Editor-In-Chief Chair Dr. Shiv Kumar

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

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

(M.P.), India

Associated Editor-In-Chief Chair Dr. Dinesh Varshney

Professor, School of Physics, Devi Ahilya University, Indore (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.

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.

Manager Chair Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair Dr. Sameh Ghanem Salem Zaghloul

Department of Radar, Military Technical College, Cairo Governorate, Egypt.

Editorial Members Dr. Uma Shanker

Professor, Department of Mathematics, Muzafferpur Institute of Technology, Muzafferpur(Bihar), India

Dr. Rama Shanker

Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea

Dr. Vinita Kumar

Department of Physics, Dr. D. Ram D A V Public School, Danapur, Patna(Bihar), India

Dr. Brijesh Singh

Senior Yoga Expert and Head, Department of Yoga, Samutakarsha Academy of Yoga, Music & Holistic Living, Prahladnagar,

Ahmedabad (Gujarat), India.

S. No

Volume-8 Issue-10S, August 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: R .Sri Devi, Dr. M. Mohan Kumar

Paper Title: Cyber Security Affairs in Empowering Technologies

Abstract: Digital world connected millions of people through internet for our day today activities, and therefore security, privacy, authentication issues is a key question today. The main objective of this paper is to study various issues in

enabling technologies such as Cyber Physical System (CPS), Internet of Things (IoT), Big Data Analytics (BDA) and

Artificial Intelligence (AI). IoT is a network of devices which link, interact and transfer data but security and privacy should

concern while moving from traditional to modern world. CPS is a combination of data processing, networking and physical

activities, through critical infrastructure attacker access computer devices and damages the system. Big data shared high

volume, velocity, variety, context and content data to distributed system and key issue is data lost by harm. Artificial

Intelligence can perform job as like human brain and solve real world problem but optical network can be easily attacked by

hackers. This paper investigate different threats, limitation and future work in the cyber physical system, IoT etc and various

methods are discussed from various articles and it is more helpful in doing further research work in this area and cyber

security is the most important tool which can be implement to protect the cyberspace from the inside and outside attacker.

Index Terms: Cyber security, poisoning attack, evasion attack, Intrusion Detection System, tools, deep learning, block

chain, algorithm, protocols.

References: 1. Wu, W., Kang, R., & Li, Z. (2015, December). Risk assessment method for cybersecurity of cyber-physical systems based on inter-

dependency of vulnerabilities. In Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference

on (pp. 1618-1622). IEEE.

2. Li, S., Bi, F., Chen, W., Miao, X., Liu, J., & Tang, C. (2018). An Improved Information Security Risk Assessments Method for Cyber-Physical-Social Computing and Networking. IEEE Access, 6, 10311-10319.

3. Siboni, S., Sachidananda, V., Meidan, Y., Bohadana, M., Mathov, Y., Bhairav, S.,& Elovici, Y. (2018). Security Testbed for Internet-of-Things Devices. IEEE Transactions on Reliability.

4. Abouzakhar, N. S., Jones, A., & Angelopoulou, O. (2017, June). Internet of Things Security: A Review of Risks and Threats to Healthcare Sector. In Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,

Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2017 IEEE International Conference on (pp. 373-378).

IEEE.

5. Singh, M., Singh, A., & Kim, S. (2018, February). Blockchain: A game changer for securing IoT data. In Internet of Things (WF-IoT), 2018 IEEE 4th World Forum on (pp. 51-55). IEEE.

6. Mahmood, T., & Afzal, U. (2013, December). Security analytics: Big data analytics for cybersecurity: A review of trends, techniques and tools. In Information assurance (ncia), 2013 2nd national conference on (pp. 129-134). IEEE.

7. Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2018). Evaluating critical security issues of the IoT world: present and future challenges. IEEE Internet of Things Journal, 5(4), 2483-2495.

8. Teoh, T. T., Nguwi, Y. Y., Elovici, Y., Cheung, N. M., & Ng, W. L. (2017, July). Analyst intuition based Hidden Markov Model on high speed, temporal cyber security big data. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and

Knowledge Discovery (ICNC-FSKD) (pp. 2080-2083). IEEE.

9. Makhdoom, I., Abolhasan, M., Lipman, J., Liu, R. P., & Ni, W. (2018). Anatomy of Threats to The Internet of Things. IEEE Communications Surveys & Tutorials.

10. Hodgson, R. (2019). Solving the security challenges of IoT with public key cryptography. Network Security, 2019(1), 17-19. 11. Thakur, K., Qiu, M., Gai, K., & Ali, M. L. (2015, November). An investigation on cyber security threats and security models. In 2015

IEEE 2nd International Conference on Cyber Security and Cloud Computing (pp. 307-311). IEEE.

12. Wolf, M., & Serpanos, D. (2018). Safety and security in cyber-physical systems and internet-of-things systems. Proceedings of the IEEE, 106(1), 9-20.

13. Sabar, N. R., Yi, X., & Song, A. (2018). A bi-objective hyper-heuristic support vector machines for big data cyber-security. IEEE Access, 6, 10421-10431.

14. Chiroma, H., Abdullahi, U. A., AlArood, A. A., Gabralla, L. A., Rana, N., Shuib, L., & Herawan, T. (2018). Progress on Artificial Neural Networks for Big Data Analytics: A Survey. IEEE Access.

15. Wu, J., Dong, M., Ota, K., Li, J., & Guan, Z. (2018). Big data analysis-based secure cluster management for optimized control plane in software-defined networks. IEEE Transactions on Network and Service Management, 15(1), 27-38.

16. Lin, H., Yan, Z., Chen, Y., & Zhang, L. (2018). A survey on network security-related data collection technologies. IEEE Access, 6, 18345-18365.

17. Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218.

18. Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., & Wang, C. (2018). Machine learning and deep learning methods for 19. cybersecurity. IEEE Access, 6, 35365-35381. 20. Phang, D. C., Wang, K., Wang, Q., Kauffman, R. J., & Naldi, M. (2019). How to derive causal insights for digital commerce in china?

a research commentary on computational social science methods. Electronic Commerce Research and Applications, 100837.

21. Abuzainab, N., & Saad, W. (2019). A graphical Bayesian game for secure sensor activation in internet of battlefield things. Ad Hoc Networks, 85, 103-109.

22. Taylor, P. J., Dargahi, T., Dehghantanha, A., Parizi, R. M., & Choo, K. K. R. (2019). A systematic literature review of blockchain cyber security. Digital Communications and Networks.

23. Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., ... & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143-174.

24. Makhdoom, I., Abolhasan, M., & Ni, W. (2018, August). Blockchain for IoT: The Challenges and aWay Forward. In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications-Volume 2: SECRYPT. INSTICC.

25. Wang, E. K., Ye, Y., Xu, X., Yiu, S. M., Hui, L. C. K., & Chow, K. P. (2010, December). Security issues and challenges for cyber physical system. In 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber,

Physical and Social Computing (pp. 733-738). IEEE.

26. Sikos, L. F. (2018). AI in Cybersecurity (Vol. 151). Springer. 27. Ville Silkamo (2018) IoT from cyber security perspective case study JYVSECTEC. (pp 1-98)

1-7

28. Saqib Ali et al (2018) Cyber Security for Cyber Physical Systems, Studies in computational intelligence, volume 768

2.

Authors: PUSHPA BALASUBRAMANIAN, KAMARASAN MARI

Paper Title: Continuous Wavelet Transform Based Gene Optimized Fuzzy C-Means Clustering For Forest Fire

Detection

Abstract: Fire detection is an important aspect of disaster preparedness, to reduce loss of lives and property damage. Conventionally, many techniques have been designed so far, to discover the forest fires through input videos. But, clustering

performance of conventional fire detection techniques was not sufficient. To overcome the above limitations, Continuous

Wavelet Transform Based Gene Optimized Fuzzy C-Means Clustering (CWT-GOFCC) technique is proposed. The proposed

CWT-GOFCC technique takes number of video files from FIRESENSE database as input and converts those input videos

into a number of frames. Next, it defines the number of clusters and centroids and consequently initializes the gene

populations with number of video frames. After that, CWT-GOFCC technique evaluates fuzzy membership with the

assistance of fitness function for all input video frames based on spatial correlation between the fire flame colors. By using

this fitness function, the technique groups the video frames into pre-fire stage or fire stage or critical fire stage with

enhanced accuracy. From that, this technique accurately clusters all the video frames into related clusters with lower time

consumption. The simulation of the technique is conducted using metrics such as fire detection accuracy, fire detection time

and false positive rate with respect to different numbers of video frames. The simulation result depicts that the technique is

able to improve the accuracy and also reduce the time of forest fire detection in video file when compared to state-of-the-art

works.

Index Terms: Continuous Wavelet Transform, Fire detection, Fire Flame Colors, Fitness function, Fuzzy Membership,

Spatial Correlation, Video Frames.

References: 1. Mahdi Hashemzadeh, Alireza Zademehdi, “Fire detection for video surveillance applications using ICA K-medoids-based color model

and efficient spatio-temporal visual features”, Expert Systems with Applications, Elsevier, Volume 130, Pages 60-78, September 2019. 2. Siva Mouni Nemalidinne, Deep Gupta, “Nonsubsampled contourlet domain visible and infrared image fusion framework for fire

detection using pulse coupled neural network and spatial fuzzy clustering”, Fire Safety Journal, Elsevier, Volume 101, Pages 84-101,

October 2018. 3. Yang Jia, Gaohua Lin, Jinjun Wang, Jun Fang, Yongming Zhang, “Light Condition Estimation Based on Video Fire Detection in

Spacious Buildings”, Arabian Journal for Science and Engineering, Springer, Volume 41, Issue 3, Pages 1031–1041, March 2016. 4. P.Tamil Mathi, Dr. L. Latha, “Video Based Forest Fire Detection using Spatio-Temporal Flame Modeling and Dynamic Texture

Analysis”, International Journal on Applications in Informaton and Communication Engineering Volume 2, Issue 4, Pages 41-47, April

2016 5. Zhijie Zhang, Tian Shen, Jianhua Zou, “An Improved Probabilistic Approach for Fire Detection in Videos”, Fire Technology,

Springer, Volume 50, Issue 3, Pages 745-752, May 2014.

6. Gudikandhula Narasimha Rao, Peddada Jagadeeswara Rao, Rajesh Duvvuru, Sridhar Bendalam, Roba Gemechu, “An enhanced real-time forest fire assessment algorithm based on video by using texture analysis”, Perspectives in Science, Elsevier, Volume 8, Pages

618-620, September 2016.

7. Zhenglin Li, Lyudmila S Mihaylova, Olga Isupovaand Lucile Rossi, “Autonomous Flame Detection in Videos with a Dirichlet Process Gaussian Mixture Color Model”, Journal Of Industrial Informatics, Special Section Of Multisensor Fusion And Integration For

Intelligent Systems, Volume 14, Issue 3, Pages 1146-1154, 2017.

8. Shixiao Wu and Chengcheng Guo, “Using Combination Methods to Improve Real Time Forest Fire Detectio n”, Journal of Statistics and Mathematical Sciences, Volume 5, Issue 1, Pages 1-10, December 2018.

9. C. E. Prema et al, “Efficient Colour Based Fire Pixel Classification Using Image Processing”, Applied Mechanics and Materials, Volume 626, Pages 52-57, 2014.

10. Rui Chen, Yuanyuan Luo, Mohanmad Reza Alsharif, “Forest Fire Detection Algorithm Based On Digital Image”, Journal of Software , Volume 8, Issue 8, Pages 1897- 1905, August 2013.

11. Amin Khatami, Saeed Mirghasemi, Abbas Khosravi, Chee Peng Lim, Saeid Nahavandi, “A new PSO-based approach to fire flame detection using K-Medoids clustering”, Expert Systems with Applications, Volume 68, Pages 69-80, February 2017.

12. Mubarak A. I. Mahmoud, and Honge Ren, “Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation”, Mathematical Problems in Engineering, Volume 2018, Article ID 7612487, Pages 1-8, 2018,

13. Rui Chi, Zhe-Ming Lu, Qing-Ge Ji, “Real-time multi-feature based fire flame detection in video”,ET Image Processing, Volume 11, Issue 1, Pages 31 – 37, 2017.

14. Kosmas Dimitropoulos, Panagiotis Barmpoutis and Nikos Grammalidis, “Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT),

Volume 25, Issue 2, Pages 339-351, February 2015.

15. FIRESENSEdatabase: https://zenodo.org/record/836749

8-14

3.

Authors: NARMATHA VENUGOPAL, KAMARASAN MARI

Paper Title: Automatic Detection of Glaucoma based on Refined Complete Local Binary Pattern and Random

Forest Classification Method

Abstract: Glaucoma is considered to be one of the main root causes of blindness. As it shows no symptoms, if not properly identified at the correct time would result in the loss of vision. This paper proposes a method for the Automatic

Detection of Glaucoma based on Refined Complete Local Binary Pattern and Random Forest Classification

Method(RCLBP-RFC), which identifies the presence or the absence of glaucoma in a patient at an early stage. The first step

is use to convert a color image into gray scale image and the second step we use Neighborhood Fuzzy K Means Clustering to

segment Optic Disc(OD) and Optic Cup(OC). In the third step Statistical Optimized and Restoration model is use to extract

the enhanced images using the restoration technique. In the Fourth step we exploit Refined Complete Local Binary Patterns

Extraction to extract the most relevant features and finally, Random Forest Classification methods are involved to classify

the features as normal, abnormal or early detected glaucoma. The experiments show that our RCLBP-RFC method achieves

state-of-the-art OD and OC segmentation result on DRIONS dataset. Experimental results indicates that the proposed

method identifies the presence or absence of glaucoma more precisely than other existing methods in terms of computational

time and complexity, and accuracy..

Keywords: Glaucoma, Random Forest, Neighborhood Fuzzy, Refined Complete Local Binary Pattern, Statistical Optimized.

.

15-22

References: 1. Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, and Xiaochun Cao, “Joint Optic Disc and Cup Segmentation

Based on Multi-label Deep Network and Polar Transformation”, Computer Vision and Pattern Recognition”, IEEE Transactions on

Medical Imaging, Jun 2017 (Multi label Deep Network (M-Net)).

2. G. Gifta Jerith, P. Nirmal Kumar, “Recognition of Glaucoma by means of Gray Wolf Optimized Neural Network”, Multimedia Tools and Applications, Springer, Jan 2019.(Gray Wolf Optimized Neural Network (GWO-NN)).

3. Q1T.R. Kausu, Varun P. Gopi, Khan A. Wahid, Wangchuk Doma, Swamidoss Issac Niwas, “Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images”, Bio Cybernetics and Bio Medical Engineering, Elsevier, Feb 2018 (Combination of Clinical and Multi Resolution Features).

4. Zaka Ur Rehman, Syed S. Naqvi, Tariq M. Khan, Muhammad Arsalan, Muhammad A. Khan, M.A. Khalil, “Multi-parametric Optic Disc Segmentation Using Superpixel Based Feature ClassiÞcation”, Expert Systems With Applications, Elsevier, Dec 2018 (Multi-parametric Optic Disk Detection and Localization) .

5. Marcos Vinõcius dos Santos Ferreira, Antonio Oseas de Carvalho Filho, Alcilene Dalõlia de Sousa, Aristofanes Corröea Silva, Marcelo Gattass, “Convolutional neural network and texture descriptor-based automatic detection and diagnosis of Glaucoma”, Expert Systems

With Applications, Elsevier, Jun 2018.

6. Rashmi Panda, N.B. Puhan, Aparna Rao, Debananda Padhy, Ganapati Panda, “Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma”, Computerized Medical Imaging and Graphics journal homepage”, Elsevier, May 2018.

7. Antonio Sousa Vieira de Carvalho Juniora, Edson Damasceno Carvalhoa, Antonio Oseas de Carvalho Filho, Alcilene Dalília de Sousa, Aristófanes Corrêa Silva, Marcelo Gattass, “Automatic methods for diagnosis of glaucoma using texture descriptors based on phylogenetic diversity”, Computers and Electrical Engineering, Elsevier, Jun 2018.

8. Shishir Maheshwari, Vivek Kanhangad, Ram Bilas Pachori, Sulatha V. Bhandary, U. Rajendra Acharya,” Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques”, Computers in Biology and Medicine, Elseiver, Nov 2018.

9. Neha Gour, Pritee Khanna, “Automated Glaucoma Detection using GIST and Pyramid Histogram of Oriented Gradients (PHOG) descriptors”, Pattern Recognition Letters, Elsevier, Nov 2018.

10. Shuang Yu Di Xiao Shaun Frost Yogesan Kanagasingam, “Robust Optic Disc and Cup Segmentation with Deep Learning for Glaucoma Detection”, Computerized Medical Imaging and Graphics, Elsevier, Feb 2019.

11. Shishir Maheshwari, Ram Bilas Pachori, and U. Rajendra Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images”, IEEE Journal of Biomedical and Health Informatics ( Volume: 21 , Issue: 3 , May 2017 ).

12. Mohammed El Amine Bechar, Nesma Settouti, Vincent Barra, Mohamed Amine Chikh, “Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease”, Multidimensional Systems and Signal Processing, Springer, Mar 2017.

13. Muhammad Nauman Zahoor and Muhammad Moazam Fraz, “A Correction to the Article Fast Optic Disc Segmentation in Retina Using Polar Transform''”, IEEE Access, Jan 2018.

14. Wei Zhou, Hao Wu, Chengdong Wu, Xiaosheng Yu, and Yugen Yi, “Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis”, Computational and Mathematical Methods in Medicine, Hindawi, Mar 2018.

15. Anindita Septiarini, Dr, Agus Harjoko, PhD, Reza Pulungan, Dr.-Ing, Retno Ekantini, Dr, “Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation”, Healthcare Informatics Research, The Korean Society of Medical

Informatics, Jun 2018

16. Guangzhou An, Kazuko Omodaka, Kazuki Hashimoto, Satoru Tsuda, Yukihiro Shiga, Naoko Takada, Tsutomu Kikawa, Hideo Yokota, Masahiro Akiba, Toru Nakazawa, “Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color

Fundus Images”, Journal of Healthcare Engineering, Hindawi, Feb 2019.

17. A. Soltania, T. Battikha, I. Jabri, N. Lakhoua, “A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis”, Biomedical Signal Processing and Control, Elsevier, Oct 2017.

18. T.R. Kausu, Varun P. Gopi, Khan A. Wahid, Wangchuk Doma, Swamidoss Issac Niwas, “Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images”, Bio Cybernetics and Bio Medical Engineering, Elsevier, Feb 2018.

19. Yuki Hagiwara, Joel En Wei Koh, Jen Hong Tan, Sulatha V Bhandary, Augustinus Laude, Edward J Ciaccio, Louis Tong, U Rajendra Acharya, “Computer-Aided Diagnosis of Glaucoma Using Fundus Images: A Review”, Computer Methods and Programs in Biomedicine, Elsevier, Jul 2018.

20. A. Sevastopolsky, “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network”, Pattern Recognition and Image Analysis, 2017, Vol. 27, No. 3, pp. 618–624. © Pleiades Publishing, Ltd., 2017

4

Authors: Sreevas R, Shanmughasundaram R, VRL Swami Vadali

Paper Title: Development of an IoT based Air Quality Monitoring System

Abstract: The proposed work aims for a large-scale air pollutant monitoring for ambient and indoor

environments. This system is developed to measure various environmental parameters. Sourceof pollutants can

be identified by analyzing the data collected from the various sensor nodes, so that air quality can be monitored

by applying engineering science and data. This is achieved by installing multiple sensor stations in various

locations such as hospitals, factories, Offices, streets and weather stations. These sensor stations measure the

environmental parameters such as PM2.5, PM10, Sulphate (SOx), Nitrate (NOx), Ozone(O3), Volatile Organic

Compounds (VOC), Temperature and Humidity. The sensor stations communicate with cloud over HTTP

protocol. Each station has ESP 8266 smart controller which captures the sensor data and creates forms

theJavaScript Object Notation (JSON) data packets that mainly consists of sensor data along with node address.

These packets will be sent to the cloud over HTTP protocol. The user can access the air quality data from the

web application.

Index Terms: Air quality; HTTP; IoT; Cloud; ESP 8266.

References: 1. S. Kumar and A. Jasuja, "Air quality monitoring system based on IoT using Raspberry Pi," 2017 International Conference on

Computing, Communication and Automation (ICCCA), Greater Noida, 2017, pp. 1341-1346.

2. SM, Shiva Nagendra, et al. "Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots." Sustainable Cities and Society 44 (2019): 520-535.

3. C. Xiaojun, L. Xianpeng and X. Peng, "IOT-based air pollution monitoring and forecasting system," 2015 International Conference on Computer and Computational Sciences (ICCCS), Noida, 2015, pp. 257-260.

4. https://thewire.in/environment/air-pollution-monitoring-diwali-pm2-5-pm10-cpcb-namp. 5. https://www.mouser.com/ds/2/588/ams_11032016_ENS210-1214724.pdf 6. Cheng, Yun, et al. "AirCloud: a cloud-based air-quality monitoring system for everyone." Proceedings of the 12th ACM

Conference on Embedded Network Sensor Systems. ACM, 2014, pp.251-265.

23-28

7. Jen-Hao Liu et al., "Developed urban air quality monitoring system based on wireless sensor networks," 2011 Fifth International Conference on Sensing Technology, Palmerston North, 2011, pp. 549-554..

8. Abraham, Sherin, and Xinrong Li. "A cost-effective wireless sensor network system for indoor air quality monitoring applications." Procedia Computer Science 34 (2014): 165-171.

9. G. Spandana and R. Shanmughasundram, "Design and Development of Air Pollution Monitoring System for Smart Cities," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 1640-1643.

10. M. V. Ramesh et al., "Water quality monitoring and waste management using IoT," 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, 2017, pp. 1-7.

5.

Authors: V.Mohana Maniganda Babu, S.Sasireka, E.Anitha

Paper Title: Visual Information Retrieval for Videos Based On Feature Extraction Using Machine Learning

Techniques

Abstract: Information retrieval is one of the important areas of research with highest scope for data mining

combined with machine learning. The proposed research focus on visual information retrieval by applying

machine learning techniques. The usage of multimedia data such as text, images, videos are abundantly

increasing day by day in this smart era. Also the need for information classification and retrieval are getting

exponential demands to fulfill the research and end user requirements. The tech giants are conducting their

researches to develop efficient retrieval systems for videos. Video retrieval is considered to be the toughest and

challenging research in the recent times. Due to large storage space, lengthy play time, multiple sequence of

frames, spatial temporal challenges, lack of visual relevancy, less hardware and processing support. The

proposed visual information retrieval has got higher scope of research with the above listed problems.

Index Terms: Bag of features (Bof), histograms, Support Vector Machines, key point locations-Means

algorithm.

.

References: 1. Retrieval Using Text and Image Content” Cybernetics and information technologies, vol. 10, no. 3 pp.20-30,2010 2. Chin-Chen Chang and Tzu-Chuen Lu, “A Color-Based Image Retrieval Method Using Color Distribution and Common Bitmap”

Springer, pp. 56–71, 2005

3. Kekre H.B, Sudeep D. Thepade, TanujaK. Sarode and Shrikant P. Sanas , ”Image retrieval using texture features extracted using LBG, KPE, KFCG, KMCG, KEVR with assorted Color spaces” International Journal of Advances in Engineering & Technology, Vol. 2, Issue 1, pp. 520-531,2012

4. Vivek Jain, Neha Sahu “A Survey: On Content Based Image Retrieval” International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 3, Issue 4, Jul-Aug 2013, pp.1166-1169

5. V.MohanaManigandaBabu, Dr.T.Santha, “Efficient Brightness Adaptive Deep-Sea Image Stitching using Biorthogonal Multi-Wavelet Transform and Harris Algorithm”, IEEE International Conference on Intelligent Computing and Control (I2C2 17),ISBN: 978-1-4673-9916-6,23-24th June 2017.

6. V.MohanaManigandaBabu, Dr.T.Santha, ”Efficient Brightness Adaptive Deep-Sea Image Stitching using Biorthogonal Multi-Wavelet Transform and Harris Algorithm”, IEEE International Conference on Intelligent Computing and Control (I2C2 17), ISBN: 978-1-5386-0374-1,23-24th June 2017.

7. Sakthi Sivkumar.V, “Organic On-Field Research Combined with Information Technology for a Fast Uptake, Sustainable and Profitable Future Using Both Macro and Micro Analysis of The System as a Whole”, (ORGATROP 2017) - International Conference on Organic Agriculture in the Tropics: State-of-the-Art, Challenges and Opportunities, Yogyakarta, Indonesia,

August 20 – 24, 2017.

8. Sakthi Sivakumar. V, “Applying both modern and ancient Management principles and planning to optimize the Organic sector from field level to increase the Sustainability and Profitability” (OWC – 2017), Organic World Congress, Greater Noida, India,

Nov 09 -11, 2017.

9. M Abhayadev, Dr.T Santha, “Object Boundary Identification using Enhanced High Pass Frequency Filtering Algorithm and Morphological Erosion Structuring Element”, Journal of Scientific & Industrial Research (SCIE),Vol. 76, pp.620-625, October

2017

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6.

Authors: K .G. Rani Roopha Devi1,Dr.R.Mahendra Chozhan2, Dr. R. Murugesan3

Paper Title: Artificial intelligence applications and future research directions

Abstract: This is an extensive study of Artificial Intelligence applications. It offers artificial neural networks (ANN) taxonomy and supplies investigators with current knowledge and raising needs in ANN based research applications and

concentration for investigators. In addition, this study offers an ANN application contributions, challenges, performance

comparison and evaluation. This study is demonstrated various ANN applications in diverse disciplines comprise science,

computing, medicine, environmental, engineering, climate, technology, mining, arts, nanotechnology, business and so on.

Based on this review, it is identified that neural network models like Feedback propagation and Feed forward artificial neural

networks performs effectually in human problems based application. Henceforth, feed forward and feed backward

propagation ANN focuses on research sourced on data analysis parameters such as accuracy, fault tolerance, latency, volume,

convergence, scalability and performance. However, this study suggests that indeed of utilizing single method, future

investigation concentrates on merging ANN models into cloud and dentistry based network wide application.

Keywords- Artificial intelligence; Artificial Neural networks; feed forward; feed backward; ANN applications; cloud

fusion; Dentistry.

.

References: 1. any H Ammar, Walid Abdelmoez and Mohamed Salah Hamdi, “Software Engineering Using Artificial Intelligence Techniques:

Current State and Open Problems” – ICCIT 2012. 2. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015.

3. C. Szegedy et al., "Intriguing properties of neural networks," presented at the International Conference on Learning Representations, 2014, pp. 1-10. 4. K. Crawford, "Opinion I Artificial Intelligence's White Guy Problem," The New York Times, 25-Jun-2016.

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5. B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. 1. Gershman, "Building machines that learn and think like people," Behav. Brain Sci., vol. 40, ed 2017.

6. W. Samek, T. Wiegand, and K.-R. Muller, "Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models," ITU J. ICT Discov. - Spec. Issue I - Impact Artif. Intell. AI Commun. Netw. Serv., vol. I, pp. 1-10, Dec. 2017. 7. G. Montavon, W. Samek, and K.-R. Muller, "Methods for interpreting and understanding deep neural networks," Digit. Signal Process, vol. 73, pp. 1-15, Feb. 2018.

8. E. Strumbelj and I. Kononenko, "Explaining Prediction Models and Individual Predictions with Feature Contributions," Knowllnf Syst, vol. 41, no. 3, pp. 647-665, Dec. 2014.

9. M. D. Zeiler and R. Fergus, "Visualizing and Understanding Convolutional Networks," in Computer Vision - ECCV 2014, 2014, pp.818-833. 10. W. Guo, K. Zhang, L. Lin, S. Huang, and X. Xing, "Towards Interrogating Discriminative Machine Learning Models," ArXiv 170508564 Cs Stat, May 2017.

11. Point, T. (2017, July 23). “Artificial Intelligence Overview” Retrieved August 22, 2017.

7.

Authors: T. Shanthi, S. U. Prabha

Paper Title: MPPT controller based on fuzzy logic for photovoltaic systems with sepic converter

Abstract: A controller for maximum power point tracking (MPPT) based on fuzzy logic was developed to

connect the solar panels and three phase grid through an inverter. MPPT controller traces the maximum power

and then feeds this power to the three phase grid irrespective of the changes in solar irradiations. The input and

output variables for the fuzzy logic controller were selected in order to vary the inverter firing angle to track the

maximum power from the solar panels. The proposed system using fuzzy logic controller was built using

MATLAB Simulink /Power System Block (PSB) set. A DSP controller has been embedded with program for

firing of the thyristors used in the inverter with appropriate pulses. Hardware of the entire system was fabricated

in the laboratory and the outputs from the PV array of 90 volts, 11 amperes are exhibited. Both the hardware and

simulation outputs have been compared which are in close agreement to validate the suggested system.

Index Terms: Line commutated inverter, photovoltaic cell, SEPIC converter, Maximum Power Point Tracking.

References: 1. Ohnishi T, Takata S. ,’Comparisons of maximum power tracking strategy of solar cell output and control characteristics using

step up/down chopper circuit’, Transactions of IEEJ, 1992,112-D(3), 250–257. 2. NurAtharahKamarzaman, Chee Wei Tan, ‘A comprehensive review of maximum power point tracking algorithms for

photovoltaic systems’, Renewable and Sustainable Energy Reviews, 2014, 37, 585–598.

3. Koutroulis E, Kalaitzakis K, Voulgaris NC., ‘Development of a microcontroller based, photovoltaic maximum power point tracking control system’, IEEE Transactions on Power Electronics, 2001, 16, (1), pp. 46–54.

4. JiradaGosumbonggot,‘Maximum Power Point Tracking Method using Perturb and Observe Algorithm for small scale DC voltage converter’, Procedia Computer Science(Elsevier), 2016, 86, pp. 421 – 424.

5. BidyadharSubudhi, RaseswariPradhan.,‘A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems’, IEEE Transactions on sustainable energy, 2013, 4, (1), pp. 89.

6. T.Shanthi.,‘Incremental Conductance Method of Maximum Power Point Tracking for Photovoltaic Array with Single Switch DC/DC Converter’, Journal of Advanced Research in Dynamical and Control Systems, 2017, 9, Sp-16, pp. 1181-1191.

7. K. K. Kumar., R. Bhaskar., and H. Koti., ‘Implementation of MPPT algorithm for solar photovoltaic cell by comparing short-circuit method and incremental conductance method’,Procedia Technology, 2014, 12, pp. 705–715.

8. Casadei D, et al.,‘Single-phase single stage photovoltaic generation system based on a ripple correlation control maximum power point tracking’, IEEE Transactions on Energy Conversion, 2006, 21, pp. 562-568.

9. Hadi m. el-helw, Ahmed magdy1, and Mostafa i. marei., ‘A Hybrid Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Arrays’, IEEE Access, 2017, 5, pp. 11900–11908.

10. Ahmad El Khateb, JeyrajSelvaraj, and Mohammad Nasir Uddin., ‘Fuzzy-Logic Based SEPIC Converter for Maximum Power Point Tracking’, IEEE Transactions on Industry Applications,2014, 50, (4), pp. 2349-2358.

11. T.Shanthi., J.MohanaPriya., ‘Standalone Hybrid Power Generation Using Photovoltaic/Wind/Fuel Cell’, International Journal of Applied Engineering Research, 2015, 10, (20), pp.18612-18616.

12. Ahmad H.ElKhateb, NasrudinAbd Rahim, JeyrajSelvaraj.,‘Fuzzy Logic Control approaches of a maximum power point employing SEPIC converter for standalone photovoltaic system’, Procedia Environmental Sciences (Elsevier), 2013, 17, pp. 529 -

536. 13. T. Shanthi,, N. AmmasaiGounden., ‘Power electronic interface for grid-connected PV array using boost converter and line-

commutated inverter with MPPT’, IEEE Conference proceedings International Conference on Intelligent and Advanced Systems,

Nov.2007, pp.882-886. 14. Rodrigues EMG, et al., ‘Simulation of a solar cell considering single diode equivalent circuit model’, International conference on

renewable energies and power, May 2011, 1, (9), pp. 369 - 373.

15. W. Xiao, F. F. Edwin, G. Spaguolo, and J. Jatskevich,, ‘Efficient Approaches for Modeling and Simulating Photovoltaic Power Systems’, IEEE Journal of Photovoltaics, 2013, 3, (1), pp. 500-508.

16. T.Shanthi., A.S.Vanmukhil., ‘ANFIS controller based MPPT Control of Photovoltaic Generation System’, Research Journal of Applied Sciences, 2013, 8, (7), pp.375-382

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8.

Authors: Haritha M, Dr. E A Parameshwar Gupta

Paper Title: Retention and turnover of teaching fraternity in Educational sector with special reference to degree

colleges in Bangalore

Abstract: The study aims the intension of understanding the employee turnover and their retention strategies

that can be practised. There exists a highExcellence education imparted in Indian educational institutions but at

present there is a shortage of excellence teaching fraternity, which is a predicament situation. Hence the turnover

rate is high in education sector and the functioning of the teaching fraternitydiffers from other professions. This

study will show the comparison of the turnover and retention issues. This research is conducted in a degree

educational institution in Bangalore on teaching fraternity by using qualitative method of data collection. The

qualitative research methodology would be used to gather data from the Respondents. Responses will be

collected with open-ended questionnaires as well structured interviews will be conducted to gain knowledge

about turnover issues..

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Index Terms: About four key words or phrases in alphabetical order, separated by commas.

References: 1. Confederation of Indian Industry, 2008. India Employee Turnover Study Research Report. Indian: Centre for Socio-Eco-Nomic

Development.

2. Ed. Shubha Tiwari (2005). Education in India. India: Atlantic Publishers & Dist. p200-250. 3. William J. Wasmuth and Stanley W. Davis,. (1983). Managing employee turnover. Cornell Hotel and Restaurant administration

quarterly. 1 (1), 15-20.

4. Gretchen Rhines Cheney, Betsy Brown Ruzzi and Karthik Muralidharan. (November 2005). A Profile of the Indian Education System. National Center on Education and the Economy. 1 (1), 1-29.

5. Rashmi ShaRetention managementa. (Feb 2, 2012). Teachers on the Move: International Migration of School Teachers From India. Journal of Studies in International Education. 1 (1), 1-23.

6. James M. Vardaman, David G. Allen, Robert W. Renn and Karen R. Moffitt. (Oct 10, 2008). Should I stay or should I go? The role of risk in employee turnover decisions. Human Relations. 61 (11), 1-34.

7. Henry Ongori. (22, May 2007). A review of the literature on employee turnover. African Journal of Business Management . 1 (1), 01-06.

8. Pawan S. Budhwar, Arup VaRetention managementa, Neeru Malhotra, Avinandan Mukherjee, (2009) "Insights into the Indian call centre industry: can internal marketing help tackle high employee turnover?", Journal of Services Marketing, Vol. 23 Iss: 5,

pp.351 – 362

9. Zheng WeiBo1*, Sharan Kaur2 and Tao Zhi3. (2010). A critical review of employee turnover model (1938- 2009) and development in perspective of perfoRetention managementance. African Journal of Business Management. 4 (19), 1-13.

9.

Authors: Ankush Kohli, H. S. Bains, Sumit Jain)

Paper Title: Modelling And Optimization Of Cutting Forces And Tool Wear In Milling Of Aerospace Al

6061(Sic) Composites

Abstract: The aftereffects of modelling and the investigation of the aluminium (Al) and aluminium based

(Al6061) silicon carbide reinforcement (SiCp) Metal matrix composite (MMCs) during milling is analysed. The

impact of processing parameters, for example, speed, feed rate, depth of cut on tool wear and the cutting forces

has been examined. The analysis of the cutting forces in the milling of Al and its MMC plays an important role

in characterizing the cutting operations through the response surface methodology (RSM) forecast model. The

predicted model used to decide the consolidated impact of machining parameters on the cutting forces (Cf) and

tool Flank wear (Vbmax.). The consequences of the model were contrasted with the experimental results and

observed that the effects of the forecast help in the evolution of process parameters to minimizing the Cf and

Vbmax.

Keywords: Milling, Aluminium MMC, cutting forces and tool Flank wear.

References: 1. R. Ramanujam, K. Venkatesan, N. Kothawade, and J. Shivangkumar, “Fabrication of Al-TiB 2 Metal Matrix Composites for

Evaluation of Surface Characterization and Machinability,” January, 2015 vol. 8, pp. no. (85–89).

2. E. Zalnezhad, A. A. D. Sarhan, and M. Hamdi, “Optimizing the PVD TiN thin film coating’s parameters on aerospace AL7075-T6 alloy for higher coating hardness and adhesion with better tribological properties of the coating surface,” Int. J. Adv. Manuf. Technol., 2013 vol. 64, no. 1–4, pp. (281–290),.

3. M. Ravi Shankar, S. Chandrasekar, W. D. Compton, and A. H. King, “Characteristics of aluminum 6061-T6 deformed to large plastic strains by machining,” Mater. Sci. Eng. A, 2005,vol. 410–411, pp. (364–368),.

4. S. Gopalakannan and T. Senthilvelan, “Application of response surface method on machining of Al-SiC nano-composites,” Meas. J. Int. Meas. Confed., 2013, vol. 46, no. 8, pp. (2705–2715).

5. S. Durante, G. Rutelli, and F. Rabezzana, “Aluminum-based MMC machining with diamond-coated cutting tools,” Surf. Coatings Technol., 1997, vol. 94–95, pp. (632–640).

6. I. Zaghbani and V. Songmene, “A force-temperature model including a constitutive law for Dry High Speed Milling of aluminium alloys,” J. Mater. Process. Technol., 2009, vol. 209, no. 5, pp. (2532–2544).

7. P. Liu, J. Xu, and Y. Fu, “Cutting force and its frequency spectrum characteristics in high speed milling of titanium alloy with a polycrystalline diamond tool,” J. Zhejiang Univ. Sci. A, 2011, vol. 12, no. 1, pp. (56–62).

8. C. K. Toh, “A study of the effects of cutter path strategies and orientations in milling,” J. Mater. Process. Technol., 2004, vol. 152, no. 3, pp. (346–356).

9. A. Manna and B. Bhattacharayya, “A study on machinability of Al/SiC-MMC,” J. Mater. Process. Technol., 2003, vol. 140, no. 1–3 SPEC., pp. (711–716).

10. Y. K. Chou and J. Liu, “CVD diamond tool performance in metal matrix composite machining,” Surf. Coatings Technol., 2005, vol. 200, no. 5–6, pp. (1872–1878).

11. T. Wang, L. Xie, and X. Wang, “Simulation study on defect formation mechanism of the machined surface in milling of high volume fraction SiCp/Al composite,” Int. J. Adv. Manuf. Technol., 2015, vol. 79, no. 5–8, pp. (1185–1194).

12. J. P. Davim, “Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays,” J. Mater. Process. Technol., 2003, vol. 132, no. 1–3, pp. (340–344).

13. I. Mukherjee and P. K. Ray, “A review of optimization techniques in metal cutting processes,” Comput. Ind. Eng., 2006, vol. 50, no. 1–2, pp. (15–34).

14. A. Aggarwal and H. Singh, “Optimization of machining techniques — A retrospective and literature review,” Sadhana, 2005, vol. 30, no. 6, pp. (699–711).

15. Y. C. Shin and Y. S. Joo, “Optimization of machining conditions with practical constraints,” Int. J. Prod. Res., 1992, vol. 30, no. 12, pp. (2907–2919).

16. R. Gupta, J. L. Batra, and G. K. Lal, “Determination of optimal subdivision of depth of cut in multipass turning with constraints,” Int. J. Prod. Res., 1995, vol. 33, no. 9, pp. (2555–2565).

17. Z. Michalewicz and M. Schoenauer, “Evolutionary Algorithms for Constrained Parameter Optimization Problems,” Evol. Comput., 1996, vol. 4, no. 1, pp. (1–32).

18. Z. Khan, B. Prasad, and T. Singh, “Machining condition optimization by genetic algorithms and simulated annealing,” Comput. Oper. Res., 1997, vol. 24, no. 7, pp. (647–657)

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10.

Authors: B. Arokia Lawrence Vijay

Paper Title: Spiritual Values: A question of existence in William Faulkner’s The Sound and the Fury and

Absalom, Absalom!

Abstract: Values are the necessary code for human conduct for harmonious life of integrity. Human beings are

rational; they have reason for each and every action. Their actions bring out their beliefs, attitudes and custom.

William Faulkner in his novels The Sound and the Fury and Absalom, Absalom! delivers perfect medium of the

values in spirituality and absence in it. This research work focuses on the absence of spiritual values which lead

to confusion and disorientation in the lives of many characters. All the characters discussed in the works of

William Faulkner gives a perfect medium of trouble not only to self but also to others just because of the least

consideration that is given to the values based on spirituality.

Key Words: values, spirituality, confusion, disorientation

References: 1. William (1990), “The Sound and the Fury”, New York: Vintage Book. 2. Faulkner William (1990), “Absalom, Absalom!”, New York: Vintage International. 3. Abadie Ann J, and Evans Harrington(1977), The South and Faulkner’s Yoknapatawpha: The Actual and the Apocryphal.

Jackson: U P of Mississippi. 4. Jacqueline Scott, Judith Treas, and Martin Richards (2004), “Parenting Practices. The Blackwell Companion to the Sociology of

Families” Malden, MA: Blackwell Publishing.

5. Brooks, Cleanth(1963). “William Faulkner. The Yoknanpatawha country”. New Haven: Yale 6. Gail, M. Morrison (2008), “The Composition of The Sound and the Fury.”, Infobase Publishing New York. 7. Howe Irving (1962), “ William Faulkner: A Critical Study”, Vintage Books, New York. 8. Miller Douglas T (1963), “Faulkner and the Civil War: Myth and Reality,” American Quarterly 15.2. 9. Miner, Ward L(1959), “The World of William Faulkner”, Pageant Book Co., New York.

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11

Authors: ShirmilaPremkumari. K

Paper Title: Oscillatory Behavior of solutions of Fourth-order Mixed Neutral Difference Equations with

Asynchronous Non Linearities

Abstract: In this article, oscillation criteria for solutions of fourth order mixed type neutral difference equation

with asynchronous non linearities of the form

where{an}, {bn}, {cn}, {qn} and {pn} are established. Examples are provided to illustrate the results.

Keywords : Oscillation, Neutral difference equation, asynchronous.

References: 1. Third Order Nonlinear Difference Equations. Appl. Anal. Discrete Math., 3:27-38, 2009. 2. M.Bohner, R.P.Agarwal, and D.O’Regan. Discrete Oscillation Theory. New York:HindawiPubl Co, 2005. 3. S.Donthaand, S.R.Grace. Oscillation of Higher Order Neutral Difference Equations of Mixed Typed. Dynam.Systems. J. Math.

Anal. Appl., 12:521- 532, 2003.

4. S.R.Grace. Oscillation of certain Neutral Difference Equations of Mixed Typed. J. Math. Anal. Appl., 224:241-254, 1988. 5. S.R.Grace and R.P.Agarwal. The Oscillation of certain Difference Equations. Math.Comput.Modelling, 30:53-66, 1999. 6. S.R.Grace and R.P.Agarwal. Oscillation of Higher Order Nonlinear Difference Equations of Neutral type. Appl. Math. Lett.,

12:77-83, 1999.

7. S.R.Grace andR.P.Agarwal. Oscillation of certain Third-Order Difference Equations.Comput.Math. Appl., 42:379-384, 2001. 8. S.R.Grace, R.P.Agarwal, and E.A.Bohner. On the Oscillation of Higher Order Neutral Difference Equations of Mixed

type.Dynam. Systems Appl., 11:459- 470, 2002.

9. S.R.Grace, R.P.Agarwal, and P.J.Y.Wong. On the Oscillation of Third Order Nonlinear Difference Equations. J. Appl. Math. Comput., 32:189-203, 2010.

10. N.Kavitha and E.Thandapani. Oscillatory Behavior of Solutions of certain Third Order Mixed Neutral Difference Equations. Acta.Math. Sci., 33B(1) : 218-226, 2013.

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12.

Authors: ShirmilaPremkumari. K

Paper Title: Riccati Techniques, Discrete Oscillation

and Conjugacy Criteria for Fourth Order Nonlinear Difference Equations

Abstract: We discuss the discrete Oscillatory properties of Fourth order non linear difference equation.

whereα >1. In particular we establish the discrete oscillation using Riccati Techniques and Conjugacy criteria.

The Proofs of all the results in this paper are based on the Riccati technique.

Keywords : Conjugacy criteria, Discrete oscillation, Fourth order difference equation, Riccati techniques.

References: 1. [Chen and Erbe, 1989] Chen, S. and Erbe, L. H. (1989).Riccati techniques and discrete oscil- lation.J. Math. Anal.Appl, 88(142):468–

487.

2. [Cheng, 1994] Cheng, S. S. (1994). Hille-WintnerComposision Theorems for Non Linear diff.eqn. Funkcial, EKvac, 88(37):531–535. 3. [Dosly and Rehak, 1998a] Dosly, O. and Rehak, P. (1998a). Conjugacy Criteria for SecondOrder Lineardiff.eqn. Arch. Math.,

88(34):301–319.

4. [Dosly and Rehak, 1998b] Dosly, O. and Rehak, P. (1998b). Non Oscillatorn Criteria for Sec- ond Order Lineardiff.eqn. Arch. Math., 88(34):301–319.

5. [Erbe and Yan, 1992] Erbe, L. H. and Yan, P. (1992). Weighted Averaging Techniques in Oscillation Theory for Second Order diff.eq.Canad - Math. Bull., 88(35):61–69.

6. [Hooker et al., 1987] Hooker, J. W., Kwong, M. K., and Patula, W. T. (1987). Oscillatory Second Order Linear diff.eqn.andRiccati

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equation,. SIAM. J. Math. Anal., 88(18):54–63. 7. [Hooker and Patula, 1981] Hooker, J. W. and Patula, W. T. (1981).Riccati Type Transfor- mations for Second-Order Linear diff.eqn.J.

Math. Anal.Appl, 88(82):451–462.

8. [Kwong et al., ]Kwong, M. K., Hooker, J. W., and Patula, W. T. Riccati Type Transformation for Second-order linear diff. eqn,.J. Math. Anal. Appl., 88(107):182–196.

9. [Mary, 1987] Mary, D. F. S. (1987). RiccatiIntesral equations and non oscillation of self-adjoint linear systems.J. Math. Anal.Appl, 88(121):109–118.

10. [Patula, 1979] Patula, W. T. (1979). Growth and Oscillation Properties of Second Order linear diff. eqns. SIAM. J. Math. Anal., 88(10):1272–1279.

11. [Selvaraj and Daphy, 2011] Selvaraj, B. and Daphy, J. (2011). Oscillatory Properties of Cer- tain First and Second order Diff. eqn. J. Comp and Math. aci. val., 88(2(3)):567–571.

12. [Selvaraj and Lovenia, ]Selvaraj, B. and Lovenia, J. D. L. Third-order newtral difference equations with positive and negative co-esticients! J. Comp. Math. Sci., 88(2(3)):531–536.

13. [Taylor, 1993] Taylor, W. E. (1993). Fourth order diff. eqn, Oscillation and non oscillation. Rocky Mountain J. Math, 88(23):781–795. 14. [Tipler, 1978] Tipler, F. J. (1978). General relativity and Conjugate ordinary differential eqn. J. Differential equation, 88(30):165–174. 15. [Wintner, 1951] Wintner, A. (1951). On the non existence of conjugate points.Amer. J. math, 88(73):368–380.

13.

Authors: Rutuja Deshmukh Jagtap, Dr.D.P.Singh

Paper Title: P06 2A Planning Aspect of balancing Sustainability and Green Form for 21st Centuries Mega Cities

Abstract: Open Spaces provides space for the expression of diversity, both personal and cultural. The social

and cultural values of open space include attitudes towards nature and the desire for contact with it. Open space

is now Inclusive part of statutory and community planning processes. Urban Spaces must be stimulating for all

age groups encouraging their activities, events and gatherings via sustainable planning and design. This paper

focuses on need, unconventional visions and principles for urban green form in this modern era of planning for

21st century Mega cities. The social, cultural and physiological values of open space include attitudes towards

nature and the desire for contact with it.

Keywords : Open Spaces, sustainability, Green Form of Mega City, community planning.

References: 1. Burgess, J., 1995. Growing in Confidence Understanding People’s Perceptions of Urban Fringe Woodlands. Countryside

Commission, Northampton

2. Davidson, N., 1999. Urban grafts: a study of residual space in urban restructuring strategies. MAThesis, Edinburgh College of Art/Heriot-Watt University

3. Dovey, K., 2000. Spaces of ‘becoming’. In: Proceedings of the 16th Conference of the International Association for People– Environment Studies, Paris.

4. Franck, K., 2000. When are Spaces Loose? In: Proceedings of the 16th Conference of the International Association for People– Environment Studies. Paris.

5. Macdonald, M. (Ed.), 1992. Patrick Geddes: Ecologist, Educator, Visual Thinker. Edinburgh Review. Vol. 88, 1992. 6. Rogers, R., et al., Urban Task Force, 1999. Towards an Urban Renaissance: Final Report of the Urban Task Force Chaired by

Lord Rogers of Riverside. Department of the Environment, Transport and the Regions, London.

7. Ward Thompson, C., 1998. Historic American parks and contemporary needs. Landsc. J. 17 (1), 1–25.

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14.

Authors: F Srinivas Daketi, Dr.AbdulRazak Mohamed, Dr.RameshSrikonda

Paper Title: Impact of Culture on Rural built environment

Abstract: The term “built environment” refers to the human made or modified physical surroundings in which

people live, work and play. These places and spaces include our homes, communities, schools, workplaces,

parks/recreations areas, business areas and transportation systems, and vary in size from large-scale urban areas

to smaller rural developments. Based on human activities, the environment was created to obtain the basic needs

of people. The regular human activities for many generations to prepare their needs are considered as culture.

Hence based on culture, the environment was built and maintained for future generation. Regions are separated

into two types based on production occurs in rural area and trading developed in urban. In olden days, most of

the places are rural because of the undevelopment in transport system. The activities involved in preparing food,

shelter and other needs are the common factors to build rural environment. Natural resources are the basic factor

that decides the build environment and culture of human in rural regions. By analyzing the natural resources, the

cultural impacts are determined based on building environment in rural areas.

Index Terms: built environment, culture, natural resource.

References: 1. Bell, M.M. (2007). The two-ness of rural life and the ends of rural scholarship. Journal of Rural Studies. [Online]. 23 (4). p.pp.

402–415. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0743016707000241.

2. Borg, J. van der & Russo, A.P. (2005). The Impacts of Culture on The Economic Development of Cities. [Online]. Available from: https://www.wien.gv.at/meu/fdb/pdf/intern-vergleichsstudie-ci-959-ma27.pdf.

3. Burr, V. (1995). An Introduction to Social Constructionism. London: Routledge. 4. CABE Space (2002). The Value of Good Design: How Buildings and Spaces create Economic and Social Value. [Online]. 2002.

Designcouncil. Available from: http://www.publichealth.ie/files/file/Health_Impacts_of_the_Built_Environment_A_Review.pdf.

[Accessed: 12 April 2017].

5. Cao, X. & Yang, W. (2017). Examining the effects of the built environment and residential self-selection on commuting trips and the related CO 2 emissions: An empirical study in Guangzhou, China. Transportation Research Part D: Transport and

Environment. [Online]. 52. p.pp. 480–494. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1361920915302455.

6. Cohen, A. (1982). Belonging: the experience of culture. In: A. Cohen (ed.). Belonging: Identity and Social Organisation in British Rural Cultures. Manchester: Manchester University Press.

7. Cohen, A. (1985). The Symbolic Construction of Community. England.: Ellis Horwood Limited.

78-84

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15.

Authors: . Jennie Sebasty Pritha, Dr.U.Karuppiah

Paper Title: Common fixed point theorems for generalized cyclic contraction pair in partial b-metric spaces

Abstract: In this paper, we introduce the notion of generalized cyclic contraction pair with transitive mapping

in partial b-metric spaces. Also, we establish some fixed point theorems for this contraction pair. Our results

generalize and improve the result of Oratai Yamaod, Wutiphol Sintunavarat and Yeol Je Cho (Fixed Point

Theory App. 2015:164) in partial-b-metric spaces.

Index Terms: altering distance function, common fixed point, b-metric spaces, partial b-metric spaces, cyclic

generalized contraction, transitive mapping..

References: 1. Shukla.S, “Partial b-metric spaces and fixed point theorems”, Mediterr. J. Math. vol.11 (2014), 703-711. 2. Bakhtin I.A, “The contraction mapping principle in quasi metric spaces”. Funct. Anal. Unianowsk Gos. Ped. Inst. Vol. 30, 26-37

(1989). 3. Czerwik. S, “Contraction mappings in b-metric spaces”, Acta Mathematica et Informatica Universitatis Ostraviensis Vol. 5-11 (1993). 4. Mathews. S.G, “Partial metric topology”, in Proc. 8th Summer conference on General Topology and Application. Ann. New York

Acad. Sci. Vol. 728, 183-197 (1994).

5. Kirk. W.A, Srinivasan. P.S, Veeramani. P, “Fixed points for mappings satisfying cyclical contractive conditions”, Fixed Point Theory Vol. 4, 79-89 (2003).

6. Sintunavarat. W, “Generalized Ulam-Hyers stability, well-posedness and limit shadowing of fixed point problems for - -

contraction mapping in metric spaces”, Sci. World J. 2014, 569174 (2014).

7. Latif. A, Mongkolkeha. C, Sintunavarat. W, “Fixed point theorems for generalized - -weakly contraction mappings in metric

spaces and applications”, Sci. World J. 2014, 784207 (2014).

8. Khan. M.S, Swaleh. M, Sessa. S, “Fixed point theorems by altering distances between the points”, Bull. Aust. Math. Soc. Vol. 30, 1-9(1984).

9. Nguyen Van Dung and Vo Thi Le Hang “Remarks on partial b-metric spaces and fixed point theorems”, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301].

85-89

16.

Authors: P. THIRUNAVUKARASU, S.BHUVANESWARI, R.MANJULA

Paper Title: Consequence Of Heat Transfer On Free Convection Flow Of Casson Fluid With Hall Effect

Abstract: : Effect of heat transfer on free convection flow of Casson fluid over a vertical plate with Hall effect

has been studied. A similarity analysis method was used to transform the system of partial differential equations

describing the problem into an ordinary differential equations, Analytical solutions are obtained by solving the

ODE to analyze the velocity and temperature fields. Variations of interesting parameters on the velocity, heat

transfer and skin friction are observed by plotting graphs. Further, it was concluded that the Casson fluid

parameter and hall parameter has an retarding influence on velocity profile and also in the skin friction.

Index Terms: Casson fluid, Hall effect, MHD, Free convection, Velocity field...

References: 1. Chang. C and Yen. J, 1959. Rayleigh’s Problem in Magnetohydrodynamics, Phys. Fluids, 2: 239.

90-95

2. Kurzweg V. Marple, 1988. United States Court of Appeals, Fifth Circuit, 7, No. 87- 3615. 3. Okelo, J.A., 2007. Heat and Mass transfer past a semi infinite vertical porous plate in MHD flows, Ph.D., Dissertation, Jomo

Kenyatta University of agriculture and technology.

4. Khen Chand, Singh, K.D. and Shavnam Sharma, 2013. Effect of Hall current and rotation on heat transfer in MHD flow of oscillating dusty fluid in a porous channel, Indian Journal of pure and applied physics, 51, 669-682.

5. Sundarnath. J.K. and Muthucumarswamy, R., 2015. Hall effect on MHD flow past an accelerated plate with heat transfer, Int. J. of applied mechanics and engineering, 20(1): 171-181.

6. Mustafa, M., Hayat, T., Pop, I. and Aziz, A, 2011. Unsteady boundary layer flow of a Casson fluid due to an impulsively started moving flat plate, Heat Transfer Asian Res. 40(6):563-576.

7. Casson, N, 1959. A flow equation for Pigment –oil suspension of the priniting ink type. In: Mill, C.C., Ed., Rheology of disperse systems, Pergamon press, Oxford, 84-104.

17.

Authors: Mr. A. Siles Appollo, Dr.R.Vijayalakshmi, Mr.L.Aruldoss

Paper Title: Representation Of The True Discipleship In Harriet Beecher Stowe’s “Uncle Tom’s Cabin”

Abstract: Harriet Beecher Stowe (1811 - 1896) a nineteenth century American female writer, rose from a

religious family and enrooted in Calvinism preached by her father Lyman Beecher, she pictures the true disciple

of Christ in her novel Uncle Tom’s Cabin. Uncle Tom, a blackish slave of Kentucky plantation in the year 1840

who plays the central character and he owns only the Bible. Throughout the novel he often found reading it with

great religious feeling and quotes it to educate Eva, Cassy, and others to find the strength to survive in their

trials. This paper aims to observe the characteristics features of the true disciples with reference to the Bible. As

the bible says, in Colossians 3:22 “Slaves, obey your earthly masters in everything; and do it, not only when

their eye is on you and to curry their favor, but with sincerity of heart and reverence for the Lord”. The Holy

book says that humans ought to treat one another as they themselves wish to be treated. Uncle Tom and Eva are

true martyrs of love, compassion, sacrifice and obedience. They stand as a symbol of saintliness, representation

and a true disciple of Jesus Christ.

Index Terms: true disciple, obedience, humility, saintliness, sacrifice..

References: 1. Stowe, Harriet Beecher, 1811-1896. Uncle Tom's Cabin. London :J. Cassell, 1852. Print. 2. https://freebooksummary.com/christianity-in-uncle-toms-cabin-46306 3. James H. Smylie, American Presbyterians: Vol. 73, No. 3 (FALL 1995), pp. 165-176

96-98

18.

Authors: P.S. Syed Ibrahim, J. Senthil Murugan, S. Chidambaravinayagam, J. Edward Jeyakumar

Paper Title:

Excess Acoustical Properties and Molecular Interactions in Ternary Liquid Mixtures of

3(Meta)Methoxy Phenol, 1 Propanol and n- Hexane at 303 K, 308 K & 313 K Using Ultrasonic

Techniques

Abstract: The Ultrasonic velocity(U), density(ρ), and viscosity(η) have been measured experimentally for the

ternary liquid mixtures of 3(meta) methoxy phenol(MMP), 1 propanol and n hexane at various temperatures viz.,

303 K, 308 K and 313 K at constant frequency of 2 MHz. for different concentrations ranges from 0.001M to

0.01M. The thermodynamic and acoustical parameters such as adiabatic compressibility(β), Rao constant(R),

absorption coefficient (α/f2), internal pressure(πi), cohesive energy(CE), free volume(Vf), free length(Lf),

acoustic impedence(z), available volume(Va), viscous relaxation time and Lenard Jones potential were calculated

from the experimental data. The various excess properties including excess Ultrasonic velocity, excess acoustic

impedence, excess free length, excess adiabatic compressibility, excess free volume and excess internal pressure

were also computed. The variation of these excess parameters with respect to concentration and temperatures

have been discussed in the light of molecular interaction. The molecular interactions were predicted based on

the results obtained for ultrasonic velocities of different concentrations of the ternary mixtures at different

temperatures.

Index Terms: molecular interactions, ultrasonic velocity, ternary liquid mixture, internal pressure, acoustic

impedence.

References: 1. Arul G. and Palaniappan L. “Ultrasonic study of 1-butanol in pyridine with benzene,” Ind. J Pure. Apple Phys. Vol. 43, pp. 755-

758, 2005.

2. Kannappan V. and Jaya Shanthi R. “Ultrasonic studies of induced dipole-dipole interactions in binary liquids mixtures,” Ind. J. Pure. Appl. Phys. Vol. 43, pp. 750-754, 2005.

3. Kannappan AN and Rajendran V “Acoustic parameters of some ternary liquid mixtures,” Ind. J. Pure. Appl. Phys. Vol. 30, pp. 240-242, 1992.

4. Anwar Ali, Anil Kumar and Abida“Ultrasonic and volumetric studies of molecular interaction in acetonitrile +1-alkanols C6, C8, C10 binary mixtures at different temperatures,” J. Chin. Chem. Soc. Vol. 51, pp. 477-485, 2004.

5. Aralaguppi M.I. and Barragi J.C. “Physicochemical and excess properties of the binary mixtures of methylcyclohexane + ethanol + propan-1-ol + propan-2-ol, + butan-1-ol, + 2-methyl-1-propanol or 3-methyl-1-butanol at T = 298.15, 303.15 and 308.15 K,” J. Chem. Therm. Vol. 38, pp. 434-442, 2006.

6. Niham P.S., Kapade V.M. and Mehdi Hasan. “Molecular interactions in binary mixtures of bromobenzene with normal alkanols C1-C4, An ultrasonic study,” Ind. J. Pure Appl. Phys. Vol. 38, pp. 170-173, 2000.G. Arul and L. Palaniappan, Ind. J. Pure. Appl. Phys,Vol. 39 pp 561-564, 2001.

7. Surjit Singh Bhatti and Devinder Pal Singh “Molecular association in some n-butanol systems,” Ind. J. Pure Appl. Phys. Vol. 21, pp. 506-509, 1983.

8. Sridevi U., Samatha K. and Viswanatha Sarma A. “Excess thermodymamic properties in binary liquids,” J. pure Appl. Ultrason. Vol. 26, pp. 1-11, 2004.

9. Islam M.R. and Quadri. “Ultrasonic velocity and viscosity of binary liquid mixtures,” Thermo. Chim. Acta. Vol. 115, p. 335, 1987.

99-105

https://www.litcharts.com/lit/uncle-tom-s-cabin/characters/uncle-tomhttps://www.litcharts.com/lit/uncle-tom-s-cabin/characters/eva-st-clarehttps://www.litcharts.com/lit/uncle-tom-s-cabin/characters/cassyhttps://www.biblestudytools.com/colossians/3-22.htmlhttps://www.litcharts.com/lit/uncle-tom-s-cabin/characters/eva-st-clare

10. Rajendran V. and Marikani. “Investigation of Thermodynamic properties of amine-alcohol mixtures at 303.15K,” Acoustics Lett., UK. Vol. 18, pp. 90-94, 1994.

11. Madhu Rastogi, Aashees Awasthi, Manisha Guptha and J.P. Shukla Ind. J. Pure Appl. Phys. vol 40, pp 256-263, 2002. 12. G. Arul and L. Palaniappan, Ind. J. Pure. Appl. Phys,Vol. 39 pp 561-564, 2001

19.

Authors: First Author Name, Second Author Name, Third Author Name

Paper Title: Relationship between Motivational Practices and Organisational Citizenship Behaviour with

special reference to employees of manufacturing industries in Coimbatore District

Abstract: This paper reports on the findings of a research to investigate the relationship between Motivational

practices and Organisational Citizenship Behaviour of employees working in select manufacturing industries in

Coimbatore District. The findings of the study highlight the importance of motivational practices to enhance

employee effectiveness.

Index Terms: Motivational practices, Organisational Citizenship Behaviour.

References: 1. Shore, L. M., & Martin, H. J. (1989).Job satisfaction and organizational commitment in relation to work performance and

turnover intentions. Human Relations, 42(7), 625-638.

2. Denton, K. (1987). Effective Appraisals: Key to Employee Motivation. Industrial Engineering, 19(12), 24. 3. Hackett, R. D., Lapierre, L. M., &Hausdorf, P. A. (2001). Understanding the links between work commitment constructs.

Journal of Vocational Behavior, 58, 392-413.

4. Meyer, J. P., Allen, N. J. & Smith, C.A. (1993). Commitment to Organizations and Occupations: Extension and Test of a Three-Component Conceptualization. Journal of Applied Psychology, 78(4), 538-552.

5. Bashaw, R.E., & Grant, E.S. (1994).Exploring the distinctive nature of work commitments: Their relationships with personal characteristics, job performance, and propensity to leave. Journal of Personal Selling & Sales Management, 14(2), 1-16.

6. Lawler, E.E. (1986). High involvement management: participative strategies for improving organizational performance. San Francisco: Jossey-Bass.

106-111

20.

Authors: S.Govindaraju, Dr B.Mukunthan

Paper Title: Improved Content Based Medical Image Retrieval using PCA with SURF Features

Abstract: In the computer era, the Content Based Image Retrieval system (CBIR) has most widely used in medical field and crime invention. During the last decade, CBIR emerged as powerful tool to efficiently retrieved images visually similar

to query image. The basic process behind this concept is representation of image as feature vector and to measure the

similarities between the images with distance between their corresponding feature vectors according to some metrics. The

finding of correct features to represent images with, as well as the similarity metric that groups visually similar image

together, are important milestone in construction of any CBIR system .The work in this paper focused on retrieve the correct

query image from a huge number of medical image databases with the help of Principal Component Analysis (PCA) through

SURF feature vector detection. The combination of this method produces an accurate and quick response than other

conventional methods like SIFT and SURF feature vector based medical image retrieval.

Index Terms: CBMIR, SURF, PCA, MRI Images, Fast-Hessian matrix.

References: 1. Müller et al, ”A review of content-based image retrieval systems in medical applications—clinical benefits and future directions”,

International journal of medical informatics, 73(1), pp:1-23,2004.

2. Agkul et al, ”Content-based image retrieval in radiology: current status and future directions”, Journal of Digital Imaging, 24(2), 208-222,2011.

3. Bajwal et al, “Feature Based Image classification by using Principal component analysis," CIST-Journal of Graphics, Vision and image processing, 2009.

4. Sharma et al, "Face Identification Using Wavelet Transform & PCA”, Proc. of the Intl. Conf. on Advances in Computer Science and Electronics Engineering, CSEE 2013, pp. 109-113.

5. Dr. K.Somasundaram and T.Kalaiselvi “Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images “, Computers in Biology and Medicine Elsevier journal volume no.40, Issue no.10, 2013.

6. R. Rajesh and N. Senthilkumaran, J. Satheeshkumar, B. Shanmuga Priya, C. Thilagavathy, K. Priya, “On the Type-1 and Type-2 Fuzziness Measures for Thresholding MRI Brain Images”, pp:992-995.

7. E. Ben George an