in Proceedings of International Conference on System Modeling & Advancement in

Research Trends, Moradabad, India, 2016, pp.109 –114.

17. V. Kumar, A. Bansal, and S. K. Muttoo, “Data Hiding Method Based on Inter-Block Difference in Eight Queens Solutions and LSB Substitution”, International Journal of Information Security and Privacy, (IGI Global), Vol.

8(2), 2014, pp. 55-68.

18. A. Bansal, S. K. Muttoo, and V. Kumar, “Data Hiding Approach Based on Eight-Queens Problem and Pixel Mapping Method”, International Journal of Signal Processing, Image Processing and Pattern Recognition,

Vol.7(5), 2014, pp.47-58.

19. M. Deshmukh, N. Nain, and M. Ahmed, "A Novel Approach for Sharing Multiple Color Images by Employing Chinese Remainder Theorem", Journal of Visual Communication and Image Representation, Vol. 49, 2017, pp.

291-302.

20. K. Joshi , S. Gill, and R. K. Yadav, “A New Method of Image Steganography Using 7th Bit of a Pixel as Indicator by Introducing the Successive Temporary Pixel in the Gray Scale Image”, Journal of Computer Networks and

Communications, Hindawi, 2018, pp. 10. 21. A. Bakshi, A. K. Patel, “Secure Telemedicine using RONI Half Toned Visual Cryptography without Pixel

Expansion”, Journal of Information Security and Applications, 46 (2019), pp. 281–295.

22. A. Kanso, M. Ghebleh, An efficient lossless secret sharing scheme for medical images,” Journal of Visual

Communication and Image Representation, Elsevier, Vol. 56, 2018, pp. 245-255.

23. X. Wua, C.-N. Yang, “A Combination of Color-Black-and-White Visual Cryptography and Polynomial Based

Secret Image Sharing,” Journal of Visual Communication and Image Representation, Elsevier, Vol. 61, 2019, pp.74-84.

24. A. A. Al-Sadi, E.S.M. El-Alfy , “An Adaptive Steganographic Method for Color Images Based on LSB

Substitution and Pixel Value Differencing,” in Proceedings of (ACC 2011) Communications in Computer and Information Science, Vol . (191), Springer.

25. https://en.wikipedia.org/wiki/Permutation

26. S. Tyagi, R. K. Dwivedi, and A. K. Saxena, "A High Capacity PDF Text Steganography Technique Based on Hashing Using Quadratic Probing", International Journal of Intelligent Engineering and Systems , Vol.12, No.3,

2019, pp. 192-202.

128.

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Authors: Dr. Dakka.Obulesu, Dr. Arunkumar G., Dr. B.P. Mishra, Spoorthi J, Dr. T.CManjunath

Paper

Title:

Recent advances in the design & development of a drone used for bio-medical engineering

applications : Medi-Sky

Abstract: The primary target or the motivation behind this paper is to build up a model of the

automaton rescue vehicle to help the ambulances in sparing human lives by sending the drugs to

the working environment where the influenced patients are available. A huge number of

individuals bite the dust in light of rescue vehicle delays. The time taken by emergency vehicle

to achieve a patient depends a great deal on the course and the traffic on the way. At the point

when a medicinal crisis happens, the reaction time can have a significant effect between a real

existence spared and an actual existence lost. Shockingly, ambulances can stall out in rush hour

gridlock and arrive of late after the crisis call has been made, in which an unfortunate casualty

may have endured a great deal of wounds and can even lose his/her life. The utilization of

Unmanned Aerial Vehicles (UAV) or "Automatons" has been utilized for quite a while for a

wide range of uses. The motivation behind this paper is to build up a model of automaton

emergency vehicle to help the ambulances in sparing human lives. The emergency vehicle

automaton enters the scene at the moment time and constant directions are given by the

administrator. The automaton can gauge different constant wellbeing parameters of the patient,

for example, temperature, pulse and heartbeat. The estimations of these basic parameters are then

transmitted to the specialists present in a rescue vehicle. Well, the idea is to implement the same

in the rural areas where penetration of healthcare is poor. The system is being designed to look

after the infants and aged people in the fast-moving urban lives. The work developed in this

paper along with the results shown depicts the effectivity of the methodology proposed.

Keywords: Drone, Application, Medicine, Hardware, Software, Arduino, UAV.

References:

1. Vangara Vamsi Krishna, Shivang Shastri, Shubhra Kulshrestha, Mrs. A. Mariajossy, “Design of Drone Ambulance”,

International Journal of Pure and Applied Mathematics, Volume 119 No. 15 2018, 1813-1818, ISSN: 1314-3395 (on-

line version), url: http://www.acadpubl.eu/hub/ Special Issue. 2. Josefin Lennartsson, “Strategic Placement of Ambulance Drones for Delivering Defibrillators to out of Hospital

Cardiac Arrest Victims”, KTH, School of Architecture and the Built Environment (ABE), Urban Planning and

Environment, Geoinformatic, 2015. 3. Josephin Arockia Dhivya, Dr. J. Premkumar, “Quadcopter based technology for an emergency healthcare,” 2017 3rd

International Conference on Biosignals, images and instrumentation (ICBSII), 16-18 March 2017.

4. Tan Han Rong, Ronald, “Collaborative UAV study ,” National University of Singapore, 2009. 5. Farin, N. Sharif, S. and Mobin, I, “An Intelligent Sensor Based System for Real Time Heart Rate Monitoring”,

(HRM), Intelligent Control and Automation, 7, 55-62,May 2016.

6. www.radio-electronics.com 7. www.ni.com

8. www.wikipedia.org

9. Trio Adiono; Renitia Murti Rahayu, “Zigbee baseband hardware modeling for Internet of Things IEEE 802.15.4 compliance”, 6th International Conference on Electrical Engineering and Informatics (ICEEI), 2017.

10. http://pharpoint.com/wp-content/uploads/2013/06/Ambulance-Drones.pdf

11. https://scholarspace.manoa.hawaii.edu/bitstream/10125/41557/paper0408.pdf 12. https://acadpubl.eu/hub/2018-119-15/4/794.pdf

13. http://www.goelectromech.in/doc/Ambulance%20Drone%20Support%20System%20(ADSS).pdf

796-800

Authors: Satvik M. Kusagur, Dr. Arun Kumar G., Spoorthi Jainar, Dr. T.C.Manjunath, Pavithra

G.

Paper

Title:

4-point minimal pick & place trajectory design in robotics

Abstract: Motion planning in robotics plays a very important role in the movement of objects

from the source to the destination. Robots are classified according to motion control as PNP,

PTP and CP robots. Hence, there are 3 basic types of trajectory motions + 1 trajectory which is

the shortest path between two points in the 3D space. This type of motion or trajectory is

exhibited by PNP robots. In this paper, we develop the theoretical background along with the

mathematical formulation relating to the design & development of a 4-point minimal pick &

place trajectory from the source to the destination during the transportation of an object in the 3

129.

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dimensional Euclidean space R3.

Keywords: Robot, Motion planning, Trajectory, Source, Destination, 4-Point, Pick, Lift-off,

Set-down, Place, Obstacle.

References: 1. Craig J, “Introduction to Robotics : Mechanics”, Dynamics & Control, Addison Wessely, USA, 1986. 2. Robert J.S., “Fundamentals of Robotics - Analysis and Control”, PHI, New Delhi.

3. Klafter, Thomas and Negin, “Robotic Engineering”, PHI, New Delhi.

4. Fu, Gonzalez and Lee, “Robotics: Control, Sensing, Vision and Intelligence”, McGraw Hill, Singapore. 5. Groover, Weiss, Nagel and Odrey, “Industrial Robotics”, McGraw Hill.

6. Ranky P.G., C.Y. Ho, “Robot Modeling, Control & Applications”, IFS Publishers, Springer, UK.

7. Crane, Joseph Duffy, “Kinematic Analysis of Robotic Manipulators”, Cambridge Press, UK. 8. Manjunath, T.C., “Fundamentals of Robotics”, Fifth edn., Nandu Publishers, Mumbai, India, 2007.

9. Ranky P G and C Y Ho, Robot Modeling, control and applications, IFS publishers, Springer, UK.

10. Asada, H., and J.E. Slotine, Robot Dynamics & Control, Wiley, NY. 11. Phillip Coiffette, Robotics Series (Vol. I to VIII ), Kogan Page, London.

12. Mohsen Shahinpoor, “Robotic Engineering Text Book”, Harper & Row Publishers.

13. Janakiraman, “Robotics and Image Processing”, Tata McGraw Hill . 14. Richard A Paul, “Robotic Manipulators”, MIT press, Cambridge.

15. Fairhunt, “Computer Vision for Robotic Systems”, CFS Pubs., New Delhi.

16. Yoram Koren, “Robotics for Engineer”, McGraw Hill. 17. Bernard Hodges, “Industrial Robotics”, Jaico Publishing House, India.

18. Tsuneo Yoshikawa, “Foundations of Robotics : Analysis and Control”, PHI, India.

19. Dr. Jain and Dr. Aggarwal, “Robotics : Principles & Practice”, Khanna Publishers, Delhi. 20. Lorenzo and Siciliano, “Modeling and Control of Robotic Manipulators”, McGraw Hill.

21. Dr. Amitabha Bhattacharya, “Mechanotronics of Robotics Systems”, Kaizen Publishing, Calculatta, India.

22. S.R. Deb, “Industrial Robotics”, Tata MacGraw Hill.. 23. Edward Kafrissen and Mark Stephans, “Industrial Robots and Robotics”, Prentice Hall Inc. , Virginia, USA.

24. Rex Miller, “Fundamentals of Industrial Robots and Robotics”, PWS Kent Pub Co., Boston, USA.

25. William Burns and Janet Evans, (2000), Practical Robotics - Systems, Interfacing, Applications, Reston Publishing Co.

26. http://www.wikipedia.org

27. Michael Dipperstein, Run Length Encoding (RLE) Discussion and Implementation. 28. Flusser, J.; Suk, T.; Saic, S., Recognition of blurred images by the method of moments, Image Processing, IEEE

Transactions.

29. Bob Bailey, Moments in Image Processing, Nov. 2002. 30. Phillip Coiffette, (1995), Robotics Series, Volume I to VIII, Kogan Page, London, UK.

31. Yoshikawa T., (1984), “Analysis and Control of Robot Manipulators with Redundancy”, Proc. First Int. Symp.

on Robotics Research, Cambridge, MIT Press, pp. 735-748. 32. Whitney DE., (1972), “The Mathematics of Coordinated Control of Prosthetic Arms and Manipulators”,

Trans. ASM J. Dynamic Systems, Measurements and Control, Vol. 122, pp. 303-309.

33. Lovass Nagy V, R.J. Schilling, (1987), “Control of Kinematically Redundant Robots Using {1}-inverses”, IEEE Trans. Syst. Man, Cybernetics, Vol. SMC-17 (No. 4), pp. 644-649.

34. Lovass Nagy V., R J Miller and D L Powers, (1978), “An Introduction to the Application of the Simplest Matrix-

Generalized Inverse in Systems Science”, IEEE Trans. Circuits and Systems, Vol. CAS-25 (No. 9), pp. 776. 35. Dr. Manjunath, “Modelling & Control of Smart Structures”, Ph.D. Thesis, IIT Bombay, 2007.

36. Dr. Arunkumar, Ph.D. Thesis, 2016.

801-806

Authors: Simran Uppal, Harpreet Kaur, Parul Sharma

Paper

Title:

High capacity reversible data hiding technique framed for experimentation with PSO

Abstract: In this paper we have studied the reversible data hiding techniques. We have seen

previous techniques and also carefully analyzed the high capacity reversible data hiding

method in MSB. We have proposed a model with particle swarm optimization which will work

in future with HCRDH to ensure improvement in several parameters like precision and PSNR.

We want to contribute towards a proposed method before actual experimentation and results

are worked for.

Keywords: High Capacity Reversible data hiding, Most Significant Bit, Reversible data

hiding.

References:

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1. Trappe, W., 2006. Introduction to cryptography with coding theory. Pearson Education India.

2. Erkin, Z., Piva, A., Katzenbeisser, S., Lagendijk, R.L., Shokrollahi, J., Neven, G. and Barni, M., 2007.

Protection and retrieval of encrypted multimedia content: When cryptography meets signal

processing. EURASIP Journal on Information Security, 2007, p.17. 3. Puech, W., Chaumont, M. and Strauss, O., 2008. A reversible data hiding method for encrypted images,

Security, Forensics, Steganography, and Watermarking of Multimedia Contents X. In Proc. SPIE (Vol. 6819).

4. Cao, X., Du, L., Wei, X., Meng, D. and Guo, X., 2015. High capacity reversible data hiding in encrypted images by patch-level sparse representation. IEEE transactions on cybernetics, 46(5), pp.1132-1143.

5. Wu, X. and Sun, W., 2014. High-capacity reversible data hiding in encrypted images by prediction error. Signal

Processing, 104, pp.387-400. 6. Rad, R.M., Wong, K. and Guo, J.M., 2014. A unified data embedding and scrambling method. IEEE

Transactions on Image Processing, 23(4), pp.1463-1475.

7. Xu, D., Wang, R. and Shi, Y.Q., 2014. Data hiding in encrypted H. 264/AVC video streams by codeword substitution. IEEE transactions on information forensics and security, 9(4), pp.596-606.

8. Dragoi, I.C. and Coltuc, D., 2014. Local-prediction-based difference expansion reversible watermarking. IEEE

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match. IEEE Signal Processing Letters, 19(4), pp.199-202.

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807-812

Authors: Vikas Kashtriya, Amit Doegar, Varun Gupta, Poonam Kashtriya

Paper

Title:

Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase

Convolutional Neural Network Model

131

.

.

Abstract: A vast number of image processing and neural network approaches are currently

being utilized in the analysis of various medical conditions. Malaria is a disease which can be

diagnosed by examining blood smears. But when it is examined manually by the microscopist,

the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear

and the expertise of microscopist in examining the smears. Among the various machine

learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy.

We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell

images taken from thin blood smear samples into infected and non-infected with the malaria

parasite. The proposed OSICNN model consists of four convolutional layers and is showing

comparable results when compared with other state of the art models. The accuracy of

identifying parasite in RBC has been found to be 98.3% with the proposed model.

Keywords: CNN, Deep Learning, Malaria, Machine Learning, Medical Diagnosis, Neural

Networks, Image Classification.

References: 1. Z. May, S. S. A. M. Aziz, and R. Salamat, “Automated quantification and classification of malaria parasites in

thin blood smears,” 2013 IEEE International Conference on Signal and Image Processing Applications, 2013.

2. World Health Organization World Malaria Report – 2018. https://www.who.int/malaria/publications/world-malaria-report-2018/en/ Accessed May 2019.

3. S. Chavan and M. Nagmode, “Malaria Disease Identification and Analysis Using Image Processing,” IJLTET, 2014.

4. M. Poostchi, K. Silamut, R. J. Maude, S. Jaeger, and G. R. Thoma, “Image analysis and machine learning for detecting malaria,” Transl. Res., vol. 194, 2018, pp. 36–55.

5. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, 2015, pp. 436–444.

6. D. K. Das, A. K. Maiti, and C. Chakraborty, “Automated system for characterization and classification of

malaria‐infected stages using light microscopic images of thin blood smears,” J. Microsc., vol. 257, no. 3, 2015,

pp. 238–252.

7. J. E. Arco, J. M. Górriz, J. Ramírez, I. Álvarez, and C. G. Puntonet, “Digital image analysis for automatic enumeration of malaria parasites using morphological operations,” Expert Syst. Appl., vol. 42, no. 6, 2015, pp. 3041–3047.

8. G. Díaz, F. A. González, and E. Romero, “A semi-automatic method for quantification and classification of

erythrocytes infected with malaria parasites in microscopic images,” J. Biomed. Inform., vol. 42, no. 2, 2009, pp.

296–307.

9. J. Soni, N. Mishra, and C. Kamargaonkar, “Automatic difference between RBC and malaria parasites based on morphology with first order features using image processing,” IJAET, vol. 1, no. 5, 2011, pp. 290–297.

10. S. S. Savkare and S. P. Narote, “Automated system for malaria parasite identification,” in 2015 International Conference on Communication, Information & Computing Technology (ICCICT), 2015, pp. 1–4.

11. F. B. Tek, A. G. Dempster, and İ. Kale, “Parasite detection and identification for automated thin blood film malaria diagnosis,” Comput. Vis. Image Underst., vol. 114, no. 1, 2010, pp. 21–32.

12. Pallavi T. Suradkar, “detection of malarial parasite in blood using image processing,” Int J Eng Inn Technol (IJEIT) 2(10):124–126, 2013.

13. N. Abbas and D. Mohamad, “Microscopic rgb color images enhancement for blood cells segmentation in ycbcr color space for k-means clustering,” J. Theor. Appl. Inf. Technol., vol. 55, no. 1, 2013, pp. 117–125.

14. N. A. Khan, H. Pervaz, A. K. Latif, A. Musharraf, and Saniya, “Unsupervised identification of malaria parasites

using computer vision,” in Computer Science and Software Engineering (JCSSE), 2014 11th International Joint Conference on, 2014, pp. 263–267.

15. M. Chayadevi, G. Raju, “Usage of art for automatic malaria parasite identification based on fractal features,”. Int J Video Image Proc Netw Sec 14(4), 2014, pp 7–15.

16. L. B. Damahe, R. K. Krishna, N. J. Janwe, and V. T. Nileshsingh, “Segmentation Based Approach to Detect Parasites and RBCs in Blood Cell Images,” Int J Comput Sci Appl 4(2), 2011, pp 71–81.

17. L. Malihi, K. Ansari-Asl, and A. Behbahani, “Malaria parasite detection in giemsa-stained blood cell images,” in 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 2013 , pp. 360–365.

18. N. A. Seman, N. A. M. Isa, L. C. Li, Z. Mohamed, U. K. Ngah, and K. Z. Zamli, “Classification Of Malaria

Parasite Species Based On Thin Blood Smears Using Multilayer Perceptron Network,” Int. J. Comput. Internet Manag., vol. 16, no. 1, 2008, pp. 46–52.

19. V. Špringl, “Automatic malaria diagnosis through microscopy imaging,” Fac. Electr. Eng., vol. 128, 2009.

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in plasmodium falciparum,” in 2011 International Conference on Advanced Computer Science and Information

813-818

Systems, 2011, pp. 347–352.

22. S. Mandal, A. Kumar, J. Chatterjee, M. Manjunatha, and A. K. Ray, “Segmentation of blood smear images using

normalized cuts for detection of malarial parasites,” in 2010 Annual IEEE India Conference (INDICON), 2010, pp. 1–4.

23. L. Yunda, A. A. Ramirez, and J. Millán, “Automated image analysis method for p-vivax malaria parasite detection in thick film blood images,” Sist. y Telemática Vol. 10 No. 20, vol. 10, no. 20, 2012, pp. 9–25.

24. M.T. Le, T. R. Bretschneider, C. Kuss, and P. R. Preiser, “A novel semi-automatic image processing approach to

determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears.,” BMC Cell Biol., vol. 9, no. 1, 2008, p. 15.

25. D. M. Memeu, “A Rapid Malaria Diagnostic Method Based On Automatic Detection And Classification Of Plasmodium Parasites In Stained Thin Blood Smear Images,” 2014.

26. K. Prasad, J. Winter, U. M. Bhat, R. V Acharya, and G. K. Prabhu, “Image analysis approach for development of

a decision support system for detection of malaria parasites in thin blood smear images,” J. Digit. Imaging, vol. 25, no. 4, 2012, pp. 542–549.

27. S. K. Kumarasamy, S. H. Ong, and K. S. W. Tan, “Robust contour reconstruction of red blood cells and parasites

in the automated identification of the stages of malarial infection,” Mach. Vis. Appl., vol. 22, no. 3, 2011, pp. 461–469.

28. D. S. Kermany et al., “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, 2018, pp. 1122–1131.

29. Y.D. Zhang, C. Pan, X. Chen, and F. Wang, “Abnormal breast identification by nine-layer convolutional neural

network with parametric rectified linear unit and rank-based stochastic pooling,” J. Comput. Sci., vol. 27, 2018, pp. 57–68.

30. S. Rajaraman et al., “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, vol. 6, , 2018, p. e 4568.

31. B. Xu, N. Wang, T. Chen, and M. Li, “Empirical Evaluation of Rectified Activations in Convolutional Network,” Journal of Machine Learning Research, 15(1), 1929–1958. 2015.

32. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, no. Feb. 2012, pp. 281–305.

33. D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” Int. Conf. Learn. Represent., 2015.

34. G. P. Gopakumar, M. Swetha, G. S. Siva, and G. R. K. S. Subrahmanyam, “Convolutional Neural Network-based malaria Diagnosis from Focus Stack of Blood Smear Images Acquired Using Custom-built Slide Scanner,” J. Biophotonics, vol. 11, no. 3, 2018.

35. D. Bibin, M. S. Nair, and P. Punitha, “Malaria Parasite Detection From Peripheral Blood Smear Images Using

Deep Belief Networks,” IEEE Access, vol. 5, 2017, pp. 9099–9108.

36. Z. Liang et al., “CNN-based image analysis for malaria diagnosis,” in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 493–496.

37. N. E. Ross, C. J. Pritchard, D. M. Rubin, and A. G. Dusé, “Automated image processing method for the diagnosis and classification of malaria on thin blood smears.,” Med. Biol. Eng. Comput., vol. 44, no. 5, 2006, pp. 427–436.

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Authors: Sandeep Kaur, Alka Jindal

Paper

Title:

Fragile water-marking based image authentication scheme using LSBs

Abstract: This paper proposed a Quick response code (QR code) based strategy to provide

authentication to our digital images. Quick response code is used to provide protection to

digital images because of its important characteristics like detection from direction and large

data encoding capacity. First of all, Least Significant Bit (LSB) approach is applied on the

original image to select LSBs from each block of the image. Next, LSB image is partitioned

into sized blocks and mean is calculated for each block of the cell. Then Singular Value

Decomposition function is performed on this cell to get singular values which are used as

authentication data. After that QR Code generator is used to generate QR code matrix from

these singular values. And finally this code is inserted into MSBs to get an authenticated

image. Experimental result shows that proposed method produces images with good quality.

Keywords: Image authentication, Least Significant bit, QR code, Singular Value

Decomposition.

References:

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1 Ansari, I. A., Pant, M., & Ahn, C. W. (2016). SVD based fragile watermarking schem e for tamper

localization and self-recovery. International Journal of Machine Learning and Cybernetics, 7(6), 1225–1239.

https://doi.org/10.1007/s13042-015-0455-1 2 Dadkhah, S., Abd Manaf, A., Hori, Y., Ella Hassanien, A., & Sadeghi, S. (2014). An effective SVD-based

image tampering detection and self-recovery using active watermarking. Signal Processing: Image

Communication, 29(10), 1197–1210. https://doi.org/10.1016/j.image.2014.09.001 3 Liu, X. L., Lin, C. C., & Yuan, S. M. (2018). Blind Dual Watermarking for Color Images’ Authentication and

Copyright Protection. IEEE Transactions on Circuits and Systems for Video Technology, 28(5), 1047–1055.

https://doi.org/10.1109/TCSVT.2016.2633878 4 Ozyurt, F., Tuncer, T., & Avci, E. (2018). A novel probabilistic image authentication method based on

universal hash function for RGB images. 2018 International Conference on Computing Sciences and

Engineering, ICCSE 2018 - Proceedings, 1–6. https://doi.org/10.1109/ICCSE1.2018.8373994 5 Qasim, A. F., Meziane, F., & Aspin, R. (2018). Digital watermarking: Applicability for developing trust in

medical imaging workflows state of the art review. Computer Science Review, 27, 45–60.

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watermarking, 39(April), 345–361.

7 Sreenivas, K., & Kamkshi Prasad, V. (2018). Fragile watermarking schemes for image authentication: a

survey. International Journal of Machine Learning and Cybernetics, 9(7), 1193–1218.

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8 Wu, W. C. (2015). Subsampling-based image tamper detection and recovery using quick response code. International Journal of Security and Its Applications, 9(7), 201–216.

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(February). https://doi.org/10.1016/j.jvcir.2016.02.005

11 Zhang, J., Zhang, Q., & Lv, H. (2013). Optik A novel image tamper localization and recovery algorithm based on watermarking technology. Optik - International Journal for Light and Electron Optics, 124(23), 6367–

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12 Zhuvikin, A. (2017). Selective Image Authentication Using Shearlet Coefficients Tolerant to JPEG Compression. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, 11, 681–688. https://doi.org/10.15439/2017f177

133.

Authors: Gulshan Sharma, Rakesh Kumar

Paper

Title:

Classifying White Blood Cells in Blood Smear Images using a Convolutional Neural

Network

Abstract: We have tried to automate the classification task of white blood cells by using a

Convolutional Neural Network. We have divided white blood cell classification in two types of

problems, a binary class problem and a 4-classification problem. In binary class problem we

classify white blood cell as either mononuclear or Grenrecules. In 4-classification problem

where cells are classified into their subtypes (monocytes, lymphocytes, neutrophils, basophils

and eosinophils). In our experiment we were able to achieve validation accuracy of 100% in

binary classification and 98.40 in multiple classification.

Keywords: Convolutional Neural Network, Deep Learning, Medical Diagnosis, White Blood

Cell Classification.

References: 1. S. H. Rezatofighi, H. Soltanian-Zadeh, “Automatic Recognition of Five Types of White Blood Cells in Peripheral

Blood,”Computerized Medical Imaging and Graphics, vol. 35, no. 4, 2011, pp. 333-343.

2. G. Liang, H. Hong, W. Xie and L. Zheng, "Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification," in IEEE Access, vol. 6, 2018, pp. 36188-36197.

3. D.-C. Huang and K.-D. Hung, "Leukocyte Nucleus Segmentation and Recognition in Color Blood-Smear Images,"

in IEEE International Instrumentation and Measurement Technology, 2012. 4. Putta madegowda and Prasanna kumar, "White Blood cell segmentation using Fuzzy C means and snake," in 2016

International Conference on Computation System and Information Technology for Sustainable Solutions

(CSITSS), 2016. 5. R. Ahasan, A. U. Ratul and A. S. M. Bakibillah, "White Blood Cells Nucleus Segmentation from Microscopic

Images of strained peripheral blood film during Leukemia and Normal Condition," in 5th International Conference

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on Informatics, Electronics and Vision (ICIEV), 2016.

6. Z. K. K. Alreza and A. Karimian, "Design a new algorithm to count white blood cells for classification Leukemic

Blood Image using machine vision system," in International Conference on Computer and Knowledge

Engineering (ICCKE 2016), 2016. 7. S. Manik, L.M. Saini, N. Vadera, Counting and classification of white blood cell using Artificial Neural Network

(ANN), in IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems

(ICPEICES), IEEE (2016). 8. G. Ongun, et al., An automated differential blood count system. Engineering in Medicine and Biology Society,

in2001 Proceedings of the 23rd Annual International Conference of the IEEE,vol. 3. IEEE (2001).

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10. LeCun, Yann & Bengio, Y & Hinton, Geoffrey. (2015). “Deep Learning,” Nature. 521, 2015. pp. 436-44.

11. Mehdi Habibzadeh, Mahboobeh Jannesari, Zahra Rezaei, Hossein Baharvand, Mehdi Totonchi, "Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception," Proc. SPIE 10696, Tenth

International Conference on Machine Vision (ICMV 2017)

12. H. Chang, “Skin cancer reorganization and classification with deep neural network,” arXiv preprint arXiv:1703.00534 (2017).

13. Y. Liu, K. Gadepalli, M. Norouzi, et al., “Detecting cancer metastases on gigapixel pathology images,”

arXiv preprint arXiv:1703.02442 (2017).

14. M. Aubreville, C. Knipfer, N. Oetter, et al., “Automatic classification of cancerous tissue in laser endomicroscopy

images of the oral cavity using deep learning,” arXiv preprint arXiv:1703.01622 (2017).

15. A. Cruz-Roa, H. Gilmore, A. Basavanhally, et al., “Accurate and reproducible invasive breast cancer detection in whole-slide images: A deep learning approach for quantifying tumor extent,” Scientific Reports 7, 46450 (2017).

16. D. Wang, A. Khosla, R. Gargeya, et al., “Deep learning for identifying metastatic breast cancer,” arXiv preprint

arXiv:1606.05718 (2016). 17. K. Sirinukunwattana, S. E. A. Raza, Y. W. Tsang, et al., “Locality sensitive deep learning for detection and

classification of nuclei in routine colon cancer histology images,” IEEE Transactions on Medical Imaging 35,

1196 (2016) 18. GitHub - Shenggan/BCCD_Dataset: BCCD Dataset is a small-scale dataset for blood cells detection. BCCD

Dataset is under MIT licence. [Online]. Available: https://github.com/Shenggan/BCCD_Dataset

19. M. J. Macawile, V. V. Quiñones, A. Ballado, J. D. Cruz and M. V. Caya, "White blood cell classification and counting using convolutional neural network," 2018 3rd International Conference on Control and Robotics

Engineering (ICCRE), Nagoya, 2018, pp. 259-263.

134.

.

Authors: Dr. Rinkesh Mittal, Navneet Kaur, Dr. Parveen Singla

Paper

Title:

Performance improvement of DVFS based 16 bit SAR ADC

Abstract: Analog-to-digital converters (ADCs) at elevated efficiency are vital components

for elevated quality image sensors growth. In order to achieve the necessary resolution at a

specific velocity, these systems need a large amount of ADCs. In addition, energy dissipation

has now become a main output for analog models, especially for mobile equipment. Such a

circuit design is a difficult job, requiring a mixture of sophisticated digital circuit design,

analog expertise and iterative design. The sharing of amplifiers was frequently employed for

reducing dissipation of energy in ADC pipelines. In this paper we present the topology of a 16-

bit ADC pipeline, developed in 45 nm CMOS. Its efficiency is likened to a standard Scaling

configuration for amplifier and a completely shared amplifier.

Keywords: Analog-digital converters (ADC), data conversion, low power, successive

approximation register architecture (SAR), digital to analog converter (DAC).

References:

1. T.Sowmya, SK. Muneer Nihal, “IMPLEMENTATION OF 16-BIT PIPELINED ADC USING 180nm CMOS TECHNOLOGY”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072.

2. Saima Bashir, Samiya Ali, Suhaib Ahmed, “Analog-to-Digital Converters: A Comparative Study and

Performance Analysis”, International Conference on Computing, Communication and Automation (ICCCA2016),2016.

3. T. Moody, "Design of a 10-bit 1.2 GS/s Digital-to-Analog Converter in 90 nm CMOS," Ohio LINK

Electronic Theses and Dissertations Center., 2015. 4. Abhishek Rai, B Ananda Venkatesan,“Analysis and design of High Speed and Low Power Comparator in

ADC”, International Journal of Engineering Development and Research (IJEDR), 2014. 5. S. Ren and J. Emmert, "Successive approximation pipelined ADC with one clock cycle conversion rate," The

Institute of Engineering and Technology 2012 ELECTRONIC LETTERS, vol. 48, no. 20, 27th September

2012.

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behavioral modeling," Antennas and Propagation Magazine, p. 197– 208, 2010.

8. I. -H. Wang, J. -L. Lin and S. -I. Liu, “5-bit, 10 G Samples/s track-and-hold circuit with input feed through cancellation," Electronics Letters, vol. 42, no. 8, pp. 457 - 459, 2006.

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letters 32.12 (1996): 1055- 1057.

12. F. Kuttner, "A 1.2v 10b 20MSample/s non-binary successive approximation ADC in0.13um CMOS," in IEEE International Solid-State Circuits Conference, 2002. [9] B.Razavi, Principles of Data Conversion System

Design, IEEE Press, 1995.

13. Yee, Y. S., L. M. Terman, and L. G. Heller. "A two- stage weighted capacitor network for D/AA/D conversion." SolidState Circuits, IEEE Journal of 14.4 (1979): 778-781.

14. J. McCreary, "Successive Approximation Analog to Digital Conversion Techniques in MOS Integrated

Circuits," University of California, Berkeley, 1975.

15. Ian Beavers contributing technical expert, "Understanding Spurious-Free Dynamic Range in Wideband GSPS

ADCs by Ian Beavers,," Analog Devices, Inc. Technical Article MS-2660.

135

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.

Authors: Er. Archana Madan, Er. Daljit Singh

Paper

Title:

An Optimized Hybrid Filter designing for Speckle and Gaussian Noise Reduction in

UltraSound Images

Abstract: Image which is a visual perception of a scene or something is a set of pixels that

have certain values which appear in the form of colors to view that particular set of pixel as

image. Image contains information about whatever is being depicted in the picture and hence it

can be said as a useful source for storing or conveying information. Image noise is apparent in

image region with low signal level, such as shadow region or under exposed images. The work

presents the concept of noising and denoising in a digital image. Noise is a kind of disturbance

that occurs in the channel at the time of transmission. Image denoising is the procedure of

improving true images from the noisy images. For ages researchers have been proposing

several techniques that were used to remove the noise from the image. Image denoising is the

procedure of improving true image from the noisy image. At the time of such process it is

difficult to reduce noise. Owing to this difficulty, numerous denoising models have been

proposed [4]. The paper presents the reduction of speckle and Gaussian noise in the biomedical

ultrasound images. In the proposed work, the butterworth filter is applied for filtering the noisy

image and then the coefficients are optimized by using the firefly algorithm mechanism to

remove noise that occurs at the time of transmission and then it is hybrid with the Weiner filter.

Experiments have been performed to check the performance of the proposed technique. The

results are analysedquantatively using PSNR and SSIM. The results are also evaluated on other

performance parameters such as BER, MSE and fitness on speckle as well as on Gaussian

noise.

Keywords: Speckle Noise, Gaussian Noise, Denoising, Butterworth filter, Firefly Algorithm,

Digital Image, Ultrasound Image

References:

1. ArchanaMadan, Daljit Singh, “ An Optimized Hybrid Filter Designing for Speckle Noise Reduction in Ultrasound

Images”, International Journal for Research in Applied Science and Engineering Technology (IJRASET), Vol 5

Issue XII December 2017 2. Rajesh Mohan R, “Speckle Noise Reduction in Images using Wiener Filtering and Adaptive Wavelet

Thresholding”, IEEE, 2016, Pp 2860-2864.

3. Mr. Hitesh, S. Asari, Ami Shah, “A Research Paper On Reducion Of Speckle Noise In Ultrasound Imaging Using Wavelet And Contourlet Transform”, Journal of information, knowledge and research in electronics and

communication engineering, Vol 2, 2016, Pp 800-806.

4. Milind Kumar V. Sarode, Prashant R. Deshmukh, “Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique”, International Journal of Advancement in Technology, Vol 2, 2011,

5. AlenrexMaity ;AnshumanPattanaik ; SantwanaSagnika ; SantoshPani, “A Comparative Study on Approaches to

Speckle Noise Reduction in Images”, IEEE, 2015. 6. T.RathaJeyalakshmi and K.Ramar, “A Modified Method for Speckle Noise Removal in Ultrasound Medical

Images”, International Journal of Computer and Electrical Engineering, Vol 2, 2010, Pp 1-5.

836-842

7. NishthaAttlas, Dr. Sheifali Gupta, “Wavelet Based Techniques for Speckle Noise Reduction in Ultrasound

Images” , International Journal of Engineering and Research and Application, Vol 4, 2014, Pp 508,513.

8. JyotiJaybhay, RajveerShastri, “speckle noise reduction filters analysis”, Research Gate, Vol 2, 2015, Pp 72-78.

9. K.M. SharavanaRaju, Mohammad ShahnawazNasir, T. Meera Devi, “Filtering Techniques to reduce Speckle Noise and Image Quality Enhancement methods on Satellite Images”, IOSR-JCE, Vol 15, 2013, Pp 10-15.

10. Karamjeet Singh, SukhjeetKaurRanade, Chandan Singh, “A hybrid algorithm for speckle noise reduction of

ultrasound images”, ELSEVIER, Vol 148, 2017, Pp 55-69. 11. NagashettappaBiradar, M.L. Dewal, Manoj Kumar Rohit, “Speckle noise reduction in echocardiographic images

of aortic valve and cardiac chambers”, ELSEVIER, Vol 126, 2015, Pp 153-169.

12. Jian Yang, Jingfan Fan, Yongtian Wang, “Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image”, ELSEVIER, Vol 195, 2016, Pp 88-95.

13. Ajitha R. Subhamathi, “Ultrasound Image Despeckling based on Local Binary Pattern and Local Homogenity”,

IEEE, 2016, Pp 1758-1761. 14. Jingyun Liu, Zegang Ding, Liangbo Zhao, Fei Dong, Dechengang Liu, “An Adaptive SAR Image Speckle

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136

.

.

Authors: Sonali Mathur, Prof. (Dr.) Vikram Bali, Prof. (Dr.) S.L. Gupta, Dr. Payal Pahwa

Paper

Title:

Data Warehouse Testing and Security: A Conspectus

Abstract: Data warehouse is a central storage facility that stores information from many

sources which can be in structured or unstructured format, queries this information for retrieval

based on certain input facts and delivers the outcome analysis to many analysts, to meet

decision support and business intelligence requirement. Not much research has been carried

out in this research area in the past few years. In this research paper, we are discussing the

data warehouse architecture and the testing techniques that are used for best suited to be used

for the data warehouses. Literature for the testing techniques is integrated at one place and the

outcome is to focus on security issues while performing data warehouse testing.

Keywords: Database, Data Warehouse, Data Warehouse Testing, Database Testing, Software

Testing

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137

Authors: Sulekha Saxena, P. N. Hrisheekesha, Vijay Kumar Gupta, Ram Sewak Singh

Paper

Title:

Detection of Congestive Heart Failure Based On Spectral Features and Extreme Learning

Machine

.

Abstract: In this paper we proposed a novel approach to evaluate the classification

performance of features derived from various spectral investigation methods for congestive

Heart Failure (CHF) analysis using ranking methods, Kernel Principal Component Analysis

(KPCA) and binary classifier as 1-norm linear programming extreme learning machine (1-

NLPELM). For this study, thirty different features are extracted from heart rate variability

(HRV) signal by using spectral methods like multiscale Wavelet packet (MSWP), higher order

spectra (HOS) and auto regression (AR) model. Top ten features were extracted by ranking

methods and then reduced to only one feature by KPCA having kernel function as radial basis

function (RBF) which was further applied to 1-NLPELM binary classifier. For this purpose,

the HRV data were taken from standard database of Normal sinus rhythm (NSR), elderly

(ELY) and Congestive heart failure (CHF) subjects. Numerical experiments were being done

on the combination of database sets as NSR-CHF, NSR-ELY, and ELY-CHF subjects. The

numerical results show that features at third level of decomposition of HRV data sets MSWP

shows lowest p-value (<0.001). Thus, third level of MSWP features are better than other

features extracted by auto regression (AR) model and higher order spectra (HOS) spectral

methods.

Keywords: 1-norm linear programming extreme learning machine (1-NLPELM), higher order

spectra (HOS), Kernel Principal Component Analysis (KPCA), ranking methods

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Authors: Gaurav Mehta, Dr. Parveen Singla, Dr. Rinkesh Mittal

Paper

Title:

Node Validation Technique to Increase Security of Wireless Sensor Networks

138

.

Abstract: A distributed sort of network in which sensor nodes can join or leave the network

when they want is identified as wireless sensor network. Due to self-arranging of the network,

attacker nodes make their entry inside networks and launch different types of active and

passive intrusions. The active attacks can be divided into various sub categories and one of

them is misdirection attack. This attack increases delay in the network. The available attacker

hub will trigger attack. In order to recognize and disengage malicious nodes a novel strategy is

proposed in this work. The malicious nodes are recognized from the networks which are in

charge of triggering the node. The attacker or malevolent node launches sinkhole intrusion.

This intrusion streams fake recognition information within the network. This study proposes a

verification approach for detecting attacker nodes present in the network. The performance of

introduced approach is tested in NS2. It is scrutinized that performance is improved as per

various parameters.

Keywords: ACTIVE ATTACKS, DOS, IDS,LEACH, NS2, SINKHOLE, WSN

References: 1. Omar Banimelhem, Muhammad Naserllah, and Alaa Abu-Hantash, “An Efficient Coverage in Wireless

Sensor Networks Using Fuzzy Logic-Based Control for the Mobile Node Movement,” 2017, IEEE 2. RanuShukla, Rekha Jain, P. D. Vyavahare, “Combating against Wormhole Attack in Trust and Energy Aware

Secure Routing Protocol (TESRP) in Wireless Sensor Network”, 2017, Recent Innovations is Signal

Processing and Embedded Systems (RISE) 3. Poonam Rolla, ManpreetKaur, “Dynamic Forwarding Window Technique against DoS Attack in WSN,”

2016, IEEE

4. Aamir Shaikh and Siraj Pathan, “Research on Wireless Sensor Network Technology”, 2012, International Journal of Information and Education Technology, Vol. 2, No. 5

5. RakshaUpadhyay, Salman Khan, HarendraTripathi, Uma Rathore Bhatt, “Detection and Prevention of DDOS

Attack in WSN for AODV and DSR using Battery Drain,” 2015, IEEE 6. Yogesh Kumar Fulara, “Some Aspects of Wireless Sensor Networks”, 2015, International Journal on AdHoc

Networking Systems (IJANS) Vol. 5, No. 1

7. Ju young Kim, Ronnie D. Caytiles, Kyung Jung Kim, “A Review of the Vulnerabilities and Attacks for Wireless Sensor Networks” Journal of Security Engineering, 2014, pp.241-250

8. Mayank Kumar Sharma, Brijendra Kumar Joshi, “Detection & Prevention of Vampire Attack in Wireless

Sensor Networks,” 2017, IEEE

9. Aditi Rani, Sanjeet Kumar, “A Low Complexity Security Algorithm for Wireless Sensor Networks,” 2017,

IEEE

10. AminaMsolli, HaythemAmeur, AbdelhamidHelali, HassenMaaref, “A new secure key management scheme for wireless sensor network,” 2017, IEEE

11. JanhaviKulkarni, Karan Nair, AdityaPappu, SarthakGadre, Ganesh Gore, Jonathan Joshi, “Using On-Chip

Cryptographic Units for Security in Wireless Sensor Networks,” 2017, IEEE 12. C. Anand, R. K. Gnanamurthy,” Localized DoS Attack Detection Architecture for Reliable Data Transmission

Over Wireless Sensor Network”, 2016 Springer Science + Business Media New York

13. Omar Said and AlaaElnashar,” Scaling of wireless sensor network intrusion detection probability: 3D sensors,

3D intruders, and 3D environments”, 2015 Springer

863-867

Authors: Simranjeet kaur, Geetanjali Babbar, Dr. Gagandeep

Paper

Title:

Image processing andclassification, approch for plant Disease detecion

Abstract: The plant disease detection is the major issue of the computer vision and machine

learning. The plant disease detection has the various phases like pre-processing, segmentation,

feature extraction and classification. In the existing technique support vector machine is used

for the classification. The support vector machine approach has the low accuracy for the plant

disease detection and also it can classify data into two classes which affect its performance.

The proposed methodology is based on the region based segmentation, textual feature analysis

and k-nearest neighbor method is applied for the classification. The proposed method is

implemented in MATLAB and results are analyzed in terms of accuracy. The proposed

technique has high accuracy and compared to existing technique

Keywords: Plant disease detection, GLCM, K-mean, KNN

References:

139

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1. Santhosh Kumar S, B. K. Raghavendra, “Diseases Detection of Various Plant Leaf Using Image Processing

Techniques: A Review”, 2019, 5th International Conference on Advanced Computing & Communication Systems (ICACCS)

2. Kriti, Jitendra Virmani and Ravinder Agarwal, “Effect of despeckle filtering on classification of breast

tumors using ultrasound images”, Biocybernetics and Biomedical Engineering, Vol. 39, No. 2, pp. 536-560, 2019, Publisher: Elsevier

3. Abirami Devaraj, Karunya Rathan, Sarvepalli Jaahnavi, K Indira, “Identification of Plant Disease using

Image Processing Technique”, 2019, International Conference on Communication and Signal Processing (ICCSP)

4. Meenakshi Garg, Manisha Malhotra and Harpal Singh, “Statistical Feature Based Image Classification and

Retrieval Using Trained Neural Classifiers”, 2018, International Journal of Applied Engineering Research, Volume 13, Number 8, pp. 5766-5771

5. M S Arya, K Anjali, Divya Unni, “Detection of unhealthy plant leaves using image processing and genetic

algorithm with Arduino”, 2018, International Conference on Power, Signals, Control and Computation

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2017, IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR)

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Journal of Chemical and Pharmaceutical Sciences

8. Sujeet Varshney, Tarun Dalal, “Plant Disease Prediction using Image Processing Techniques- A Review”,

2016, International Journal of Computer Science and Mobile Computing 9. Shivani K. Tichkule, Dhanashri. H. Gawali, “Plant diseases detection using image processing techniques”,

2016, Online International Conference on Green Engineering and Technologies (IC-GET)

10. R Anand, S Veni, J Aravinth, “An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method”, 2016, International Conference on Recent Trends in

Information Technology (ICRTIT)

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Conference on Computing Communication Control and Automation

868-871

140

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Authors: Shaifali Sharma, Geetanjali Babbar, Ishdeep singla,gagandeep jindal

Paper

Title:

Efficient Scale Invariant and Back Propagation Neural Network Method using LIP Region

Segmentation

Abstract: With the advent of technological sensor devices and human interface machine

technology, there has been extensive research done in lip segmentation methods by several

researchers — some linguistic features required for interaction with the machine equipment.

Therefore, research work has been done in the audio speech detection scheme for recognition

of lip reading . Visual lip reading technology developed based on the extraction of features of

the lip. Lip segmentation is an essential approach to recognize lip reading scheme. Meanwhile,

it helps to improve parameters. Several methods studied to segment the lip area based on

localized active contour method using twice contour finding and combined color-space

method. Apply the illumination histogram equalization to real color images to reduce the

distortion of uneven illumination. The proposed method implemented can get better accuracy

rate and segmentation results and compare with the existing process using area or circle as the

region to segment grayscale images and combined in the color-space image. Using SIFT and

BPNN, the inner region of the lip found in the result. The experiment tool is used MATLAB

2016a and designs a PROJECT APPLICATION. Improve the success rate and reduce the

segmented error and compared with the current metrics. The experimental analysis determines

the accuracy rate with 94%; error rate reduces with Segmented Error 14.1 % and Overlap Error

rate value with 79.73%.

Keywords: Lip Segmentation, Feature Extraction – Scale Invariant Feature Transformation,

BPNN – Back Propagation Neural Network, and Active Contour.

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system.”,In Proceedings of the international multi conference of engineers and computer scientists ,Vol. 1, pp.

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Detection and Tracking System,In International Visual Informatics Conference

(pp.254-265).Springer, Berlin, Heidelberg. 3. Karlsson.al, S. M., and Bigun.al, J. (2012, June), “Lip-motion events analysis and lip segmentation using

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6. Munot.al, K., Mehta, N., Mishra.la, S. and Chaturvedi.la, R. N.(2017),”Areview on image segmentation techniques with an application perspective.”,International Journal of Advanced Research in Computer

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7. Li, M. and Cheung, Y. M. (2010, September),”Automatic segmentation of color lip images based on morphological filter”,In International Conference on Artificial Neural Networks (pp. 384-387).Springer,

Berlin, Heidelberg.

8. Kalbkhani.al, H. and Amirani.al, M. C. (2012),”An efficient algorithm for lip segmentation in color face

images based on local information”, Journal of World's Electrical engineering and technology, vol.1(1), 2012.

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survey. In 2010 6th international conference on emerging technologies (ICET) (pp. 181-186).IEEE. 10. Liew.al, A. C., Leung, S. H and Lau, W. H. (2003),” Segmentation of color lip images by spatial fuzzy

clustering”,IEEE transactions on Fuzzy Systems, vol 11(4), pp 542-549.

11. Zhang, D and Lu, G. (2001),”Segmentation of moving objects in image sequence: A review”, Circuits, Systems and Signal Processing, vol 20(2), pp 143-183.

12. Stillittano.al, S and Caplier.al, A. (2008, January),”Inner Lip Segmentation by Combining Active Contours

and Parametric Models”, 13. In VISAPP (1) (pp. 297-304).

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contour”, Neural Computing and Applications, 29(5), 1417-1424. 15. Liévin.al, M., Delmas.al, P., Coulon.al, P. Y., Luthon.al, F andFristol.al, V. (1999, July),” Automatic

lip tracking: Bayesiansegmentation and active contours in a

cooperative scheme”,In Proceedings IEEE International Conference on MultimediComputing and Systems (Vol. 1, pp. 691-696).IEEE.

16. Saeed.al, U and Dugelay.al, J. L. (2010, July),”Combining edgedetection and region segmentation for lip

contour extraction “,In International Conference on Articulated Motion and Deformable Objects (pp. 11-

20).Springer, BerlinHeidelberg.

17. Lu, Y and Liu, Q. (2018),”Lip segmentation using automatic selected initial contours based on localized

active contour model “,EURASIP Journal on Image and Video Processing, 201 vol 8(1), 7. 18.

19. Liu, G., and Li, H. (2018),” Robust evolution method of active contour models and application in

segmentation of image sequence.”,Journal of Electrical and Computer Engineering, 2018. 18 Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J and Yang, X. (2017),”An active contour model based on local

fitted images for image segmentation,” Information sciences,vol. 418,pp. 61-73.

19 Shele.ali, L. and Mengxing.al, H. (2018, March).,”Research ofUnderwater Image Segmentation Algorithm Based on the ImprovedGeometric Active Contour Models”, In 2018 International Conference on

Intelligent Autonomous Systems (ISO IAS) (pp. 44-50).IEEE.

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1-5).IEEE.

Authors: Ashima Rani, Gaurav Aggarwal

Paper

Title:

Algorithm for Automatic Detection of Ambiguities from Software Requirements

Abstract: Software Systems are built by the Software engineers and must ensure that

software requirement document (SRS) should be specific. Natural Language is the main

representation of Software requirement specification document, because it is the most flexible

and easiest way for clients or customers to express their software requirements [2]. However

being stated in natural language, software requirement specification document may lead to

ambiguities [28]. The main goal of presented work to automatically detection of the different

types of ambiguities like Lexical, Syntactic, Syntax and Pragmatic. Then an algorithm is

proposed to early detection the different types of ambiguities from software requirement

141

document. Part of Speech (POS) technique and regular expression is used to detect each type

of ambiguities. An algorithm presented in this paper have two main goals (1) Automatic

detection of different types of ambiguities. (2) Count the total number of each types of

ambiguities found and evaluate the percentage of ambiguous and non- ambiguous statements

detected from software requirement document. The suggested algorithm can absolutely support

the analyst in identifying different kinds of ambiguities in Software requirements specification

(SRS) document.

Keywords: Lexical ambiguity, Syntax Ambiguity, Software Requirement Specification,

Syntactic ambiguity, Pragmatic ambiguity, Part of Speech Tagging.

References: 1. Ashima Rani, Gaurav Aggarwal, “Advanced Practices to Detect Ambiguities and Inconsistencies from

Software Requirements”, IEEE, 7th international conference on System Modeling & Advancement in

Research Trends, November 2018, pp. 17-21.

2. Benedikt Gleich, Oliver Creighton, Leonid Kof, “Ambiguity Detection: Towards a toll explaining ambiguity

sources”, International Working Conference on Requirements Engineering: Foundation for Software Quality,

2010, pp 218-232.

3. Ayan Nigam, Neeraj Arya, Bhawna Nigam, Dipika Jain, “Tools for automatic discovery of ambiguity in requirements”, International Journal of Computer Science Issues, Vol 9, Issue 5, 2012, pp 350-356.

4. ALI OLOW JIM, WAN MOHD NAZMEE WAN ZAINON, “An approach for detecting syntax and syntactic

ambiguity in software requirement specification”, Journal of Theoretical and applied Information Technology, Vol 96, Issue. 8, April 2018, pp 2275-2284.

5. Gulia, S. and T. Choudhury, “An efficient automated design to generate UML diagram from Natural

Language specifications” , IEEE 6th International Conference Cloud System and Big Data Engineering (Confluence), 2016.

6. Beg, R., Q. Abbas, and A. Joshi, “A method to deal with the type of lexical ambiguity in a software

requirement specification document”, ICETET’08 in Emerging Trends in Engineering and Technology, 2008. 7. Gleich, B., O. Creighton, and L. Kof, “Ambiguity detection: Towards a tool explaining ambiguity sources.

International Working Conference on Requirements Engineering: Foundation for Software Quality”. 2010.

Springer. 8. Arendse, B., “A thorough comparison of NLP tools for requirements quality improvement”, 2016.

9. . Umber, A. and I.S. Bajwa. Minimizing ambiguity in natural language software requirements specification.

2011 Sixth International Conference on Digital Information Management (ICDIM),. 2011. IEEE.

10. Takoshima, A. and M. Aoyama. Assessing the Quality of Software Requirements Specifications for

Automotive Software Systems. in Software Engineering Conference (APSEC), 2015 Asia-Pacific. 2015.

IEEE. 11. Bano, M. Addressing the challenges of requirements ambiguity: A review of empirical literature. in 2015

IEEE Fifth International Workshop on Empirical Requirements Engineering (EmpiRE). 2015. IEEE.

12. Popescu, D., Rugaber, S., Medvidovic N., & Berry, D.M. Reducing ambiguities in requirements specifications via automatically created object-oriented models. LNCS 5320, 2007. Springer.

13. Femmer, H., Fernandez, D.M., & Juergens, E., Rapid requirements checks with requirements smells: two

case studies. in Proceedings of the 1st International Workshop on Rapid Continuous Software Engineering. 2014. ACM.

14. . Shah, U.S. and D.C. Jinwala, Resolving ambiguities in natural language software requirements: a

comprehensive survey. ACM SIGSOFT Software Engineering Notes, 2015. 40(5): p. 1-7. 15. Korner, S.J. and T. Brumm. Resi-a natural language specification improver. in Semantic Computing, 2009.

ICSC'09. IEEE International Conference on. 2009. IEEE. 16. . Bajwa, I., M. Lee, and B. Bordbar, Resolving syntactic ambiguities in natural language specification of

constraints. Computational Linguistics and Intelligent Text Processing, 2012: p. 178-187.

17. . Soares, H.A. and R.S. Moura. A methodology to guide writing Software Requirements Specification document. in Computing Conference (CLEI), 2015 Latin American. 2015. IEEE.

18. Fockel, M. and J. Holtmann. ReqPat: Efficient documentation of high-quality requirements using controlled

natural language. in 2015 IEEE 23rd International Requirements Engineering Conference (RE). 2015. IEEE. 19. Gill, K.D., Raza, A., Zaidi, A.M., & Kiani, M.M. Semi-Automation For Ambiguity Resolution, 27th

Canadian Conference on Open Source Software requirements. in Electrical and Computer Engineering

(CCECE). 2014. IEEE. 20. . Sandhu, G. and S. Sikka. State-of-art practices to detect inconsistencies and ambiguities from software

requirements. 2015 International Conference on. Computing, Communication & Automation (ICCCA), 2015.

IEEE. 21. de Bruijn, F. and H.L. Dekkers. Ambiguity in natural language software requirements: A case study.

International Working Conference on Requirements Engineering: Foundation for Software Quality. 2010.

Springer. 22. Unnati S Shah, Devesh C. Jinawala, “Resolving Ambiguties in Natural language Software Requirements: A

Comp rehensive Survey”, ACM SIGSOFT Software Engineering Notes, Vol 40 No. 5, September 2015.

23. . Gang Liu, Shaobin Huang, Xiufeng Piao, “Study on Requirement Testing Method Based On Alpha-Beta Cutoff Procedure” Collage of computer Science and Technology, Harbin Engineering University, Harbin,

878-882

Heilongjiang, China, 2008 IEEE.

24. Ravi Prakash Verma, Bal Gopal, Md. Rizwan Beg, ”Algorithm for Generating Test Case for Prerequisites of

Software Requirement” Department of Computer Science and Engineering, Integral University. International

Journal of Computer Application, September 2010 IEEE. 25. Ronald Kirk Kndt “Software Requirement Engineering: Practice and Techniques”, Jet Propulsion

Laboratory, California Institute of Technology, November 7, 2003.

26. . Massila Kamalrudin, Safiah Sidek, Sharifah Sakinah, Syed Ahmad, Nadiah Daud, “A review of requirements engineering tools for requirements validation software engineering process”,vol.1, no.1,

International journal of software engineering and technology (IJSET), 2014.

27. . Dr. Sohail Asghar, Mahrukhumar, “Requirement engineering challenges in development of software applications and selection of customer-offthe-Shelf (COTS) components”, International journal of software

engineering (IJSE), vol. 1, no. 2, August 2010.

28. . Khtira, A.; Benlarabi, A.; El Asri, B. Detecting feature duplication in natural language specifications when evolving software product lines. In Proceedings of the 10th International Conference on Evaluation of Novel

Approaches to Software Engineering, Barcelona, Spain, 29–30 April 2015.

142

Authors: Pradeep Kumar Gaur, Anupma Marwaha

Paper

Title:

Wide-WANET Architecture for extension of Internet facilities in remote regions

Abstract: A definitive research of intrigue nowadays is in the field of internetworking

identified with overseeing and keeping up of correspondence between nodes of various

network standards inside an ad-hoc domain. The communication between heterogeneous nodes

is established by the computation of the instantaneously selected metric values i.e. hop count

and routing queue length of individual gateways candidates. A wide-WANET structure is

proposed conforming to four different network standards and simulated using enhanced AODV

and IPv6 addressing format. It has been proved through simulations that the parameters in

consideration has shown improved better performance by implementing Multiple-metrics

Gateway Selection Criterion whereby facilitating extension of Internet facilities by cohering

nodes of varying network standards.

Keywords: Hop Count; Infrastructure less; Multiple-metrics Gateway Selection Criterion;

Routing Queue Length; wide-WANET;

References: 1. Gaur PK, Marwaha A “Scalability Analysis using Auto-configuration process for coalescing of

heterogeneous Wireless ad-hoc networks” International Journal of Applied Engineering Research, Research India Publications, 12(14), 2007, pp. 4427-4432.

2. Bayer N, Xu B, Hischke S, “An Architecture for connecting Ad hoc Networks with the IPv6 Backbone

(6Bone) using a Wireless Gateway”, European Wireless, vol. 100, 2004. 3. Khan KUR, Reddy AV, Zaman RU, Kumar M, “An effective gateway discovery mechanism in an integrated

internet-MANET (IIM)” Int Conf Adv Comput Eng, 2010, pp. 24–28.

4. Ratanchandani P, Kravets R, “A Hybrid Approach to Internet Connectivity for Mobile Ad Hoc Networks”, Wirel Commun Netw, vol. 3, 2003, pp.1522–1527.

5. Ahlund C, Zaslavsky A, “Integration of ad hoc network and IP network capabilities for mobile hosts”, 10th

Int Conf Telecommun ICT, vol. 1, 2003, pp. 482–489. doi: 10.1109/ICTEL.2003.1191288 6. Armuelles I, Robles T, Siebert M, “On Ad Hoc Networks in the 4G Integration Process”, Third Annual

Mediterranean Ad Hoc Networking Workshop, 2004.

7. Bader F, Pinart C, Christophi C, “User-centric analysis of perceived QoS in 4G IP mobile/wireless networks” IEEE Int Symp Pers Indoor Mob Radio Commun PIMRC, vol. 3, 2003, pp. 2047–2053.

8. Djouama A, Mokdad L, Abdennebi M, Tohmé S, “Topology control for enhanced QoS on infrastructure-less

heterogeneous radio networks” Proc - Conf Local Comput Networks, LCN, 2009, pp. 414–419. 9. Zachariah T, Klugman N, Campbell B, “The Internet of Things Has a Gateway Problem”, Proc 16th Int Work

Mob Comput Syst Appl - HotMobile’15, 2015, pp. 27–32.

10. Glória A, Cercas F, Souto N, “Design and implementation of an IoT gateway to create smart environments”, Procedia Comput Sci, vol. 109, 2017, pp. 568–575.

11. Villiers G De, Byl A Van der, Wilkinson RH, “Developing a WSN internet gateway for an African context”,

Int J Sens Networks, vol. 20(1), 2016. 12. Choi J, Dagefu FT, Sadler BM, Sarabandi K, “Low-Power Low-VHF Ad-Hoc Networking in Complex

Environments”, IEEE Access, vol. 5, 2017, pp. 24120–24127.

13. El-Moshrify H, Mangoud MA, Rizk M, “Gateway discovery in Ad hoc On-Demand Distance Vector (AODV) routing for internet connectivity”, Natl Radio Sci Conf NRSC, Proc, vol 2, 2007, pp. 1–8.

14. Perkins, C. E., Royer, E. M. & Das, S., “Ad hoc on demand distance vector (AODV) routing for IP version

6” Internet Draft, 2000.

883-886

143

Authors: Tejinder Kaur, Rachna Manchanda, Chanpreet Kaur

Paper

Title:

Parameters for stability of reconfigurable memory and 6T SRAM cell

Abstract: As the technology is improving, channel length of MOSFET is scaling down. In

this environment stability of SRAM becomes the major concern for future technology. Static

noise margin (SNM) plays a vital role in stability of SRAM. This paper gives an introduction

to the reconfigurable memory and 6T SRAM cell. It includes the implementation,

characterization and analysis of reconfigurable memory cell and its comparison with the

conventional 6T SRAM cell for various parameters like read margin, write margin, data

retention voltage, temperature and power supply fluctuations and depending upon these

analysis we find SNM for 6T and 8T SRAM cell. The tool used for simulation purpose is IC

Station by Mentor Graphics using 350nm technology at supply voltage of 2.5volts.

Keywords: SNM, 6T SRAM, DRV, CR, 8T SRAM

References: 1. Ken Mai, “Design and analysis of Reconfigurable Memories”, Dissertation, Stanford University, 2005. 2. Andrei Pavlov & Manoj Sachdev, “CMOS SRAM Circuit Design and Parametric Test in Nano-Scaled

Technologies”, Intel Corporation, University of Waterloo, 2008 Springer Science and Business Media B.V.,

pp: 1-202. 3. K. Mai et al., “Smart Memories: A Modular Reconfigurable Architecture,” Proceedings, International

Symposium on Computer Architecture, pp. 161-71, June 2000.

4. Ken Mai, Ron Ho, Elad Alon, Dean Liu, Younggon Kim, Dinesh Patil, and Mark A. Horowitz, “Architecture and Circuit Techniques for a 1.1-GHz 16-kb Reconfigurable Memory in 0.18 micron CMOS”, IEEE Journal

of Solid-State Circuits, vol. 40, 2005, pp:261-275.

5. Qiaoyan Yu and Paul Ampadu “Cell Ratio Bounds for Reliable SRAM Operation”, IEEE, 2006, pp: 1192-1195.

6. Kumar, A.; Qin, H.; Ishwar, P.; Rabaey, J.; Ramchandran, K.; “Fundamental Bounds on Power Reduction

during Data-Retention in Standby SRAM” ; IEEE International Symposium, vol. 27(30), 2007, pp. 1867 – 1870.

7. Debasis Mukherjee, Hemanta Kr. Mondal and B.V.R. Reddy, “Static Noise Margin Analysis of SRAM Cell

for High Speed Application”, IJCSI International Journal of Computer Science Issues, vol. 7, 2010.

8. B. Amrutur and M. Horowitz, “Speed and Power Scaling of SRAM’s,” IEEE Journal of Solid-State Circuits,

2000.

9. E. Seevink and F. List, “Static Noise Margin Analysis of MOS SRAM Cells”, Solid-State Circuits, IEEE Journal of, vol. 5, 1987, pp. 748-754.

10. L. Chang, D. Fried and J. Hergenrother, “Stable SRAM cell design for the 32 nm node and beyond”, VLSI Technology, 2005. Digest of Technical Papers. 2005 Symposium on, 2005, pp. 128-129.

887-892

Authors: Nidhi Syal, Preeti Bansal

Paper

Title:

3D NoC Network using Adaptive Algorithm for 8x8x4 Mesh

Abstract: This paper presents a qualitative analysis of 3D routing algorithm in 8x8x4 mesh

network topology. The traffic distribution in 3D routing algorithm has limited bandwidth

along vertical links. Different traffic patterns were used during simulation. The simulation is

performed on different traffic pattern. The proposed 3D algorithm has been used to perform

better in terms of latency and throughput in comparison with existing routing algorithm. The

simulation is done with synthetic traffic pattern in a 8×8×4 3D mesh system design which

shows that with existing routing algorithm the network is powerful and steady under various

traffic patterns, A weighted adaptive routing algorithm for 8×8×4 3D mesh NoC frameworks

with arbitrary traffic pattern reveals to accomplish critical execution improvement in terms of

Maximum delay and throughput with existing XYZ routing algorithm. Throughput for WARA

at packet injection ratio 0.26 is 0.0009893 and maximum delay at packet injection ratio 0.26 is

976.

Keywords: 3D NoC Mesh; Adaptive routing; Vertical interconnect; CMOS technology;

Packaging density

893-899

144

References: 1. W.J. Dally and B. Towles, “Route packets, not wires: On-chipinterconnection networks,” in Proc. DAC, 2001,

pp. 684-689.

2. I. Loi, S. Mitra, T.H. Lee, S. Fujita, and L. Benini, “A low-overhead fault tolerance scheme for TSV-based

3D network on chip links,” inProc. ICCAD, 2008, pp. 598-602. 3. A.-M. Rahmani et al., “Congestion aware, fault tolerant, and thermally

4. efficient inter-layer communication scheme for hybrid NoC-bus 3D architectures,” in Proc. NOCS, 2011, pp.

65-72 5. A. Jantsch and H. Tenhunen, Networks on Chip. Kluwer AcademicPublishers, 2003.

6. B.S. Feero and P.P. Pande, “Networks-on-chip in a three-dimensionalnvironment: a performance evaluation,”

IEEE Trans. Comput., vol. 58, no. 1, pp. 32-45, Jan. 2009. 7. H. Matsutani and M. Koibuchi,“Tightly-coupled multi-layer topologies for 3-D NoCs,” in Proc. ICPP,

2007, pp. 75-75.

8. C. Seiculescu, S. Murali, L. Benini and G. De Micheli, “Sunfloor 3D : a tool for networks on chip topology synthesis for 3D systems on chips,” inProc. DATE, 2009, pp. 9-14.

9. V.F. Pavlidis and E.G. Friedman, “3-D topologies for networks-on- chip,” IEEE Trans. VLSI Syst., vol. 15,

no. 10, pp. 1081-1090, Oct. 2007. 10. V.F. Pavilidis and E.G. Friedman, Three-dimensional IntegrationCircuit Design, Morgan Kaufmann, 2008.

11. A.-M Rahmani et al., “Power and area opitmization of 3D networks-on- chip using smart and efficient

vertical channels,” in Proc. PATMOS, 2011, pp. 278-287. 12. H. Sangki, “3D super-via for memory applications,” Micro-SystemsPackaging Initiative (MSPI) Packaging

Workshop, 2007.

13. C. Liu et al., “Vertical interconnects squeezing in symmetric 3D meshNetwork-on-chip,” in Proc. ASP-DAC, 2011, pp. 357-362.

14. K. Puttaswamy and G.H. Loh, “Thermal herding: microarchitecturetechniques for controlling hotspots in high-performance 3D-integratedprocessors,” in Proc. ISCA, 2008, pp. 251-261.

15. S. Saito et al., “MuCCRA-Cube : a 3D dynamically reconfigurableprocessor with inductive-coupling link,” in

Proc. FPL, 2009. 16. J. Lee, M. Zhu, K. Choi, J.H. Ahn, and R. Sharma, “3D network-on-chip with wireless links through

inductive coupling,” in Proc. International SoC Design Conference (ISOCC), pp.353-356, Nov. 2011.

17. S. Pasricha, “Exploring serial vertical interconnects for 3D ICs,” in Proc. DAC, 2009, pp.581-586. 18. A.-M Rahmani et al., “ARB-NET: a novel adaptive monitoring platform for stacked mesh 3D NoC

architecturs,” in Proc. ASP-DAC, 2012, pp.413-418.

19. Intel Corporation, “A touchstone delta system description,” in: IntelAdvanced Information, 1991. 20. C.J. Glass and L.M. Ni, “The turn model for adative routing,” in Proc.ISCA, 1992, pp. 278-287.

21. M. Li, Q. Zeng, and W. Jone, “DyXY – a proximity congestion-awaredeadlock-free dynamic routing method

for network on chip,” in Proc.DAC, 2006, pp. 849-852.

22. G. Ascia, V. Catania, M. Palesi, and D. Patti, “Implementation andanalysis of a new selection strategy for

adaptive routing in networks-on-chip,” IEEE Trans. Comput., vol. 57, no. 6, pp. 809-820, June. 2008.

23. S. Ma, N.E. Jerger, and Z. Wang, “DBAR: an efficient routing algorithm to support multipleconcurrent applications in networks-on-chip”, in Proc. ISCA, 2011, pp. 413-424.

24. M. Ebrahimi et al., “CATRA- congestion aware trapezoid-based routing algorithm for on-chip networks,” in

Proc. DATE, 2012, pp. 320-325. 25. S.Y. Lin et al., “Traffic-and thermal aware routing for throttled three-dimensional network-on-chip systems,”

in Proc. VLSI-DAT, 2011, pp. 1-4.

26. .Sehgal,V.K., 2015. Markovian models based stochastic communication in networks-in-package. IEEE Transactions on Parallel and Distributed Systems, 26(10), pp.2806-2821.

27. Rui Ben, Fen Ge, Xintian Tong, Ning Wu, Ying Zhang, Fang Zhou, “ A Multicast Routing Algorithm for 3D

Network-on-Chip in Chip Multi_Processors”, Proceedings of the World Congress on Engineering 2018 Vol I WCE 2018, July4-6, 2018, London, U.K..

Authors: Sunayna Khurana, Baljinder Kaur, Jaswinder Singh

Paper

Title:

The Impact of Demographic Factors on Satisfaction of Users for various Digital Payment

Methods

Abstract: There has been significant progress in the usage of digital payment methods as

alternative payment options instead of using cash. Although a majority of researches are still

focusing on factors of adoption of digital payment methods, the present work moved on to the

next level by examining users’ present level of satisfaction. Further, this work augmented by

investigating the impact of demographic factors (gender, age, education, occupation, marital

status and income) on users’ satisfaction with use of various digital payment methods in

Amritsar, Punjab, India. A structured questionnaire was used to collect the data from 163 users

of various digital payment methods. The data were analyzed using statistical techniques. The

results show a significant effect of age, gender, education, occupation, marital status and

145.

income of respondents on users’ satisfaction. The results of the present work revealed valuable

insight into users’ satisfaction with six prevailing digital payment methods vis-à-vis

demographic factors.

Keywords: Digital Payment Methods, Demographic Factors, Satisfaction, Age, Gender,

Education and Occupation.

References: 1. Izogo, E.E., Nnaemeka, O.C., Ama, O., Onuoha, Ezema, K.S., “Impact of Demographic Variables on

Consumers’ Adoption of E-banking in Nigeria: An Empirical Investigation”, European Journal of Business and

Management, vol. 4(17), 2012. ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online). 2. G. Worku G , A. Tilahun , MA Tafa,“The Impact of Electronic Banking on Customers’ Satisfaction in

Ethiopian Banking Industry (The Case of Customers of Dashen and Wogagen Banks in Gondar City)”, 4 J Bus

Fin Aff, BSFA an open access journal, vol. 5(2), 2013. ISSN: 2167-0234 3. Goh Mei Linga , Yeo Sook Ferna , Lim Kah Boona , Tan Seng Huata , “Understanding Customer Satisfaction

of Internet Banking: A Case Study In Malacca”, Fifth International Conference on Marketing and Retailing (5TH

INCOMaR), vol. 37, 2016, pp. 80-85.

4. http://cashlessindia.gov.in/digital_payment_methods.html

5. Jeanne M. Hogarth, Jane Kolodinsky, Tatiana Gabor, “Consumer payment choices: paper, plastic, or

electrons?” Int. J. Electronic Banking, vol. 1(1), 2008. 6. Muslim Amin, Sajad Rezaei, Maryam Abolghasemi, "User satisfaction with mobile websites: the impact of

perceived usefulness (PU), perceived ease of use (PEOU) and trust", Nankai Business Review International, vol.

5(3), 2014, pp.258-274.https://doi.org/10.1108/NBRI-01-2014-0005 7. Shamsher Singh and Ravish Rana, “Study of Consumer Perception of Digital Payment Mode”, Journal of

Internet Banking and Commerc, vol. 22(3), 2017. (http://www.icommercecentral.com)

8. Shraddha Parab and Supriya Bhalerao, “Choosing statistical test” ,Int J Ayurveda Res., vol. 1(3), 2010, pp. 187–191.

doi: 10.4103/0974-7788.72494

9. Siddiqui U.A. & Khan M. S., “An Exploratory Study on Effect of Demographic Factors on Consumer Satisfaction and its Determinants in E-Retailing”, Management Studies and Economic Systems (MSES), vol.

3(3), 2017, pp. 159-171.

10. T. Y Chang, “Dynamics of Banking Technology Adoption: An Application to Internet Banking”, University of Warwick workshops, the European Association for Research in Industrial Economics Conference, 2003

11. The Report of High Level Committee on Deepening of Digital Payment, 2019.

https://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/CDDP03062019634B0EEF3F7144C3B65360B280E420

AC.PDF

12. Vijay M. Kumbhar, “Alternative Banking Channels and Customers’ Satisfaction: An Empirical Study of Public

and Private Sector Banks”, International Journal of Business and Management Tomorrow, vol. 1(1), 2011. 13. Winfred Yaokumah, Peace Kumah, and Eric Saviour Aryee Okai “Demographic Influences on E-Payment

Services”, International Journal of E-Business Research, vol. 13(1), 2017.

900-905

146

Authors: Shakiv Pandit, Dr. Sumit Kaur, Ms. Lofty Saini

Paper

Title:

Automated Edge-based Segmentation and Evolutionary-SVM Algorithm of Fingertips

Bones for Radiographs

Abstract: Skeletal Bone is world-wide used to standard growth for prediction and

assessment for children in endocrinology. A survey of the various papers and found these two

methods used for BAA are GPs and TW2 (Greulich Pyle and Tanner Whitehouse) methods.

Radiograph bone of the patient matched with SR (Standard Radiographs) using Graphics and

consequences, determined in GP technique, whereas in TW2 technique scoring method is used

for assessment for BA. The clinical practice depends on the level of maturity for 20*20

features including the features based on Epiphysis, Diaphysis and Metaphysis in radius

features, Ulna, 1st and 3rd fingers and the carpal Bones, so it is difficult and time-consuming.

The major problems are time-consuming to assess the bone using clinical methods and

scanning phase challenging to predict the X-ray images. The research work has implemented a

novel method using evolutionary support vector machine method to assess the bone age based

on hand, wrist X-ray images, and resolve the issues in existing processes. To develop a

filtration and optimized feature vector extraction and selection method to smooth the hand

wrist X-ray images. To implement in-depth learning approach using ESVM to classify the

assessment rate based on the X-ray Bone Images. After that evaluation of the performance

metrics such as error rate, and Accuracy Rate and compared with the various methods. In the

proposed work’s conclusion has accurately achieved value is 98.7% and existing method

906-911

performance in 1-year assessment 98%, and 2years 97.5 %.

Keywords: Bone Age Assessment, Evolutionary Support Vector Machine, GP and TW2

Method, and Hand Wrist X-ray Images.

References: 1. Thangam, P., Mahendiran, T. V., & Thanushkodi, K, “Skeletal Bone Age Assessment-Research

Directions”, Journal of Engineering Science & Technology Review, vol. 5(1), 2012.

2. Iglovikov, V. I., Rakhlin, A., Kalinin, A. A., & Shvets, A. A, “Paediatric Bone age assessment using deep

convolutional neural networks”, In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, Cham, 2018, pp. 300-308.

3. Choi, J. A., Kim, Y. C., Min, S. J., & Khil, E. K, “A simple method for bone age assessment: the capitohamate

planimetry”, European radiology, vol. 28(6) , 2018, pp. 2299-2307. 4. B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.

5. Pietka, E., Gertych, A., Pospiech, S., Cao, F., Huang, H. K., & Gilsanz, V, “Computer-assisted bone age

assessment: Image preprocessing and epiphyseal/metaphyseal ROI extraction”, IEEE transactions on medical imaging, vol. 20(8), 2001, pp. 715-729.

6. Tristán-Vega, A., & Arribas, J. I, “A radius and ulna TW3 bone age assessment system”, IEEE Transactions on

Biomedical Engineering, vol. 55(5), 2008, pp. 1463-1476. 7. Yuh, Y. S., Liu, C. C., Chang, J. D., & Yu, S. S, “Later stage bone age assessment on hand radiographs”, In 2012

7th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2012, pp. 1329-1332.

8. Hsieh, C. W., Jong, T. L., & Yang, C. S, “Development of the cloud platform for bone age assessment based on tanner and whitehouse method”, In 2013 International Conference on Machine Learning and Cybernetics IEEE,

vol. 3, 2013, pp. 1185-1188.

9. Son, S. J., Song, Y., Kim, N., Do, Y., Kwak, N., Lee, M. S., & Lee, B. D, “TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks”, IEEE Access, vol. 7, 2019, pp. 33346-33358.

10. Tristán-Vega, A., & Arribas, J. I, “A radius and ulna TW3 bone age assessment system”, IEEE Transactions on

Biomedical Engineering, vol. 55(5), 2008, pp. 1463-1476. 11. Thangam, P., Saravanan, R., & Thanushkodi, K, “Robust techniques for automated Bone Age Assessment”,

In 2013 International Conference on Current Trends in Engineering and Technology (ICCTET) IEEE, 2013, pp.

92-94 12. Pietka, E., Gertych, A., Pospiech, S., Cao, F., Huang, H. K., & Gilsanz, V, “Computer-assisted bone age

assessment: Image preprocessing and epiphyseal/metaphyseal ROI extraction”, IEEE transactions on medical

imaging, vol. 20(8), 2001, pp. 715-729. 13. Mora, S., Boechat, M. I., Pietka, E., Huang, H. K., & Gilsanz, V, “Skeletal age determinations in children of

European and African descent: applicability of the Greulich and Pyle standards”, International Pediatric Research

Foundation, vol. 50(5), 2001, pp. 624. 14. Mansourvar, M., Asemi, A., Raj, R. G., Kareem, S. A., Antony, C. D., Idris, N., & Baba, M. S, “A fuzzy

inference system for skeletal age assessment in living individual”, International Journal of Fuzzy Systems, vol.

19(3), 2017, pp. 838-848. 15. Schulze, P. J., & Buurman, R, “Absence of the posterior arch of the atlas”, American Journal of

Roentgenology, vol. 134(1), 1980, pp. 178-180.

16. Ohalloran, R. L., & Lundy, J. K, “Age and ossification of the hyoid bone: forensic implications”, Journal of Forensic Science, vol. 32(6), 1987, pp. 1655-1659.

17. Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., “Fully automated deep learning system

for bone age assessment”, Journal of digital imaging, vol. 30(4), 2017, pp. 427-441. 18. Choi, J. A., Kim, Y. C., Min, S. J., & Khil, E. K, “A simple method for bone age assessment: the capitohamate

planimetry”, European radiology, vol. 28(6), 2018, pp. 2299-2307.

19. Koitka, S., Demircioglu, A., Kim, M. S., Friedrich, C. M., & Nensa, F, “Ossification area localization in pediatric hand radiographs using deep neural networks for object detection”, PloS one, vol. 13(11), 2018.

20. Son, S. J., Song, Y., Kim, N., Do, Y., Kwak, N., Lee, M. S., & Lee, B. D, “TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks”, IEEE Access, vol. 7, 2019, pp. 33346-33358.

147

Authors: Arun Goel, Alka Jindal

Paper

Title:

Use of Periocular Biometric in Face Recognition

Abstract: Biometrics is a technology to identify people based on their physical and

behavioral traits. Enhancement in the field of biometrics have led to development of a trait that

is non intrusive, widely acceptable and can be captured remotely with effective features. Face

biometric is widely used trait all over world. Due to less efficiency of face biometric in non

cooperative environment, researchers suggest to use a sub region of face that is more effective.

Periocular biometric is region surrounding the eyes having modalities including eyebrows,

eyelashes, eyelids, tear duct and skin texture. This paper explores use of periocular region and

its modalities in face recognition including periocular databases and its use in future for

912-917

.

various applications.

Keywords: eyebrow, eyelash, eyelid, face, periocular, skin texture, tear duct

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Applications of Computer Vision (WACV), 2012, pp. 201–208.

11. R. Jillela and A. Ross, “Matching face against iris images using periocular information,” in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 4997–5001.

12. J. Xu, M. Cha, J. L. Heyman, S. Venugopalan, R. Abiantun, and M. Savvides, “Robust local binary pattern

feature sets for periocular biometric identification,” in 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2010, pp. 1–8.

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Extraction for Periocular Biometric Recognition: Less is More,” in 2010 20th International Conference on Pattern Recognition, 2010, pp. 205–208.

14. F. Juefei-Xu and M. Savvides, “Subspace-Based Discrete Transform Encoded Local Binary Patterns

Representations for Robust Periocular Matching on NIST’s Face Recognition Grand Challenge,” IEEE Transactions on Image Processing, vol. 23(8), 2014, pp. 3490–3505.

15. D. L. Woodard, S. J. Pundlik, J. R. Lyle, and P. E. Miller, “Periocular region appearance cues for biometric

identification,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010, pp. 162–169.

16. A. Joshi, A. Gangwar, R. Sharma, A. Singh, and Z. Saquib, “Periocular recognition based on Gabor and

Parzen PNN,” in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 4977–4981. 17. M. Uzair, A. Mahmood, A. Mian, and C. McDonald, “Periocular biometric recognition using image sets,”

in 2013 IEEE Workshop on Applications of Computer Vision (WACV), 2013, pp. 246–251.

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2011, pp. 1–7.

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22. J. R. Lyle, P. E. Miller, S. J. Pundlik, and D. L. Woodard, “Soft biometric classification using periocular

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in 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2016, pp. 1–8.

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Dataset (CK+): A complete dataset for action unit and emotion-specified expression,” in 2010 IEEE

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texture,” in Proceedings of the 2010 ACM Symposium on Applied Computing - SAC ’10, 2010, p. 1496.

29. R. Abiantun and M. Savvides, “Tear-duct detector for identifying left versus right iris images,” in 2008 37th IEEE Applied Imagery Pattern Recognition Workshop, 2008, pp. 1–4.

30. K. Hollingsworth, K. W. Bowyer, and P. J. Flynn, “Identifying useful features for recognition in near-

infrared periocular images,” in in IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems, 2010, pp. 1–8.

31. K. P. Hollingsworth, S. S. Darnell, P. E. Miller, D. L. Woodard, K. W. Bowyer, and P. J. Flynn, “Human

and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light,” IEEE Transactions on Information Forensics and Security, vol. 7(2), 2012, pp. 588–601.

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study,” British Journal of Oral and Maxillofacial Surgery, vol. 44(2), 2006, pp. 89–93. 33. W.-K. Kong and D. Zhang, “Detecting Eyelash and Reflection for Accurate Iris Segmentation,”

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1–8.

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identification,” in IEEE International Joint Conference on Biometrics, 2014, pp. 1–8.

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Iris and Periocular Features,” p. 4. 37. D. L. Woodard, S. Pundlik, P. Miller, R. Jillela, and A. Ross, “On the Fusion of Periocular and Iris

Biometrics in Non-ideal Imagery,” in 2010 20th International Conference on Pattern Recognition, 2010,

pp. 201–204. 38. A. Joshi, A. K. Gangwar, and Z. Saquib, “Person recognition based on fusion of iris and periocular

biometrics,” in 2012 12th International Conference on Hybrid Intelligent Systems (HIS), 2012, pp. 57–62.

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Authors: Mukesh Kumar Mann, Vishal Ahlawat, Sunil Nain, Malkit Gir, Aman Beniwal

Paper

Title:

Wear and Friction Behaviour of Kans Grass Fiber/Polyester Composites

Abstract: The friction and wear behaviour of kans grass fiber (KGF) polyester composites

were examined. An amount of 0, 10, 13.35, 18 and 20.08 vol % was reinforced into the

polyester resin for composite development using hand layup technique. The tribo-test

specimens of size 4×4×50 mm were cut from the fabricated composites and tested on wear and

friction testing machine at 0.5-1.5 m/s of sliding velocity and a constant load of 5 N for 60

seconds. It was found that the neat polyester specimen possessed less wear resistance than the

KGF composites at almost all the sliding conditions. The fiber content and variation in sliding

velocity slightly influenced the friction behaviour. The increase in fiber vol% from 10 to 20.08

marginally increased the average friction coefficient of the KGF composite specimens at 0.5

m/s and 1.5 m/s whereas fluctuating behaviour was noticed at intermediate sliding velocity.

Keywords: friction and wear behaviour, kans grass fiber, polyester composites.

References: 1. A. M. Eleiche and G. M. Amin, “The effect of unidirectional cotton fibre reinforcement on the friction and

wear characteristics of polyester,” Wear, vol. 112, no. 1, pp. 67–78, 1986. 2. L. T. Drzal, A. K. Mohanty, and M. Misra, “Bio-composite materials as alternatives to petroleum-based

composites for automotive applications,” Magnesium, vol. 40, no. 60, pp. 1–3, 2001.

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148

3. V. K. Thakur, M. K. Thakur, and R. K. Gupta, “raw natural fiber–based polymer composites,” International Journal of Polymer Analysis and Characterization, vol. 19, no. 3, pp. 256–271, 2014.

4. A. Nourbakhsh and A. Ashori, “Wood plastic composites from agro-waste materials: Analysis of mechanical

properties,” Bioresource technology, vol. 101, no. 7, pp. 2525–2528, 2010. 5. U. Nirmal, B. F. Yousif, D. Rilling, and P. V. Brevern, “Effect of betelnut fibres treatment and contact

conditions on adhesive wear and frictional performance of polyester composites,” Wear, vol. 268, no. 11–12,

pp. 1354–1370, 2010. 6. J. Holbery and D. Houston, “Natural-fiber-reinforced polymer composites in automotive applications,” Jom,

vol. 58, no. 11, pp. 80–86, 2006.

7. H. Ku, H. Wang, N. Pattarachaiyakoop, and M. Trada, “A review on the tensile properties of natural fiber reinforced polymer composites,” Composites Part B: Engineering, vol. 42, no. 4, pp. 856–873, 2011.

8. B. Barari et al., “Mechanical characterization of scalable cellulose nano-fiber based composites made using

liquid composite molding process,” Composites Part B: Engineering, vol. 84, pp. 277–284, 2016. 9. R. A. Ibrahim, “Tribological performance of polyester composites reinforced by agricultural wastes,”

Tribology International, vol. 90, pp. 463–466, 2015.

10. C. E. Correa, S. Betancourt, A. Vázquez, and P. Gañan, “Wear resistance and friction behavior of thermoset matrix reinforced with Musaceae fiber bundles,” Tribology International, vol. 87, pp. 57–64, 2015.

11. N. A. Nordin, F. M. Yussof, S. Kasolang, Z. Salleh, and M. A. Ahmad, “Wear rate of natural fibre: long

kenaf composite,” Procedia Engineering, vol. 68, pp. 145–151, 2013.

12. V. Ahlawat, A. Malik, and C. Singh, “Parametric Optimization and Wear Behavior of Fiber-Reinforced

Polyester Composites,” IUP Journal of Mechanical Engineering, vol. 8, no. 3, 2015.

13. V. Ahlawat, S. Kajal, and A. Parinam, “Experimental analysis of tensile, flexural, and tribological properties of walnut shell powder/polyester composites,” Euro-Mediterranean Journal for Environmental Integration,

vol. 4, no. 1, p. 1, 2019.

14. V. Ahlawat, A. Parinam, and S. Kajal, “Experimental study of tensile and flexural properties of kans grass fiber reinforced polyester composites,” 2018.

15. P. K. Bajpai, I. Singh, and J. Madaan, “Tribological behavior of natural fiber reinforced PLA composites,”

Wear, vol. 297, no. 1–2, pp. 829–840, 2013.

149

Authors: Manmeet Kaur, Surinder Singh, Preeti Bansal, Gaurav Kumar

Paper

Title:

Smart Routing with Segmental Routing mechanism for Wireless Mesh Network (WMN)

Abstract: The wireless mesh networks (WMN) are the growing mediums of connectivity for

the purpose of internet or intranet connectivity. The routing among the WMN becomes a

challenging task with the rise in the number of nodes across the network and the larger data

volumes. In this paper, the work has been carried out on the growing WMNs with the dynamic

number of nodes. The node availability aware neighbor formation and status tracking

mechanism has been applied in this scheme in order to keep the network updated about the

working nodes and to eliminate the non-functional nodes from the connected mesh network.

The neighbor query process is utilized for the path building towards the base stations (BTS) or

Sink node in the given WMN networks. Additionally; the routing information is collected from

all of the neighboring nodes towards the destination paths using the desired mechanism for the

path discovery under the segmental routing mechanism for WMN, which has been created with

the capability of handling the network path failures dynamically in the local domain of the

given network zone. The performance of the proposed model has been analyzed in the form of

energy consumption, end-to-end delay and detailed energy & packet based analysis

Keywords: Segmental routing, WMN routing, Mesh networking, Route recovery

References: 1. Kwon, Kiwoong, Minkeun Ha, Taehong Kim, Seong Hoon Kim, and Daeyoung Kim. "The stateless point to

point routing protocol based on shortcut tree routing algorithm for IP-WSN." In Internet of Things (IOT), 2012 3rd International Conference on the, 2012, pp. 167-174.

2. Bechkit, Walid, Mouloud Koudil, Yacine Challal, Abdelmadjid Bouabdallah, Brahim Souici, and Karima

Benatchba. "A new weighted shortest path tree for convergecast traffic routing in WSN." In Computers and Communications (ISCC), 2012 IEEE Symposium on, 2012, pp. 187-192.

3. Delaney, D., Russell Higgs, and G. O'Hare. "A stable routing framework for tree-based routing structures in

wsns." 2014, pp. 1-1. 4. Ghadimi, Euhanna, Olaf Landsiedel, Pablo Soldati, Simon Duquennoy, and Mikael Johansson. "Opportunistic

Routing in Low Duty-Cycled Wireless Mesh networks." ACM Transactions on Mesh networks vol. 10(4),

2014.

5. Sahin, Dilan, Vehbi Cagri Gungor, Taskin Kocak, and Gurkan Tuna. "Quality-of-service differentiation in single-path and multi-path routing for wireless mesh network-based smart grid applications." Ad Hoc

922-926

Networks, 2014.

6. Singh, Dharmendra, Shubhanjali Sharma, Vinesh Jain, and Jyoti Gajrani. "Energy efficient source based tree

routing with time stamp in WSN." In Signal Propagation and Computer Technology (ICSPCT), 2014

International Conference on, 2014, pp. 120-124. 7. Tunca, Can, Sinan Isik, M. Donmez, and Cem Ersoy. "Distributed Mobile Sink Routing for Wireless Mesh

networks: A Survey." 2014, pp. 1-21.

8. Chakraborty, Dibakar. "i-QCA: An intelligent framework for quality of service multicast routing in multichannel multiradio wireless mesh networks." Ad Hoc Networks vol. 33, 2015, pp. 221-232.

9. Lall, Shruti, B. T. J. Maharaj, and PA Jansen van Vuuren. "Null-frequency jamming of a proactive routing

protocol in wireless mesh networks." Journal of Network and Computer Applications, 2015.

10. Lai, I-Wei, Wen-Chung Kao, and Nobuo Funabiki. "Cross-layer selective routing for active access-point

minimization in wireless mesh network." InConsumer Electronics-Taiwan (ICCE-TW), 2015 IEEE International Conference on, 2015, pp. 482-483.

11. Al-Saadi, Ahmed, Rossitza Setchi, Yulia Hicks, and Stuart M. Allen. "Routing Protocol for Heterogeneous Wireless Mesh Networks." IEEE Transactions on Vehicular Technology, vol. 65 (12), 2016, pp. 9773-9786.

12. Zhou, Anfu, Min Liu, Zhongcheng Li, and Eryk Dutkiewicz. "Joint Traffic Splitting, Rate Control, Routing,

and Scheduling Algorithm for Maximizing Network Utility in Wireless Mesh Networks." IEEE Transactions

on Vehicular Technology, vol. 65(4), 2016, pp. 2688-2702.

13. Murugeswari, R., S. Radhakrishnan, and D. Devaraj. "A multi-objective evolutionary algorithm based QoS

routing in wireless mesh networks." Applied Soft Computing, vol. 40, 2016, pp. 517-525.

14. Sakamoto, Shinji, Tetsuya Oda, Makoto Ikeda, Leonard Barolli, and Fatos Xhafa. "Implementation of a New

Replacement Method in WMN-PSO Simulation System and Its Performance Evaluation." In Advanced Information Networking and Applications (AINA), 2016 IEEE 30th International Conference on, 2016, pp.

206-211.

15. Hassan, Sharma, Singh “Detection and Mitigation of Selfish Node in Wireless Mesh Networks (WMN’s)” international journal of technology and computing (ijtc), ISSN-2455-099X, vol. 5(5), 2019.

150

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Authors: Alankrita Aggarwal, Kanwalvir Singh Dhindsa, P. K. Suri

Paper

Title:

Usage Patterns and Implementation of Random Forest Approach for Software Risk and

Bugs Predictions

Abstract: The software bugs predictions whereby the datasets of different types of bugs are

evaluated for further predictions. In this research manuscript, the pragmatic evaluation of

random forest approach is done and compared with results with traditional artificial neural

networks (ANN) so that the results can be compared. From the outcome, the extracts from

random forest are better on the accuracy level with the test datasets used in a specific format.

The process of Random Forest (RF) Approach is adopted in this work that gives the effectual

outcomes in most of the cases as compared to ANN and thereby the usage patterns of RF are

performance aware. The paradigm of RF is used widely for the engineering optimization to

solve the complex problems and generation of the dynamic trees. The outcomes and results

obtained and presented in this work is giving the variations in favor random forest based

optimization for the software risk management and predictive mining. The need of the

proposed work and background of the study includes the effective and performance based

software bugs detection. The current problem addressed includes the accuracy and multi-

dimensional evaluations. The key methodology adopted here to solve the existing problem is

the integration of Random Forest approach and the findings are quite effective and cavernous

in assorted aspects.

Keywords: Artificial Neural Network, Random Forest Approach, Software Risk

Management, Software Risk Prediction, Soft Computing for Software Bugs Prediction

References: 1. Yavari, M. Zavvar, S. M. Mirhassannia, M. R. Nehi, A. Yanpi, and M. H. Zavvar, “Classification of Risk in

Software Development Projects using Support Vector Machine”, Journal of Telecommunication Electronics

Computer Engineering, vol. 9(1), 2017, pp. 1–5.

2. D. Greer, E. E. Odzaly, and D. Stewart, “Agile risk management using software agents”, Journal of Ambient Intelligence and Humanized computing, vol. 9(3), 2018, pp. 823-841.

3. F. Lu, H. Bi, M. Huang, and S. Duan, “Simulated Annealing Genetic Algorithm Based Schedule Risk

Management of IT Outsourcing Project”, Mathematical Problems in Engineering, 2017, pp. 1-17. 4. M. Tießler, D. M. Fernández, M. Kalinowski, M. Felderer, and M. Kuhrmann, “On Evidence-based Risk

Management in Requirements Engineering,”, International Conference on Software Quality, 2018, pp. 39–59.

5. T. Rauter, N. Kajtazovic, and C. Kreiner , “Asset-Centric Security Risk Assessment of Software Components,” 2nd International workshop on MILS: Architecture and Assurance for Secure Systems, 2016, pp. 1-17

6. Abdelrafe MS, ”Managing Software Project Risks Using Stepwise and Fuzzy Regression Analysis Modeling

927-932

Techniques”, 2016.

7. C. Krishna and K. Subrahmanyam, “A Decision Support System for Assessing risk using Halstead approach and

Principal Component Analysis,”, vol. 9(4), 2016, pp. 3383–3387.

8. P. Purandare, “An entropy based approach for risk factor analysis in a software development project” International Journal of Applied Engineering Res., vol. 11(4), 2016, pp. 2258–2262.

9. M. Choetkiertikul, H. K. Dam, T. Tran, and A. Ghose, “Predicting delays in software projects using networked

classification,”, Proc. - 2015 30th IEEE/ACM International Conference of Automation Software engineering , ASE 2015, 2016, pp. 353–364.

10. Elzamly, B. Hussin, S. S. A. Naser, and M. Doheir, “Predicting Software Analysis Process Risks Using Linear

Stepwise Discriminant Analysis: Statistical Methods,” Int. J. Adv. Inf. Sci. Technol., vol. 38(38), 2015, pp. 108–115.

11. Mohanty, R., & Ravi, V, “Machine Learning Techniques to Predict Software Defect. In Artificial Intelligence:

Concepts, Methodologies, Tools, and Applications”, IGI global, 2017, pp. 1473-1487. 12. Singh, R., Raja, R., & Chopra J, ” Software Defect Prediction Using Averaging Likelihood Ensemble

Technique”, 2017

13. Ali, M. M., Huda, S., Abawajy, J., Alyahya, S., Al-Dossari, H., & Yearwood, J. A, ” Parallel framework for software defect detection and metric selection on cloud computing”, Cluster Computing, vol. 20(3), 2017, pp.

2267-2281.

14. Osman, H., Ghafari, M., & Nierstrasz, O, “Automatic feature selection by regularization to improve bug

prediction accuracy.”, In Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), IEEE

Workshop on IEEE, 2017, pp. 27-32.

15. Maddipati, S. S., Pradeepini, G., & Yesubabu, A, ” Software Defect Prediction using Adaptive Neuro Fuzzy Inference System.” International Journal of Applied Engineering Research, vol. 13(1), 2018, pp. 394-397.

16. Lalit Mohan Goyal, Mamta Mittal, Iqbaldeep Kaur, Sumit Kaur, Amit Verma, D. Jude Hemanth, “Performance

Enhanced Growing Convolutional Neural Network Based Approach for Brain Tumor Segmentation in Magnetic Resonance Brain Images”, Applied Soft Computing, (SCIE Indexed journal), 4.004 (Second Revision Submitted)

17. Xuan, J., Jiang, H., Hu, Y., Ren, Z., Zou, W., Luo, Z., & Wu, X, ”Towards Effective Bug Triage with Towards

Effective Bug Triage with Software Data Reduction Techniques”, arXiv preprint, 2017. arXiv: 1704.04761

151

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Authors: Ajit Singh, Sultan Ahmad, Mohammad Imdadul Haque

Paper

Title:

Big Data Science and EXASOL as Big Data Analytics tool

Abstract: Big data and Data science are the two top trends of recent years. Both can be

combined together as big data science. This leads to the demand for new system architectures

which facilitates the development of processes which can handle huge data volumes without

deterring the agility, flexibility and the interactive feel which suits the exploratory approach of

a data scientist. Businesses today have found ways of using data as the principal factor for

value generation. These data-driven businesses apply a variety of data tools as data analysis is

one of the chief elements in this process. In order to raise data science to the new

computational level that is required to meet the challenges of big data and interactive advanced

analytics, EXASOL has introduced a new technological approach. This tool enables us more

effective and easy data analysis.

Keywords: Big Data Science, EXASOL, Big Data, Data Science.

References:

1. Chen, J., Chen, Y., , Xiaoyong D.U., Cuiping L.I., Jiaheng L.U. ,Suyun Z., Xuan Z., “Big

data challenge: a data management perspective”, Front. Comput. Sci., vol. 7(2), 2013, pp. 157–164.

2. Kalra, B., Yadav, S., and Chauhan, D.K. . “A Review of Issues and Challenges with Big Data”, International

Journal of Computer Science and Information Technology Research, vol. 2(4), 2014, pp.97-101. 3. Jaseena K.U and David, J.M., “Issues, Challenges, and Solutions: Big Data Mining”, Computer Science &

Information Technology, 2014, pp. 131–140.

4. Fan, J.,Han, F., Liu, H., “Challenges of Big Data analysis . National Science Review”, vol. 1, 2014 , pp. 293–314. 5. Mandale, A. and Gadage, S., “Big Data Analytics: Challenges, Tools”. International Journal of Innovative

Research in Computer Science & Technology, vol. 3(3), 2015, pp. 10-14.

6. Jin, X., Wah, B.W., Cheng, X., and Wang, Y., “Significance and Challenges of Big Data Research”, Big Data Research, vol. 2, 2015, pp. 59–64.

7. Kubina, M., Varmus, M., and Kubinova, I., “Use of big data for competitive advantage of company” Procedia

Economics and Finance, vol. 26, 2015, pp. 561-565. 8. Pai, V. “Big Data New Challenges, Tools and Techniques”, International Journal of Engineering Research and

Modern Education, vol. 1(1), 2016, pp. 743-750.

9. Mohan, A. Big Data Analytics: Recent Achievements and New Challenges. International Journal of Computer Applications Technology and Research, vol. 5(7), 2016, pp. 460-464.

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10. Schroeder, R. “Big data business models: Challenges and opportunities”. Cogent Social Sciences, vol. 2, 2016, pp.

1-15.

11. Jelonek, D. “Big data analytics in the Management of Business”. MATEC Web of Conferences, vol. 125, 2017.

12. Alsghaier, H., Akour, M., Shehabat, I., and Aldiabat. S., “The importance of big data analytics in business: A case study”, American Journal of Software Engineering and Applications, vol. 6(4), 2017, pp. 111-115.

13. Tukkoji, C and Seetharam, “A Comprehensive Survey on Big-Data Issues, Challenges and Management

Approaches on Cloud Environment”, International Journal of Advanced Research in Computer and

Communication Engineering, vol. 6(2), 2017.

14. Naganathan, V, “Comparative Analysis of Big Data, Big Data Analytics: Challenges and Trends”, International

Research Journal of Engineering and Technology, vol. 5(5), 2018, pp. 1948-1964. 15. Rahaman.A., Rajesh. S, Rani, G., “Challenging tools on Research Issues in Big Data Analytics” International

Journal of Engineering Development and Research, vol. 6(1), 2018, pp. 637-644.

16. https://www.exasol.com/en/community/resources/resource/a-peek-under-the-hood/ Whitepaper: EXASOL :A Peek Under The Hood

17. https://www.exasol.com/en/community/resources/resource/a-drill-down-into-exasol/ Whitepaper : A Drill-Down

into EXASOL

152.

Authors: Rakhi Sharma, Dr. Sukhdeep Kaur, Dr. P N Hrisheekesha, Dr. Pooja Sahni

Paper

Title:

TREE shape micro-strip Antenna using stack layer DGS

Abstract: This paper describes a tree structure antenna with defected ground structure &

CPW feeding. In this proposed antenna, Swasthik layer defected ground structure has been

considered to achieve wide-band & ultra-wideband (UWB) characteristics for impedance

matching. The simulation bandwidth matching from S11<-10dB and frequency from 5GHz-

20GHz. The Swasthik shape antenna is designed for the enhancement of multiband frequency

applicable for L-band, D-band & SHF-band. The antenna performance evaluating the dense

antenna that is applicable for portable communication devices.

Keywords: defected ground structure, CPW feeding, microstrip antenna, FR4 epoxy

References:

1. ParulDawar, N. S. Raghava, Asok De. “UWB Metamaterial-Loaded Antenna for C-Band Applications”,

International Journal of Antennas and Propagation, 2019

2. Tale Saeidi, Idris Ismail, Wong Peng Wen, Adam R. H. Alhawari, Ahmad Mohammadi. “Ultra-Wideband Antennas

for Wireless Communication Applications”, International Journals of Antennas and Propagation, 2019

3. A. Nouri, G. R. Dadashzadeh. “A Compact UWB Band-Notched Printed Monopole Antenna With Defected Ground Structure”, IEEE Antennas and Wireless Propagation Letters, 2011

4. D. Guha, M. Biswas, Y.M.M Antar. “Microstrip Patch Antenna With Defected Ground Structure for Cross

Polarization Supression”, IEEE Antennas and Wireless Propagation Letters, 2005 5. Pei, Jing, An Guo Wang, and Wen Leng. “A Novel Arc-Shaped Printed Antenna for WLAN Applications”, Applied

Mechanics and Materials, 2011

6. Geetanjali Singla, Rajesh Khana. “Modified CPW-fed rotated E-slot antenna for LTE/WiMAX applications”, International Journal of Microwave and Wireless Technologies, 2014

7. SonaliKatoch, Harshita Jotwani, ShuchismitaPani, AsmitaRajawat. “A Compact dual band antenna for IOT

applications”, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT),2015 8. AshnaKakkar, Nirdosh, Shalini Sah. “A tri-band circular patch microstrip antenna with different shapes in DGS for

Ku and K applications”, 2017 2nd International Conference on

9. Anil Kr Gautam, SwatiYadav, Binod Kr Kanaujia. “A CPW-Fed Compact UWB Microstrip Antenna”, IEEE Antennas and Wireless Propagation letters, 2013

10. “Table of contents”, IEEE Transactions on Antenna and Propagation, 2018

938-943

Authors: Ankur Singhal, Vinay Bhatia, Abhishek Sharma

Paper

Title:

Optical Networks for Optimized and cost effective Performance

Abstract: Recently there is rapid increase of multimedia applications in the access networks.

The focus of this paper is to suggest a novel system architecture that can provide efficient and

cost effective solutions for the access networks. To offer economical solutions with higher

bandwidth optical networks are designed with passive components. In the presented system,

512 subscribers can access information for aggregated system line rate of 80Gbps. Proposed

frameworks are subjected to intensive investigation in terms of Modulation Format, External

Modulator, Photo Detector so that system architectures perform at an optimized level. Also, the

access network is designed without the use of reach extender devices like optical amplifier or

153

repeater so that installation and the recurring cost are minimized.

Keywords: Access networks, Central office, Cost, Modulation formats, Optical networks,

References: 1. Kulkarni, S., andSayed, M., “FTTH based Broadband Access Technologies: Key Parameters for Cost Optimized

Network Planning,” Bell Labs Technical Journal, 14 (04), (2010), pp. 297-310. 2. Breuer, D.,Geilhardt, F.,Hulsermann, R., Kind, M., Lange, C.,Monath,T., andWeis, E. “Opportunities for Next-

Generation Optical Access,” IEEE Communications Magazine, 49 (02), (2011),pp. S16-S24.

3. Arevalo, G., Hincapie, R., and Gaudino, R., “Optimization of Multiple PON Deployment Costs and Comparison between GPON, XGPON, NGPON2 and UDWDM PON,”Optical Switching and Networking, 25, (2017), pp. 80-

90.

4. Goyal, R., and Kaler, R.S., “A Novel Architecture of Hybrid (WDM/TDM) Passive Optical Networks with Suitable Modulation Format,” Optical Fiber Technology, 18(06), (2012), pp. 518-522.

5. Dixit, A.,Lannoo, B.,Das, G.,Colle, D., Pickavet, M.,andDemeester, P., “Flexible TDMA/WDMA Passive Optical

Network: Energy Efficient Next-Generation Optical Access Solution,” Optical Switching and Networking,10 (04), (2013), pp. 491-506.

6. Urban, P.J.,Pluk, E.G.C.,Laat, M.M.,Huijskens, F.M.,Khoe, G.D.,Koonen, A.M.J.,and Waardt, H., “1.25-Gbps

Transmission over an Access Network Link with Tunable OADM and a Reflective SOA,” Photonics Technology Letters, 21 (06), (2009), pp. 380-382.

7. Kaur, A.,Singh, M.L.,and Sheetal, A., “Simulative Analysis of Co-Existing 2.5 G/10G Asymmetric XG-PON

System using RZ and NRZ Data Formats,” Optik-International Journal of Light and Electron Optics, 125 (14), (2014), pp. 3637-3640.

8. Pandey, G.,andGoel, A., “Performance Analysis of Symmetrical 10 Gbps Colorless WDM-PONusing Subcarrier Modulated Downstream and Wavelength Converted Upstream through RSOA,” Optik-International Journal of

Light and Electron Optics, 125 (17), (2014), pp. 4951-4954.

9. Nhat, N.D.,Elsherif, M.A., Minh, H.L.,and Malekmohammadi, A., “NRZ versus RZ over Absolute added Correlative Coding in Optical Metro Access Networks,” Optics Communications, 387, (2017), pp. 30-36.

10. Elmagzoub, M.A.,Mohammad, A.B.,Shaddad, R.Q., and Gailani, S.A., “Physical Layer Performance Analysis of

Hybrid and Stacked TDM-WDM 40G-PON for Next Generation PON,” Optik-International Journal of Light and Electron Optics, 125 (20), (2014), pp. 6194-6197.

11. Yin, A.,Li, L.,and Zhang, X., “Analysis of 2.5 Gbit/s GPON Downlink Optical-Receiver Performance,” Optics

Communications, 282 (02), (2009), pp. 198-203. 12. Anuar, M.S.,Aljunid, S.A.,Arief, A.R.,and Saad, N.M., “PIN versus Avalanche Photodiode Gain Optimization in

Zero Cross Correlation Optical Code Division Multiple Access System,” Optik-International Journal of Light and

Electron Optics, 124 (04), , (2013), pp. 371-375.

944-948

154

Authors: Akanksha Gaur, Mohammad Arif

Paper

Title:

Software Test Suite Minimization using Ant Colony Optimization

Abstract: In the fast pacing technological era, the key to a successful software industry is

quick delivery of high quality software to the clients. This high quality is achieved by

performing software testing on the product. The high quality product ensures stakeholder’s

satisfaction which in turn spreads good word about the software industry making it a success.

In this paper, we will focus on the problems faced during regression testing and how the same

can be handled. Regression testing is a critical activity done during the software maintenance

phase of the software development cycle. However, it has countless underlying issues like

effective test case generation and prioritization, etc which need to be dealt with. These issues

demand effort, time and cost of the testing. Different techniques and methodologies have been

proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite

minimization has been an area of interest for many researchers. This paper presents an

implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO

helps choose an optimal solution to the problem.

Keywords: Analysis, Ant Colony Optimization, Software testing, Test suite minimization

References: 1. G.M. Kapfhammer, “Software Testing,” Chapter in book, Department of Computer Science, Allegheny College,

June 2003. 2. Basu, Anirban (2015). Software Quality Assurance, Testing and Metrics. PHI Learning, ISBN 978-81-203-5068-7.

3. S. Yoo. and M. Harman, “Regression testing minimization, selection and prioritization: a survey.” , Software

Testing Verification and Reliability, 2010. doi: 10.1002/stvr.430.

949-953

4. E. Engström, P. Runeson, "A Qualitative Survey of Regression Testing Practices," Lecture Notes on Computer Science (LNCS), Springer Verlag, 2010, pp. 3-16.

5. Caro, G. Di and Dorigo, M., “AntNet: Distributed stigmergetic control for communications networks” Journal of

Artificial Intelligence Research, vol. 9, 1998, pp. 317-365. 6. M. Dorigo, V. Maniezzo, and Colorni, A. “Ant System: Optimization by a colony of cooperating agents” IEEE

Transactions on Systems, Man and Cybernetics, vol. 3(26), 1996, pp. 29-41.

7. L. Marsh, C. Onof, “Stigmergic epistemology, stigemergic cognition”, Cognitive Systems Research, vol. 9, 2008, pp. 136.

8. A. Ramarajan, S. Usha “Diversity Based Genetic Algorithm for Efficient Test Case Selection”, International

Journal of Advanced Research in Computer and Communication Engineering, vol. 5(4), 2016. 9. B. Suri, S. Singhal “Evolved regression test suite selection using BCO and GA and empirical comparison with

ACO”, CSIT, vol. 3(2–4), 2015, pp. 143–154.

10. M. Arif, K. I. Rahmani “Adaptive ARA (AARA) for MANETs”, IEEE Xplore in the Proceedings of 3rd Nirma University International Conference on Engineering [NUiCONE 2012], 2012. ISBN 978-1-4673-1720-7.

11. M. Arif, T. Rani. “ACO based Routing for MANETs”, International Journal of Wireless & Mobile Networks

(IJWMN). vol. 4(2),2012, pp. 163-174. ISSN: 0975 - 3834[Online]; 0975 - 4679 [Print]. 12. M. Arif, T. Rani. “Enhanced Ant Colony based Routing in MANETs”. Proceedings of 5th IEEE International

Conference on Advanced Computing & Communication Technologies [ICACCT-2011], 2011, pp. 48-54. ISBN

81-87885-03-3.

13. H. Li and C. Peng Lam, “Software Test Data Generation Using Ant Colony Optimization,” Transactions on

Engineering, Computing and Technology, 2005.

14. R.S. Parpinelli, H.S. Lopes, and A.A. Freitas, “Data mining with an ant colony optimization algorithm,” IEEE Transactions on Evolutionary Computation, vol. 6, 2002, pp. 321–332.

15. P. Zhao, P. Zhao, and X. Zhang, “New Ant Colony Optimization for the Knapsack Problem,” Proceedings of the

7th International Conference on Computer-Aided Industrial Design and Conceptual Design, 2006, pp 1-3. 16. K. Doerner, W.J. Gutjahr, “Extracting Test Sequences from a Markov Software Usage Model by ACO,”

Proceedings of GECCO 2003, LNCS Springer-Verlag Berlin Heidelberg, vol. 2724, 2003, pp. 2465-2476.

17. F.P. McMinn, M. Holcombe, “The State Problem for Evolutionary Testing,” Proceedings of GECCO 2003, LNCS, Springer Verlag, vol. 2724, 2003, pp. 2488-2500.

18. B. Suri and S. Singhal, “Implementing Ant Colony Optimization” for Test Case Selection and Prioritization”,

International Journal on Computer Science and Engineering, vol. 3(5), 2011, pp. 1924-1932. 19. L. M Goyal, M. Mittal and J. K.. Sethi, “Fuzzy Model Generation using Subtractive and Fuzzy C–Means

Clustering”, CSI Transaction on ICT, Springer, 2016, pp 129-133

20. A. Saxena, M. Mittal and L.M. Goyal, “Comparative Analysis of Clustering Methods”, International Journal of Computer Applications, vol.118(21), 2015, pp. 30-35.

21. M. Mittal, L.M. Goyal, D. J. Hemanth and J. K. Sethi, “Clustering Approaches for High-Dimensional Databases:

A Review”, WIREs Data Mining KnowlDiscov, John Wiley & Sons, 2019, pp. 1-14. DOI: 10.1002/widm.1300

22. M. Mittal, L.M. Goyal, J. K. Sethi and D.J. Hemanth, “Monitoring the Impact of Economic Crisis on Crime in

India Using Machine Learning”, Computational Economics, Springer, 2018, pp. 1-19.

23. Rajesh Singh, Anita Gehlot, Mamta Mittal, Rohit Samkaria and Sushabhan Choudhury, “Application of iCloud and Wireless Sensor Network in Environmental Parameter Analysis”, International Journal of Sensors, Wireless

Communications and Control, vol 7(3), 2018, pp. 170-177.

24. Singh A., Mittal M., Kapoor N, “Data Processing Framework Using Apache and Spark Technologies in Big Data” Big Data Processing Using Spark in Cloud. Studies in Big Data, vol 43, 2018, pp 107-122.

25. Mamta Mittal, D. Jude Hemanth, Valentina Emilia Balas and Ragavendra Kumar, “BigData for Parallel

Computing” Advances in Parallel Computing Series ,IOS Press, 2018. 26. Mamta Mittal, Lalit Mohan Goyal, Jasleen Kaur Sethi and D Jude Hemanth, “Monitoring the Impact of Economic

Crisis on Crime in India Using Machine Learning”, Computational Economics, Springer , 2018, pp. 1-19.

27. Goyal L. M., M. Mittal and Sethi, J. K., “Fuzzy Model Generation using Subtractive and Fuzzy C–Means Clustering”, CSI Transaction on ICT, Springer, 2016, pp 129-133

28. Nidhi Beniwal, Mittal M., Goyal L.M. & Monika, “Enhance Ad-hoc On-Demand Distance Vector Routing Protocol”, Indian Journal of Computer Science and Engineering, vol 8 (3), 2017, pp. 463-469.

29. Aakash Saini, Mamta Mittal, Shweta Singh, “An FPGA Based Efficient Surveillance System: A Split Processing

Approach”, International Journal of Imaging and Robotics, vol. 18(3), 2018. 30. Shivani Chauhan, Mamta Mittal, Aakash Saini, “Microstrip Patch Antenna: A Design to Study the Parametric

Trade-off”, International Journal of Research, vol. 4(14), 2017, pp 2646- 2654. ISSN 2348-6848

Authors: Nikhat Azhar, Mohd. Haroon

Paper

Title:

Dynamic Load balancing by Round Robin and Warshall Algorithm in Cloud Computing

Abstract: Cloud Computing is a well- known technology that delivers scalable, fault–

tolerant, consistent, secure and reliable computational services. Cloud figure out grants its

services in subscriptions means user have to pay as much they use. Due to the continuously

progressing and increasing area, cloud figure out is a very good topic for researchers. The main

area of interest in cloud figure out is virtualization, collates, software defined network, network

function virtualization and many more. There are limited load collates algorithms are used in

155

cloud, and all algorithms having its own advantages and disadvantages. This is the requirement

of time to invent a new efficient algorithm in load collate in cloud figure out, through which

we can get maximum throughput, minimum response time, etc. Here in this research paper

authors are proposing a new approach of dynamic load collate, which mainly depends on the

distance between datacenter to host. Simulation is done by CloudSim toolkit. Authentication

and effectiveness of the proposed load collate algorithm are checked by comparative analysis

with existing load collate algorithm.

Keywords: load collate, cloud figure out, host, virtual machine, cloud simulator

References: 1. Rajat1, Dr. Sanjeev Kumar, ”cloud based load balancing architecture: a study”, IJCSE March 2017-Sept, vol. 8(2)

, 2017, pp.112-116. 2. Aanjey Mani Tripathi*, Sarvpal Singh. “A literature review on algorithms for the load balancing in cloud

environments and their future trends" , Computer Modelling & New Technologies 2017, vol. 21(1), 2017, pp 64-

73,

3. Kumar Mishra, "needs, objective and major challenges in cloud-A Systematic review", International Journal of

Computer Applications, vol.. 131(18), 2017.

4. Shreedhar g. Domanal, G. Ram Mohana reddy.”Optimal Load Balancing in cloud computing by efficient utilization of virtial machine”. 978-1-4799-36309/14@ IEEE 2014.

5. Ritu Garg, Mamta Mittal, Le Hoang Son , “Reliability and Energy Efficient Workflow Scheduling in Cloud

Environment”, Cluster Computing, 2019, pp. 1-15 6. Ponam Kumari, mohit Saxena, “A round robin based load approach Scalable demands and maximized resourse

availability ”, International Journal of engineering and Computer science, ISSN: 2319-7242, Val. 5(8), 2016 . 7. Geethu Gopinath PP(1), Shriram K.Vasudevan, "An in-depth analysis and study Load technique in the Cloud

environment". ELSEVIER 2015.

8. Prashant Maheta, "utilizing round robin Concept for load balancing algorithm at virtual machine level in cloud environment", conference paper in International Journal of Computer Application, May 2014, Doi:

10.5120/16332-5612.

9. Divya Chaudhry, Rajinder Singh chiller. " A New Load technology in cloud environment" International

journal of computer application, vol. 69(23), 2013.

10. Hamid Shoja, Hossein Nahid, Reza Azizi, “A Comparative Survey on Load Balancing Algorithms in Cloud ”, 5th ICCCNT 2014, 2014, pp.11- 13, IEEE – 33044

11. Supreeth S 1, Shobha Biradar 2, “Scheduling Virtual Machines for Load balancing in Cloud Platform”,

International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064, vol. 2(6), 2013. 12. Nikhat Azhar, Mohd Haroon,” A Novel Method for Load Balancing In Cloud Computing: Round Robin with

Floyd-Warshall Algorithm”. International Journal of Science and Research(IJSR), vol. 8(2), 2019.

. Akinniyi Ojo∗, Ngok-Wa Ma∗, Isaac Woungang, “Modified Floyd-Warshall Algorithm for Equal Cost Multipath

in Software-Defined Data Center", IEEE ICC 2015-Workshop on advance in software-defined and context-aware

cognitive networks 2015 (IEEE SCAN-2015). 14. Rajkumar Buyya, Rajiv Ranjan and Rodrigo N.Calheiros, “Modeling and Simulation of Scalable Cloud

Environments and the Clouds Toolkit: Challenges and Opportunities”. published online 24 August in Wiley

Online Library, vol. 41(1), 2011, pp. 23–50. 15. Komal Mahajan*, Ansuyia Makroo* and Deepak Dahiya *, "Round Robin with Server Affinity: A VM Load

balancing Algorithm for Cloud-Based Infrastructure". J Inf Process Syst, vol. 9(3)

954-964

Authors: Loveleen Kaur, Dr.Rajbir Kaur, Dr.Navroop Kaur

Paper

Title:

Energy Efficient Decision based Routing Technique using Fog Computing Paradigm

Abstract: In the era of new technologies, Fog computing becomes very popular in today’s

scenario. Fog computing paradigm brings a concept that extends cloud computing to the edge

and close proximity to the Internet of Things (IoT) network. The fundamental components of

fog computing are fog nodes. Additionally, fog nodes are energy efficient nodes. Numerous

fog nodes are deployed in the associated fields that will handle the Internet of Things (IoT)

sensors computation. Meanwhile, the Internet of Things (IoT) faces challenges, among which

energy efficiency is one of the most prominent or critical challenges in the current scenario.

However, sensor devices are an energy constraint that creates hotspot during the routing

process. For this reason, to handle such constraints, this paper presents an effective hotspot

mechanism using fog nodes that demonstrate the routing process and directed the sensors to

choose the routing path as selected by the fog node. Moreover, fog node will act as a decision

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maker node and maintain the energy efficiency of sensors during the routing as fog nodes are

energy efficient nodes. As it moves towards the emergency situation, the most appropriate and

effective routing approach has been designed who maintain the energy level of sensors will be

high during the routing process. The proposed routing technique could be better performance

for the sake of efficient routing in terms of energy consumption and prolonging network

lifetime.

Keywords: Fog Computing, IoT, Energy Consumption, Fog nodes and Hotspot.

References: 1. O. Bello and S. Zeadally, “Intelligent Device-to-Device Communication in the Internet of Things,”

Ieeexplore.Ieee.Org, vol. 10, no. 3, pp. 1–11, 2015.

2. Kevin, “That ‘internet of things’ thing,” RFID J., vol. 22, no. 7, pp. 97–114, 2009. 3. K. Gusmeroli, S., Haller, S., Harrison, M., Kalaboukas, K., Tomasella, M., Vermesan, O., & Wouters, Vision

and challenges for realizing the internet of things, vol. 1, no.

APRIL. 2009.

architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.

4. S. Engineering, M. Program, G. Square, and E. Zayed, “An Energy-Efficient Multi-Objective Scheduling Model

for Monitoring in Internet of Things,” vol. 4662, no. c, pp. 1–12, 2012.

5. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016.

6. J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A Survey on Internet of Things: Architecture,

Enabling Technologies, Security and Privacy, and Applications,” IEEE Internet Things J., vol. 4, no. 5, pp.

1125–1142, 2017. 7. S. He, J. Chen, and Y. Sun, “Coverage and connectivity in duty-cycled wireless sensor networks for event

monitoring,” IEEE Trans. Parallel Distrib. Syst., vol. 13, no. 3, pp. 475–482, 2012.

8. P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V. C. M. Leung, and Y. L. Guan, “Wireless energy harvesting for the Internet of Things,” IEEE Commun. Mag., vol. 53, no. 6, pp. 102–108, 2015.

9. N. Kaur and S. K. Sood, “An Energy-Efficient Architecture for the Internet of Things (IoT),” IEEE Syst. J., pp.

1–10, 2015. 10. M. Dong, K. Ota, and A. Liu, “RMER: Reliable and Energy-Efficient Data Collection for Large-Scale Wireless

Sensor Networks,” IEEE Internet Things J., vol. 3, no. 4, pp. 511–519, 2016.

11. Y. Bi, L. Sun, J. Ma, N. Li, I. A. Khan, and C. Chen, “HUMS: An autonomous moving strategy for mobile sinks in data-gathering sensor networks,” Eurasip J. Wirel. Commun. Netw., vol. 2007, 2007.

12. R. Mahmud, R. Kotagiri, and R. Buyya, “Fog Computing: A Taxonomy, Survey and Future Directions,” pp. 1–

28, 2016. 13. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things,” Proc.

first Ed. MCC Work. Mob. cloud Comput., pp. 13–16, 2012. 14. A. Dastjerdi, R. B.- Computer, and undefined 2016, “Fog computing: Helping the Internet of Things realize its

potential,” Ieeexplore.Ieee.Org, 2016.

15. C. C. Byers, “Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks,” IEEE Commun. Mag., vol. 55, no. 8, pp. 14–20, 2017.

16. P. Hu, S. Dhelim, H. Ning, and T. Qiu, “Survey on fog computing: architecture, key technologies, applications

and open issues,” J. Netw. Comput. Appl., vol. 98, pp. 27–42, 2017. 17. M. Aazam and E. N. Huh, “Fog Computing: The Cloud-IoT/IoE Middleware Paradigm,” IEEE Potentials, vol.

35, no. 3, pp. 40–44, 2016.

18. M. Hajibaba and S. Gorgin, “A review on modern distributed computing paradigms: Cloud computing, jungle computing and fog computing,” J. Comput. Inf. Technol., vol. 22, no. 2, pp. 69–84, 2014.

19. M. Yannuzzi and R. Milito, “Key ingredients in an IoT recipe Fog Computing,.pdf,” pp. 325–329.

20. M. Elappila, S. Chinara, and D. R. Parhi, “Survivable Path Routing in WSN for IoT applications,” Pervasive Mob. Comput., vol. 43, pp. 49–63, 2018.

21. F. Zawaideh, “An Energy Efficient Clustering Algorithm for Wireless Sensor Networks ( EECA ),” no.

September, pp. 39–44, 2012. 22. F. Li, M. Xiong, L. Wang, H. Peng, J. Hua, and X. Liu, “A novel energy-balanced routing algorithm in energy

harvesting sensor networks,” Phys. Commun., 2018.

23. K. Wang, Y. Wang, Y. Sun, S. Guo, and J. Wu, “Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective,” IEEE Commun. Mag., vol. 54, no. 11, pp. 48–54, 2016.

24. J. Luo, J. Hu, D. Wu, and R. Li, “Opportunistic routing algorithm for relay node selection in wireless sensor

networks,” IEEE Trans. Ind. Informatics, vol. 11, no. 1, pp. 112–121, 2015. 25. T. Qiu, Y. Lv, F. Xia, N. Chen, J. Wan, and A. Tolba, “ERGID: An efficient routing protocol for emergency

response Internet of Things,” J. Netw. Comput. Appl., vol. 72, pp. 104–112, 2016.

26. S. B. Shah, Z. Chen, F. Yin, I. U. Khan, and N. Ahmad, “Energy and interoperable aware routing for throughput optimization in clustered IoT-wireless sensor net,” Futur. Gener. Comput. Syst., vol. 81, pp. 372–381, 2018.

27. D. Sun, X. Huang, Y. Liu, and H. Zhong, “Predictable Energy Aware Routing based on Dynamic Game Theory

in Wireless Sensor Networks,” Comput. Electr. Eng., vol. 39, no. 6, pp. 1601–1608, 2013. S. Wen, C. Huang, X. Chen, J. Ma, N. Xiong, and Z. Li, “Energy-efficient and delay-aware distributed routing

with cooperative transmission for Internet of Things,” J. Parallel Distrib. Comput., 2017.

965-970

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.

Authors: Maneet Kaur Bohmrah, Harjot Kaur

Paper

Title:

Autonomic Fog Computing: Towards Sustainable and Reliable Integration of Cloud &

Internet of Things

Abstract: With the growth of IoT based applications day by day huge volume of data is

generated, which becomes a challenging issue for researchers. Fog computing is seem to be an

effective solution for managing huge volume of data which is mainly security critical and time

sensitive produced by IoT devices or sensors. In this paper we first present an integration of

cloud and IoT as substantial number of application scenarios empowered by their Integration

and discuss threats challenges & existing solutions related to it. Followed by this, we discussed

fog computing which supports the integration of cloud and IoT, further the issues related to fog

has been explored. We proposed a concept of self-awareness in Fog computing termed as

Autonomic Fog Computing. Autonomic fog computing is introducing the features of Self-

management and hence increase the efficiency and enhance the overall system performance.

Keywords: Cloud Computing, Internet of Things, CloudIoT, Self-management, Self-

configuration, Fog Computing, and Autonomic Fog Computing.

References: 1. Mell, T. Grance, The nist definition of cloud computing, 2011. 2. L.M. Vaquero, L. Rodero-Merino, J. Caceres, M. Linder, A break in the clouds: towards a cloud definition, ACM

SIGCOMM Comput. Commun. Rev. vol. 39(1)2008 50-55.

3. T. Dillon, C. Wu, E. Chang, Cloud Compuing: issues and challenges, 24th IEEE international conference on Advanced information Networking and Applications, IEEE, 2010, pp.1-6.

4. Commission of the European Communities, Early Challenges 1801, Regarding the ‘‘Internet of Things”, 2008. 5. D. Giusto, A. Iera, G. Morabito, L. Atzori (Eds.), The Internet of Things, 1661 Springer, 2010. ISBN: 978-1-4419-

1673-0.

6. J. Zhou, T. Leppanen, E. Harjula, M. Ylianttila, T. Ojala, C. Yu. H. Jin Cloudthings: A common architecture for integrating the internet of things and cloud computing, in: CSCWD, IEEE, 2013.

7. H. C. Chao, internet of things and cloud computing for future Internet, Ubiquitous Intelligence and Computing,

in: Lecture Notes in Computer science, 2011. 8. A. Botta, W. de Donato, V. Persico, A. Pescape, Integration of Cloud Computing and Internet of Things: A

survey, 0167-739X, Elsevier 2015.

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20. Singh S, Chana I, Singh, Buyya R. SOCCER: self-optimization of energy-efficient cloud resources. ClustComput. vol. 19(4), 2016, pp. 1787-1800.

21. Chen G, Jin H, Zou D, Zhou BB, Qiang, Hu G. SHelp: automatic self-healing for multiple application instances

in a virtual machine environment. IEEE International Conference on Cluster Computing, 2010. 22. Lama P, Zhou X. AROMA: automated resource allocation and configuration of MapReduce environment in the

cloud. Proceedings of the 9th International Conference on Autonomic Computing, 2012.

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complex distributed networks. J. Supercomp, vol. 67(2), 2014, pp. 585–613. DOI:http://dx.doi.org/10.1007/s11227-013-1019-3

971-979

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approach for cloud computing. Clust Comput. 2017, pp. 1-39. https://doi.org/10.1007/s10586-017-1040-z.

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technique for provisioned cloud resources.J Netw Syst Manag. vol. 26(2), 2018, pp. 361-400. 27. Gill SS, Buyya R, Chana I, Singh, RADAR: Self-configuring and self-healing in resource management for

enhancing quality of cloud services Concurrency Computat Pract Exper. 2019; 31: e4834.

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158.

Authors: Dr. Krupesh A. Chauhan, Rushabh A. Shah

Paper

Title:

Application of Time and Motion study for Performance Enhancement of Building

Construction Industry

Abstract:Construction industry is, as we all know growing at very fast rate to match the

current and innovative requirements. Still there are so many reasons of delaying and cost

overruns in construction industry. Out of which one of the main reason is Lean waste. These

wastes are always hidden and we often neglect or do not give them importance for any

analysis or scheduling work. To identify and to avoid those wastes, lean tools need to be

implemented on such kind of projects. In this study lean tool in terms of Time and motion

study is applied on plastering work of Three building construction sites of Surat, Gujarat, India

(Site 1: Pratham, Site 2: IFM, Site 3: Celebration Home). Leant tool is applied on plastering

work, which is divided in 3 Main activities, and those main 3 activities further divided in 9

different part. Time study is a direct and continuous observation of a task, using a timekeeping

device (e.g., decimal minute stopwatch, computer-assisted electronic stopwatch, and videotape

camera) to record the time taken to accomplish a task. Motion studies are performed to

eliminate waste. Data was collected for sites during different time and of different types of

labors. The analysis result show that some of the activities are having higher time than the

average time that will force the task to overruns.

Keywords:Lean Tool, Motion Study, Plastering work, Time Study.

References: 1. Abdul Talib Bon, Aliza Ariffin. An Impact Time Motion Study on Small Medium Enterprise Organization.

2. Adnan Enshassi, Sherif Mohamed, Ziad Abu Mustafa1 and Peter Eduard Mayer (2007) Factors affecting labour

productivity in building projects in the Gaza strip. J. of Civil Engg and Management .p245-254

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services research, Health Service Research, Chicago. Dec 1993. Vol. 28, Issue 5.

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Edition.

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Of Manufacturing Systems, Vol. 22 / No 2 2003 Pages 157 – 171.

25. Puniavathi Puranam, Pramila. & R. Adavi, Time and Motion Study, Analysis Through Statistics.

26. Ralph M. Barnes (2001). Motion and Time Study – Design and Measurement of Work. Seventh Edition. John

Wiley and Sons Inc.J. Jones.

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Journal of Innovative and Emerging Research in Engineering, ISSN: 2394 – 3343

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Journal for Scientific Research & Development| Vol. 3, Issue 02, 2015 | ISSN (online): 2321-0613

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159.

Authors: Mouamil Khan, Sandeep Singla, Sualiheen Ahmad

Paper

Title:

Accident Mitigation and Management Measures for NH-44(India) from Khannabal to

Qazigung in Jammu-Kashmir

Abstract:Accidents are not natural but they are caused is a common cliché in the area of

traffic safety. Thus, if accidents are caused, surely the reasons responsible for them could be

identified and appropriate remedial measures developed and implemented to the extent

feasible. This study lays emphasis on the safety analysis and accident studies of National

Highway -1A(NH-44) on the 24 Km long stretch of road from Khanabal to Qazigund which is

the main connecting link of Kashmir valley to Jammu and the rest of the country. As such this

road is subjected to heavy vehicular traffic carrying passengers and goods and is the main

strategic road of the state of Jammu and Kashmir State, it is one of the busiest and dangerous

roads of the country. The problem of traffic flow is very acute due to complex flow of

vehicular traffic, presence of mixed traffic along with pedestrians. Traffic accidents lead to

loss of life and property. Also the stretch under study is subjected to rough weather conditions

particularly during the winters. The primary objective of this study is to carry out the safety

analysis of National Highway-1A(NH-44) on the 24 Km long stretch of road from Khanabal to

Qazigund and provide a solution to the traffic accidents which occur on this 24 Km long

stretch of road from Khanabal to Qazigund. The National Highway-44 (formerly known as

NH 1A) connecting Kashmir Valley to the rest of the country has always been susceptible to

accidents.

Keywords: Accident Mitigation, Jammu and Kashmir, Counter Measures, Priority Index,

highway.

References: 1. M. D., "Road accidents in India," ATSS research, 2009.

2. T. O. S. M. F. M. Mohan D, "Road safety in India: challenges and opportunities.". 3. S. SK., "Road traffic accidents in India: issues and challenges.," Transportation research procedia., 2017.

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Rehabilitation., 2013. 5. F. kallu, "www.greaterkashmir.com," [Online].

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1980. 11. IRC-SP-44-1996, Highway safety Code, The Indian Road Congress(IRC) special publication44, New Delhi,

Indian Road Congress, 1996.

12. IRC-SP-55-2001, Guidelines on safety in road construction zones, National Highway Authority of India(NHAI), Ministry of Surface Transport, New Delhi: The Indian Road Congress(IRC) publication, 2001.

985-997

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Procedia-Social and Behavioral Sciences., 2013.

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in Kenya.," 2010.

160.

Authors: Yogita Patra, Shruti Tripathi

Paper

Title:

Agile Software Development: Reusability and Customizability is key to Competitive

Advantage

Abstract:The study addresses the research issue of how software firms can compete in a

dynamic industry through creating agile software development processes. It gives an insight

into what a firm needs to do to equip its software process engineers to create flexible processes

which can be customized specifically for the customer or reused for another customer without

having to recreate them. Can these perceived process dimensions impact the competitive

advantage of the firm? Survey design was used to collect responses from 100 software firms.

Senior managers in the delivery departments were taken as respondents. The model and

hypothesis were assessed on AMOS using structural equation modelling for path analysis. The

results suggested that firms that can reuse the codes and customize the services specific to

customers through training and development initiatives taken for employees are able to create

competitive advantage in the industry. The improvement in the processes be having direct

impact on the economic efficiency of the firm.

Keywords: Competitive advantage, Software development, Training and development,

Structural Equation Modelling.

References: 1. Armstrong & Shimizu, C. E. (2007). A Review of Approaches to Empirical Research on the Resource-Based

View of the Firm. Journal of Management, 33(6), Pp. 959-986.

2. Batt and Banerjee, R. M. (2011). The scope and trajectory of strategic HR research: evidence from American and

British journals. The International Journal of Human Resource Management,Vol. 23, No. 9, Pp. 1739–1762.

3. Browning Edgar Gray & Garrett, V. F. (2009). Realising competitive advantage through HRM in New Zealand

service industries. The Service Industries Journal, Pp 741–760.

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Drivers and Organizational Culture . ProQuest Dissertations and Theses database (UMI No. 3394575). , Pp. 203–209.

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Management Research: A Bibliometric. Strategic Management Journal, Vol. 25, No. 10, Pp. 981-1004.

13. Reddington Martin & Bondarouk, M. G. (2015). Chapter 4 Linking HR Strategy, e-HR Goals, Architectures, and

Outcomes: A Model and Case Study Evidence. In Electronic HRM in Theory and Practice (pp. Pp. 55-81). Emerald publication.

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and Banking (AP15Singapore Conference) ISBN: 978-1-63415-751-3 .

15. Schoonover, S. C. (2000). HumanResource Competencies for the Year 2000: The Wake-Up Call. Society for

Human Resource Management. , Pp 27.

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Management 14:4 , Pp. 530–543.

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Values, Knowledge, and Behavior. Journal of the Academy of Marketing Science, Volume 25, No. 4, Pp 305-

318.

18. Sung & Choi, S. Y. (2018). To invest or not to invest: strategic decision making toward investing in training and

development in Korean manufacturing firms. The International Journal of Human Resource Management, VOL.

29, NO. 13, Pp. 2080–2105.

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Greece. The International Journal of Human Resource Management Vol. 19, No. 1, Pp. 74-97.

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21. Wang, H.-L. (2013). Theories for competitive advantage. In Being practical with theory: a window into business

research (pp. Pp. 33-43). University of Wollongong: http://ro.uow.edu.au/buspapers/408.

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Window into Business Research (pp. Pp. 33-43). Wollongong, Australia: University of Wollongong.

161.

Authors: Sunita Chaki, Dr Kshamta Chauhan, Anita Daryal

Paper

Title: Financial Performance of Banks in India: Are Banks Sound Enough to be Banked Upon

Abstract: The seemingly untamable Non-Performing Assets are leading the Indian banks

towards a highly unstable environment. The financial soundness of the banks is mandatory for

any economy considering it is one of the most significant and a pre-requisite of a stable

economy.The present study examines the financial performance parameters of banks with a

probable variation among public and private sector banks for a period between 2005 and 2018.

The study is divided into three sections. The first section studies the financial performance of

the Scheduled Commercial Banks (SCBs), Public & Private Sector Banks in three identified

time bands of last thirteen years. The second section assesses the probable variation in asset

quality among Private and Public Sector Banks through statistical inferences. The third section

finally examines the probability of variations in asset quality in the three time bands identified

in the study. The study concludes a very high volatility among the SCBs during the said period

and found Private Sector Banks to be more consistent and bore better stability parameters

compared to Public Sector Banks. The statistically inferred results through T-test, Welch test

and Post-Hoc test support a significant variation among both the sectors along with presence

of significant variations in asset quality.

Keywords: financial performance, asset quality, profitability, ROA, GNPA, CRAR, capital

adequacy, global financial crisis.

References: 1. W. Bank, “Doing Business-India 2019,” 2019.

2. Assocham and E&Y, “ARCs – at the crossroads of making a paradigm shift,” 2016.

3. Reserve Bank of India, “Report on Trend and Progress of Banking in India 2012-13,” 2013. 4. S. Mundra, “Financial Stability in a Weak Global Environment,” in Financial Stability in a Weakening Global

Economic Environment, 2016, p. 2016.

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7. Reserve Bank of India, “Report on Trend and Progress of Banking in India 2008-09,” 2009.

8. Reserve Bank of India, “Report on Trend and Progress of Banking in India 2014-15,” 2014. 9. A. Derviz and J. Podpiera, “Predicting Bank CAMELS and S & P Ratings The Case of the Czech Republic,” vol.

44, no. 1, pp. 117–130, 2008.

10. K. B. L. Mathur, “Should They be privatised,” Econ. Polit. Wkly., vol. 37, no. 23, pp. 2245–2256, 2012. 11. M. Sathye, “Privatization, Performance, and Efficiency: A Study of Indian Banks,” vol. 30, no. 1, pp. 7–16,

2005. 12. J. B. Thomson, “Predicting Bank Failures in the 1980s,” no. 1988, 1990.

13. S. Kumar and R. Sharma, “Performance Analysis Of Top Indian Banks Through Camel,” Int. J. Adv. Res.

Manag. Soc. Sci., vol. 3, no. 7, pp. 81–92, 2014. 14. C. S. Balasubramanium, “Non Performing Assets and Profitability of Commercial Banks in India:Assessment

and Emerging Issues,” Abhinav,Journal Res. Commer. Manag., vol. 1, no. 7, pp. 41–52, 2012.

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16. A. Roman and A. Camelia, “Analysing the Financial Soundness of the Commercial Banks in Romania : An

Approach Based on the Camels Framework,” Procedia Econ. Financ., vol. 6, no. 13, pp. 703–712, 2013. 17. N. Rozzani and R. A. Rahman, “Camels and Performance Evaluation of Banks in Malaysia : Conventional

Camels and Performance Evaluation of Banks in Malaysia : Conventional Versus Islamic,” no. July 2014, 2013.

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the Select Financial Soundness Indicators – An Empirical Approach,” vol. VII, no. 3, pp. 72–83, 2011.

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2014. 22. W. K. Wang, W. M. Lu, and Y. H. Wang, “The relationship between bank performance and intellectual capital

in East Asia,” Qual. Quant., vol. 47, no. 2, pp. 1041–1062, 2013.

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Econ., 2015. 25. S. Kumar and R. Gulati, “Measuring efficiency , effectiveness and performance of Indian public sector banks,”

2010.

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27. M. Mercan, A. Reisman, R. Yolalan, and A. B. Emel, “The effect of scale and mode of ownership on the

financial performance of the Turkish banking sector: Results of a DEA-based analysis,” Socioecon. Plann. Sci.,

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29. P. Manoj, “Financial Soundness of Old Private Sector Banks In India With a Focus on Kerala Based OPBs: A ‘CAMEL’ Approach,” Am. J. Sci. Res., no. 11, pp. 132–149, 2010.

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162.

Authors: Evren Kanalici, Gokhan Bilgin

Paper

Title: Scattering Wavelet Hash Fingerprints for Musical Audio Recognition

Abstract: Fingerprint design is the cornerstone of the audio recognition systems in which

aims robustness and fast retrieval. Short-term Fourier transform and Mel-spectral

representations are common for the task in mind, however these extraction methods suffer

from being unstable and having limited spectral-spatial resolution. Scattering wavelet

transform (SWT) provides another approach to these limitations by recovering information

loss, while ensuring translation invariance and stability.We propose a two-stage feature

extraction framework using SWT coupled with deep Siamese hashing model for musical audio

recognition. Similarity-preserving hashes are the final fingerprints and in the projected

embedding space, similarity is defined by a distance metric. Hashing model is trained by

roughly aligned and non-matching audio snippets to model musical audio data via two-layer

scattering spectrum. Our proposed framework provides competitive performance results to

identify audio signals superimposed with environmental noise which can be modeled as real-

world obstacles for music recognition. With a very compact storage footprint (256 bytes/sec.),

we achieve 98.2% ROC AUC score on GTZAN dataset.

Keywords:Audio fingerprinting, CNNs, Scattering wavelet transform, Siamese networks,

Embedding hash models.

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DC, 2003.

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audio processing, 10(5):293–302, 2002.

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17. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer.

Automatic differentiation in pytorch. 2017.

18. M. Andreux, T. Angles, G. Exarchakis, R. Leonarduzzi, G. Rochette, L. Thiry, J. Zarka, S. Mallat, E. Belilovsky,

J. Bruna, et al. Kymatio: Scattering transforms in python. arXiv preprint arXiv:1812.11214, 2018.

19. A. Hermans, L. Beyer, and B. Leibe. In defense of the triplet loss for person re-identification. arXiv preprint

arXiv:1703.07737, 2017

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conference on Multimedia, pages 411–412. ACM, 2013

163.

Authors: Catarina de Nazaré Pereira Pinheiro, Adriene Rodrigues Barbosa

Paper

Title:

Analysis of Pathological Manifestations in Buildings at The University City Prof. José da

Silveira Netto, Located in Belém-PA

Abstract: Abstract: The reinforced concrete structures are present in most constructions

around the world and can be defined as a composite material of concrete reinforced with steel

bars. Those structures may exhibit pathologies during the construction stage or after, due to

several types of failures, whether is in the project, execution or maintenance. The

Universidade Federal do Pará, has great influence over Brazil, being considered the biggest

university in the north of the country. Despite its relevance, the university campus presents

numerous problems in their building structures. The purpose of this paper is to identify the

pathologies in the older and most recent reinforced concrete buildings existing at the Cidade

Universitária Prof. José da Silveira Netto, Guamá campus, located in Belém, state's capital,

enabling the evaluation of corrective maintenance needs of the most deteriorated

constructions. To support the case study, it was performed a visual analysis with photographic

register, allied to theoretical study regarding the subject, enabling the identification of possible

causes of the alterations. The results show pathologies from many fields, from structure design

until foundation of the buildings. The reduction in the occurrence of those manifestations

would be possible, with better supervision during the construction process and preventive

maintenance, whose lack of were the main cause of the identified pathologies.

Keywords:Building Pathologies Evaluation; Case Study; Pathological Manifestations

References: 1. ABNT, Associação Brasileira de Normas Técnicas. NBR 15575 Edificações habitacionais – Desempenho.

Brasil, 2013.

2. ALVES, J. R. Levantamento das manifestações patológicas em fundações e estruturas nas edificações, com até

dez anos de idade, executadas no estado de Goiás. 131f. Dissertação. Escola de Engenharia Civil – Universidade Federal de Goiás, 2009.

3. AZEREDO, H. A. O Edifício Até sua Cobertura. São Paulo. Ed. Edgar Blucher Ltda, 1977.

1016-1020

4. BASTOS, P. S. S. Fundamentos do concreto armado: Notas de aula. Unesp, Bauru, 2006.

5. HELENE, P. R. L. Manual para Reparo, Reforço e Proteção de Estruturas de Concreto. 2. ed. São Paulo: Ed.

Pini, 1992.

6. HELENE, P. R. L. Contribuição ao estudo da corrosão em armaduras de concreto armado. 231 f. Tese (Livre-Docência) – Escola Politécnica da Universidade de São Paulo, São Paulo, 1993.

7. LAPA, J.S. Patologia, Recuperação e Reparo das Estruturas de Concreto. 56f. Monografia – Curso Engenharia

Civil, Universidade Federal de Minas Gerais, Minas Gerais, 2008. 8. MAFRA, J.; BALTAZAR, B.; ARAÚJO, A.; GUSMÃO, R.; SOUZA, A. Análise das manifestações patológicas

visando concepção de plano de manutenção corretiva para o Centro de Convenções Benedito Nunes, em Belém-

Pa, Congresso Internacional sobre Patologia e Reabilitação de Estruturas. Crato (Ceará), 2017. 9. MEHTA, P. K.; MONTEIRO, P. J. M. Concreto: estrutura, propriedades e materiais. São Paulo: Ed. PINI, 1994.

10. SOUZA, V.C.M.; RIPPER, Thomaz. Patologia, recuperação e reforço de estruturas de concreto. 262 p. São

Paulo. Ed. PINI, 1998. 11. RIPPER, T. Patologia, recuperação e reforço de estruturas de concreto. 262f. 1. ed. São Paulo: Editora PINI

Ltda, abril, 2009.CUNHA, Maria Clementina Pereira. (Org.) O Direito à Memória: patrimônio histórico e

cidadania. São Paulo: Departamento do Patrimônio Histórico, 1992, p. 25.

164.

Authors: Catarina de Nazaré Pereira Pinheiro, Adriene Rodrigues Barbosa

Paper

Title:

Pathological Manifestation and Restoration Procedures Analysis of the Historic Building,

located in Belém-PA

Abstract:The city of Belém, capital of the Brazilian state of Pará, a four hundred years old

city, is considered rich with history and cultural diversity. Amongst its historical

constructions, the Library “Arquivo Público” - Public Archives – stands out for its great

importance, not only for its own history and architecture, but also for keeping very relevant

documents in its collection. Among the archives, the oldest document is dated from 1649,

close to the year of the city’s foundation. However, until the beginning of 2014, the building

was in a dreadful preservation state. Aiming the conservation of the city’s historic and cultural

heritage, the renovation of the building was initiated, focusing especially on the restoration of

the original architectural pieces, preserving the artistic heritage of the construction. This paper

aims to analyze the pathologies found on site and evaluate the restoration process of this

historical building, to verify the repair procedures made and to present the results of the

construction work.

Keywords:Pathologies; Restoration; Construction Repair; Building Maintenance; Historic

Heritage; Structural Rehabilitation.

References: 1. ABNT, Associação Brasileira de Normas Técnicas. NBR 15575 Edificações habitacionais — Desempenho.

2013.

2. ABNT, Associação Brasileira de Normas Técnicas. NBR 5674 – Manutenção de Edificações — Requisitos para

o sistema de gestão de manutenção. 2012.

3. ARENDT, C. O. (1997) Exaustivo Caminho da Recuperação. Congresso Ibero Americano de Patologia das

Construções, Porto Alegre: CPGEC/UFRGS, Porto Alegre.

4. BERTOLINI, L. Materiais de Construção: patologia, reabilitação, prevenção; tradução Leda Maria Marques Dias

Beck. São Paulo: Oficina de Textos, 2010.

5. CUNHA, Maria Clementina Pereira. (Org.) O Direito à Memória: patrimônio histórico e cidadania. São Paulo:

Departamento do Patrimônio Histórico, 1992, p. 25.

6. CARRASCO, E. V. M., “Apostila: Estruturas Usuais de Madeiras”, Dpto de Estruturas,UFMG, 2009.

7. POSSA, E.; Demoliner, C. A. (2013) Desempenho, durabilidade e vida útil das edificações: abordagem geral.

8. PERES, R. M. (2001) Levantamento e Identificação de Manifestações Patológicas em Prédio Histórico – Um

estudo de caso. Dissertação (Mestrado em Engenharia Civil) – programa de Pós-Graduação em Engenharia Civil – UFRGS, Porto Alegre. 142 p.

9. TOMAZ, Paulo Cesar. A preservação do Patrimônio Cultural e sua trajetória no Brasil. São Paulo: Fênix –

Revista de História e Estudos Culturais Maio/ Junho/ Julho/ Agosto de 2010 v. 7 year VII no 2.

10. VIANA, A. O. V. N. A biblioteca e arquivo público (do pará): resumo histórico. Revista de Biblioteconomia de

Brasília, v. 3, n. 1, p. 85-102, 1975. Available in: <http://www.brapci.inf.br/v/a/3697>. Access in: 15 Jan. 2018..

1021-1027

165.

Authors: Sandeep Kumar Chandel, Dr. Rajesh Goyal, Dr.Sandeep Singla

Paper

Title:

Utilization of Construction Waste as Partial Replacement of Aggregates in Cement

Concrete

Abstract:This paper contains study of marble waste as replacement of fine and coarse

aggregates in concrete. Entire planet is facing an issue of environment and climate. In this era

sustainable development is in huge demand. Sustainable development has way of possibility

by revising, rethinking, reducing, reusing. The use of construction waste materials in concrete

industrial is playing key role to its economic, eco-friendly, green paybacks and engineering.

This review reports on the utilization of waste marble as aggregate in concrete production

industry. On the base of the reviewed studies, it was experiential that construction waste used

in place of coarse aggregate contribute to the workability and mechanical properties of

concrete. When natural aggregates relaced with coarse marble aggregates, ceramic tiles,

recycled aggregates, it attained the best results at full replacement ratio. Additionally, waste

construction materials in coarse aggregate form improves the mechanical properties over the

dust form. These sustainable alternatives not only enhance mechanical properties of concrete

but also boost economy.

Keywords:Environment friendly, waste of Construction, waste from marble, tile waste,

aggregates, sustainability, Aggregates, strength of concrete.

References: 1. Andre, A., de Brito, J., Rosa, A., Pedro, D., 2014. Durability performance of concrete incorporating coarse

aggregates from marble industry waste. J. Clean. Prod. 65, 389e396.

2. Andr_e, A.J.N.P., July 2012. Performance in Durability Terms of Concrete Incorporating Waste Coarse

Aggregates from the Marble Industry. Instituto Superior Tecnico, Universidade Tecnica de Lisbona, Lisbon. 3. Aruntas H.Y., Gürü, M., Dayı, M., Tekin, _I., 2010. Utilization of waste marble dust as an additive in cement

production. Mater. Des. 31 (8), 4039e4042.

4. Binici, H., Shah, T., Aksoganc, O., Kaplan, H., 2008. Durability of concrete made with granite and marble as recycle aggregates. J. Mater. Proc. Technol. 208 (1e3), 299e308.

5. Corinaldesi, V., Moriconi, G., Naik, T.R., 2010. Characterization of marble powder for its use in mortar and

concrete. Constr. Build. Mater. 24 (1), 113e117. 6. Gameiro, F., de Brito, J., Correia da Silva, D., 2014. Durability performance structural concrete containing fine

aggregates from waste generated by marble quarrying industry. Eng. Struct. 59, 654e662.

7. Gautam, N., Krishna, V., Srivastava, A., 2014. Sustainability in the concrete construction. Int. J. Environ. Res. Dev. ISSN: 2249-3131 4 (1), 81e90.

8. Elçi, H., Utilisation of crushed floor andwall tilewastes as aggregate in concrete production,Journal of Cleaner

Production(2015), http://dx.doi.org/10.1016/j.jclepro.2015.07.003 9. Hameed, M.S., Sekar, A.S.S., 2009. Properties of green concrete containing quarry rock dust and marble sludge

powder as fine aggregate. ARPN J. Eng. Appl. Sci. ISSN: 1819- 6608 4 (4), 83e89

10. Hasan Sahan Arel “Recyclability of waste marble in concrete production” Journal of Cleaner Production 131 (2016) 179e188

11. Hebhoub, H., Aoun, H., Belachia, M., Houari, H., Ghorbel, E., 2011. Use of waste marble aggregates in

concrete. Constr. Build. Mater. 25 (3), 1167e1171. 12. Shahid Kabir, A.Al-Shayeb, Imran M Khan 2016 Recycled construction debris as concrete aggregates for

sustainable construction material, International conference on Sustainable Design, Engineering and Construction,

Procedia Engineering 145 ( 2016 ) 1518 – 1525Published by Elsevier Ltd. 13. Marco Pepe, Romildo D. Toledo Filho, Eduardus A.B. Koenders, Enzo M. (2014), Alternative processing

procedures for recycled aggregates in structural concrete, Construction and Building Materials 69 (2014) 124–

132, http://dx.doi.org/10.1016/j.conbuildmat.2014.06.084 14. Monica, Ms, Dhoka, C., 2013. Green concrete: using industrial waste of marble powder, quarry dust and paper

pulp. Int. J. Eng. Sci. Invent 2 (10), 67e70. ISSN (online): 2319- 6734, ISSN (print):2319-6726

15. Nagarajan, V.K., Devi, S.A., Manohari, S.P., Santha, M.M., 2014. Experimental study on partial replacement of cement with coconut shell ash in concrete. Int. J. Sci. Res. ISSN: 2278-3075 3 (3), 651e661.

16. Omar, O.M., Abd Elhameed, G.D., Sherif, M.A., Mohamadien, H.A., 2012. Influence of limestone waste as

partial replacement material for sand and marble powder in concrete properties. HBRC J. 8 (3), 193e203.

17. Pathan, V.G., Pathan, G., 2014. Feasibility and need of use of waste marble powder in concrete production.

IOSR J. Mech. Civ. Eng. (IOSR-JMCE) 23e26 e-ISSN: 2278- 1684, p- ISSN: 2320-334X. 18. Paul O Awoyera, Julius M. Ndambauki, Joseph O Akinmusuru, David O. Omole, (2016)Characterization of

ceramic waste aggregate Concret, HBRC Journal, http://dx.doi.org/10.1016/j.hbrcj.2016.11.003

19. Rai, B., Khan, N.H., Abhishek, Kr, Tabin, R.S., Duggal, S.K., 2011a. Influence of marble powder/granulesinconcretemix. Int. J. Civ. Struct. Eng. ISSN: 0976-43991 (4), 827e834.

20. Rai, B., Khan, N.H., Abhishek, Kr, Tabin, R.S., Duggal, S.K., 2011a. Influence of marble

powder/granulesinconcretemix. Int. J. Civ. Struct. Eng. ISSN: 0976-43991 (4), 827e834. 21. Rai, B., Naushad, K.H., Abhishek, Kr, Rushad, S.T., Duggal, S.K., 2011. Influence of marble powder/granules

in concrete mix. Int. J. Civ. Struct. Eng. 1 (4).

22. Sadek, D.M., El-Attar, M.M., Ali, H.A., 2016. Reusing of marble and granite powders in self-compacting concretefor sustainable development. J. Clean. Prod. 121, 19e32

23. Silva, R.V., de Brito, J., Dhir, R.K., 2014. Properties and composition of recycled aggregates from construction

and demolition waste suitable for concrete production.Constr. Build. Mater. 65, 201e21

1028-1032

24. Sudarshan, D.K., Vyas, A.K., 2016. Impact of marble waste as coarse aggregate on properties of lean cement

concrete. Case Stud. Constr. Mater

25. Talah, A., Kharchi, F., Chaid, R., 2015. Influence of marble powder on high performance concrete behavior.

Proced. Eng. 114, 685e690. 26. Tennich, M., Kallel, A., Ouezdou, M.B., 2015. Incorporation of fillers from marble and tile wastes in the

composition of self-compacting concretes. Constr. Build. Mater. 91, 65e70.

27. Uyguno_glu, T., Topçu, _I.B., Çelik, A.G., 2014. Use of waste marble and recycled aggregates in self-compacting concrete for environmental sustainability. J. Clean. Prod.84, 691e700

28. Sadek, D.M., El-Attar, M.M., Ali, H.A., 2016. Reusing of marble and granite powders in self-compacting

concretefor sustainable development. J. Clean. Prod. 121, 19e32 29. Silva, R.V., de Brito, J., Dhir, R.K., 2014. Properties and composition of recycled aggregates from construction

and demolition waste suitable for concrete production.Constr. Build. Mater. 65, 201e217

30. Sudarshan, D.K., Vyas, A.K., 2016. Impact of marble waste as coarse aggregate on properties of lean cement concrete. Case Stud. Constr. Mater

31. Talah, A., Kharchi, F., Chaid, R., 2015. Influence of marble powder on high performance concrete behavior.

Proced. Eng. 114, 685e690

166.

Authors: Amit Bhatia, Sandeep Singla

Paper

Title: Ergonomic Evaluation and Customized Design of Kitchen

Abstract: Ergonomics is the science of planning the environment for comfortable working.

Ergonomic plays an important role in designing a kitchen area free from fatigue, decreasing

the unnecessary movements and excessive expenditure of workers energy and time. A poorly

planned kitchen construction affects work efficiency requiring more effort and more time

while working on the poorly designed kitchen counters. Kitchens vary from area to area which

may or may not be designed on the basis of ergonomics. However, some women working in

the kitchen experience discomfort or injury when working in the kitchen. A standard-design of

a kitchen was considered as reference to have the optimal dimensions of the various

components of the kitchen. For this, first a survey questionnaire was prepared to know about

the problems encountered by the women working in kitchen. Anthropometric data of 30

participants from different cities was collected. The kitchen and its counters heights were

designed on the basis of the anthropometric data of the same participants. Rapid upper limb

assessment (RULA) and Rapid entire body assessment (REBA) Employee assessment

worksheet was used for the analysis of postures of kitchen workers while working in the

Standard-designed kitchen. Images of various postures of all the women volunteers were

captured while working in the kitchen. After analysis of which, it was concluded that the

participants were working exceeding the safe limit. Again the participants were asked to work

in the kitchen designed on the basis of anthropometric data. The same procedure was followed

and the results were evaluated for both standard-designed kitchen and ergonomically designed

kitchen. Subsequently, it was inferred that there is a lack of ergonomics awareness among the

kitchen workers and its designers. Assessment of postures using REBA and RULA shows that

the majority of women are working beyond their safe limit in the standard-designed kitchen.

In future, the work can be done to rationalize the kitchen dimensions that should be used in its

designing.

Keywords: Anthropometric data, Ergonomics, Kitchenette, MSD (Musculoskeletal disorders),

Optimal dimensions, REBA, RULA, Standard-designed kitchen.

References: 1. J Kishtwaria, P Mathur and A Rana, “Ergonomic Evaluation of Kitchen work with reference to space

designing”, in J Hum Ecol, 2007 21:43-46.

2. Nowakowski, Przemyslaw, “Kitchen Chores Ergonomics: Research and Its Application”, 2018, pp. 43-52.

3. Braton, N.J., “Occupational causes of disorders in the upper limb”, in British Medical. J., 1992, pp. 309-311.

4. Sultana, Sajida and Prakash, Chitra, “The ergonomic perspective of the home makers in using kitchens”, in

Asian J. Home Sci., 2014, 9 (1): 25-28.

5. S Gangopadhyay, T Bandyopadhyay, “Ergonomics study on analysis of normal activities of Indian women in

kitchen”, in Biomedicine, 1999, pp. 123-128.

6. N Agrawal, D & A Madankar, T & S Jibhakate, M., “Study and Validation of Body Postures Of Workers

Working In Small Scale Industry through RULA,” in International Journal of Engineering Science and Technology, Vol. 3 No.10 October 2011

1033-1039

7. Maguire et al, 2011, “Age friendly kitchens: a study based on social history and ergonomics”, in 6th

International Conference on Inclusive Design: The Role of Inclusive Design in Making Social Innovation

Happen. Royal College of Art, London, UK, 2011.

8. Lynn McAtamney and E Nigel Corlett, “RULA: a survey method for the investigation of world-related upper

limb disorders”, Applied Ergonomics, 1993 24(2):91-99.

9. Charu,. “Developing Ergonomically Designed Kitchen Aid for Reducing Physiological Stress of Women

Working in Standing Type Kitchen”, 2014. Available:

10. http://krishikosh.egranth.ac.in/handle/1/5810016061

11. Mahajan, Swati Ashok and Patwardhan, S.L., “A study of ergonomic approach to kitchen work centers”, Asian J.

Home Sci., 2015, 10 (2): 371-374.

12. Baroto Tavip Indrojarwo Eko Nurmianto, Ellya Zulaikha, “Design Study of Ergonomic Kitchen for Small

Dwelling With Family Interaction Concept”.

13. Kiran Shete, Harshal Tukaram Pandve and Tanmayee Puntambekar. “Role of Ergonomics in Kitchen Related

Back Problems”, J Ergonomics 2015.

167.

Authors: Neeraj Parashar, Sandeep Singla, Anuj Sachar and Anjali Gupta

Paper

Title: Optimal Municipal Solid Waste Management of a City in North India

Abstract: This paper studies the present system of collection, transportation and disposal of

Municipal Solid Waste (MSW) being followed in the city of Panchkula, in North India. It is

found that the present system of MSW management in Panchkula is unscientific, inefficient

and a major potential environment and health hazard. The trends of waste generation and its

qualitative as well as quantitative characteristics have been studied in this paper and paired

comparison techniques tool has been used to arrive at the most suitable, sustainable and

environment friendly solution for the management of MSW in Panchkula. Each element of the

proposed MSW management system has been discussed in detail. The paper leads to the

conclusion that a participative, inclusive and decentralized system would be best suited for the

city.

Keywords: Municipal solid waste, composting, ranking, land filling, segregation.

References: 1. Population Census of India https://census2011.co.in/ 2. Bhalla Anuj, Dr, 2014 , “Decadal Population Growth Rate of Haryana: A Spatial Temporal Analysis”,

Geography-An International Journal-125,Vol-XIII(6), Aug 2017, PP 74-75.

3. Official Website of Panchkula http://panchkula.nic.in on 24 May 2019. 4. Kaushal Yuvraj,2018, “Spotlight: The Smoky Mountain of Panchkula”, Hindustan Times, Panchkula, 28 Jun

2018.

5. CPCB website www.cpcb.nic.in/ 6. Peavy Howard S., Rowe Donald R., Tchobanologous G.,(1985), “Environmental Engineering”, McGraw Hill,

NY.

7. Baetz B. W and Korol R. M. (1995), “Evaluating Technical Alternatives on Basis of Sustainability”, J. of Professional Issues in Engg. Education and Practice. Vol.121, no.2, p. 102.

8. Gupta Vivek 2017, “Panchkula Pangs: No Work All Fight”, HT, Panchkula, 01 May 2017 and “City Laid to

Waste: Great Mess Called Panchkula Garbage”, HT, Panchkula,04 May 2017. 9. Thakur Bhartesh Singh, 2017, “How MC Panchkula Failed its People”, HT 23 April 2017.

10. Dutta Saptarishi, 2017, “Panchkula Garbage Problems Mount Despite a Proposed New Landfill”, HT, 06b Oct

2017. 11. The Times Of India, Panchkula, 11 Mar 2019, “Waste Segregation Training for Panchkula Residents”.

12. Singhal S and Pandey S (2001) “ Solid waste management in India – Status and future directions”, TERI

Information monitor on Environmental Science, Vol.6, No.1, pp 1-4. 13. Goel Sudha, 2008, “Municipal Solid Waste Management in India – A Critical review”. Journal of Environment

Science and Engineering.50.319-328.

14. Parashar Neeraj, 2007, “A Comprehensive and Critical Study of Solid Waste Management System of A Modern And Planned City of North India”, A thesis submitted to PEC Chandigarh, in partial fulfillment of the

requirements for the award of the degree of Master of Engineering in Environmental Engineering.

1040-1048

168.

Authors: Anuj Sachar, Manish Kaushal, Ashish Kumar, Sandeep Singla

Paper

Title: Analysis of Chandigarh Periphery for the Urban Containment

Abstract: The vision of great architect Le Corbusier for an agricultural greenbelt surrounding

Chandigarh was to provide an outer beauty by linking rural India to the city. The goal of the

greenbelt was to create distinction between rural and urban practices, although it has suffered

to restrict expansion and protect functional rural landscape. Today, the symmetry of

Chandigarh periphery is destroyed due to rapid urban encroachment and unregulated

construction. The present conditions were surveyed, analyzed and solutions were prepared

from the assessments of contemporary urban containment practices in the developed countries.

All the solution was examined on the basis of their effectiveness. In this case if the assessed

solutions are successfully implemented, the desired measures will provide agricultural

protection, urban development and rural aspect.

Keywords: Periphery, Chandigarh, Urban Containment, Greenbelt and Encroachment.

References: 1. Prakash, Aditya. "Concepts and principles for Haryana's new capital: beyond Corbusier (Chandigarh)."

Environments 19, no. 2 (January 1988): 5-11

2. Nelson, Arthur C., and Casey J. Dawkins. Urban Containment in the United States: History, models and

techniques for regional and metropolitan growth management. Planning Advisory Service, Chicago: American

Planning Association, 2004..

3. Town and Country Planning Association. "Nothing Gained by Overcrowding! A Centenary Celebration and Re-

¬‐exploration of Raymond Unwin's Pamphlet -¬‐ 'How the Garden City Type of Development May Benefit Both

Owner and Occupier'." London: Town and Country Planning Association, April 2012.

4. Easley, V. Gail. Staying Inside the Lines: Urban Growth Boundaries. Chicago: American Planning Association,

1992.

5. Anthony, Jerry. "Do State Growth Management Regulations Reduce Sprawl?" Urban Affairs Review 39, no. 3

(January 2004): 376-¬‐397.

6. DeGrove, John M., and Deborah A. Miness. Planning and Growth Management in the States. Cambridge, MA:

Lincoln Institute of Land Policy, 1992.

7. Jacobs, Allan B. "Observations on Chandigarh." Journal of the American Institute of Planners, November 2007.

8. Chalana, Manish. "Centering the Periphery." Journal of Planning History. Forthcoming article, accessed 2014.

9. Abigail Weber. ‘’Within the Edge: A Revised approach to Urban Containment within Chandigarh Periphery”

2014.

10. Chandigarh Administration. "Draft Chandigarh Master Plan 2031".

11. Greater Mohali Area Development Authority. "Regional Plan 2008-2058." Plan.

12. Haryana Urban Development Authority. Historical Background.

1049-4052

169.

Authors: Mohammed Noor Shaida, Sandeep Singla

Paper

Title: Global Biomedical Waste Management Issues and Practices

Abstract:Biomedical waste is a special type of waste which carries high potential of infection

and injury. Hospital waste management means the management of waste produced by

hospitals using techniques that will check the spread of diseases through hospital waste. This

study was conducted to examine health care waste management practices in different

hospitals. The related data has been collected from various international journals, books and

websites. The data is analyzed by finding biomedical waste management issues and challenges

around the world by gap analysis. Hospital waste generation, segregation, collection,

transportation and disposal practices were not in accordance with standard guidelines. The

average waste generation in most of the hospitals was almost equivalent to other under

developed countries but less than that of developed countries. Conclusions: The hospital

waste in the majority of hospitals was mismanaged. No proper hospital waste management

plan existed has been done except at few hospitals. In this research the analysis of current

biomedical wastes management, and some steps for management of healthcare is proposed.

Keywords:Biomedical waste management, generation, segregation, disposal, guidelines

References: 1. Al-Habash. M and Al-Zubi. 2012. Efficiency of medical waste management performance, health sector and its

impact on environment in Jordan applied study. Al-Balqa’ Applied University, Amman University College,

Amman-11118- Jordan. World applied Sciences Journal 19 (6): 880-893, 2012, ISSN 1818 – 4952 @ IDOSI

1053-1059

Publications, 2012. DOI: 10.5829/idosi.wasj.2012.19.06.2775

2. Biljana ShikoskaCena. Dimova, Gjorgji Schumanovand Vlado Vankovski. 2016. biomedical waste

management. Mac Med Review 2016; 70(1): 1-8. DOI:10.1515/mmr-2016-0001. Retrieved on April 11. 2019

from 3. Central Pollution Control Board. 2017. Protocol for Performance Evaluation and Monitoring of the Common

Hazardous Waste Treatment Storage and Disposal Facilities including Common Hazardous Waste Incinerators.

Ministry of Environmental and Forest. Parivesh Bhawan, East Arjun Negar, Delhi-110 032. Retrieved on May 25. 2019 from

http://cpcb.nic.in/displaypdf.php?id=aHdtZC9SZXZpc2VkX0d1aWRlbGluZXNfZm9yX0Jpby1tZWRpY2FsX1

dhc3RlX0luY2luZXJhdG9yLnBkZg== 4. Gupta. R. 2018. Manual for biomedical waste management. Government Medical College and Hospital – 32,

Chandigarh. Retrieved on May 25. 2019 from

https://www.worksafe.qld.gov.au/__data/assets/pdf_file/0006/88710/guide-handling-cytoxic-drugs-related-waste.pdf

5. HWM guideline. 2018. Guideline for Management of Healthcare waste as per biomedical waste management

rules, 2016. By Directorate general of Health Services Ministry of Health and Family Welfare and Central Pollution Control Board Ministry of Environment, Forest and Climate Change. Retrieved on May 25. 2019 from

http://cpcb.nic.in/uploads/hwmd/Guidelines_healthcare_June_2018.pdf

6. ICRC. 2011. Biomedical waste management. International committee of the Red Cross, 1202 Geneva,

Switzerland, November 2011. Retrieved on May 25. 2019 from

https://www.icrc.org/en/doc/assets/files/publications/icrc-002-4032.pdf

7. Imani Centre for policy and education. 2016. Biomedical waste management in Ghana. East Legon, Accra, Ghana, March 4.2016. Retrieved on February 20. 2019 from http://www.imaniafrica.org/wp-

content/uploads/2016/05/BIOMEDICAL-WASTE-MANAGEMENT-IN-GHANA.pdf

8. Melanen. M. 2016. Waste Management in Hospitals, Case project with Ecosir Oy and Eksote. Saimaa University of Applied Sciences, Faculty of Business Administration Lappeenranta, International. Bachelor's Thesis.

Retrieved on March 06. 2019 from https://www.theseus.fi/handle/10024/116209

9. Pandey. A, Ahuja. S, Madan. M and Asthana. A. K. 2016. Bio-Medical Waste Management in Tertiary Care Hospital: An Overview. Journal of Clinic and Diagnostic Research. Retrieved on March 12. 2019 from

10.7860/JCDR/2016/22595.8822

10. Qiao. Z, Nie. L and Wu. H. 2014. Medical Waste Management in China: Case Study of Xinxiang. China Agricultural University, Beijing and Xinxiang Medical University, Xinxiang, China. Copyright © 2014 by

authors and Scientific Research Publishing Inc.

11. This work is licensed under the Creative Commons Attribution International License (CC BY). 12. http://creativecommons.org/licenses/by/4.0/

13. Queensland Government. 2018. Guide for handling cytotoxic drugs and related waste. Office of industrial

relations, Workplace Health and Safety Queensland. PN10522 Version 4. Retrieved on June 05. 2019 from

https://www.worksafe.qld.gov.au/__data/assets/pdf_file/0006/88710/guide-handling-cytoxic-drugs-related-

waste.pdf

14. Soyam1. G. C, Hiwarkar1. P. A, Kawalkar1. U. G, Soyam. V. C, Gupta. V. K. 2017. KAP study of bio-medical waste management among health care workers in Delhi. International Journal of Community Medicine and

Public Heal, th. Soyam GC et al. Int J Community Med Public Health. pISSN 2394-6032 | eISSN 2394-

60402017 Sep;4(9):3332-3337 15. Training Component of the Project “Environmentally Sound Management of Medical Wastes in India”

Endeavour of GEF, UNIDO, MoEFCC and State Governments of Gujarat Karnataka, Maharashtra, Odisha and

Punjab, First Published 2018, by the United Nations Industrial Development Organization. Retrieved on February 22. 2019 from

http://envfor.nic.in/sites/default/files/5.%20Waste%20handlers%20manual_FLIP%20CHART.pdf

16. Vasistha. P, Ganguly. R, Gupta. A. K. 2015. Questionnaire Method for Assessing Biomedical Waste Management in Shimla City–Case Studies of Public and Private Hospitals. Journal of Civil Engineering and

Environmental Technology. p-ISSN: 2349-8404; e-ISSN: 2349-879X; Volume 2, Issue 16; October-December, 2015 pp. 11-14, Krishi Sanskrit Publications

170.

Authors: Sayed Behbood Hosaini, Sandeep Singla

Paper

Title: Significant Factors of Delay in Construction Projects in Afghanistan

Abstract: This research investigates the factors, which cause delay in construction projects in

Afghanistan. Delay is one of the major challenges during the implementation of the

construction projects, and it is the late completion of the activities or works of a project

compared to the planned. A large number of construction projects in Afghanistan are facing

delay during the implementation. Project delay negatively affects the prestige and dignity of

the government organizations and in general, failures and weaknesses of the projects created

distance between the people and the government and shows incapacity of the Government in

the implementation of the projects all around the country in Afghanistan. Several similar

studies from developing countries have been reviewed and a survey has been conducted for

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data collection from the different public organizations of Afghanistan. The analysis of the

collected data points out the significant causes of delay in construction project in Afghanistan

as; ineffective planning and scheduling of a project by contractor, delay in progress payments

by client, poor site management and supervision of contractors by consultant and client,

financial difficulties by contractor, insufficient controlling and monitoring by consultant and

client, non-availability of experienced technical staff of contractor, late in reviewing and

approving design documents by client, lack of communication and coordination between the

parties, delay in delivery of materials to site, inadequate contractor experience, political

influences and warlords intervention. Finally, this thesis provides applicable recommendations

to minimize or eliminate the causes of delay in construction projects in Afghanistan.

Keywords: significant factors, delay, construction projects, Afghanistan.

References: 1. Asian Development Bank (ADB) completion report. (2010). Afghanistan: Andkhoy–Qaisar Road Project.

2. 2. Retrieved May 12, 2016, Special Inspector General for Afghanistan Reconstruction (SIGAR). (2011a). Better

Planning and Oversight Could Have Reduced Construction Delays and Costs at the Kabul Military Training

Center. SIGAR Audit-12-2

3. 3. Special Inspector General for Afghanistan Reconstruction (SIGAR). (2011b). Afghan National Security

University Has Experienced Cost Growth and Schedule Delays, and Contract Administration Needs

Improvement. SIGAR Audit-12-3.

4. 4. Special Inspector General for Afghanistan Reconstruction (SIGAR). (2012). Fiscal Year 2011 Afghanistan

Infrastructure Fund Projects Are behind Schedule and Lack Adequate Sustainment Plans. SIGAR Audit-12-12.

5. 5. David, M., & Ore, K.J. (2010). Business Q&A, what major challenges have you observed with completing

construction projects in Afghanistan. Retrieved April 25, 2015

6. 6. Sadeqi, H. (2014). The main challenges of development projects in Afghanistan. Retrieved August 18, 2015.

7. 7. Barmak, W. (2013). Main challenges of National Solidarity projects in Afghanistan. Retrieved June 2015.

8. 8. Nushin, W. (2012). Factors which are Causing Projects Failure in Afghanistan [Web log post].

9. 9.The World Bank. (2012). Restructuring Paper on a Proposed Project Restructuring of Afghanistan Kabul

Aybak Mazar e Sharif Power Project.

10. 10.Special Inspector General for Afghanistan Reconstruction (SIGAR). (2009). Actions needed to resolve

construction delays at the counter-narcotics justice centre. SIGAR Audit-09-4.

11. 11.Retrieved Special Inspector General for Afghanistan Reconstruction (SIGAR). (2011a). Better Planning and

Oversight Could Have Reduced Construction Delays and Costs at the Kabul Military Training Center. SIGAR Audit-12-2. Retrieved May 12, 2016

12. 12.Special Inspector General for Afghanistan Reconstruction (SIGAR). (2010b). ANP District Headquarters

Facilities in Helmand and Kandahar Provinces Have Significant Construction Deficiencies Due to Lack of

Oversight and Poor Contractor Performance. SIGAR Audit-11-3.

13. 13. Special Inspector General for Afghanistan Reconstruction (SIGAR). (2012c). Fiscal Year 2011 Afghanistan

Infrastructure Fund Projects Are behind Schedule and Lack Adequate Sustainment Plans. SIGAR Audit-12-12.

Retrieved May 12, 2016.

14. 14. Special Inspector General for Afghanistan Reconstruction (SIGAR). (2013). Knduz Afghan National Police

Provincial Headquarters: After Construction Delays and Cost Increases, Concerns Remain About the Facilities Usability and Sustainability. SIGAR Inspection-13-4. Retrieved May 12, 2015.

15. 15. Special Inspector General for Afghanistan Reconstruction (SIGAR), (2010c).

16. 16.Significant Factors Causing Cost Overruns in the Construction Industry in Afghanistan By: Ghulam Abbas

Niazia, *, Noel Paintingb.2017.

171.

Authors: Deependra Prashad Bhatta, Sandeep Singla, Manish Kaushal, Anuj Sachar

Paper

Title: Effect of Retrofitting on Beam-Column Joints

Abstract: During the serviceable life of R.C.C structures they are found to show evidence of

distress due to various reasons. To bring such structures back to their functional/serviceable

condition these structures need urgent attention and enquiry for finding out reasons of distress

along with appropriate remedial treatments so as to increase serviceable life of such structures

and bring them back to their functional use. So the process of retrofitting involves upgrading

and enhancing the strength of deficient structures and their components. Safety of life is a

priority issue to be addressed in process of retrofitting. For preventing injury and death of

occupants and for preventing damage to structural components and collapse of structure as a

whole, some retrofitting techniques try to deal with the issue to avoid damage. As a retrofitting

1070-1076

technique, ferrocement technique is one of commonly used procedure of improving strength

which is due to their good durability, less cost, easy availability and ease in application with

requirement of intricate formwork. Application of ferrocement can be quickly done on the

damaged structural elements without any requirements of chemical bonding agents. Also the

ferrocement application requires less skill labour in comparison to other retrofit solutions

available these days. Ferrocement is light in weight, easy to construct & have low self weight

which is why it is preferred to other techniques of retrofitting. It has higher tensile strength

then R.C.C. Also the thickness of ferrocement is a fraction of thickness of R.C.C. structural

elements which makes it a complementary material for prefabricated structures. In the present

study six R.C.C. Beam column joint specimens were casted. Two controlled specimens were

initially stressed to ultimate load (100% damage) and other four specimens are stressed to

prefixed percentages of ultimate load. All six specimens were then retrofitted using

ferrocement for upgrading the strength of Beam column joints in flexure and shear. Chicken

wire mesh is wrapped all around the specimen. From the study it is observed that retrofitted

specimens has shown considerable decrease in deflection if we compare them to controlled

specimens. Also the percentage decrease in deflection for specimens subjected to 75% and

50% of ultimate loads is considerably higher to those specimens which are subjected to 100%

damage i.e. ultimate load

Keywords: Retrofitting, Beam-column joint, bond, development length, jacketing, Wire

mesh, deflection, shear, ferrocement.

References: 1. Makki Ragheed Fatehi “Response of Reinforced Concrete Beams Retrofitted by Ferrocement” International

journal of Scientific and Technology Research, Vol 3, Issue 9, Sep 2014, ISSN 2277-8616.

2. S Patil Sandesh, Ogale R.A , Dwivedi Arun Kumar, “Performance of Chicken Mesh on Strength of Beams

Retrofitted using Ferrocement Jackets” Journal of Engineering Vol.2, Issue 7, July 2012, PP01-10 3. Kaushik, S.K; Gupta.V.K.and Rahman.M.K. “Efficiency of mesh overlays of ferrocement elements” Journal of

ferrocement 17(4) 1987.

4. Desia, R., “Field Shake Test Programme at Latur, Western India,” News letter,Earthquake Hazard Centre, New Zealand, V. 3, No.2, pp. 4-5 (1999).

5. Ong, K.C.G., Paramasivam, P., and Lim, C.T.T.,” Flexural strengthening of reinforced concrete beams using

ferrocement laminates”. Journal of ferrocement, Vol 22, No 4, pp 331-342 (1992). 6. Desayi, P., and El-Kholy, S.A., “Deflection and cracking behaviour of lightweight fiber reinforced ferrocement”,

Journal of Ferrocement, Vol. 22, No2, pp 135-150 (1992).\

7. Mukherjee, Abhijit, Joshi, Mangesh, “FRPC reinforced concrete beam-column joints under cyclic excitation”. Composite Structures 70 (2005) 185–199.

8. Al-Salloum, Y.A., Alsayed, S. H., Almusallam, T. H. & Siddiqui, N. A. “seismic performance of shear deficient

exterior rc beam-column joints repaired using CFRP composite. 9. Ahmad Gobarah et al 2002, “Seismic rehabilitation of beam-column joints using FRP laminates”. (2002)

10. Wasti, S.T., Erberik, M.A., Sucuoglu, H.., and Kaur, C., “Studies on Strengthening of Rural Structures

Damaged in the 1995 Dinar Earthquakes,” Proceedings of the Eleventh European Conference on Earthquake Engineering, Paris, France (1998).

11. IS 1489:1991 (Part -1) Portland-Pozzolana Cement Specifications.

12. IS 13920 Ductile Detailing Of Reinforced Concrete Structures Subjected To Seismic Forces -Code Of Practice

172.

Authors: Prahlad Prasad and Brajkishor Prasad

Paper

Title: Performance Behavior of Eccentrically Braced Steel Frame under Seismic Loading

Abstract: Recent past growth of multi-story buildings structures with emphasis on steel has

been found satisfactory. For solving the better-quality accommodation in the region where the

chances of earthquakes are likely, role of bracing system enhances the performance of

building under lateral load effect of earthquakes. Various methods of bracing systems are

available in practices however, eccentrically braced frames (EBFs) are fairly new lateral force

resisting system established to resist seismic event in a probable manner. Properly designed

and detailed EBFs perform in a ductile manner through shear or flexural yielding of link

element. The ductile yielding indicates wide hysteresis loop, which is excellent energy

dissipation essential for high seismic event. The seismic performance of multi-story steel

frame is designed according to the Indian code (IS800:2007). A simple computer-based

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pushover analysis is a procedure for performance-based design of buildings subjected to

earthquake loading. Pushover analysis gets much importance in the past due to its ease and

efficiency of the results. In this study eight frames were taken,among these, six frames which

were braced as V, Inverted V, and Diagonal and remaining two are frames without bracing in

two alternate heights (4 and 8 story). Seismic response of frames is studied using non-linear

static analysis (pushover analysis) in terms of base shear, roof displacement, spectral

displacement, spectral acceleration and story drift.

Keywords: Steel Frame, Braced Frame, Pushover Analysis, Base Shear, Story Drift.

References: 1. Popov, Egor P., and Michael D. Engelhardt. "Seismic eccentrically braced frames." Journal of Constructional

Steel Research 10 (1988): 321-354.

2. Popov, E. P., J. M. Ricles, and K. Kasai. "Methodology for optimum EBF link design." Proceedings, Tenth

World Conference of Earthquake Engineering. Vol. 7. 1992.

3. World Conference on Earthquake Engineering. Beijing. 2008.

4. Mohammed Idrees Khan and Mr. Khalid Nayaz Khan, “Seismic Analysis of Steel Frame with Bracings using

Pushover analysis.”International Journal of Advanced Technology in Engineering and Science, Volume No.02,

Issue No. 07, July 2014.

5. Goel, Rakesh K. "Evaluation of nonlinear static procedures using strong-motion building records." Civil and

Environmental Engineering (2003): 70.

6. Chopra, Anil K., and R. Goel. "Modal pushover analysis of SAC buildings."Proceedings SEAOC Convention,

San Diego, California. 2001.

7. Federal Emergency Management Agency (FEMA),( 1997).NEHRP

8. IS 1893 Part 1 (2002),”Indian Standard Criteria for Earthquake Resistant Design of Structures”, Bureau of Indian

Standards, New Delhi

9. IS: 800 (2007), General Construction in Steel – Code of Practice, Bureau of Indian Standards, New Delhi, 2007

10. Applied Technology Council, ATC-40. Seismic evaluation and retrofit of concrete Buildings, California, 1996;

Vols. 1 and 2.

11. SAP2000, C. S. I. "Computers and Structures Inc." Berkeley, CA, USA (2013).

173.

Authors: Rashbha Sharma, Geetanjali and Rajesh Khanna

Paper

Title:

An H-Shaped Microstrip Antenna with Meandered Slot Lines and H-Shaped DGS For

Multiband Operation

Abstract: In this paper, a microstrip antenna is presented. It has an H-shaped patch which

uses meandered slots an H-shaped DGS beneath the microstrip line to support multiband

operation with enhanced bandwidth. The simulated and measured results are plotted to see the

performance of the antenna in terms of S11 parameter. The proposed designed resonates at

3.56, 8.04 and 10.57 GHz with a peak gain of 8.39 dB with considerable impedance

bandwidth and return loss values at the desired bands. The radiation pattern plots show the

conformability with the application it is designed for. The planar structure with a water-

resistant substrate makes it suitable for weather radar and other 5G applications.

Keywords: Defected ground structure (DGS), meandering, MSA: microstrip antenna,

transverse-electromagnetic mode (TEM).

References:

1. Girish Kumar, K.P. Ray , Broadband Microstrip Antennas. ARTECH HOUSE, INC 2003. 2. Juhua Liu, Shaoyong Zheng, Yuanxin Li, And Yunliang Long, “Broadband Monopolar Microstrip Patch

Antenna With Shorting Vias And Coupled Ring," Ieee Antennas And Wireless Propagation Letters, Vol. 13,

2014. 3. S. W. Lee and Y. J. Sung, “Reconfigurable Rhombus-Shaped Patch Antenna With Y-Shaped Feed for

Polarization Diversity,” DOI 10.1109/LAWP.2014.2358651, IEEE Antennas and Wireless Propagation Letters.

4. Neng-Wu Liu, Lei Zhu, Wai-Wa Choi and Jin-Dong Zhang, “A Low Profile Differentially-Fed Microstrip Patch Antenna with Broad Impedance Bandwidth under Triple-Mode Resonance”, DOI

10.1109/LAWP.2018.2850045, IEEE Antennas and Wireless Propagation Letters.

5. Olivier Caytan, Sam Lemey, Sam Agneessens, Dries Vande Ginste, Piet Demeester, Caroline Loss, Rita Salvado and Hendrik Rogier, “Half-Mode Substrate-Integrated-Waveguide Cavity-Backed Slot Antenna on Cork

Substrate”, DOI 10.1109/LAWP.2015.2435891, IEEE Antennas and Wireless Propagation Letters.

6. Symon K. Podilchak, Jonathan C. Johnstone, Mathieu Caillet, Michel Clenet, ´and Yahia M. M. Antar,“A

1091-1097

Compact Wideband Dielectric Resonator Antenna with a Meandered Slot Ring and Cavity Backing,” DOI

10.1109/LAWP.2015.2480547, IEEE Antennas and Wireless Propagation Letters.

7. Chow-Yen-Desmond Sim, Tuan-Yung Han and Yan-Jie Liao , “A Frequency Reconfigurable Half Annular Ring

Slot Antenna Design”, DOI 10.1109/TAP.2014.2314314, IEEE Transactions on Antennas and Propagation. 8. Jiade Yuan, Jiamin Zheng, and Zhizhang (David) A Compact Meandered Ring Antenna Loaded with Parasitic

Patches and a Slotted Ground for Global Navigation Satellite Systems (GNSS)”,DOI

10.1109/TAP.2018.2869209, IEEE Transactions on Antennas and Propagation. 9. Md. Mehedi Hasan, Mohammad Rashed Iqbal Faruque and Mohammad Tariqul Islam, “Dual Band Metamaterial

Antenna For LTE/Bluetooth/WiMAX System”, Scientific Reports (2018) 8:1240 | DOI:10.1038/s41598-018-

19705-3. 10. Mukesh Kumar Khandelwal, Binod Kumar Kanaujia and Sachin Kumar, “Defected Ground Structure:

Fundamentals, Analysis, and Applications in Modern Wireless Trends”, Hindawi International Journal of

Antennas and Propagation Volume 2017, Article ID 2018527. 11. D. Guha, S. Biswas, and Y. M. M. Antar, Defected Ground Structure for Microstrip Antennas, in Microstrip and

Printed Antennas: New Trends, Techniques and Applications, John Wiley & Sons, London, UK, 2011.

12. H. Saini, A. Kaur, A. Thakur, R. Kumar, and N. Kumar, "Compact multiband ground slotted patch antenna for X-band applications," 2nd IEEE International Conference on Recent Advances in Engineering & Computational

Sciences (RAECS), Chandigarh, 2015, pp. 1-6

13. James J. R., Hall P. S., and Wood C., “Microstrip Antenna Theory and Design”, London, United Kingdom, Peter

Peregrinus, pp. 87-89, 1981-include it in references and Put ref no. for this here

14. Constantine A. Balanis, “ANTENNA THEORY ANALYSIS AND DESIGN”, John Wiley & Sons,2005..

174.

Authors: Anudeep Arora, Dr.Durgesh Batra

Paper

Title:

Export Competitiveness of Indian Oilseeds: The Method of Constant Market Share

(CMS) with Special Reference to Groundnut

Abstract: This research Paper examines the export competiveness of oilseeds. The main and

huge production in agriculture is oilseeds which make India on the first number in Production

of oilseeds in the world. Oilseeds compose one of the significant groups of cash crops in

Indian agriculture. India has a proportional benefit in agriculture and there is a considerable

potential in raising farm returns and employment by stepping up agro base exports. The

constant market share (CONSTANT MARKET SHARE) study framework is used to

decompose changes in India’s share of the worldwide market for goods export in to

competitiveness and structural consequence over 2001-2017. The CONSTANT MARKET

SHARE Method is universally used to observe empirically the country’s export performance.

This paper is addressed to examine the regions export performance by applying CONSTANT

MARKET SHARE Method.

Keywords: CONSTANT MARKET SHARE, Export, Export Competitiveness, Trade

Specialization, Groundnut, Oilseed, Export Potential.

References: 1. Balassa, Bela (1977), “'Revealed' Comparative Advantage Revisited: An Analysis of Relative Export Shares of

the Industrial Countries, 1953-1971”, The Manchester School of Economic & Social Studies, 1977, vol. 45, issue

4, pp. 327-44.

2. Verghese SK (1979) Developments in International Competitiveness in India in 1970. Economic and Political

Weekly 14: 1718-1726.

3. Balance, Robert H, Helmut Forstner and Tracy Murray (1987), “Consistency Tests of Alternative Measures of

Comparative Advantage”, the Review of Economics and Statistics, Vol. 69, No. 1, pp. 157-161. 2.

4. Reddy, B. D. R., Lalith Achoth and B. V. Chenappa Reddy., 1998, Export Competitiveness of Groundnut

Empirical Evidence from Karnataka. Astha Vijnana, Vol.15 (3): pp. 263-270.

5. Bhavani T.A (2001), “Determinants of firm-level export performance: a case study of Indian Textile garments

and apparel industry”, The Journal of International Trade & Economic Development.10:1, 65-92.

6. Datta, S.K., 2001. How to Judge Global Competitiveness of Indian Agribusiness: Methodological Issues and

Lessons for India. In S.K.Datta and S.Y.Deodhar (Eds), Implications of WORLD TRADE ORGANIZATION

Agreements for Indian Agriculture. Oxford and IBH Publishing Co.Pvt.Ltd, New Delhi.

7. Samar Verma (November, 2002) “Export Competitiveness Of Indian Textile And Garment Industry” .

8. Verma S (2002) Export Competitiveness of Indian Textile and Garment Industry Working.Indian Council for

Research on International Economic Relations 94.

9. Ranjana Kumar (2005) “Constrains facing Indian agriculture: Need for policy intervention”, Indian journal of

agricultural economics, Vol. 60, No. 01, Jan –March.

10. RS Deshpande (March, 2005) “Karnataka Oilseed Production”.

11. Zhang, Xiu-ling and Liu, M., 2008. Experimental study on the international competition power of China’s peanut

industry. Journal of Henan Agricultural Sciences, 11.

1098-1105

12. Bhatt PR (2010) China’s Competitiveness in World Economy Foreign Trade 44: 19-41.

13. Jiang Y, Zhang Q, Chai J (2010) The Empirical Research of the Competitiveness based on the

Informationization of China’s Textile & Clothing Industry. The Conference on Web Based Business Management.

14. HailegiorgisBiramoAllaro (2011) “Export Performance of Oilseeds and ITS Determinants in Ethiopia”.

15. Devendra S (2013) Performace of Indian Textile and Clothing Industry in the United States Market A Post ATC

Analysis. Journal of Research in Commerce & Management 2: 64-76.

16. Sharma SK, Bugalya K (2014) Competitiveness of Indian agriculture sector a case study of cotton crop Procedia

- Social and Behavioral Sciences 133: 320-335.

17. Dr. N C Pahariya (2014) “Impact Assessment of Trade Liberalisation in Oilseeds Sector : Rajasthan”.

18. Akmal, N., W. Akhtar, H. Shah, M. A. Niazi and T. Saleem, 2014. The structure and competitiveness of

Pakistan’s Basmati Rice exports. Asian Journal of Agriculture and Rural Development, 4 (4): 304-312.

19. Sunil Kumar Niranjan (2016) “WORLD TRADE ORGANIZATION Agreement on Indian Oilseed Agriculture”.

20. Cann O (2016) what is competitiveness? World Economic Forum.

175.

Authors: Nik Alif Amri Nik Hashim, Sathish Kumar Velayuthan, Abdullah Muhamed Yusoff,

Zaimatul Awang, Fauzan Hafiz Muhammad Safri

Paper

Title:

Validating the Measuring Instrument for Motivation Factors towards Visiting Spa and

Wellness Tourism Destinations in Kuala Lumpur

Abstract: Visiting Spa and wellness tourism destinations is a new social trend among

wellness tourist around Malaysia as this industry is still at a growth stage in the product

lifecycle. However, limited interest has been paid on the study of domestic tourist motivation

to visit the spa and wellness tourism destination. Thus, by applying the quantitative approach,

the purpose of this pilot study is to assess the validity and reliability of the instrument used in

measuring the motivation factors towards visiting spa and wellness tourism destinations in

Kuala Lumpur. A total of 150-sample data were analysed using the statistical software IBM

SPSS version 23. Before that, content and face validity, reliability and data normality were

examined based on expert assessment. The result of the pilot study indicated that the

measuring instruments used in this study are reliable, and the data is proved of rational

normality. The findings of this study provided overall support for the proposed measuring

instrument for further research.

Keywords: Motivation, Visit, Spa, Wellness, Domestic Tourist.

References: 1. Athena H.N Mak, K. K. (2009). Health or Self-indulgence? The Motivation And Characteristic Among Spa

Goers. International Journal of Tourism Research, 185-199.

2. Bulmer, M.G. (1970). Principles of Statistics. United States: Dover Publication Inc.

3. Das, K. R., & Imon, A. H. M. R. (2016). A brief review of tests for normality. American Journal of Theoretical and Applied Statistics, 5(1), 5-12.

4. Global Wellness Summit. (2016). Summit Mission. Retrieved from http://www.globalwellnesssummit.com

5. Hashemi, S.M., Jusoh, J., Kiumarsi, S., & Mohammadi, S. (2015). Influence Factors Of Spa and Wellness Tourism on Revisit Intention: The Mediating Role of International Tourist Motivation and Tourist Satisfaction.

International Journal of Research, 3(7),1-11.

6. Henna Konu and Tommi Laukkanen. (2009). Roles Of Motivation Factors In Predicting The Tourist's Intention To Make Wellbeing Holidays - A Finnish Case. ANZMAC , 1-9.

7. Hyde-Smith, M. J., (2012).The Wellness Spa: Construct Definition and Performance Evaluation(Unpublished

Doctoral dissertation) Auckland University of Technology. 8. Sekaran, U., Bougie, R. (2017). Research Methods for Business: A Skill Building Approach. United Kingdom:

John Wiley & Sons, Ltd, Publication.

9. Smith, M., & Puczkó, L., (2008).Health and wellness tourism. Routledge.

1106-1108

176.

Authors: Manjit Singh, Mukesh Mann

Paper Title: Thermal Diffusion Flow Analysis in Unsaturated Loamy Soil using UPWIND and

QUICK Numerical Simulation

Abstract: Understanding of energy transport in saturated porous media and unsaturated

porous media provides vital information regarding the development of thermal porous

reservoirs, industrial filter applications, water reservoirs and low-temperature fluid flow

applications. The present paper focus is on presenting the solution of thermal energy diffusion

through unsaturated loamy soil using finite difference method approach. The unsteady

temperature profile comparison as time function at different spatial locations and as function

1109-1112

of spatial location at different time interval are compared using UPWIND and QUICK

approach method using thermal diffusion case. MATLAB software is used to simulate above

approach for simulation.

Keywords : Mathematical modeling, Finite difference method, Loamy soil

References: 1. H. Darcy, "Les fontainesPubliques de la Ville de Dijon," Vols. Dalmont, Paris, 1856.

2. M. Muskat, The flow of homogeneous fluid through porous media, United State of America: Mcgraw Hill,

1937. 3. J. Dupuit, "EstudesThéoriques et Pratiques sur le mouvement des Eaux dans les canauxdécouverts et à

travers les terrains perméables," (Second ed.)., Paris, Dalmont, 1863.

4. H. Brinkman, "A calculation of viscous force exerted by a flowing fluid on a dense swarm of particles," Applied Scientific Research, vol. 1, pp. 27-34, December 1949.

5. Mukesh Kumar; Manjit Singh; Chanpreet Singh; Ganga Charyulu;, "Modeling for heat conduction transfer

through porous media," International Journal of Advance in computing and information technology, Issue-5, pp. 469-474, 2012.

6. W. Stephen, "Flow in Porous Media I: A Theoretical Derivation of Darcy's Law," Transport in Porous

Media 1, D. Reidel Publishing Company, pp. 3-25, 1986. 7. S. Irmay, "Sur le mouvement des eaux dans le sol," Rev. Universelle des Mines, vol. 3, no. 4, 1947.

8. Manjit Singh; Sandeep Jindal; Amit Gupta; Virender Chahal, "Numerical Analysis of soil-water flow in

fixed Horizontal pipe," International journal of theoretical and applied mechanics ISSN 0973-6085, pp. volume 12, number 3, 533-541, 2017.

9. H. K. Dahle, M. A. Celia and S. M. Hassanizadeh, "Bundle-of-Tubes Model for Calculating Dynamic

Effects in the Capillary- Pressure-Saturation Relationship," Transport in Porous Media, vol. 58, pp. 1-2, 2005.

10. D. Yang, R. P. Currier and Duan Z. Zhang, "Ensemble phase averaged equations for multiphase flows in

porous media. Part 1: The bundle-of-tubes model," International Journal of Multiphase Flow, vol. 35, p. 628–639, 2009.

11. Manjit Singh; Chanpreet Singh; D. Gangacharyulu, "Modelling for flow through unsaturated porous media

with constant and variable density conditions using local thermal equilibrium," International Journal of Computer Application (0975-8887), pp. 24-30, 2016.

12. Manjit Singh; Chanpreet Singh; D. GangaCharyulu, "Numerical Analysis on variations of thermal and

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