international journal of innovative technology and exploring … · 2019. 4. 27. · 4. r. n....

S. No

Volume-8 Issue-6C, April 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Rajeev Tiwari, Shuchi Upadhyay, Parv Singhal, Ujla Garg, Shefali Bisht

Paper Title: Air Pollution Level Prediction System

Abstract: Nowadays, the levels of air pollutants in the environment are increasing manifold. This has led to

deterioration of human lifestyle. Various methods like ‘Climatology’ (based on the assumption that the past is a

good predictor of the future) have been used for air quality forecasting. These approaches are usually used to

predict exceeding limits from specific thresholds, not ambient concentrations. As a result, a lot of improvement

is still required in this field for prediction analysis. With incomplete data parameters and their significance

(priority), most of the methods fail to predict the pollution levels significantly. The advantage of artificial

neural networks includes the problem-solving efficiency in the cases of unavailability of complete information,

with no information about the analytical relationship among the input and processed output data. The aim is to

develop an artificial neural network for air quality prediction that can perform with constrained dataset with

highly robust feature in order to handle the data including noise and errors. Dataset used deals with pollution in

the U.S. involving four major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide and Ozone) on

daily basis for the time period of year 2008 to 2017. We use prediction models like ARIMA etc. to validate our

predicted AQI. This AQI analysis helps in telling the status of present air pollution and forecasted pollution

levels in coming time. So, it plays a vital role for decision maker and for individual also to know about air

pollution quality.

Keywords: Artificial neural network; Environmental engineering; Air Quality index; ARIMA; Forecasting.

References: 1. Air Pollution – Monitoring, Modelling and Health, Edited by Mukesh Khare p. cm. ISBN 978-953-51-0424-7, InTech Janeza Trdine 9,

51000 Rijeka, Croatia

2. Finardi, S.; de Maria, R.; D’Allura, A.; Cascone, C.; Calori, G.; Lollobrigida, F. A deterministic air quality forecasting system for Torino urban area, Italy. Environ. Model. Softw. 2008, 23, 344–355.

3. Pai, T.Y.; Ho, C.L.; Chen, S.W.; Lo, H.M.; Sung, P.J.; Lin, S.W.; Lai, W.J.; Tseng, S.C.; Ciou, S.P.; Kuo, J.L.; Kao, J.T. Using seven

types of GM (1, 1) model to forecast hourly particulate matter concentration in Banciao City of Taiwan. Water Air Soil Pollut. 2011, 217, 25–33.

4. Comrie, A.C. Comparing neural networks and regression models for ozone forecasting. J. Air Waste Manag. Assoc. 1997, 47, 653–

663. 5. Schlink, U.; Dorling, S.; Pelikan, E.; Nunnari, G.; Cawley, G.; Junnine, H.; Greig, A.; Foxall, R.; Eben, K.; Chatterton, T.; et al. A

rigorous inter-comparison of ground-level ozone predictions. Atmos. Environ. 2003, 37, 3237–3253.

6. Yi, J.; Prybutok, V.R. A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environ. Pollut. 1996, 92, 349–357. 14.

7. Grivas, G.; Chaloulakou, A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos. Environ. 2006, 40, 1216–1229.

8. Małgorzata Pawul1 , Małgorzata S´liwka, 2016, ‘Application of artificial neural networks for prediction of air pollution levels in

environmental monitoring’ AGH University of Science and Technology, Journal of Ecological Engineering.

1-8

2.

Authors: Manmohan Sharma, V.K. Jain

Paper Title: Load Balancing in Cloud using Hybrid Approach

Abstract: Cloud computing is up and coming era of registering what's more, a creating processing worldview

in the present-day industry, either might be government associations or people in general associations. In basic

terms we will outline Cloud computing is assortment of assorted servers that take into consideration want of

numerous customers in lightweight of their demands. Clouds have very capable knowledge centers to handle

expansive variety of client's demands. Cloud is defined as a shared reservoir of dynamic resources and

virtualization. Load Balancing is needed to cater the needs of customer in an efficient manner. In LB the

workload is distributed between numerous virtual machines (VM’s) on a Server over the system, to accomplish

ideal resource utilization, diminish data processing time, diminish in normal response time, and stay away from

over-burden. The target of this paper is to propose effective and productive furthermore, upgraded composite

scheduling algorithm that can be used to keep up the load and gives proficient resource distribution procedures.

This paper outlines the advantages of combining Equally Spread Current Execution (ESCE) and Priority

algorithms. The entire simulation is performed on Cloudsim 3.0 toolbox which is JAVA based simulation.

Keywords: Cloud Computing, Load balancing (LB), Virtual Machine (VM), CloudSim, and Priority

References: 1. Ranjan Dinesh, Canino Anthony, Izaguirre A Jesus and Douglas Thain “Converting a High-Performance Application to an Elastic

Cloud Application” 3rd IEEE International Conference on Cloud Computing Technology and Science, Nov 11. 2. A. Y. Zomaya,& Y. H. Teh. (2014). Observations on using genetic algorithms for dynamic load-balancing. IEEE Transaction on

Parallel and Distributed Systems, vol. 12, no. 9, pp. 899-911.

3. Buyya, Rajkumar.,Broberg, James., Goscinski, Andrzej. “Cloud Computing Principles and Paradigms” (1sted.). Hoboken, New

Jersey, USA: Wiley, 2011.

4. R. N. Calheiros and R. Buyya, “Meeting deadlines of scientific workflows in public clouds with tasks replication,” IEEE Transactions

on Parallel and Distributed Systems, vol. 25, no. 7, pp. 1787–1796, 2014. 5. Eddy Caron , Luis Rodero-Merino ―Auto-Scaling , Load Balancing And Monitoring In Commercial And OpenSource Clouds ―

9-13

Research Report, January2012. 6. Mishra ,Ratan , Jaiswal, Anant,P―Ant Colony Optimization: A Solution Of Load Balancing In Cloud‖,April 2012, International

Journal Of Web & Semantic Technology;Apr2012, Vol. 3 Issue 2, P33.

7. R. Basker, V. RhymendUthariaraj, and D. Chitra Devi, “An enhanced scheduling in weighted round robin for the cloud infrastructure services,” International Journal of Recent Advance in Engineering & Technology, vol. 2, no. 3, pp. 81–86, 2014.

8. BhathiyaWickremasinghe ,Roderigo N. Calherios Cloud Analyst: A Cloud-Sim-Based Visual Modeler For Analyzing Cloud

Computing Environments And Applications‖. Proc Of IEEE International Conference On Advance Information Networking And Applications ,2010.

9. GenaudStephane and GossaJulien “Cost-wait Tradeoffs in Client-side Resource Provisioning with Elastic Clouds”, IEEE 4th

International Conference on Cloud Computing, 2011. 10. R.N. Calheiros, R. Ranjan, A. Beloglazov, C. Rose, R. Buyya, “Cloudsim:A toolkit for modeling and simulation of cloud computing

environments and evaluation of resource provisioning

algorithms”,inSoftware:PracticeandExperience(SPE),Vol:41,No:1,ISSN:00380644,Wiley Press,USA,pp:23-50,2011. 11. Buyya R, Ranjan R, Calheiros R N. “Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit:

Challenges and opportunities” Proceedings of the Conference on High Performance Computing and Simulation (HPCS 2009),

Leipzig, Germany. IEEE Press: New York, U.S.A., 21–24 June 2009; 1–11. 12. Dr.S. Suguna and R. Barani,” Simulation of Dynamic Load Balancing Algorithms”, Bonfring International Journal of Software

Engineering and Soft Computing, Vol. 5, No.1, July 2015.

13. Ishwari Singh Rajput, Deepa Gupta, "A Priority Based Round Robin CPU Scheduling Algorithm for Real Time Systems", IJIET, vol. 1, no. 3, October 2012, ISSN 2319-1058.

3

Authors: Shekhar Singh, Mayank Singh, Sandhya Tarar

Paper Title: Improved Facial Recognition based Authentication approach to Secure Big Data

Abstract: When deploying biometric identification techniques over the massive data available on web for user

authentication purposes, maintaining quality, security and integrity of confidential data are imperative. It is

required to make sure the data is captured and stored over a trusted server and is readily available for

authentication/ user identification without any interference. In this paper, facial recognition is used as a

measure of biometric authentication to address the security issues in Big data. Discrete Wavelet Transform

(DWT) is applied to normalize and de-noise the input image, in order to eradicate the unwanted variations

preserved while storing the biometric data using traditional methods such as Principle Component Analysis

(PCA). Following this, Gabor Filter bank is used to extract the facial features. Further, Expansive Discrete

wavelet Transform (EDWT) is used to linearize the dimensional sub-space, using its high expansiveness to

curb the number of features extracted from the facial data. The approach uses the spatial orientation of the

processed image’s high-frequency textural features to improve the accuracy of the trained data for overcoming

the shortcomings which results in a 74% efficient algorithm which viably and feasibly achieves the objective of

minimizing the expanse of features extracted.

Keywords: Biometric Identification, Big Data, Discrete Wavelet Transform (DWT), Facial Recognition.

References: 1. Haghighat, Mohammad, SamanZonouz, and Mohamed Abdel-Mottaleb. CloudID: Trustworthy cloud-based and cross-enterprise

biometric identification.Expert Systems with Applications. November, 2015. 7905-7916. 2. Selesnick, Ivan W. A higher density discrete wavelet transform.IEEE Transactions on Signal Processing. July, 2006. 3039-3048.

3. Haghighat, Mohammad, Mohamed Abdel-Mottaleb, and WadeeAlhalabi. Fully automatic face normalization and single sample face

recognition in unconstrained environments.Expert Systems with Applications. July, 2016. 23-34. 4. Sadeghi, Ahmad-Reza, Thomas Schneider, and ImmoWehrenberg. Efficient privacy-preserving face recognition.International

Conference on Information Security and Cryptology. Springer Berlin Heidelberg. December, 2009.

5. Bringer, Julien, HervéChabanne, and Bruno Kindarji. Error-tolerant searchable encryption.International Conference on. IEEE. June, 2010.

6. Lahmiri, Salim, and MounirBoukadoum. Hybrid discrete wavelet transform and Gabor filter banks processing for features extraction

from biomedical images.Journal of medical engineering. March, 2013. 7. Belhumeur, Peter N., João P. Hespanha, and David J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear

projection.IEEE Transactions on pattern analysis and machine intelligence. June, 1997. 711-720.

8. Jain, Anil K., Arun Ross, and SalilPrabhakar. An introduction to biometric recognition.IEEE Transactions on circuits and systems for video technology. 2004. 4-20.

9. Haghighat, Mohammad, Mohamed Abdel-Mottaleb, and WadeeAlhalabi. Discriminant correlation analysis for feature level fusion with

application to multimodal biometrics.2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. July, 2016.

10. Jain, Anil K., and Ajay Kumar. Biometric recognition: an overview.Second Generation Biometrics: The Ethical, Legal and Social

Context. Springer Netherlands. May, 2012. 49-79. 11. S. Tarar, and E. Kumar. Fingerprint image enhancement: iterative fast Fourier transform algorithm and performance evaluation. Int. J.

Hybrid. Inf. Technology. July, 2013.

12. S. Tarar, and E. Kumar. Fingerprint Mosaicking Algorithm to Improve the Performance of Fingerprint Matching System. Computer Science and Information Technology. June, 2014.

13. S. Tarar, and E. Kumar. Design Paradigm and Risk Assessment of Hybrid Re-engineering with an approach for development of Re-

engineering Metrics. International Journal of Software Engineering & Applications. January, 2012. 14. S. Zaidi, S.K. Singh, and S. Tarar.To evaluate the performance of fingerprint enhancement techniques. India Conference (INDICON),

2015 Annual IEEE, 2015.

15. H. Chauhan, and S. Tarar.Image Processing Edge Detection Technique using Iterative Enhancement Wavelet used for Traffic Control Problem. International Journal of Engineering Science. May, 2016.

16. S. Tarar, and C. Verma. Secure Random Sequence based Frequency Hoping Spread Spectrum Audio Watermarking. International

Journal of Engineering Science. 2016. S. Tarar, A.P. Singh, and S. Singh.Speech recognition approach: Desktop items activation with comparative analysis.International

Conference of Computer Science and Information Technology (ICCSIT). October, 2010.

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4

Authors: Harsh Purohit, Bhupal Bhattacharya, Pawandeep Bindra

Paper Title: Need of Digital Literacy and E-Content Crisis Management in India: A Study from the Bharatiya

Perspectives

Abstract: Social media crisis management has become the need of the hour in ensuring proper effective

implementation of it in its true letter and spirit. Tweeting on Twitter or posting on Facebook or forwarding 21-24

https://scholar.google.co.in/scholar?oi=bibs&cluster=346614329652199953&btnI=1&hl=en













messages through Whatsapp and other social network sites/ Apps have become a common phenomenon in the

present days in communicating one’s view to the other. Though these media platforms have assumed a good,

effective and irreversible position in more or less every person’s life, still proper and effective management of

such is somewhere lacking. Thoughts of Bharataiya Culture somewhere differ from the modern approach of

using of gadgets and mobile technology which in large scale diverting the energy and attention of youths from

productive outcome.

Developing social media crisis response plan is the pressing requirement of present time. The current study

aims to explore these patterns in an intervention study for the oppressed adolescents highlighting few case

studies and observations.

The Researchers have carried out the research through Doctrinal Research by way of extensive surveys of

documents and the reports available concerning the research area.

Keywords: BMDL Social Media management, Cyber Security, Family-based intervention, Teenage

depression.

References: 1. Baym, N. K. (2015). Personal connections in the digital age. John Wiley & Sons. 2. Baym, N. K. (2015). Personal connections in the digital age. John Wiley & Sons.

3. Cooper, A. (2004). The inmates are running the asylum:[Why high-tech products drive us crazy and how to restore the sanity].

Indianapolis: Sams.

4. Crystal, D. (2013). A global language. In English in the World (pp. 163-208). Routledge.

5. Drotner, K. (2008). Leisure is hard work: Digital practices and future competencies. Youth, identity, and digital media, 167-184.

6. Hanna, J. L. (2008). A nonverbal language for imagining and learning: Dance education in K–12 curriculum. Educational Researcher, 37(8), 491-506.

7. Hiltz, S. R., & Turoff, M. (1993). The network nation: Human communication via computer. Mit Press.

8. Ho, A. (2008). Relational autonomy or undue pressure? Family’s role in medical decision‐making. Scandinavian journal of caring

sciences, 22(1), 128-135.

9. Palmer, P. J. (2017). The courage to teach: Exploring the inner landscape of a teacher's life. John Wiley & Sons. 10. Polat, R. K. (2005). The Internet and political participation: Exploring the explanatory links. European journal of communication,

20(4), 435-459.

11. Purohit, H., Bharti, N., & Joshi, A. (2015). Partnering for Promotion of Digital Literacy among Women in Rajasthan through Bhartiya Model of Digital Literacy.

12. Rajagopal, A. (2017). On Media and Politics in India: An Interview with Paranjoy Guha Thakurta. South Asia: Journal of South Asian Studies, 40(1), 175-190.

13. Schmidt, E., & Cohen, J. (2013). The new digital age: Reshaping the future of people, nations and business. Hachette UK.

14. Sunstein, C. R. (1994). The partial constitution. Harvard University Press. 15. Turkle, S. (2017). Alone together: Why we expect more from technology and less from each other. Hachette UK.

16. Wackernagel, M., & Rees, W. (1998). Our ecological footprint: reducing human impact on the earth (Vol. 9). New Society Publishers.

17. Weimann, G. (2016). Going dark: Terrorism on the dark Web. Studies in Conflict & Terrorism, 39(3), 195-206.

18. Ziegeldorf, J. H., Morchon, O. G., & Wehrle, K. (2014). Privacy in the Internet of Things: threats and challenges. Security and

Communication Networks, 7(12), 2728-2742.

5

Authors: Manoj Kumar Sharma, Tanveer J Siddiqui

Paper Title: Combining Semantics and Visual Content for Museum Information Retrieval

Abstract: Searching useful information from huge and unstructured museum multimedia data has been a

difficult problem in information retrieval. In this paper, we propose the design of an image retrieval system that

combines two important modalities- text and image – along with ontological concepts for retrieving digitized

museum artifacts. The combination of text and domain ontology facilitates retrieval based on semantics. The

evaluation of the proposed method has been done on a dataset comprising of 1200 images of artifacts displayed

in various galleries of Allahabad museum and their textual descriptions. A domain ontology has been manually

constructed and used as a source of knowledge to aid in the retrieval and browsing. The organization of the

artifacts in the museum has been used to arrange domain concepts in a hierarchy.

Keywords: Artefacts; Framework; Feature Extraction; Ontology; Query Processing.

References: 1. A. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain,” Content based Image retrieval at the end of the early years”, IEEE,

Vol.22, pp.1349, 2000. 2. T. J Siddiqui ,U.S. Tiwary, , “Words and Pictures”, An HCI Perspective, Proceeding of the First International Conference on Intelligent

Human Computer Interaction, pp. 59-70, 2009.

3. J. F. Canny , “A Computational Approach to Edge Detection”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol8, Nov 1986.

4. S. Sarwar, Z.UI. Qayyum, S. Majeed, “Ontology based image retrieval framework using qualitative semantics image descriptions”, In

17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems(KES), Procedia Computer Science 22, 285-294, 2013.

5. A. Pentland, Picard WR, S. Sclaroff, “Photobook: Content based manipulation of image database”, Int J Computing Vis 18, 233-54,

1996. 6. B .Holt, L. Hartwick, "Retrieving art images by image content: the UC Davis QBIC project,” Proc first international conference on

Electronic library and visual information system Research, de mohtfor University, Mitton keyners, pp.93-100, 1994.

7. R.J. Bach , C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, ”Virage image search engine: an open framework for image management”, In: Sethi IK,Jain RC Eds. Proceedings of SPIE 2670 (1), 76-87, 1996.

8. G. Chechik, V. Sharma, U. Shalit, Bengio US,”Large scale online learning of image similarity through ranking”, J Mach Learn Res 11,

1109-35, 2010. 9. Y. Liu, D. Zhang, G. Lu, and W.Y. Ma, “A survey of content based image retrieval with high level semantics”, Pattern recognition,

vol. 40, pp. 262-282, 2007.

10. F. A. Mahdi and A. Ibadi, “MIRS: Museum Image Retrieval System using Most Appropriate Low-Level Feature Descriptors,”

25-28

International Journal of Computer Science, vol. 11, Issue 5, No.2, pp. 1694-0784, September 2014. 11. G. Chechik, V. Sharma, U. Shalit, Bengio US,”Large scale online learning of image similarity through ranking”, J Mach Learn Res 11,

1109-35, 2010.

12. W.Y Ma, H. J. Zhang,"Content Based Image Indexing and Retrieval,” In: Handbook of Multimedia Computing,(Ed.) Furht, B.crc Press, Boca Raton,1998.

13. M. Flickner, H Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafne, D. Lee, D. Petkovic, D. Steele and P.

Yanker, “Query by image and video content the QBIC system”, IEEE Computer, 23-32 1995. 14. Natalya F. Noy,Deborah L., McGuinness,”Ontology Development 101: A guide to creating your first ontology”,Stanford

University,Stanford,,CA,94305.

15. K. Barnard,D. Forsyth,”Learning the semantics of words and pictures”,International conference on computer vision 2,University of California,Berkeley,2001.

16. K. Barnard, P. Duygulu, N. de Freitas, D.Forsyth, D, Blei , M. I. Jordan,”Matching words and pictures”,Journal of Machine Learning

Research,3:1107-1135,2003.

6

Authors: V Kakulapati, B.S.S. Deepthi, S. Mahender Reddy

Paper Title: Predictive Analytics of Emotional Intelligence in Women Suffering with Breast Cancer

Abstract: Now a day’s women suffer with Breast cancer due to depression and anxiety result in life threat

which is second place in various types of cancers. The goal of this study is to investigate the association

between resilience and cognitive sentiment analysis and mental health of the woman who suffered by breast

cancer. Develop a statistical model by utilizing the multiple regressions for predicting breast cancer patients,

which involves the least square’s inference problem to approximation the parameters. The implementation

results exhibit significant association between refusal resilience and cognitive sentiment analysis and mental

health of breast cancer patients. Also, predicting obsessive rumination of breast cancer patients and their

sentiment analysis is the preeminent refusal for the healthiness of the model.

Keywords: statistical model; Negation resilience; cognitive emotion; obsessive rumination; cancer.

References: 1. Smith RA et al., Cancer screening in the United States, 2013: a review of current American Cancer Society guidelines, current issues in

cancer screening, and new guidance on cervical cancer screening and lungcancer screening. CA Cancer J Clin. 2013;63(2):88- 105.

2. Kashani F et al. The effects of relaxation on reducing depression, anxiety and stress in women who underwent mastectomy for breast cancer. Iran J Nurs Midwifery Res. 2012; 17(1):30-3.

3. Wells, H.B. Depressive rumination: Nature, theory, and treatment (107-124). New York: Wiley 2009.

4. Fariba et al., “The Relationship between Resilience and Cognitive Emotion Regulation and Obsessive Rumination of Woman with Breast Cancer” European Online Journal of Natural and Social Sciences 2015, Vol.4, No.1 Special Issue on New Dimensions in

Economics, Accounting and Management ISSN 1805-3602. 5. Arora S, Ashrafian H, Davis R, et al. Emotional intelligence in medicine: a systematic review through the context of the ACGME

competencies. Med Educ. 2010; 44(8):749-64.

6. Birks YF, Watt IS. Emotional intelligence and patient-centred care. J R Soc Med. 2007; 100(8):368-74. 7. Fineman,S et al., Emotion and Organizing. Handbook of organization studies. London: Sage, 1996

8. Sandelands, L. E. The concept of work feeling. Journal for the theory of social behaviour, 18, 437-457, 1988.

9. Zahra Nikmanesh et al., “Examining the Predictive Role of Emotional Self-Regulation in Quality of Life and Perception of Suffering among Patients with Breast Cancer “,Middle East Journal of Cancer; April 2017; 8(2): 93-101.

10. Khosravi, M., Mehrabi, H.A., & Azizi Moghaddam, M. (2008). A comparative study in patients with depression and obsessive-

compulsive rumination component with ordinary people, Journal of Medical Sciences, 1 (1), 43-56. 11. Lo, C.S.L., Ho, S.M.Y., &Hollon, S.D. (2014). The effects of rumination and negative cognitive styles on depression: A mediation

analysis, Behavior Research Therapy; 46, 487-495.

12. Mirzamani, M et al., Validation of Haven and Yale Multidimensional Pain Inventory in patients with chronic pain, Qom University School of Medicine, 1 (3), 22-34, 2007.

13. Chochinov HM. Depression in cancer patients. Lancet Oncol. 2001;2(8):499–505.

14. Fann JR, Thomas-Rich AM, Katon WJ, Cowley D, Pepping M, McGregor BA, et al. Major depression after breast cancer: a review of epidemiology and treatment. Gen Hosp Psychiatry. 2008;30(2):112–26.

15. Hopwood P, Sumo G, Mills J, Haviland J, Bliss JM. The course of anxiety and depression over 5 years of follow-up and risk factors in

women with early breast cancer: results from the UK Standardisation of Radiotherapy Trials (START). Breast. 2010;19(2):84–91. 16. Badr H, Milbury K. Associations between depression, pain behaviours, and partner responses to pain in metastatic breast cancer.

Pain.2011;152(11):2596–604.

17. Satin JR, Linden W, Phillips MJ. Depression as a predictor of disease progression and mortality in cancer patients. Cancer. 2009;115(22):5349–61.

18. Henselmans I. Psychological well-being and perceived control after a breast cancer diagnosis [unpublished doctoral dissertation].

Groningen, The Netherlands: University of Groningen; 2009. 19. Gross, J.J. The emerging field of emotion regulation: An integrative review. Rev. General Psychol. 1998, 2, 271–299. [CrossRef].

20. Boyes, A.W.; Girgis, A.; D’Este, C.A.; Zucca, A.C.; Lecathelinais, C.; Carey, M.L. Prevalence andpredictors of the short-term

trajectory of anxiety and depression in the first year after a cancer diagnosis:A population-based longitudinal study. J. Clin. Oncol. 2013, 31, 2724–2729. [CrossRef] [PubMed]

21. Linden, W.; Vodermaier, A.; MacKenzieb, R.; Greig, D. Anxiety and depression after cancer diagnosis:Prevalence rates by cancer

type, gender, and age. J. Affect. Disord. 2012, 141, 343–351. [CrossRef] [PubMed].

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7

Authors: Vani Ramesh

Paper Title: Digitization and Audit Profession

Abstract: ‘Digitization’ has restructured the auditing profession with upgraded technical skills and knowledge

of auditors in India. Soft wares such as, CAATT, eaudit, DMS, Aura, on spring, Audit Board, iAuditor and

larger safety culture suite assists the auditors to stay ahead with the audit process efficiently and effectively.

Digitization helped the auditors to spend less time on paper work and more time lending their knowledge to

other high-risk areas of the business. The present study aims at understanding the impact of Digitization on the

audit profession in India by using a well-structured questionnaire of 400 respondents from auditor`s profession

across India. Factor Analysis (FA) and confirmatory factor analysis (CFA) approach is exploited to generate the

results. SEM has been deployed to evaluate the original and modification indices of the model, which further

establishes the improvement of path analysis in SEM`s effectiveness with the help of Reliability and Validity

33-39

tests. The model establishes the significant impact of Digitization on the audit profession in India. Based on

results in Structural Equation Modelling, contributions of digitization on the audit profession in India is

analysed. Various parameters established based on Force Field Theory (Kurt Lewin`s, 1948) [1] of driving

forces and restraining forces such as, auditor`s perception, demographic, socio economic, political,

environmental, technological, infrastructural and political. The outcome of the study will help the auditors and

corporates practitioners to frame the guidelines to meet global challenges.

Keywords: Auditing, Automation, Technology, Digitization, Software.

References: 1. Kurt Lewin`s (1947), Force Field Analysis, the 1973 Annual Handbook for Group Facilitators, 111-13.

2. Wachal Robert (1971), A Personal View on Humanitiesand Computers, The North American Review (New), Spring 1971. 3. Crutchley, C., Jensen, M, and Marshall, B, (2007), Climate for Scandal: Corporate Environment that Contribute to Accounting Fraud.

The Financial Review, 42,53-73.

4. Swanson D, L Tynan, D Wolstencroft, B Edmondson(2013), Communication for Business. xxiv, 431 pages: colour illustrations; 25 cm.South Melbourne, Victoria : Oxford University Press, 2013. https://trove.nla.gov.au/version/192726396

5. Xiao Z, Sangster A, Dodgson JH (1997). Therelationship between information technology and corporate financial reporting.

http://www.emeraldinsight.com/Insight/Articles/0510200604.html44,56-78. 6. IIFAC, 2001, https://www.ifac.org › About IFAC › Publications & Resource; 56,98,102. Harrison MJ, Datta P (2007). An empirical

assessment of user perceptions of feature versus application level usage. Communication Assoc Inf Syst. 20: 300-321

7. DeAngelo, L. (1981), Auditor size and audit quality, Journal of Accounting and Economics, 3(3), 189–199. 8. Zahargier, M.S., and Balasundaram, N. (2011). Factorsaffecting Employees Performance in Ready-aide Garments (RMGs) Sector in

Chittagong, Bangladesh. Economic Sciences series, 63(1). 9-15

9. Masood, A., & Lodhi, R.N. (2015). Factors Affecting the Success of Government Audits: A Case Study of Pakistan. Universal Journal of Management, 3(2), 52-62.

10. Gupta, (2008) 5 Comp LJ 512 Del (Delhi High Court, 2008) Council of Institute of Chartered Accountants of

India vs Praveen, 17.54(3),98. 11. Alan W. Anderson, The 13th and Final Article in a Series of “ANDERSON’S AUDIT EXPRESS”; KSCPA Skyscapes. 88-23-654(1).

[13] David C Yang, Liming Guan, (2004) "The evolution of IT auditing and internal control standards in financial statement audits:

The case of the United States", Managerial Auditing Journal, Vol. 19 Issue: 4, pp.544 12. M Iqbal Khadaroo. Corporate communications: An international journal 10 (1), 58-68, 2005. 110, 2005. A framework for examining

accountability and value for money in the UK's private finance initiative. I Demirag. Corporate Social Responsibility, Accountability

and Governance, 7792, 2017. 86, 2017. 13. Steven A. Taylor, Kevin Celuch, Stephen Goodwin, (2004) "The importance of brand equity to customer loyalty", Journal of Product

& Brand Management, Vol. 13 Issue: 4, pp.217- 227, https://doi.org/10.1108/10610420410546934

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esteem, involvement, and sociability on the use of Internet dating services.100-45-11-90.

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8

Authors: Franklin Salazar L., Jorge Buele, Homero J. Velasteguí, Angel Soria, Edith Elena Tubón Núñez,

Clara Sánchez Benítez, Gabriela Orejuela T.

Paper Title: Teleoperation and Remote Monitoring of a ScorBot ER-4U Robotic Arm in an Academic

Environment

Abstract: The present research work develops a system that uses a programming method implemented for

tele-operation of a ScorBot ER-4U. Its objective is remote control by a human operator, adding the possibility

of creating routines from the remote site. This indicates a transformation in processes of production or control

within industry and academic environments. To achieve the telecommunication purpose, two interfaces named

client/server were developed in the LabVIEW software, which will process information through a TCP/IP

communication protocol. Through the client interface, the operator gives specific orders for the robot to execute

them. While the server interface allows the visualization of the instructions that arrive from the client and

determines if there are changes in the requested positions to be executed in the robot. It transmits a frame with

the information to the ScorBot through Serial communication. In ScorBase, a routine that reads the serial port

data detecting errors and executing the movement requested by the TCP client is executed permanently. The

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http://www.ktu.lt/en/science/journals/econo/infaut038.html

http://www.metagroup.com/webhost/ONLINE/73974

https://doi.org/10.1136/eb-2015-102054

drawback of the project is the delays in the execution of the movements, produced by characteristics of the

environment of handling and programming of the robot. Although they are not lapses greater than a second,

generate a vision of discontinuity in movements determined

Keywords: Process control, robotic arm, TCPIP, teleoperation.

References: 1. M. Shakir, M. Hammood and K. Muttar, “Literature review of security issues in saas for public cloud computing: a meta-analysis,”

International Journal of Engineering & Technology, vol. 7, no. 3, pp. 1161-1171, 2017.

2. S. M. Zanoli, L. Barboni, F. Cocchioni and C. Pepe, “Advanced process control aimed at energy efficiency improvement in process

industries,” 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 57-62, 2018. 3. J. J. Ng, “Statistical process control chart as a project management tool,” IEEE Engineering Management Review, vol. 46, no. 2, pp.

26-28, 2018.

4. A. Şipoş, “The alcoholic fermentation process temperature automatic control,” 2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp. 1-6, 2018.

5. L. Jhang, C. Santiago and C. Chiu, “Multi-sensor based glove control of an industrial mobile robot arm,” 2017 International Automatic

Control Conference (CACS), pp. 1-6, 2017. 6. Y. Xie, J. Pan, J. Yan and J. Li, “Design of the fast speed two-arm robot in limited space,” 2017 IEEE International Conference on

Robotics and Biomimetics (ROBIO), pp. 652-656, 2017.

7. H. H. Kim, S.O. Park, J. H. Kyung, H. M. Do and M. C. Lee, “A study for estimating reaction force of robot arm by using PDSPO,” 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 258-262, 2017.

8. D. He and Y. Guo, “Finite element analysis of humanoid robot arm,” 2016 13th International Conference on Ubiquitous Robots and

Ambient Intelligence (URAI), pp. 772-776, 2016. 9. M. E. Shoshiashvili and I. S. Shoshiashvili, “Principles of construction of control devices for mechatronic pipe-lay complexes,” 2016

2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1-4, 2016.

10. Y. Tang, X. Xing, H. R. Karimi, L. Kocarev and J. Kurths, “Tracking Control of Networked Multi-Agent Systems Under New Characterizations of Impulses and Its Applications in Robotic Systems,” IEEE Transactions on Industrial Electronics, vol. 63, no.2, pp.

1299-1307, 2016.

11. Z. Yan, S. Guo, L. Shi, Y. Wang, G. Li and W. Peng, “Study on slave side of interventional surgery robotic system focused on the feed-back force detection,” 2016 IEEE International Conference on Mechatronics and Automation, pp. 420-425, 2016.

12. R. Szabo and A. Gontean, “SCORBOT-ER III robotic arm control with FPGA using image processing with the possibility to use as

them as sun trackers,” 2017 40th International Conference on Telecommunications and Signal Processing (TSP), pp. 563-566, 2017. 13. M. S. Kazemi and M. J. Dominguez, “Simulation and evaluation of neuro-controllers applied in a SCORBOT,” 2016 IEEE

International Conference on Automatica (ICA-ACCA), pp. 1-9, 2016.

14. R. R. Kumar and P. Chand, “Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u,” 2015 6th International Conference on Automation, Robotics and Applications (ICARA), pp. 364-369, 2015.

15. X. Xu, B. Cizmeci, C. Schuwerk and E. Steinbach, “Model-Mediated Teleoperation: Toward Stable and Transparent Teleoperation

Systems,” IEEE Access, vol. 4, pp. 425-449, 2016. 16. P. Malysz and S. Sirouspour, “Cooperative teleoperation control with projective force mappings,” 2010 IEEE Haptics Symposium, pp.

301-308, 2010. 17. A. S. Anthony and A. P. Pallewatta, “Four Legged Walking Robot with Smart Gravitational Stabilization,” Kelaniya International

Conference on Advances in Computing and Technology (KICACT - 2017), pp. 29, 2017.

18. D. N. S. R. Kumar and D. Kumar D, “VNC server based robot for military applications,” 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), pp. 1292-1295, 2017.

9

Authors: Ninni Singh, Amit Kumar, Neelu Jyothi Ahuja

Paper Title: Implementation and Evaluation of Personalized Intelligent Tutoring System

Abstract: This research article illustrates a generic architecture for intelligent tutoring system christened as

SeisTutor. SeisTutor adapts itself according to the learner learning preferences by determining the learning

style and pre knowledge level. The aim of SeisTutor is to mimic similar the human intelligence by implicitly

adjudge the tutoring strategy prior to tutoring session and custom-tailored the tutoring concepts to enhance the

learning gain. SeisTutor was implemented using I2A2 index of learning style model. An Empirical analysis

has been performed for graduation pursing students. The experimental analysis reveals that learning style

model were accurately predicted with an accuracy of 61-100 %. The applicants found SeisTutor is helpful with

an average of 13 % learning gain, attains 24 % engagement at the beginning of the tutoring session.

Keywords: Learning style, Pedagogy flipping, Intelligent tutoring system, e-learning system, domain

knowledge, knowledge management.

References: 1. Etienne, Wenger. "Artificial intelligence and tutoring systems." Computational and Cognitive Approaches to the Communication of

Knowledge. Morgan Kauffmann, Los Altos, San Francisco, CA USA (1987).

2. Ohlsson, Stellan. "Some principles of intelligent tutoring." Instructional science 14.3-4 (1986): 293-326

3. Shute, Valerie J. "Rose garden promises of intelligent tutoring systems: Blossom or thorn." (1991). 4. Koedinger, Kenneth R., et al. "Intelligent tutoring goes to school in the big city." International Journal of Artificial Intelligence in

Education (IJAIED) 8 (1997): 30-43.

5. Buchanan, Thomas. "The efficacy of a World‐Wide Web mediated formative assessment." Journal of Computer Assisted Learning 16.3

(2000): 193-200.

6. Wang, Tzu-Hua. "What strategies are effective for formative assessment in an e‐learning environment?." Journal of Computer Assisted

Learning 23.3 (2007): 171-186.

7. Shi, Hongchi, et al. "Integrating adaptive and intelligent techniques into a web-based environment for active learning." Intelligent

Systems: Technology and Applications 4 (2002): 229-260.

8. Berlyne, Daniel E. "Curiosity and learning." Motivation and emotion 2.2 (1978): 97-175.

9. Craig, Scotty, et al. "Affect and learning: an exploratory look into the role of affect in learning with AutoTutor." Journal of

educational media 29.3 (2004): 241-250.

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10. Csikszentmihalyi, Mihaly. "Flow:ThePsychology of Optimal Experience". Harper and Row, NewYork (1990). 11. D’Mello, Sidney, et al. "Collaborative lecturing by human and computer tutors." International Conference on Intelligent Tutoring

Systems. Springer, Berlin, Heidelberg, 2010.

12. Mann, Sandi, and Andrew Robinson. "Boredom in the lecture theatre: An investigation into the contributors, moderators and outcomes of boredom amongst university students." British Educational Research Journal 35.2 (2009): 243-258.

13. Moss, Jarrod, et al. "They were trained, but they did not all learn: individual differences in uptake of learning strategy training." Poster

presented at the 29th Annual Conference of the Cognitive Science Society, Washington, DC. 2008. 14. Pekrun, Reinhard, et al. "Boredom in achievement settings: Exploring control–value antecedents and performance outcomes of a

neglected emotion." Journal of Educational Psychology 102.3 (2010): 531.

15. Pekrun, Reinhard, et al, “Academic emotions. In: Urdan, T.(Ed.)”, APA Educational Psychology Handbook , American Psychological Association, Washington, DC vol 2 2010.

16. Vassileva, Julita, and Ralph Deters. "Dynamic courseware generation on the WWW." British Journal of Educational Technology 29.1

(1998): 5-14. 17. Singh, Ninni, Neelu Jyothi Ahuja, and Amit Kumar. "A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive

Intelligent Tutoring System." Journal of Cases on Information Technology (JCIT) 20.3 (2018): 1-20.

18. Ekman, Paul, and Wallace V. Friesen. "Facial Action Coding System (FACS): Investigator’s guide (Part Two). Palo Alto." (1978).

10

Authors: Ramamurthy Venkatesh, Tarun Kumar Singhal, Liju Mathew

Paper Title: Emergence of Digital Services Innovation as A Path to Business Transformation: Case of

Communication Services Providers in GCC Region

Abstract: Changing business dynamics and pace of digital transformation imperatives are forcing

Communication Service Providers (CSPs) with more opportunities as well as threats. Purpose of this research

article is to qualitatively explore and recommend a business model canvas perspective for incumbent

communication services providers who are aspiring to transform as digital services providers. This study will

also focus on how digital services innovations are emerging as a crucial path to business transformation,

especially for global CSPs and how regional incumbent CSPs and telecom operators in GCC region are coping

with their digital transformation challenges. Due to the contemporary and topical nature, this study is based on

literature based exploratory investigation of latest industry reports and academic research with a combination of

insights from a wide range of published sources. Telecommunications industry specific reports of international

organizations’ such as TM Forum and World Economic Forum are duly included for better comprehension and

practicality. Comprehending the trends of current digital transformation priorities by global CSPs and

consultative recommendations from industry re-search reports, this study provides a new perspective to

understand digital services innovation options for CSPs in business model elements perspective. A comparison

of current digital transformation approaches of regional CSPs in the GCC region are provided for better

contextualization. As practical implication, inferences made by this study in the form of business model canvas

components will help managers and practitioners as a useful tool for further detailed operational planning

towards their digitalization goals.

Keywords: Business Models, Platform Models, Digital Services; Digital Transformation.

References: 1. World Economic Forum. Digital Transformation Initiative. Telecommunication Industry. http://reports.weforum.org/digital-

transformation/wp-content/blogs.dir/94/mp/files/pages/files/dti-telecommunications-industry-white-paper.pdf. Accessed 06 January

2018 2. Berman, Saul J. "Digital transformation: opportunities to create new business models." Strategy & Leadership 40, no. 2 (2012): 16-24.

3. Lynn, T., N. O’Carroll, J. Mooney, M. Helfert, D. Corcoran, G. Hunt, L. Van Der Werff, J. Morrison, and P. Healy. "Towards a

framework for defining and categorizing business Process-As-A-Service (BPaaS)." In 21st International Product Development Management Conference. 2014.

4. Venkatesh, Ramamurthy, and Tarun Kumar Singhal. "Innovating Managed Services Business Models." Indian Journal of Science and

Technology 10, no. 29 (2017). 5. Williams, Kevin, Samir Chatterjee, and Matti Rossi. "Design of emerging digital services: A Taxonomy." In Design Research in

Information Systems, pp. 235-253. Springer, Boston, MA, 2010.

6. Gawer, Annabelle, and Michael A. Cusumano. "Industry platforms and ecosystem innovation." Journal of Product Innovation Management 31, no. 3 (2014): 417-433.

7. Bouwman, Harry, Shahrokh Nikou, Francisco J. Molina-Castillo, and Mark de Reuver. "The impact of digitalization on business

models." Digital Policy, Regulation and Governance 20, no. 2 (2018): 105-124.

8. IBM. The digital service provider: The transformation of the telecommunications industry. https://www-

935.ibm.com/services/multimedia/The_Digital_Service_Provider.pdf. Accessed 06 June 2018.

9. Venkatesh, Ramamurthy, and Tarun Kumar Singhal. "Clarifying Determinants of Business Innovation Capabilities for Technology Driven Entrepreneurial Firms." International Journal of Applied Engineering Research 13, no. 13 (2018): 11305-11315.

10. McKinsey & Company. How telecom companies can win in the digital revolution. https://www.mckinsey.com/business-

functions/digital-mckinsey/our-insights/how-telecom-companies-can-win-in-the-digital-revolution. Accessed February 20, 2018. 11. Evans, Peter C., and Annabelle Gawer. "The rise of the platform enterprise: a global survey." (2016).

12. Frost & Sullivan. New Business Models of the Future Analysis of Innovative and Emerging Best Practices and Implications to Future

Value Chains to 2025, (2015). 13. BearingPoint. Monetizing Digital Services and Partner Ecosystems.

https://www.bearingpoint.com/files/Monetizing_Digital_Services_EN.pdf. Revised March 2017. Accessed January 03, 2018.

14. Venkatesh, R, Mathew, L. and Singhal, T.K. "Imperatives of Business Models and Digital Transformation for Digital Services Providers". International Journal of Business Data Communications and Networking (IJBDCN), no. 7, 15(1),2018.

15. TM Forum. Vision 2020: Future CSP Business Models. http://inform-digital.tmforum.org/vision-2020-future-csp-business-models.

Revised May 2018. Accessed January 03, 2018. 16. Morakanyane, Resego, Audrey A. Grace, and Philip O'Reilly. "Conceptualizing Digital Transformation in Business Organizations: A

Systematic Review of Literature." (2017).

17. Osterwalder, Alexander, Yves Pigneur, and Christopher L. Tucci. "Clarifying business models: Origins, present, and future of the concept." Communications of the association for Information Systems 16, no. 1 (2005): 1.

18. Johnson, M. W., Christensen ,C. M and Kagermann, H. Reinventing your Business Model. Harvard Business Review Press, 2008.

19. Tokarski, Andrzej, Maciej Tokarski, and Jacek Wójcik. "The Possibility Of Using The Business Model Canvas In The Establishment Of An Operator’s Business Plan." Torun Business Review 16, no. 4 (2017): 17-31.

20. Dasí, À., Elter, F., Gooderham, P. N. and Pedersen, T, New Business Models In-The-Making in Extant MNCs: Digital Transformation

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in a Telco. In Breaking up the Global Value Chain: Opportunities and Consequences, Emerald Publishing Limited, 2017; 29-53. 21. Dasí, Àngels, Frank Elter, Paul N. Gooderham, and Torben Pedersen. "New Business Models In-The-Making in Extant MNCs: Digital

Transformation in a Telco." In Breaking up the Global Value Chain: Opportunities and Consequences, pp. 29-53. Emerald Publishing

Limited, 2017. 22. AT&T Inc. 2017 Annual report. https://investors.att.com/~/media/Files/A/ATT-IR/financial-reports/annual-reports/2017/complete-

2017-annual-report.pdf. Accessed 12 April 2018.

23. Deutsche Telekom AG. 2017 annual report. https://www.telekom.com/resource/blob/512796/2428939591e7f0bca2b6631f25a74c7f/dl-180222-q4-allinone-data.pdf. Accessed 12 April 2018.

24. Emirates Integrated Telecommunications Company PJSC. 2017 Annual report. Http: //www.du.ae/Files/1430748361197. Accessed 12

April 2018. 25. Emirates Telecommunication Corporation. 2017 annual report. http://etisalat.com/en/system/docs/2018/Etisalat-Group-

AnnualReport2017-English.pdf. Accessed 12 April 2018.

26. MTN Group Ltd. 2017 annual report.https://www.mtn.com/MTN%20Service%20Detail%20Annual%20Reports1/Booklet2017.pdf. Accessed 12 April 2018.

27. Vodafone (2017). 2017 annual report.http://www.vodafone.com/content/annualreport/annual_report17/index.html. Accessed 12 April

2018. 28. Atkearney. The Future of Telecom Operators in the SAMENA Region. http://www.middle-

east.atkearney.com/documents/787838/13795768/The Future of Telecom Operators in the SAMEA Region.pdf/137d5cd7-c67b-4855-

9f5b-d3b4da9e647c. Published June 2017. Accessed 12 April 2018.

11

Authors: Shrish Bajpai, Naimur Rahman Kidwai, Harsh Vikram Singh

Paper Title: 3D Wavelet Block Tree Coding for Hyperspectral Images

Abstract: A novel hyperspectral image compression scheme based on set partitioned compression scheme is

proposed. This compression scheme uses the 3D wavelet transform to exploits the both, inter sub-band & intra

sub-band correlation, among the wavelet coefficients of transformed hyperspectral images. The compression

scheme is based on the spatial oriented trees (SOT) which is the basic unit in block. Block is in cube shape

having the coefficients m*m*m in contrast to a single coefficient in three dimension set partitioning in

hierarchical trees compression scheme. Each SOT has a root node in LLL band with the child and descendent

blocks in high frequency sub-band. So, proposed wavelet based compression scheme uses the features of both

zeroblock & zerotree base compression schemes.

Keywords: Compression Schemes, Hyperspectral Imaging, Performance Comparison, Set Partition

Compression, Wavelet Transform.

References: 1. Mohan BK & Porwal A (2015), Hyperspectral image processing and analysis. Current Science 108, pp. 833-841.

2. Bajpai S, Singh HV & Kidwai NR (2017), Feature extraction & classification of hyperspectral images using singular spectrum analysis & multinomial logistic regression classifiers. Multimedia, Signal Processing and Communication Technologies (IMPACT), pp. 97-

100.

3. Uthayakumar J, Vengattaraman T & Dhavachelvan P (2018), 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.

https://doi.org/10.1016/j.jksuci.2018.05.006..

4. Fowler JE & Rucker JT (2007), Three-dimensional wavelet-based compression of hyperspectral imagery. Hyperspectral Data Exploitation: Theory and Applications, pp. 379-407.

5. Motta, G., Rizzo, F., & Storer, J. A. (Eds.), (2006). Hyperspectral data compression. Springer Science & Business Media..

6. Tang, X., Pearlman, W. A., & Modestino, J. W. (2003), Hyperspectral image compression using three-dimensional wavelet coding. Image and Video Communications and Processing, pp. 1037-1048.

7. Tang, X., Cho, S., & Pearlman, W. A. (2003), Comparison of 3D set partitioning methods in hyperspectral image compression

featuring an improved 3D-SPIHT. Data Compression Conference, pp. 1-4. 8. Athar A. Moinuddin, E. Khan & M. Ghanbari (2008), Efficient algorithm for very low bit rate embedded image coding. IET Image

Processing 2, pp. 59-71.

9. Naimur Rahman Kidwai, Ekram Khan & Martin Reisslein (2016), ZM-SPECK: A fast and memoryless image coder for multimedia sensor networks. IEEE Sensors Journal 16, pp. 2575-2587.

10. S. Jia & Y. Qian (2007), Spectral and spatial complexity-based hyperspectral unmixing. IEEE Trans. Geosci. Remote Sensing 45, pp.

3867–3879.

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12

Authors: Shaheema hameed , Meera Mathur

Paper Title: The Disruption of the Z Gen Employee: Change Strategies for A Smoother Workforce Entry

Abstract: Studies on Generations have gained momentum in recent times as businesses realize that each

generation has different approaches towards work and workplaces. The classification of generations is only a

theoretical attempt to classify people of a particular behavioral similarities and differences but generational

studies is essential for understanding workplace traits and the multi-faceted approaches people take towards

corporate issues on an individual and organizational perspective. This research paper seeks to study the relevant

aspects of this latest entry into the workforce with especial focus on what makes them a critical value addition

to the workforce. Despite the fact that Z Generation employees are projected to bring about a marked change in

organizations, the very term ‘disruption’ needs an operational definition in this context. Disruption is often

relegated only to technological advances and innovation. The research methodology incorporates Focus Group

discussions and a survey instrument (Structured questionnaire) to fulfill objective 1 and 2. The study was done

in a time frame of 2.5 years with a sample of 350 drawn from Southern Rajasthan. SPSS 20.0 and AMOS 21.0

were used to test hypotheses. Descriptive and Inferential statistics have been used to arrive at the findings and

conclusions of this research study. Findings from the study substantiated the literature review showing that Z

Generation employees are entrepreneurial, learning driven and altruistic in addition to being brand conscious

about their workplaces. Certain change strategies have been suggested towards the end of the research paper,

both for the corporate bodies as well as for the Z Generation employees.

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Keywords: Competency, Change Management, Managerial Competency, Z Generation

References:

1. Dierdroff, E. C., & Rubin, R. S. (2006). Towards a comprehensive empirical model of managerial competencies. MER Institute of the

Graduate Management Admission Council.

2. Drewery, K. (2008). GenUp: How the recession has changed attitudes to work. CIPD. 3. Han, J. (2007). Marketers Brace for Generation Z Consumers. Korea Times.

4. Gale, F. S. (2015). Forget Millenials: Are you ready for Generation Z?New York: CLO Media

5. Montana, J.& Petit, F. (2008). Motivating Generation X and Y on the job.Global Journal of Business Research.2(2).pp.139-149 6. Savitt,K.(2011). 3 Ways companies reach Generation Z. Retrieved from: http://www.mashable.com/2011/04/08/marketing-

generation-z/.

7. Godbold, J. (2017, June). Retrieved February 2018, from Generation Z is redefining HR practices in 2017 and beyond Retrieved from: https://www.michaelpage.com.au/advice/management-advice/engagement-and-retention/generation-z-redefining-hr-practices-

2017 8. Dishman, L. (2015). Millennials have a different definition of Diversity and Inclusion. New York. Retrieved from:

https://www.fastcompany.com.

9. FiServe Corporate Solutions. (2018, January). Retrieved January 2018, from https://www.fiserv.com/blog/the-point/technologically-savvy-gen-z-steps-into-spotlight-blog.aspx

10. Lipinski, P. (2018). 3 Ways Gen Z Will Start to Change the Workplace in 2018. Retrieved January 27, 2018.from:

https://www.hrtechnologist.com. 11. Mastroianni. (2016, March). CBS News. Retrieved January 2018, from: https://www.cbsnews.com/news/social-media-fuels-a-

change-in-generations-with-the-rise-of-gen-z/

12. Tulgan, B. (2016). The Great Generational Shift. New Haven: Rainmaker Thinking Inc. 13. The Nielson Company (US) LLC. (2018). Youth Mpovement: GenZ boasts the largest and most diverse media users. New York.

14. Raisova, T. (2012). The comparison between effectiveness of CBI and BEI. Human Resources Management & Ergonomics , 52-63.

15. Portell, G. (2017). Here’s To the Class of 2026: The Most ‘Vocal’ Consumers Ever. Huffington Post 16. Raina, S. (2015). How can India’s Gen Z prepare better for their careers. Indiaspora Inc.

17. O’Neill, M. (2010). Supporting Generation Y at Work: Implications for Business, Knoll White Paper, Knoll Inc., New York, NY

18. Mahdieh Sadat Khoshouei, H. R. (2013). Essential Competencies for Twenty first Century Managers. Iranian Journal of Management Studies, 6 (2), 131-152.

19. Gumbs, A. (2017). Will Generation Z Be The Most Entrepreneurial Generation Yet? New York: The Black Enterprise

20. Patel, D. (2017). How Gen Z Will Shape The Future Of Business. New York: Forbes. 21. Looper, L. (2016). How Generation Z Works. HowStuffWorks/Culture.

22. Königová Martina, U. H. (2012). Identification of Managerial Competencies in Knowledge Based organizations. Journal of

Competetitiveness, 4 (1), 129-142. 23. George, D. and Mallery, P. (2010) SPSS for Windows Step by Step: A Simple Guide and Reference 17.0 Update. 10th Edition,

Pearson, Boston.

24. Tavakol M, Mohagheghi MA, Dennick R. Assessing the skills of surgical residents using simulation. J Surg Educ. 2008;65(2):77-83. 10.1016/j.jsurg.2007.11.003

13

Authors: Rakesh Roshan, Om Prakash Rishi

Paper Title: Smart Solar Street Light Using WiFi, IR Motion Sensor and LDR for the Smart City

Abstract: Smart Solar Street Light is the fully automated street light , which aim is to reduce the power

consumption by automatically switch on/off the lights and transfer the saved energy to other neighbour street

lights. This paper gives the solution to two problems: First light will on/off according to the vehicle movement

on the road and Second Lights will be automatically on/off by sensing the day/night. This automation of street

lights do not need the human intervention and saved energy will be redirected to other poles or it can be used in

other work. At the same time status of the street lights, battery life and other information are shared by the

server using the Wi-Fi module integrated with the poles. Sensing of vehicle/day/night will be done by the

sensors and program will run in Ardunio Uno R3 microcontroller. The sensors sense the vehicle on the road

and switch on the lights but lights will be switched off when vehicle passes the pole. So, this paper will discuss

lots of situation and accordingly the Smart Street Lights work.

Keywords: Smart Street Light, IR sensors, Arduino Uno R3, Light Dependent Register, Smart City.

References: 1. Y.K.Tan, T.P.Huynh, Z.Wang, “Smart Personal Sensor Network Control for Energy Saving in DC Grid Poweredf LED Lighting

System”,IEEE Transaction of Smart Grid , Volume 4 Issue 2 pp 669-676.

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Malaysia”, IEEE International Sysposium of Wireless Technology and Applications(ISWTA), 22-25 Sept 2013.

5. Adele Sittoni, Davide Brunelli and David Macii, “Street lighting in smart cities: A simulation tool for the design of systems based on narrowband PLC”, IEEE First International Smart Cities Conference (ISC2),25-28 Oct 2015.

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75-79

14

Authors: Mrityunjay Singh, Niranjan Lal and Shashank Yadav

Paper Title: Rule-Based Wrappers for a Dataspace System

Abstract: Many organizations/individuals face the problem of managing a large amount of distributed and

heterogeneous data in an efficient manner. The dataspace technology addresses this problem in an efficient

manner. A dataspace system is a new abstraction for integrating heterogeneous data sources distributed over the

80-90

https://www.michaelpage.com.au/advice/management-advice/engagement-and-retention/generation-z-redefining-hr-practices-2017

https://www.michaelpage.com.au/advice/management-advice/engagement-and-retention/generation-z-redefining-hr-practices-2017

https://www.fastcompany.com/

sites that offers on-demand data integration solution with less effort and provides an integrated way of

searching & querying capability over heterogeneous data sources. We require the set of automatic wrappers to

extract the desired data from their data sources. A wrapper extracts the requested data from their respective data

sources, and populates them into the dataspace in desired format (e.g., triple formate).

This work presents a set of rule-based wrappers for a dataspace system that wrappers operate in ”pay-as-you-

go” manner. We have divided our work into two parts: discussing a set of Transformation Rules (TRSs) and

designing of a set of wrappers based on the TRSs. First, we explain the working of the TRSs for structured,

semi-structured, and unstructured data model, then, we discuss the designing of rule-based wrappers for

dataspace system based on TRSs. We have successfully implemented the wrapper for some real and synthetic

data sets. Our some of the wrappers are semi-automatic because they requires the human involvement during

the data extraction and translation.

Keywords: Dataspace System; Triple Model; Rule-based Wrappers; TripletDS; Pay-as-you-go

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15

Authors: Llewellyn Naidoo, Viranjay M. Srivastava

Paper Title: Effect of CSDG MOSFET based High Pass Filter on Signaling

Abstract: This research work looks at the design of active high pass filter that can be used in mobile

communication systems. These systems make use of trans-receiver system. This type of system is commonly

used in handsets. In this research work the proposed filter has been designed with a Cylindrical Surrounding

Double-Gate (CSDG) MOSFET and operates at cutoff frequency of 100 GHz (0.1 THz). A CSDG MOSFET is

an extension of DG MOSFET technology. It is structured by rotation of DG MOSFET with respect to its

reference point to form a hollow cylinder. It consists of two gates, one drain and one source. The gain, phase,

and return loss of the high pass filter has been analyzed with and without CSDG MOSFET using electronic

device simulator. Finally, it has been demonstrated that the third order high pass filters performs better with the

CSDG MOSFET.

Keywords: Active High Pass Filter, CSDG MOSFET, Gain, Phase, Return Loss, Microelectronics, VLSI.

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Antennas and Wireless Propagation Letters, vol. 14, pp. 799-802, 2015. 6. Narseo V. Rodriguez and Jon Crowcroft, “Energy Management Techniques in Modern Mobile Handsets,” IEEE Communications

Surveys & Tutorials, vol. 15, no. 1, pp. 179-198, 2013.

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16

Authors: Nitin Tyagi, Sandhya Tarar, Sandeep Gupta

Paper Title: A Deep Learning Mechanism for Medical Image Investigation using Convolutional Autoencoder

Neural Network

Abstract: In today’s scenario, computer tomography (CT) is broadly used to help illness determination.

Particularly, Computer aided diagnosis (CAD) in light of Artificial Intelligence (AI) as of late shows its

significance in clever medicinal services. In any case, it is an extraordinary test to set up a satisfactory marked

dataset for CT investigation help, because of the protection what's more, collateral affair. Subsequently, this

paper presents a convolutional autoencoder (CAE) deep learning structure to help unsupervised picture

highlights learning for lung knob via unlabeled data, which just needs a little measure of named information for

proficient element learning. By complete analysis, it demonstrates that the plot proposed here is better than

different methodologies, which viably takes care of the characteristic work serious issue amid counterfeit

picture marking. In addition, it checks that the proposed work approach can be stretched out for similitude

estimation of lung knobs pictures. Particularly, the highlights separated through unsupervised learning (USL)

are too material in other related situations.

Keywords: Autoencoder (AE); Convolutional Autoencoder Neural Network (CANN); Convolutional Neural

Network (CNN); Deep Learning;, Highlight Learning; Unsupervised Learning (USL)

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17

Authors: Moirangthem Romesh Singh , Romesh Laishram, Ghaneshwori Thingbaijam, Czenita Oinam,

Pheiga Gangmei

Paper Title: Development of Efficient Multi-Hop Protocols for Wireless Body Area Network (WBAN)

Abstract: A steady high throughput and energy efficient wireless body area network(WBAN) is created in

this paper. WBAN is quite helpful in medical health care service for early detection of human health problems.

Heterogeneous sensor nodes are deployed on human body to quantify physiological parameters like blood

glucose, pulse, EMG and so on. Sensor nodes data are transmitted to a sink node forwarded through

intermediate nodes. The information available in the sink node can be accessed by end users for further

analysis. Minimization of energy consumption by sensor nodes is one of the important parameter in the design

of WBAN protocols therefore multi-bounce method of correspondence is used. In this paper a new cost

function is characterized to choose a forwarder node; a node with high residual energy and least separation to

sink. Residual energy parameter settles vitality utilization among the sensor node while least separation

enhances successful delivery to sink. The simulation results demonstrated the proposed protocol in contrast to

contemporary schemes, maximizes the packets received at the sink node i.e. the throughput of the network.

Keywords: Wireless body area network; energy efficient; Throughput; Cost function; Path loss.

References: 1. IEEE P802.15.6-2012 Standard for Wireless Body Area Networks.

2. A. Salehi, M. A. Razzaque, I. Tomeo-Reyes and N. Hussain, "IEEE 802.15.6 standard in wireless body area networks from a

healthcare point of view," 2016 22nd Asia-Pacific Conference on Communications (APCC), Yogyakarta, 2016, pp. 523-528, https://doi.org/10.1109/APCC.2016.7581523.

3. M. M. Alam and E. B. Hamida , “Surveying wearable human assistive technology for life and safety critical applications: Standards,

challenges and opportunities,” Sensors, vol. 14 , no. 5, (2014), pp. 9153–9209, http://doi.org/10.3390/s140509153. 4. Mainwaring A., Culler D., Polastre J., Szewczyk R. and Anderson, J., " Wireless sensor networks for habitat monitoring", In: Proc. of

the 1st ACM Int. Workshop on Wireless sensor networks and applications, (2002), pp.88–97, https://doi.org/10.1145/570738.570751.

5. Quwaider M. and Biswas S. "DTN routing in body sensor networks with dynamic postural partitioning", Ad Hoc Networks, Vol. 8, No.8, (2010), pp. 824-841, https://dx.doi.org/10.1016%2Fj.adhoc.2010.03.002.

6. N. A. Pantazis, S. A. Nikolidakis and D. D. Vergados, "Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey,"

in IEEE Communications Surveys & Tutorials, vol. 15, no. 2, (2013),pp.551-591, https://doi.org/10.1109/SURV.2012.062612.00084. 7. Tang Q., Tummala N., Gupta S.K.S., Schwiebert L.," TARA: Thermal-Aware Routing Algorithm for Implanted Sensor Networks",

International Conference on Distributed Computing in Sensor Systems , Springer, Berlin, Heidelberg , (2005), pp. 206-217,

https://doi.org/10.1007/11502593_17. 8. B. Latre et al., "A Low-delay Protocol for Multihop Wireless Body Area Networks", Fourth Annual International Conference on

Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous), Philadelphia, PA, (2007), pp. 1-8,

https://doi: 10.1109/MOBIQ.2007.4451060. 9. T. Watteyne, S. Augé-Blum, M. Dohler, D. Barthel, "Anybody: A self-organization protocol for body area networks", Proc. Bodynets,

Jun. 2007.

10. A. Ehyaie, M. Hashemi and P. Khadivi, "Using relay network to increase life time in wireless body area sensor networks," 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, Kos, 2009, pp. 1-6.

https://doi: 10.1109/WOWMOM.2009.5282405.

11. Nabi, Majid et al., “A robust protocol stack for multi-hop wireless body area networks with transmit power adaptation.” Proceedings of

the Fifth International Conference on Body Area Networks, ACM, (2010), pp. 77-83.

12. C. Guo, R. V. Prasad and M. Jacobsson, "Packet Forwarding with Minimum Energy Consumption in Body Area Sensor Networks",

2010 7th IEEE Consumer Communications and Networking Conference, 2010, pp. 1-6, https://doi: 10.1109/CCNC.2010.5421768.

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13. N. Javaid, M. Yaqoob, M. Y. Khan, M. A. Khan, A. Javaid and Z.A.Khan, "Analyzing Delay in Wireless Multi-hop Heterogeneous Body Area Networks", Research Journal of Applied Sciences, Engineering and Technology, vol. 7, no.1, (2013), pp.123-136.

14. N. Javaid, I. Israr, M. Khan, A. Javaid, S. H. Bouk, and Z. Khan, "Analyzing medium access techniques in wireless body area

networks", Research journal of applied Science, engineering and technology, Vol. 7 No.3, 2013. 15. N. Javaid, Z. Abbas, M.S. Fareed, Z.A. Khan, N. Alrajeh, "M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless

Body Area Sensor Networks", Procedia Computer Science, Vol.19, (2013),pp. 224-231, https://doi.org/10.1016/j.procs.2013.06.033.

16. Q. Nadeem , N. Javaid , S. N. Mohammad , M. Y. Khan , S. Sarfraz and M. Gull, "SIMPLE: Stable Increased-Throughput Multi-hop Protocol for Link Efficiency in Wireless Body Area Networks", Proceedings of the 2013 Eighth International Conference on

Broadband and Wireless Computing, Communication and Applications, 2013, pp.221-226, https://doi.org/10.1109/BWCCA.2013.42.

17. Javaid N, Ahmad A, Nadeem Q, Imran MA, Haider N., "iM-SIMPLE: iMproved stable increased -through-put multi-hop link efficient routing protocol for wireless body area networks", Comput Hum Behav , (2015); 1003-11.

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Comput. Inf. Sci. (2017) 7: 24. https://doi.org/10.1186/s13673-017-0105-6. 20. M. Roy, C. Chowdhury and N. Aslam, "Designing an energy efficient WBAN routing protocol", 2017 9th International Conference on

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22. Sangwan Aarti and Bhattacharya Partha P., "Reliable Energy Efficient Multi-hop Routing Protocol for Heterogeneous Body Area

Networks", International Journal of Sensors Wireless Communications and Control, vol. 8,no.1, (2018), pp. 47-56, https://doi.org/10.2174/2210327908666180517075536.

23. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor

networks," Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, pp. 10.

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24. N. Javaid, N. A. Khan, M. Shakir, M. A. Khan, S. H. Bouk and Z. A. Khan, “Ubiquitous HealthCare in Wireless Body Area Networks-

A Survey”, J. Basic Appl. Sci. Res. vol. 3 no.4,(2013),pp.747-759.

18

Authors: Jyotirmaya Mishra, Jitendra Sheetlani, Kumari Renuka, K Hemant Kumar Reddy

Paper Title: An Evolutionary Based Heuristic Approach towards Energy Efficient Software Defined Data

Centers

Abstract: Today’s world mainly comprises of service computing, where everything is available as a service

which in turn have led to exponential increase the user service demands. In order to meet the increasing service

demands of the users, there has been rapid development in cloud computing. Due to these huge demands of

cloud services many Data Centers (DC) have been deployed. This have led to rapid increase in the number of

Data Centers and number of servers within the DC, while increasing the consumption of energy by these DCs.

The main issue which is to be considered is the energy consumption by these Data Centers. A sincere

consideration to the solution of the problem mentioned above is required for optimization of energy

consumption without violating the QoS degradation. This paper proposes a management strategy for an

efficient energy network that will lead to the fulfillment of demands in network traffic in SD-DCNs. The

proposed model addresses three main issues, the subset of switch which is to be activated by using selective-

switch-activation approach, an efficient routing for a multipath and for all the flow to be scheduled, and

Installation of forwarding rule in SDN switches. The above-mentioned issues combined together are taken into

consideration and formulated in the form of ILP problem. Finally, a GA based heuristic approach is formulated

for solving the problem so as to tackle the computational complexity of the same. The presented simulation

result lays the efficacy of the proposed algorithm.

Keywords: Data center networking, Energy Optimization, Multipath Routing, SDN, ILP, And Heuristic

Approach

References: 1. S F. Bonomi, R. Milito, J. Zhu, S. Addepalli, "Fog computing and its role in the Internet of Things", Proc. 1st Edition MCC Workshop

Mobile Cloud Comput., pp. 13-16, 2012.

2. N. Yamada, H. Takeshita, S. Okamoto, and T. Sato, “Using optical approaches to raise energy efficiency of future central and/or linked

distributed data center network services,” International Journal of Networking and Computing, vol. 4, no. 2, pp. 209–222, 2014. 3. D. Li, Y. Shang, and C. Chen, “Software defined green data center network with exclusive routing,” in INFOCOM, 2014 Proceedings

IEEE, pp. 1743–1751, IEEE, 2014. 4. S Yi, C Li, Q Li “A survey of fog computing: concepts, applications and issues” - Proceedings of the 2015 Workshop on Mobile Big

Data, 2015.

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6. B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown, “Elastictree: Saving energy in

data center networks.,” in NSDI, vol. 10, pp. 249–264, 2010. 7. N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner, “Openflow: enabling

innovation in campus networks,” ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69–74,2008.

8. Lebiednik, Brian, Aman Mangal, and Niharika Tiwari. "A survey and evaluation of data center network topologies." arXiv preprint arXiv:1605.01701 (2016).

9. F. Giroire, J. Moulierac, and T. K. Phan, “Optimizing rule placement in software-defined networks for energy-aware routing,”2014.

10. Abraham, R. Buyya, and B. Nath., “Nature’s Heuristics for Scheduling Jobs on Computational Grids”, Proceedings of 8th IEEE International Conference on Advanced Computing and Communications, 2000 .

11. M. Srinivas and L.M. Patnaik, “Genetic Algorithms: A Survey,” IEEE Computer 27, 617-26, June 1994.

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Company, New York, 1979.

14. A.Y. Zomaya and Y.H. Teh. Observations on Using Genetic Algorithms for Dynamic Load-Balancing. IEEE Transactions on Parallel

and Distributed Systems, 12(9):899–911, 2001.

15. K. Hemant K. Reddy, Manas Patra, Diptendu Sinha Roy, B. Pradhan, “An Adaptive Scheduling Mechanism for Computational

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http://dl.acm.org/citation.cfm?id=2757397

Desktop Grid Using GridGain”, Procedia Technology, Elsevier, Volume 4, pp. 395 – 400, 2012. 16. Mishra, J., Sheetlani, J., & Reddy, K. H. K. Data center network energy consumption minimization: a hierarchical FAT-tree approach.

International Journal of Information Technology, 1-13.

19

Authors: Dmitry Topchiy, Ekaterina Kochurina

Paper Title: Formation of normative and legal regulatory criteria of as-assessment of organizational process

management in the impel-mentation of projects of repurposing of major urban territories

Abstract: In this article, the authors analyzed the effectiveness of applying the standards of the ISO 9000

series in the construction industry in foreign countries. In Russia, the regulatory and technical framework

governing the organization of construction in urban areas does not provide for an integrated approach to the

conduct of this construction. In foreign countries, as well as in Russia, there are no normative documents

defining special rules of construction in conditions of dense urban development. At the same time, in contrast

to the domestic construction industry, on foreign sites they use an integrated approach to the management of

the construction industry and apply the standards from the ISO 9000 series as a quality control document.

Update and correct adaptation of the international quality standard ISO 9001 "Quality management system.

Requirements" on domestic market, under the requirements of the construction sector, will allow to introduce a

system of full control and interaction of all construction processes from the development stage to the stage of

delivery and operation of objects of any complexity and to bring the organization of construction production in

Russia to the World market.

Keywords: Construction; ISO 9000; Organizational and Technological solution; Quality management

systems; QMS; Quality in construction

References: 1. Energy audit of buildings commissioned after the conversion of industrial facilities. Topchy D.V. Scientific review. 2017. № 9. P.

114-117. 2. Adaptation of industrial buildings to objects of social sphere. Topchy D.V. Housing construction. 2007. № 7. P. 16-19.

3. Local expansion of the span of industrial buildings. Topchy D.V. Bulletin of MGSU. 2007. № 4. P. 95-99.

4. Changing the grid of columns of reconstructed single-storey multi-span buildings with their adaptation to civilian objects. Topchy D.V. Bulletin of MGSU. 2010. № 4-1. Pp. 294-303.

5. Preparation of former industrial sites for the construction of civilian objects. Topchy D.V. Architecture and construction of Russia. 2011. № 5. P. 14-21.

6. Integrated construction supervision: requirements and necessity. Topchy D.V. Technology and organization of construction. 2014.

No. 1. P. 46-47. 7. Evaluation of the potential for the conversion of industrial facilities. Topchy D.V. Technology and organization of construction. 2014.

No. 3 (8). Pp. 40-42.

8. Assessment of organizational and technological and economic parameters in the output of enterprises outside the city limits. Topchy D.V. Technology and organization of construction. 2014. № 4. P. 34-41.

9. Assessment of organizational and technological and economic parameters in the output of enterprises outside the city limits. Topchy

D.V. Technology and organization of construction. 2015. No. 4-1 (9). Pp. 34-41. 10. Evaluation of the correlation dependence of the material consumption of building structures of various types of industrial buildings

subject to dismantling in the re-profiling of industrial areas. Topchy D.V. European Research. 2015. № 6 (7). Pp. 6-9.

11. Assessment of the structure of industrial enterprises subject to conversion and located within the boundaries of large megacities. Topchy D.V. In the collection: innovative technologies in construction and geoecology Materials of the II International Scientific and

Practical Conference. Petersburg State Transport University named after Emperor Alexander I, Department "Engineering Chemistry

and Natural Science". 2015. P. 37-41. 12. Development of an organizational and management model for the implementation of projects for the redesign of industrial sites.

Topchy D.V. In the collection: innovative technologies in construction and geoecology Materials of the II International Scientific and

Practical Conference. Petersburg State Transport University named after Emperor Alexander I, Department "Engineering Chemistry and Natural Science". 2015. P. 42-60.

13. Comprehensive verification construction. Topchii DV, Skakalov VA, Yurgaitis A.Yu. International Journal of Civil Engineering and

Technology (IJCIET) Volume 9, Issue 1, January 2018, pp. 985-993 14. A. Lapidus., I Abramov, A. Lapidus, I. Abramov // E3S Web of Conferences. - 2018. - No. 33.

15. A. Lapidus, A. Makarov, A. Lapidus, A. Makarov // MATEC Web Conf. - 2016. - No. 86.

16. P. Oleinik, Method for creating a work management plan of a construction company / Oleinik P. //

17. Integrated construction supervision as a tool to reduce the developer’s risks when implementing new and redevelopment projects.

Dmitriy Topchiy, Anastasia Shatrova1 and Alexey Yurgaytis, MATEC Web of Conferences 193, 05032 (2018), ESCI 2018,

https://doi.org/10.1051/matecconf/201819305032 18. Environmental situation in construction, reconstruction and re-profiling of facilities in high-density urban development. Dmitry

Topchiy and Ekaterina Kochurina. MATEC Web of Conferences 193, 05012 (2018), ESCI 2018,

https://doi.org/10.1051/matecconf/201819305012 19. Formation of the organizational-managerial model of renovation of urban territories. Dmitriy Topchiy and Andrey Tokarskiy.

MATEC Web of Conferences 196(1):04029 · January 2018, XXVII R-S-P Seminar 2018, Theoretical Foundation of Civil

Engineering, https://doi.org/10.1051/matecconf/201819604029 20. Formation of a basic management strategy for a construction organization in the implementation of projects of redevelopment of

major urban areas. Topchiy, D.V., Shatrova, А.I. International Journal of Mechanical Engineering and Technology, 2018

21. Designing of structural and functional organizational systems, formed during the re-profiling of industrial facilities. Topchiy, D., Tokarskiy, A., IOP Conference Series: Materials Science and Engineering, 2018

22. Optimization of the annual construction program solutions. Oleinik P., Yurgaytis A., MATEC Web of Conferences. - 2017. - Volume

117. - Article Number 00130. RSP 2017 – XXVI R-S-P Seminar 2017 Theoretical Foundation of Civil Engineering https://doi.org/10.1051/matecconf/201711700130

23. The method of forming solutions for non-critical activities in the preparation and optimization of the construction complex

organizations’ annual program Oleinik P., Yurgaytis A. , MATEC Web of Conferences 193, 05010 (2018), ESCI 2018, https://doi.org/10.1051/matecconf/201819305010

24. Abramov I.L., Lapidus A.A. Formation of production structural units within a construction company using the systemic integrated

method when implementing high-rise development projects. E3S Web of Conferences 33. D. Safarik, Y. Tabunschikov and V. Murgul (Eds.). 2018. С. 03066. https://doi.org/10.1051/e3sconf/20183303066

25. Ivan Abramov, Formation of integrated structural units using the systematic and integrated method when implementing high-rise

117-120

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construction projects HRC 2017 (HIGH-RISE CONSTRUCTION-2017) E3S Web of Conferences 33. D. Safarik, Y. Tabunschikov and V. Murgul (Eds.). 2018. 03075 https://doi.org/10.1051/e3sconf/20183303075

20

Authors: Yogendra Narayan Prajapati, Ravindra Chauhan

Paper Title: Sentiment Analysis of User Game: After play Analysis

Abstract: This research is to conclude the sentiment of the gamers and the sentiments of them in a whole

game play. The evaluation of the information that you extract form the method of play. The player will have a

score card and that will be considered as the information for the sentiment analysis report. The tally will be

from the prefixed strategy of the genre of the game.

The player will be evaluated for the improvement in the game for the upcoming round and can get a detailed

analysis of the drawbacks and the flaws faced by the players. The tactic need to be detailed analysis for the

improvement. This will also focus on the addictiveness and the player and the proper schedule to play the game

and switch the play mode and the genre of game. So that the mood and the effect of virtual reality will be lessen

and good mental health of a player.

Keywords: Sentiment, Game genre, After play Analysis

References: 1. Unity3d www.unity.com a game engine, until 2018.

2. Jeremy Parish (2014). "Montezuma's Revenge, an Atari Quest to Make Adventure Proud". USGamer.

3. Retrieved 2017-10-18. By borrowing from Atari's action RPG, Utopia created a platformer classic.

4. Carter, Chris (June 9, 2017). "Understanding Playerunknown's

5. Battlegrounds". Polygon. Archived from the original on June 9, 2017. Retrieved June 9, 2017.

6. "Invisible, Inc. (pc)". Metacritic. Retrieved May 17, 2015.

7. "Nasir Gebelli and the early days of Sirius Software". The Golden Age Arcade Historian. August 28, 2015.

8. Purchase, Robert (February 1, 2013). "Temple Run 2 is the fastest-spreading mobile game ever". Euro gamer. Retrieved February 1,

2013.

9. Pidd, Helen (19 April 2012). "Anders Breivik 'trained' for shooting attacks by playing Call of Duty". The Guardian. Retrieved 2 December2017.

10. The use of first research in 2016 by Björn Strååt and Harko Verhagen http://ceur-ws.org/Vol- 1956/GHItaly17_paper_01.pdf

121-124

21

Authors: Kamalpreet Singh, Amit Aggarwal, Kamal Kumar

Paper Title: Mobility Management in Constrained Wireless Nodes: A Review

Abstract: The key aspect of IOT is interoperability, the potential of different technologies, different hardware

platforms, various communication protocols, different operating systems to communicate with each other.

Mobile support is required for interoperability between fixed and mobile nodes. As wireless nodes have certain

limitations, like reduced power, low-energy and limited resources, attention is required while designing the

mobility management scheme. The main challenge in Low Power and Mobile Network is to provide Quality of

Service along with the support of mobility. In this paper, latest work in the mobility support of RPL has been

discussed in detail along with their contributions and shortcomings.

Keywords: RPL; LPWN; LLN; Mobility; LPLN; IOT

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157.

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1–26, 2010.

6. H. Fotouhi, D. Moreira, M. Alves, and P. M. Yomsi, “mRPL+: A mobility management framework in RPL/6LoWPAN,” Computer

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2015.

8. P. O. Kamgueu, E. Nataf, and T. D. Ndie, “Survey on RPL enhancements: A focus on topology, security and mobility,” Computer Communications, vol. 120, no. February, pp. 10–21, 2018.

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17. H. R. Kermajani and C. Gomez, “Route change latency in low-power and lossy wireless networks using RPL and 6LoWPAN neighbor discovery,” Proceedings - IEEE Symposium on Computers and Communications, pp. 937–942, 2011.

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and Computer Applications, vol. 49, pp. 68–77, 2015. 23. X. Wang, “A mobility frame for 6LoWPAN WSN,” IEEE Sensors Journal, vol. 16, no. 8, pp. 2755–2762, 2016.

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2, pp. 585–594, 2015. 28. F. Gara, L. Ben Saad, E. Ben Hamida, B. Tourancheau, and R. Ben Ayed, “An adaptive timer for RPL to handle mobility in wireless

sensor networks,” 2016 International Wireless Communications and Mobile Computing Conference, IWCMC 2016, no. 978, pp.

678–683, 2016. 29. M. Barcelo, A. Correa, J. L. Vicario, A. Morell, and X. Vilajosana,“Addressing Mobility in RPL with Position Assisted Metrics,”

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wireless sensor networks using Corona mechanism,” Proceedings of the 9th IEEE International Symposium on Industrial Embedded

Systems, SIES 2014, pp. 200–209, 2014.

31. K. Sha, J. Gehlot, and R. Greve, “Multipath routing techniques in wireless sensor networks: A survey,” Wireless Personal Communications, vol. 70, no. 2, pp. 807–829, 2013.

32. M. Tarique, K. E. Tepe, S. Adibi, and S. Erfani, “Survey of multipath routin g protocols for mobile ad hoc networks,” Journal of

Network and Computer Applications, vol. 32, no. 6, pp. 1125–1143, 2009. 33. W. Tang, X. Ma, J. Huang, and J. Wei, “Toward Improved RPL: A Congestion Avoidance Multipath Routing Protocol with Time

Factor for Wireless Sensor Networks,” Journal of Sensors, vol. 2016, 2016.

34. A. Woo, T. Tong, and D. Culler, “Taming the underlying challenges of reliable multihop routing in sensor networks,” Proceedings of the first international conference on Embedded networked sensor systems - SenSys ’03, p. 14, 2003.

22

Authors: Shamimul Qamar, Niranjan Lal

Paper Title: Power Efficient Data Collection in Wireless Sensor Networks

Abstract: Wireless sensor networks experiencing exponential growth in the past decade. In almost all areas it

is required to manage and monitor the devices with low cost solutions and proper localization process for

communication that manages the sensor nodes across the globe, there are many applications those are possible

by wireless sensor networks in today’s era like smart home, environmental monitoring, smart traffic

management etc. proper identification and communication setup is very important task, especially in Internet of

Things or Internet of Everything, among the different sensor nodes in the network and other mobile devices.

Designing an efficient, reliable, scalable, and cost-effective localization process is required for the effective

communication. Using wireless sensor network, we can access, monitor, and collect the useful information

remotely like temperature, humidity, vibration, acceleration. In this paper we have identified issues related to

consumption of energy in wireless sensor network (WSN). In wireless sensor network, sensor nodes are

deployed randomly to collect the useful data and information from different applications for further processing.

Sensor nodes may be located on different locations, in staring we collect the raw data from sensor nodes that

will store at remote base station known as sink. As the sensor nodes are located on different geographical

locations which have limited battery power and life time. The sensors node battery life is different parameters

like traffic intensity, communication channel. There are many researches are going on this area to maximize

sensor’s lifetime by using routing mechanism. In this paper, we have proposed LEACH and PEGASIS

approaches for improve lifetime of sensor node in wireless sensor networks. Firstly, we have discussed the

primary routing challenges, and secondly, in this paper we have covered design area of routing in wireless

sensor network with modulation techniques to design efficient routing protocols for wireless sensor networks. Keywords: Data collection; node; style; wireless sensor network;

References: 1. Swami, Q. Zhao, Y. Hong and L. Tong, “Wireless Sensor Networks: Signal Processing and Communications Perspectives”, John Wiley

& Sons Ltd, 2007 2. Peng, J.; Chen, Y. A low energy consumption WSN node. Int. J. Embed. Syst. 2015, 7, 318–323.

3. A. Hac, “Wireless Sensor Network Designs”, John Wiley & Sons Ltd, 2003.

4. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Communications Mag., Vol. 40, No. 8, Aug. 2002, pp.102-114.

5. J. N. Al-Karaki, and A. E. Kamal, ”Routing Techniques In Wireless Sensor Networks: A Survey”, IEEE Wireless Communication, Vol.

11, 2004, pp.6-28. 6. Shi, Y.; Xie, L.; Hou, Y.T.; Sherali, H.D. On renewable sensor networks with wireless energy transfer. InProceedings of the IEEE

INFOCOM 2011, Shanghai, China, 10–15 April 2011.

7. I. F. Akyildiz and M. C. Vuran, “Wireless Sensor Network”, John Wiley & Sons Ltd., 2010. 8. A. Nayak and I. Stojmenovic, “Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scalable Coordination and Data

Communication”, John Wiley & Sons, Inc., 2010.

9. C. Hua and T. P. Yum, “Optimal Routing And Data Aggregation For Maximizing Lifetime Of Wireless Sensor Networks”, IEEE ACM Transection on Network., Vol. 16, No. 4, pp. 892–903, Aug. 2008.

10. H. R. Karkvandi, E. Pecht, and O. Yadid, “Effective Lifetime-Aware Routing In Wireless Sensor Networks”, IEEE Sensors Journal,

Vol. 11, No. 12, pp. 3359–3367, Dec. 2011. 11. D. Dasgupta, “Artificial Immune Systems and Their Applications”, Springer-Verlag Berlin Heidelberg, 1999.

133-137

12. D. Dasgupta, “Advances in Artificial Immune System”, IEEE Computational Intelligence Magazine, pp.40-49, November 2006. 13. K. Maraiya, K. Kant, and N. Gupta, "Architectural Based Data Aggregation Techniques in Wireless Sensor Network: A Comparative

Study," International Journal on Computer Science and Engineering (IJCSE), vol. 3, no. 3, pp.1131-1134, 2002.

14. S. Lindsey, C.S. Raghavendra, "PEGASIS: Power-Efficient Gathering in Sensor Information Systems,” Proceedings of IEEE on Conference Aerospace, Los Angeles, CA. 2002, pp. 3-8

23

Authors: Ankit Khare, Rashmi Sharma, Neelu Jyoti Ahuja

Paper Title: Analysis of Various Light Weight Protocols in Internet of Things-A Comparative Study

Abstract: Internet of Things (IoT) incorporates the physical world with computing devices enabling them to

be operate from remote region. Sensors and actuators with the assistance of communication protocols (MQTT,

CoAP, HTTP, and REST) exchange the data, enabling the smart devices to interact with each other. These sort

of devices having restricted limit, the protocols are intended to be handle low data transfer capacity,

communication issues and high latency rate. In this paper, existing lightweight protocols in IoT are analyze

based on eight different parameters(Architecture, Need of broker, Transport protocol, Security protocol, Scope,

Design Methodology, Message size, Service levels) and these protocols can be used based on their application

area.

Keywords: COAP, IoT, MQTT, Web Services, REST, SOAP

References: 1. Kevin Ashton,”That ‘Internet of Things’ Thing”, Jun, 2009, https://www.rfidjournal.com/articles/view?4986

2. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805. 3. Garcia-Morchon, O., Falck, T., Heer, T., & Wehrle, K. (2009, July). Security for pervasive medical sensor networks. In Mobile and

Ubiquitous Systems: Networking & Services, MobiQuitous, 2009. MobiQuitous' 09. 6th Annual International(pp. 1-10). IEEE.

4. Wu, M., Lu, T. J., Ling, F. Y., Sun, J., & Du, H. Y. (2010, August). Research on the architecture of Internet of Things. In Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on (Vol. 5, pp. V5-484). IEEE.

5. Brachmann, M., Garcia-Morchon, O., & Kirsche, M. (2011). Security for practical coap applications: Issues and solution

approaches. GI/ITG KuVS Fachgesprch Sensornetze (FGSN). Universitt Stuttgart. 6. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., Mccann, J., & Leung, K. (2013). A survey on the ietf protocol suite for the internet of

things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91-98.

7. Chen, X. (2014). Constrained application protocol for internet of things. URL: https://www. cse. wustl. edu/~ jain/cse574-14/ftp/coap. 8. Gluhak, A., Krco, S., Nati, M., Pfisterer, D., Mitton, N., & Razafindralambo, T. (2011). A survey on facilities for experimental internet

of things research. IEEE Communications Magazine, 49(11), 58-67.

9. Luo, H., Ci, S., Wu, D., Stergiou, N., & Siu, K. C. (2010). A remote markerless human gait tracking for e-healthcare based on content-aware wireless multimedia communications. IEEE Wireless Communications, 17(1).

10. Nussbaum, G. (2006, July). People with disabilities: assistive homes and environments. In International Conference on Computers for Handicapped Persons (pp. 457-460). Springer, Berlin, Heidelberg.

11. Alkar, A. Z., & Buhur, U. (2005). An Internet based wireless home automation system for multifunctional devices. IEEE Transactions

on Consumer Electronics, 51(4), 1169-1174. 12. Darianian, M., & Michael, M. P. (2008, December). Smart home mobile RFID-based Internet-of-Things systems and services.

In Advanced Computer Theory and Engineering, 2008. ICACTE'08. International Conference on (pp. 116-120). IEEE.

13. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645-1660.

14. Yun, M., & Yuxin, B. (2010, June). Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid.

In Advances in Energy Engineering (ICAEE), 2010 International Conference on (pp. 69-72). IEEE. 15. Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer

networks, 51(4), 921-960.

16. Liqiang, Z., Shouyi, Y., Leibo, L., Zhen, Z., & Shaojun, W. (2011). A crop monitoring system based on wireless sensor network. Procedia Environmental Sciences, 11, 558-565.

17. Moritz, G., Golatowski, F., & Timmermann, D. (2011, October). A lightweight SOAP over CoAP transport binding for resource

constraint networks. In Mobil\e Adhoc and Sensor Systems (MASS), 2011 IEEE 8th International Conference on (pp. 861-866). IEEE. 18. Guinard, D., Ion, I., & Mayer, S. (2011, December). In search of an internet of things service architecture: REST or WS-*? A

developers’ perspective. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp.

326-337). Springer, Berlin, Heidelberg. 19. Klas, G., Rodermund, F., Shelby, Z., Akhouri, S., & Hoeller, J. Lightweight M2M: Enabling Device Management and Applications for

the Internet of Things. 2014.

20. Weber, R. H. (2010). Internet of Things–New security and privacy challenges. Computer law & security review, 26(1), 23-30. 21. Hunkeler, U., Truong, H. L., & Stanford-Clark, A. (2008, January). MQTT-S—A publish/subscribe protocol for Wireless Sensor

Networks. In Communication systems software and middleware and workshops, 2008. comsware 2008. 3rd international conference

on (pp. 791-798). IEEE. 22. Lee, S., Kim, H., Hong, D. K., & Ju, H. (2013, January). Correlation analysis of MQTT loss and delay according to QoS level.

In Information Networking (ICOIN), 2013 International Conference on (pp. 714-717). IEEE.

23. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad hoc networks, 10(7), 1497-1516.

24. Karakostas, B., & Bessis, N. (2016, March). Intelligent Brokers in an Internet of Things for Logistics. In Proceedings of the

International Conference on Internet of things and Cloud Computing (p. 5). ACM. 25. Stankovic, J. A. (2014). Research directions for the internet of things. IEEE Internet of Things Journal, 1(1), 3-9.

26. 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. 27. Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on industrial informatics, 10(4), 2233-

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28. Leiner, Barry M., et al. "A brief history of the Internet." ACM SIGCOMM Computer Communication Review 39.5 (2009): 22-31. 29. Oen, H. M. (2015). Interoperability at the Application Layer in the Internet of Things (Master's thesis, NTNU).

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directions. Future generation computer systems, 29(7), 1645-1660.

33. Zhang, B., Zou, Z., & Liu, M. (2011, May). Evaluation on security system of internet of things based on fuzzy-AHP method. In E-

Business and E-Government (ICEE), 2011 International Conference on (pp. 1-5). IEEE.

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35. Hammami, R., Kacem, Y. H., Souissi, S., Bellaaj, H., & Kacem, A. H. (2017). Weighted Priority Queuing: A New Scheduling Strategy

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for Web Services. Information Technology and Computer Science, 2, 11-17. 36. Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: scientific research and commercially

available devices. Healthcare informatics research, 23(1), 4-15.

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Computing and Applications, 1-15.

24

Authors: Varun Sapra, Madan Lal Saini

Paper Title: Computational Intelligence for detection of Coronary Artery Disease with optimized features

Abstract: Coronary Artery Disease (CAD) is one of the foremost cause of mortality in almost all over the

world. It falls under the category of non-communicable diseases, that are spreading at a faster pace nowadays.

The factors that create a domino effect on the disease are changing life styles, unhealthy food habits, lack of

exercise and other socioeconomic factors. In the past few years, with the advancement in information

technology services, health sector is transformed largely and is transmitting a massive amount of medical

information. With the advancement of machine learning intelligent computational methods have proved their

effectiveness in almost every field. Medical field is also getting benefitted from machine learning because of its

capabilities to model complex relations. This paper discusses the use of Firefly for feature subset selection with

different machine learning schemes for the identification of CAD. The different techniques implemented are

Random Forest, Fuzzy Unordered Rule Induction, Logistic regression and Multilayer perceptron using Keras.

Deep learning based method outperforms other learning schemes with the accuracy of 89.77%. Thus, the

method can pose as a promising tool for screening CAD patients more accurately.

Keywords: Cardiovascular Disease, Coronary Artery Disease, Feature Subset selection, Multilayer Perceptron

References: 1. International Heart Protection Summit, September (2011) Cardiovascular diseases in India: Challenges and way ahead. India:

ASSOCHAM.

2. Randa El-Bialy, Mostafa A. Salamay, Omar H. Karam and M.Essam Khalifa, Feature Analysis of Coronary Artery Heart Disease Data

Sets, International Conference on Communication, Management and Information Technology , Procedia Computer Science 65, (2015), pp:459-468.

3. Chung, J. (2017), Association between Carotid Artery Plaque Score and SYNTAX Score in Coronary Artery Disease Patients. General

Medicine: Open Access, 5(5). 4. Zhou, H., Wang, X., Zhu, J., Fish, A., Kong, W., & Li, F. et al. (2017). Relation of Carotid Artery Plaque to Coronary Heart Disease

and Stroke in Chinese Patients: Does Hyperglycemia Status Matter?. Experimental And Clinical Endocrinology & Diabetes, 126(03),

134-140. 5. Ceponiene, I., Nakanishi, R., Osawa, K., Kanisawa, M., Rahmani, S., & Nezarat, N. et al. (2017), Association of Coronary Artery

Calcium Progression with Coronary Plaque Progression Determined by Quantitative Coronary Artery Plaque Analysis. Journal of The

American College of Cardiology, 69(11), 1552. doi: 10.1016/s0735-1097(17)34941-0 6. Acharya, U., Sree, S., Muthu Rama Krishnan, M., Krishnananda, N., Ranjan, S., Umesh, P., & Suri, J. (2013). Automated classification

of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images. Computer Methods And

Programs In Biomedicine, 112(3), 624-632. 7. Escolar E, Weigold G, Fuisz A, Weissman NJ. (2006) New imaging techniques for diagnosing coronary artery disease. Canadian

Medical Association Journal. 2006 Feb 174(4), pp. 487-95.

8. Giri D, Acharya UR, Martis RJ, Sree SV, Lim TC, Ahamed T, Suri JS. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowledge-Based Systems. 2013 Jan 31;37, pp.274-282.

9. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA. (2013), A data mining

approach for diagnosis of coronary artery disease. Computer methods and programs in biomedicine. 2013 Jul 31;111(1), pp. 52-61 10. Kahramanli, H., & Allahverdi, N. (2008), Design of a hybrid system for the diabetes and heart diseases. Expert systems with

applications, 35(1-2), 82-89.

11. Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017), Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Computer methods and programs in biomedicine, 141, 19-26.

12. Babič, F., Olejár, J., Vantová, Z., & Paralič, J. (2017), Predictive and descriptive analysis for heart disease diagnosis. In Computer

Science and Information Systems (FedCSIS), 2017 Federated Conference on (pp. 155-163). IEEE. 13. Verma, L., Srivastava, S., & Negi, P. C. (2016), A hybrid data mining model to predict coronary artery disease cases using non-

invasive clinical data. Journal of medical systems, 40(7), 178.

14. Lin, K. C., & Hsieh, Y. H. (2015), Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. Journal of medical systems, 39(10), 119.

15. Jou-Fan Chen, Wei-Lun Chen, Chun-Ping Huang, Szu-Hao Huang, An-Pin Chen (2016), Financial Time-series Data Analysis using

Deep Convolutional Neural Networks, 7th International Conference on Cloud Computing and Big Data, p.p 87-92. 16. P. Melillo, R. Izzo, A. Orrico, P. Scala, M. Attanasio, M. Mirra, N. De Luca and L. Pecchia (2015), Automatic Prediction of

Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analysis, PLOS ONE, vol. 10, no. 3, p. e0118504, 2015.

17. Alfonso Rojas-Domínguez, Luis Carlos Padierna, Juan Martín Carpio Valadez, Hector J. Puga-Soberanes, Héctor J. Fraire (2017),

Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis, IEEE Access, Vol 6, 2018, p.p. 7164-

7176.

18. X.S. Yang (2010), Firefly algorithm, levy flights and global optimization in Research and Development in Intelligent Systems XXVI.

144-148

Springer, pp. 209–218. 19. X.S. Yang (2011), Chaos-enhanced firefly algorithm with automatic parameter tuning, International Journal of Swarm Intelligence

Research (IJSIR), Vol. 2, No. 4, pp. 1–11.

20. Han, J., & Kamber, M. (2011). Data mining: Concepts and techniques (3rd ed.). Burlington, MA: Elsevier. 21. Cohen, W. W. (1995), Fast effective rule induction. In Proceedings of the twelfth international conference on machine learning, pp.

115–123.

22. J. Hühn, and E. Hüllermeier (2009), FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, vol. 19, no. 3, pp. 293-319.

23. Le Cessie, and J Houwelingen (1992), "Ridge Estimators in Logistic Regression", Applied Statistics, vol. 41, no. 1, pp. 191-201.

25

Authors: Luxmi Verma, Manish Kumar Mathur

Paper Title: Deep Learning based model for decision support with Case Based Reasoning

Abstract: Cardiovascular diseases (CVD) the most common reason for deaths worldwide, are not easily

diagnosed in initial stages. Early and accurate detection of CVD is highly required to prevent this leading cause

of mortality. In the last few years, with the advancement of technology and increased potential of digital

procedures, almost every business sector is adapting the automation and hence generating a large volume of

data. Health sector is also affected by this outburst of technology and almost all the hospitals are generating a

huge volume of data every day. The need of the hour is how to handle such a huge data and finding the hidden

correlations among it so that it can be used by clinical experts in disease diagnosing and helps them in decision-

making. This paper presents an intelligent decision support model for detection of coronary artery disease

(CAD) with the integration of cuckoo algorithm for feature subset, analysis of various classification techniques

to diagnose the disease more accurately and case base reasoning (CBR) for detecting the severity of the disease.

The results seems promising and the integrated technique shows the accuracy of MLP is 85.48 %.The model

can be used as a promising decision making tool for medical experts for detecting cardio vascular diseases in

their early stages.

Keywords: Cardiovascular Disease, Multilayer Perceptron, Case Based Reasoning

References: 1. Lin, R. H. (2009). An intelligent model for liver disease diagnosis. Artificial Intelligence in Medicine, 47(1), 53-62. 2. International Heart Protection Summit, September (2011) Cardiovascular diseases in India: Challenges and way ahead. India:

ASSOCHAM.

3. El-Bialy, R., Salamay, M. A., Karam, O. H., & Khalifa, M. E. (2015). Feature analysis of coronary artery heart disease data sets. Procedia Computer Science, 65, 459-468.

4. Chung, J. (2017), Association between Carotid Artery Plaque Score and SYNTAX Score in Coronary Artery Disease Patients. General

Medicine: Open Access, 5(5). 5. Zhou, H., Wang, X., Zhu, J., Fish, A., Kong, W., & Li, F. et al. (2017). Relation of Carotid Artery Plaque to Coronary Heart Disease

and Stroke in Chinese Patients: Does Hyperglycemia Status Matter?. Experimental And Clinical En-docrinology & Diabetes, 126(03),

134-140.

6. Yeow, W. L., Mahmud, R., & Raj, R. G. (2014). An application of case-based reasoning with machine learning for forensic

autopsy. Expert Systems with Applications, 41(7), 3497-3505.

7. Anggrawan, A., Hidjah, K., & Jihadil, Q. S. (2016, October). Kidney failure diagnosis based on case-based reasoning (CBR) method and statistical analysis. In Informatics and Computing (ICIC), International Conference on (pp. 298-303). IEEE.

8. Singh, P., Singh, A. P., & Ahmad, S. (2016, October). Case based reasoning model in the diagnosis of psychiatric disorder.

In Communication and Electronics Systems (ICCES), International Conference on (pp. 1-6). IEEE. 9. Babič, F., Olejár, J., Vantová, Z., & Paralič, J. (2017, September). Predictive and descriptive analysis for heart disease diagnosis.

In Computer Science and Information Systems (FedCSIS), 2017 Federated Conference on (pp. 155-163). IEEE.

10. Pandey, B., & Kundra, D. (2017). Diagnosis of EEG-based diseases using data mining and case-based reasoning. International Journal of Intelligent Systems Design and Computing, 1(1-2), 43-55.

11. Vedayoko, L. G., Sugiharti, E., & Muslim, M. A. (2017). Expert System Diagnosis of Bowel Disease Using Case Based Reasoning

with Nearest Neighbor Algorithm. Scientific Journal of Informatics, 4(2), 134-142. 12. https://archive.ics.uci.edu/ml/index.php

13. Yang, X. S., & Deb, S. (2014). Cuckoo search: recent advances and applications. Neural Computing and Applications, 24(1), 169-174.

14. Richter, M. M., & Weber, R. O. (2016). Case-based reasoning. Springer-Verlag Berlin An. 15. Bichindaritz, I., & Marling, C. (2006). Case-based reasoning in the health sciences: What's next?. Artificial intelligence in

medicine, 36(2), 127-135.

16. Wang, W. M., Cheung, C. F., Lee, W. B., & Kwok, S. K. (2007). Knowledge‐based treatment planning for adolescent early

intervention of mental healthcare: a hybrid case‐based reasoning approach. Expert Systems, 24(4), 232-251.

17. Xu, W., Xiong, G., Gao, F., & Zhang, X. (1999). Case based reasoning in conflict negotiation in concurrent engineering. Tsinghua Science and Technology, 4(2), 1397-1402.

18. Tsai, C. Y., & Chiu, C. C. (2009, April). Developing a Significant Nearest Neighbor Search Method for Effective Case Retrieval in a

CBR System. In Computer Science and Information Technology-Spring Conference, 2009. IACSITSC'09. International Association of (pp. 262-266). IEEE.

19. Bach, K., Sauer, C., Althoff, K. D., & Roth-Berghofer, T. (2014, August). Knowledge modelling with the open source tool myCBR.

CEUR Workshop Proceedings. 20. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.

149-153

26

Authors: Amit Kumar, Neelu Jyothi Ahuja

Paper Title: Assessment of Learning Style of Learner using I2A2 Learning Style Model

Abstract: E-learning mode of education is rapidly progressing due to the advancements in education delivery

through internet and web innovation. Learning can be beneficial and enhanced by considering the qualities of

learner such as knowledge level, cognitive ability, psychological style and learning style facilitating

illustrations. Diverse learners have distinctive intellectual and learning abilities. Learning style, which refers to

the learners favored approach to learning, is a standout as vital amongst the other parameters for deciding the

individual difference of learner. The present work deal with the critical assessment of the I2A2 learning styles

154-159

model through the I2A2 questionnaire. 147 learners showed up for this assessment and an experimental study

was conducted in e-learning environment and the findings were categories using parameter such as reason,

method, learner type, age-group, learner level, subject field/area, and learning style preferences through the

modality of I2A2 learning style model.

Keywords: learning style, e-learning, cognitive style, learner characteristics, adaptivity.

References: 1. Chrysafiadi, K., & Virvou, M. (2012). Evaluating the integration of fuzzy logic into the student model of a web-based learning

environment. Expert Systems with Applications, 39(18), 13127-13134.

2. Graf, S., & Liu, T. C. (2010). Analysis of learners' navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131.

3. Graf, S., Liu, T. C., Chen, N. S., & Yang, S. J. (2009). Learning styles and cognitive traits–Their relationship and its benefits in web-

based educational systems. Computers in Human Behavior, 25(6), 1280-1289. 4. Brusilovsky, P. (2004, May). KnowledgeTree: A distributed architecture for adaptive e-learning. In Proceedings of the 13th

international World Wide Web conference on Alternate track papers & posters (pp. 104-113). ACM.

5. Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674-681.

6. Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press.

7. Honey, P., & Mumford, A. (1992). The manual of learning styles. 8. Van Zwanenberg, N., Wilkinson, L. J., & Anderson, A. (2000). Felder and Silverman's Index of Learning Styles and Honey and

Mumford's Learning Styles Questionnaire: how do they compare and do they predict academic performance? Educational

Psychology, 20(3), 365-380.

9. Felder, R. M., & Spurlin, J. (2005). Applications, reliability, and validity of the index of learning styles. International journal of

engineering education, 21(1), 103-112.

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27

Authors: Jolly Upadhyaya, Neelu Jyoti Ahuja, Kapil Dev Sharma

Paper Title: Cloud Computing in Libraries and Higher Education: An Innovative User-Centric Quality of

Service Model

Abstract: Cloud Computing has emerged as an effective system to support the implementation and growth of

e-learning in the higher education sector, as well as in institutional libraries. This rising trend has attracted

several service providers to the market in a very short span of time. Students and faculties associated with

higher education are experiencing an increased need for the use of CC Services to aid development and

enhancement of their knowledge base and to help meet the requirement of constant access to the latest research

data. While providing such services, however, sufficient consideration and importance has not been attributed

to the Quality of Service (QoS) provided and as experienced by the user thus referenced as Quality of

Experience (QoE). Currently, no standard model exists that could effectively define the QoE parameters from

the users’ point of view. Hence it has become increasingly necessary to monitor, track, and quantify the

variables of QoE for cloud computing-based e-learning applications and develop a new QoE Metrics Model.

This information would help compare and identify the gaps between user expectations and the real QoS

experience. In the current work, various variables of the functional and runtime layers of cloud computing

applications, along with user demographic factors, and CC Service dimensions are studied. Dimension and

scope are defined using the SERVQUAL model and its factors as a baseline. The new Innovative User-Centric

Quality of Experience Metrics Model proposes to improve QoE by providing recommendations that increase

the adaptability and effectiveness of cloud computing systems.

Keywords: Cloud Computing; Higher Education; Innovative model; Metrics; Quality of Experience

References:

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28

Authors: Rahul Bhatt, Mayanak Agarwal

Paper Title: A Technological Review on Scheduling Algorithm to Improve Performance of Cloud Computing

Environment

Abstract: Cloud computing is today’s latest technology in computing for storing and managing data in IT

sector. It is the upcoming era of registering what's more, a creating processing worldview in the present day

industry, either might be government associations or people in general associations.

It is shifted from purchase of a product to pay as you go service, that is delivered to the users using internet and

datacenters to store the data, known as Cloud, to store and maintain by different cloud service providers like ;

Google, Salesforce etc. In cloud computing environment there should be an effectively and efficiently way to

access the data with minimum time and limited resources with proper security, for this purpose, we can use the

allocation and scheduling. To speed up the operations, various task scheduling techniques and algorithms were

proposed so far but still most of them are not considering both Quality of Service (QoS) and virtual machine

optimization factor, which is the upmost important parameter to satisfy the needs of user and effective

utilization of resources.

By using the cloud simulator, we can create applications using different components like Datacenters to for

cloud services, host, cloudlets, different types of users, virtual machines, and other utilities for configuration

and analyzing the cloud computing environment. This paper presented a technological survey on task

scheduling algorithms with specifying the important features on the CloudSim simulator.

Keywords: Cloud Computing, Scheduling, Task Scheduling, CloudSim, Pay as you go, QoS, CloudSim

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Applications, March 2012, Volume 3, Number 1, pp. 1-10.

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Environment” International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol. 4, No. 1, February 2014.

3. Niranjan Lal, Shamimul Qamar, Mayank Singh “Detailed Dominant Approach Cloud Computing Integration with WSN “

International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness QShine 2013 : Quality, Reliability, Security and Robustness in Heterogeneous Networks pp. 507-516. 2013.

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50-57, DOI= March/April 2011.

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for Delivering Computing as the 5th Utility, Future Generation Computer Systems 25(6), 599–616 (2009).

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166-172

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35. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, C´esar A. F. De Rose and Rajkumar Buyya “CloudSim: a toolkit for modeling

and simulation of cloud computing environments and evaluation of resource provisioning algorithms” Published online 24 August 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/spe.995

36. Saxena, P., Sharma, S. K., & Verma, N. K. An Analysis of Energy Efficiency& Traffic Using Cloud Computing In Portable Devices.

i-manager's Journal on Cloud Computing, 4(1), 1. 2017.

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29

Authors: Alok Singh

Paper Title: Review and Research Agenda on Supply Chain of Poultry and Meat Products

Abstract: I conducted a review of the literature with the purpose of revamping poultry and meat products

supply chain as a relevant research topic. I performed a review of various academic articles published in

refereed peer-reviewed international journals in domain of poultry and meat products and its supply chain

management. A review has been developed that emphasized the need for alignment of the major issues and key

aspects of poultry and meat products and its supply chain processes and the linkage between its supply chain

processes and strategy. A final sample of 22 articles out of 56 articles constituted the knowledge base of the

study published from 1998 to 2018. The scope of the research is to study the various levels and distinct forms

of poultry and meat products supply chain. Literature Survey indicated that most of research has been

conducted in the field of products having longer life cycles than the products having shorter life cycle like

perishable (poultry and meat) products. The results showed the publication pattern with respect to time and

provided the methodology, evidence about the journals and the content elements of poultry and meat products

supply chain. The research findings are applicable to a large extent for managerial decisions. There is a wide

research scope available in this area as only a limited research has been done in this field. This research work

and future researches in this field would be helpful for managers, students as Ill as academicians.

Keywords: Meat products, Poultry, Review Paper, Supply Chain.

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3. David Ubilava, Kenneth Foster, (2009), "Quality certification vs. product traceability: Consumer preferences for informational attributes of pork in Georgia", Food Policy 34, pp. 305–310.

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technologies for application within the meat industry: An overview", Meat Science 120, pp. 2–9. 5. Dimitrios Panagiotou, Athanassios Stavrakoudis, (2017), "Vertical price relationships betIen different cuts and quality grades in the

U.S. beef marketing channel: A wholesale-retail analysis", The Journal of Economic Asymmetries 16, pp. 53–63.

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Food Policy 59, pp. 44–54.

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meat production in Lombardy (Italy)", Journal of Cleaner Production 153, pp. 474-482.

17. Maitri Thakur, Eskil Forås, (2015), "EPCIS based online temperature monitoring and traceability in a cold meat chain", Computers and Electronics in Agriculture 117, pp. 22–30.

18. Sava Buncic, George-John Nychas, Michael R.F. Lee, Kostas Koutsoumanis, Michel Hébraud, Mickaël Desvaux, Nikos

Chorianopoulos, Declan Bolton, Bojan Blagojevic, Dragan Antic, (2014), "Microbial pathogen control in the beef chain: Recent research advances", Meat Science 97, pp. 288–297.

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