Editor-In-Chief Chair Dr. Shiv Kumar

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

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

(M.P.), India

Associated Editor-In-Chief Chair Dr. Dinesh Varshney

Professor, School of Physics, Devi Ahilya University, Indore (M.P.), India

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

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

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

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

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

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

Transport, Egypt

Dr. Mayank Singh

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

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

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

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

Dr. Moinuddin Sarker

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

Stamford, USA.

Dr. Shanmugha Priya. Pon

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

Africa, Tanzania

Dr. Veronica Mc Gowan

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

China.

Dr. Fadiya Samson Oluwaseun

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

Cyprus, Turkey.

Dr. Robert Brian Smith

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

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

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

Executive Editor Chair Dr. Deepak Garg

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

Executive Editor Members Dr. Vahid Nourani

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

Dr. Saber Mohamed Abd-Allah

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

Dr. Xiaoguang Yue

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

Dr. Labib Francis Gergis Rofaiel

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

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

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

Dr. Sunandan Bhunia

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

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

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

Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail

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

Technical Program Committee Members Dr. Haw Su Cheng

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

Dr. Hasan. A. M Al Dabbas

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

Dr. Gabil Adilov

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

Dr. Ch.V. Raghavendran

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

Dr. Thanhtrung Dang

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

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

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

Manager Chair Mr. Jitendra Kumar Sen

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

Editorial Chair Dr. Sameh Ghanem Salem Zaghloul

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

Editorial Members Dr. Uma Shanker

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

Dr. Rama Shanker

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

Dr. Vinita Kumar

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

Dr. Brijesh Singh

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

Ahmedabad (Gujarat), India.

S. No

Volume-8 Issue-2S7, July 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Raksha S, B G Prasad

Paper Title: Anomalous Human Activity Recognition in Surveillance Videos

Abstract: This paper is a survey on different approaches for Human Activity recognition which has utmost

significance in pervasive computing due to its many applicaions in real-life. Human-oriented problems such as

security can be easily taken care of by detecting abnormal behavior. Accurate human activity recognition in

real-time is challenging because human activities are complicated and extremely diverse in nature. The

traditional Closed-circuit Television (CCTV) system requires to be monitored all the time by a human being,

which is inefficient and costly. Therefore, there is a need for a system which can recognize human activity

effectively in real-time. It is time-consuming to determine the activity from a surveillance video, due to its size,

hence there is a need to compress the video using adaptive compression approaches. Adaptive video

compression is a technique that compresses only those parts of the video in which there is least focus, and the

rest is not compressed. The objective of the discussion is to be able to implement an automated anomalous

human activity recognition system which uses surveillance video to capture the occurrence of an unusual event

and caution the user in real-time. So, the paper has two parts that include adaptive video compression

approaches of the surveillance videos and providing that compressed video as the input to detect anomalous

human activity

Keywords: Human Activity Recognition, Adaptive Video compression, Vision-based Human Activity

Recognition, Anomaly detection.

References: 1. G. O. Young, “Synthetic structure of industrial plastics (Book style Wassima Aitfares, Abdellatif kobbane,Abdelaziz Kriouile,

“Suspicious Behaviour Detection of People by Monitoring Camera” IEEE 2016 2. Yong Shean Chong,Yong Haur Tay, “Abnormal Event Detection in Videos using Spatiotemporal Auto encoder” arXiv:1801.03149v2

[cs.CV] Jan 2017

3. Karuna B. Ovhal,Sonal S. Patange,Vaishnavi K. Tarange,Vijay A. Kotkar, “Analysis of anomaly detection techniques in video

surveillance,” IEEE conference June 2018

4. B. Zhang, L. Wang, Z. Wang, Y. Qiao, and H. Wang, “Real-time Action Recognition with Enhanced Motion Vector CNNs,” Apr.

2016 5. Sathyashrisharmilha, Pushparaj, Sakthivel Arumugam, “Using 3D Convolutional Neural Network in Surveillance Videos for

Recognizing Human Actions” The International Arab Journal of Information Technology, Vol. 15, No. 4, July 2018

6. Yancheng Bai, Huijuan Xu, Kate Saenko, Bernard Ghanem, “ Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection” arXiv:1801.09184v1 [cs.CV] 28 Jan 2018

7. Andrew D. Bagdanov, Marco Bertini, Alberto Del Bimbo, Lorenzo Seidenari, “Adaptive Video Compression For Video Surveillance

Applications” IEEE conference June 2011 8. Oluwatoyin P. Popoola, Member, IEEE, and Kejun Wang “Video-Based Abnormal Human Behaviour Recognition—A Review” IEEE

conference April 2016 9. B Ravi Kiran, Dilip Mathew Thomas, Ranjith Parakkal “An overview of deep learning-based methods for unsupervised and semi-

supervised anomaly detection in videos” arXiv:1801.03149v2 [cs.CV] 30 Jan 2018

10. Yan Zhang, He Sun, Siyu Tang, Heiko Neumann “Temporal Human Action Segmentation via Dynamic Clustering” arXiv:1803.05790v2 [cs.CV] 18 Mar 2018

11. Rensso Mora Colque, Carlos Caetano, Victor C. de Melo,Guillermo Camara Chavez and William Robson Schwartz “Novel

Anomalous Event Detection based on Human-object Interactions” In Proceedings of the 13th International Joint Conference on

Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP, pages 293-

300 ISBN: 978-989-758-290-5

12. N. Kumaran1, Dr. U. Srinivasulu Reddy "Article Location and Following in Group Condition - A Survey" Procedures of the Global Gathering on Innovative Registering and Informatics (ICICI 2017) IEEE Xplore Agreeable - Part Number: CFP17L34-Craftsmanship,

ISBN: 978-1-5386-4031-9Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9

13. Ong Chin Ann, Lau Bee Theng “Human Activity Recognition: A Review” IEEE International Conference on Control System, Computing and Engineering, 28 - 30 November 2014, Penang, Malaysia

14. Waqas Sultani, Chen Chen, Mubarak Shah “Real-world Anomaly Detection in Surveillance Videos” arXiv:1801.04264v2 [cs.CV] 31

Mar 2018 15. N. Patil, Prabir Kumar Biswas “A Survey of Video Datasets for Anomaly Detection in Automated Sureillance” Sixth International

Symposium on Embedded Computing and system Design (ISED) 2016

16. Angela A. Sodemann, Matthew P. Ross, and Brett J. Borghetti “A Review of Anomaly Detection in Automated Surveillance” IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, VOL. 42, NO. 6, November 2012

17. J. Yin and Y. Meng, “Abnormal behavior recognition using self-adaptive hidden Markov models,” in Pro. 6th Int. Conf.Image Anal.

Recognit., Jul. 6–8, 2009, pp. 337–346.Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., Zhang, Z.: Spatio-temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Processing: Image Communication 47,358-368 Sep

2016

18. Neha Gaba, Neelam Barak and Shipra Aggarwal “Motion Detection, Tracking and Classification for Automated Video Surveillance” IEEE conference April 2016

19. Vijay Mahadevan, Weixin Li, Viral Bhalodia and Nuno Vasconcelos “Anomaly Detection in Crowded Scenes” IEEE conference

March 2010 20. Ramin Mehran and Alexis Oyama “Anomaly in road side scene” IEEE conference March 2017

21. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In NIPS’14, pages 568–576,

2014. 22. V. Kantorov and I. Laptev. Efficient feature extraction, encoding, and classification for action recognition. In CVPR’14, pages 2593–

2600, 2014.

23. Yao B., Liu Z., Nie B., and Zhu S., “Animated Pose Templates for Modelling and Detecting Human Actions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, pp. 436-452, 2013.

24. Huo F. furthermore, Hendriks E., "Numerous Individuals Following and Posture Estimation with Impediment Estimation," PC Vision

and Picture Understanding, vol. 116, no. 5, pp. 634-647, 2012.

1-6

25. Huo F. and Hendriks E., “Real Time Multiple People Tracking and Pose Estimation,” in Proceedings of the 1st ACM International Workshop on Multimodal Pervasive Video Analysis, Firenze, pp. 5-10, 2010.

26. K. Simonyan and A. Zisserman. Two-stream convolutional networks for action recognition in videos. In Advances in neural

information processing systems, pages 568–576, 2014. 27. D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. “Learning spatiotemporal features with 3d convolutional networks” In

Proceedings of the IEEE international conference on computer vision, pages 4489–4497, 2015.

28. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp.2672–2680.

2.

Authors: Mr. Brijesh Singh, Dr. Praveen Kumar Sinha, Dr. N Babitha Thimmaiah

Paper Title: A Behaviour Model on Investors' Savings Pattern and Choices of Investment Options in the

Financial Market

Abstract: India has reckoned itself as the fast-paced economy in the present time. The GDP growth rate is

the indicator to define the Indian economy and growth in investors’ confidence. As per the World Economic

outlook, the IMF said India will grow 7.3% in financial year 2019 and 7.4% in financial Year 2020. India is

having the positive growth prediction in the coming year. A vibrant economy has paved the way for

development and raising the standard of living of people. Even the investors’ in the financial market has

radically changed over the period of time. Currently the investors’ think of multiplying the income and

effective utilization of savings in multiple channels of investment. An individual forfeits his present utilization

to create reserve funds which thus are put resources into different venture openings. It is fundamental for any

person to have legitimate understanding of all the significant issue which can have bearing on his investment

choices. The study will focus on understanding the relationship between investors’ savings and investment

preferences as well as developing the Investment model.

Keywords: Financial market, GDP, Investors, Annual Savings, Investment Preferences, Investment model

etc.

References: 1. L. Bakker a, W. Hareb, H. Khosravi a, B. Ramadanovicc ( 2010) , “A social network model of Investment Behaviour in the Stock

Market” Physica A 389 (2010) P. 1223- 1229.

2. A. Ganesh-Kumara, Kunal Senb and Rajendra R. Vaidyac , (2002), “Does the source of financing matter? Financial markets, financial

intermediaries and investment in India” Journal of International Development J. Int. Dev. 14, 211–228 (2002), DOI: 10.1002/jid.873 3. Shalini Kalra Sahi, (2010), “Individual investor biases: A segmentation analysis”, Qualitative Research in Financial Markets Vol. 4

No. 1, 2012 pp. 6-25.

4. Suman Chakraborty, Dr.Sabat Kumar Digal, (2010), “A study of saving and investment behaviour of individual Households – an empirical evidence from Orissa” http://ssrn.com/abstract=2168305, pp.1-19

5. Mandeep Kaura, Tina Bohrab, (2012), “Understanding Individual Investors Behavior: A Review of Empirical Evidences”, pacific business Review International, Vol. 5, Issue 6, December, pp. 10-18

6. Thomas J. Flavina, Margaret J. Hurleyb and Fabrice Rousseauc, (2001), “Explaining Stock Market Correlation: A Gravity Model

Approach” National University of Ireland, Maynooth, pp.1-26

7-11

3

Authors: Padma Srinivasan, Punit Cariappa and M.D.Saibaba

Paper Title: Audit committees, Auditors and Corporate Governance: A theoretical bricolage for epistemological

guidance correlating with the Indian context

Abstract: The last two decades offer a pantheon of business failures, which drove modern corporate

governance reforms, is demanding a strong assurance function, particularly from Auditors. This paper

examines both qualitatively and quantitatively the ramifications of interfaces among the corporate triad

members-Audit committee, External auditors and Internal Auditors. Focused on the backstage of corporate

governance fiascos such as Carrilion, IL&FS and others, an attempt is made to understand through the Meta

study their encumbrance. It is observed from this study is that ,while the plethora of reforms are premised on

Blue Ribbon Committee’s recommendation, Shareholder activism has brought in paradigm changes in the CG

landscape, particularly the activities of the committees and the Auditors. The notable one is what we term it as

“Carrilion effect”. Aftermath of Carrilion’s Corporate Governance fiasco, investors are fueling calls for the

breakup of the Big 4 audit firms. Another critical observation is that globally, revenue from consulting and

advisory services have risen dramatically vis-a vis the marginal increases in audit revenues. It is drawing flak

from the investors and regulators. From the flip side perspective, qualitatively in the Indian context, the results

of regressions shows that independent audit committees are perceived to be adding value over a short periods

of time, as reflected by Tobin’s Q, a proxy measure of financial performance .

Keywords: Corporate Governance, Audit committees, Auditors, Tobin’s Q, Firm performance

References: 7. Aloke Ghosh, Antonio Marra Doocheol Moon (2010), Corporate Boards, Audit Committees, and Earnings Management: Pre and

Post SOX Evidence Overseeing the External Audit Function:Evidence from Audit Committees' ReportedActivities;https://doi.org/10.1111/j.1468-5957.2010.02218. 37(9), 1145-1176.

8. Blue Ribbon Committee (1999), “Report And Recommendations of the Blue Ribbon Committee on Improving the Effectiveness of

Corporate Audit Committees”, The New York Stock Exchange and the National Association of Securities Dealers, New York. 9. Bratten, B., Payne, J. L., & Thomas, W. B. (2016). Earnings management: Do firms play “follow the leader”? Contemporary

Accounting Research, 33(2), 616-643.

10. Bratten, B., Causholli,M., & Sulcaj,V. (2019).Overseeing the external audit function: Evidence from audit committees’ reported activities. SSRN 3314334 - papers.ssrn.com

11. Christensen, B. E., S. M. Glover, T. C. Omer, and M. K. Shelley. (2016). Understanding audit quality: Insights from audit

professionals and investors. Contemporary Accounting Research, 33 (4), 1648-1684. 12. Cohen, J. R., U. Hoitash, G. Krishnamoorthy, and A. M Wright. (2014).The effect of audit committee industry expertise on monitoring

12-16

the financial reporting process. The Accounting Review 89 (1), 243-273. 13. Cohen, J.,G. Krishnamoorthy, and A. Wright (2010). Corporate governance in the post-Sarbanes-Oxley era: Auditors' experiences.

Contemporary Accounting Research 27 (3), 751-786.

14. DeZoort, F.,Todd Dana, R., Hermanson, B and Richard, W.Houstona.(2002).Audit committee support for auditors: The effects of materiality justification and accounting precision, Journal of Accounting and Public Policy, 22 (2), 175-199

15. DeZoort, F.,Todd,Dana, R., Hermanson, Deborah, S. Archambeault, and Scott A. Reed.(2006). Audit Committee Effectiveness: A

Synthesis of the Empirical Audit Committee Literature, Journal,21(1),38-75 16. Fama, E. F., and M. C. Jensen. 1983. Separation of ownership and control. The Journal of Law & Economics 26 (2), 301-325.

17. Gal-Or, R., R. Hoitash, and U. Hoitash.(2017).Shareholder elections of audit committee members: A Journal of Practice & Theory

(forthcoming). 18. Healy, P. M., and K. G. Palepu.(2001). Information asymmetry, corporate disclosure, and the capital markets: A review of the

empirical disclosure literature. Journal of Accounting and Economics 31 (1), 405-440.

19. Jean Be´dard, Sonda, C., and Lucie, C. (2004).The Effect of Audit Committee Expertise, Independence, and Activity on Aggressive Earnings Management. AUDITING: A Journal of Practice & Theory, 23(2), 13-35.

20. Cohen,J.,Ganesh.K.,Wright.A.M.(2010).Corporate Governance and the Audit Process .Contemporary Accounting research,19,(4),

573-594 21. Jensen, M. C., and W. H. Meckling. (1976).Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of

Financial Economics 3 (4), 305-360.

22. Reid, L. C., J. V. Carcello, C. Li, and T. L. Neal. (2018). Impact of auditor and audit committee report changes on audit quality and costs: Evidence from the United Kingdom. Contemporary Accounting Research (Forthcoming).

23. Spence, M. (1973). Job market signaling. The quarterly journal of Economics 87 (3):355-374.

24. Shepardson, Marcy L. (2018).Accounting, Organizations and Society Effects of individual task-specific experience in audit committee

oversight of financial reporting outcomes https://doi.org/10.1016/j.aos.

25. Turley,S.,and M. Zaman, (2003), Public Policy on Corporate Audit Committees: Case Study Evidence of Current Practice. Occasional

Research Paper No. 35, Association of Chartered Certified Accountants, London.

4

Authors: RATAN SINGH SOLANKI, BHUPAL BHATTACHARYA

Paper Title: Right to Dignity and Human Rights: Tracing the Values from Indian Perspective

Abstract: The present paper intends to focus on correlation of human dignity with human rights in Indian

philosophical perceptive. The philosophy of India as a nation and Hinduism as a major religion sets a platform

for the origin of human dignity and human rights and their correlation. In Hinduism, the importance of human

dignity is evident from the fact that human beings are introduced as Amritasya Putrah Vayam – meaning

thereby, we are all begotten of the immortal.

The understanding of human identity and dignity is more ethical-spiritual than material. Right from the Vedic

times, an invisible Atman - the Soul; Paramaatman - the Divine whole and ‘Chetna’ - Universal oneness

always find mention in Hindu classical thought. Lastly the ideal of Vasudhaiva Kutumbakam – the whole

world as One Family – also becomes unique in this age of Globalizations. In present age what we are actually

achieving is not Globalization, but Mc Donaldization.

Keywords: The paper is purely conceptual and only available literatures have been taken in updating the

paper following the doctrinal method of study.

References: 1. Ahmad, I., Ghosh, P. S., & Reifeld, H. (Eds.). (2000). Pluralism and Equality: Values in Indian Society and Politics. SAGE

Publications India. 2. Arendt, H. (2013). The human condition. University of Chicago Press.

3. Barth, K. (1960). The humanity of God. Westminster John Knox Press.

4. Benhabib, S. (2012). Is there a human right to democracy? Beyond interventionism and indifference. In Philosophical Dimensions of Human Rights (pp. 191-213). Springer, Dordrecht.

5. Bhargava, R. (2007). The distinctiveness of Indian secularism. The Future of Secularism, Oxford University Press, New York.

6. Bhatia, V. P. (2017). The Upanishads Demystified: Ethical values. Notion Press. 7. Bhawuk, D. P. (2012). India and the culture of peace: Beyond ethnic, religious, and other conflicts. In Handbook of ethnic conflict (pp.

137-174). Springer, Boston, MA.

8. Bishop, T., & Grau, D. (Eds.). (2018). Ways of Re-thinking Literature. Routledge. 9. Broughton, R., up as a Flower, C., Good-bye, S., Nancy, J., but too Well, N. W., Norris, W., ... & Max, U. (1891). Messrs. Methuen's

new books. R. Pryce. The quiet mrs. Fleming. By Richard TRYCE. Crown 8vo,.'is. 6d. [Ready. The Academy and Literature, 39,

248. 10. Brownsword, R. (2002). An interest in human dignity as the basis for genomic torts. Washburn LJ, 42, 413.

11. Chesler, P. (2018). Women and madness. Chicago Review Press. 12. Corbridge, S., & Harriss, J. (2013). Reinventing India: Liberalization, Hindu nationalism and popular democracy. John Wiley & Sons.

13. Dembour, M. B. (2010). What are Human Rights-Four Schools of Thought. Hum. Rts. Q., 32, 1.

14. Donnelly, J. (2013). Universal human rights in theory and practice. Cornell University Press. 15. Feinberg, J., & Narveson, J. (1970). The nature and value of rights. The Journal of Value Inquiry, 4(4), 243-260.

16. Feuerstein, G. (2014). The Hindu Experience and Perspective. The World's Great Wisdom: Timeless Teachings from Religions and

Philosophies, 87. 17. Flowers, N. (2000). The Human Rights Education Handbook: Effective Practices for Learning, Action, and Change. Human Rights

Education Series, Topic Book. Human Rights Resource Center, University of Minnesota, 229 19th Avenue South, Room 439,

Minneapolis, MN 55455. 18. Galanter, M. (1971). Hinduism, secularism, and the Indian judiciary. Philosophy East and West, 21(4), 467-487.

19. Glensy, R. D. (2011). The right to dignity. Colum. Hum. Rts. L. Rev., 43, 65.

20. Glensy, R. D. (2011). The right to dignity. Colum. Hum. Rts. L. Rev., 43, 65. 21. Gore, M. S. (1997). Unity and Differentiation.

22. Hanegraaff, W. J. (1996). New age religion and western culture: Esotericism in the mirror of secular thought (Vol. 72). Suny Press.

23. Hinduism is a way of life: Firth, S. (2005). End-of-life: a Hindu view. The Lancet, 366(9486), 682-686. 24. Lebech, M. (2004). What Is Human Dignity? Maynooth Philosophical Papers.

25. Lebech, M. (2004). What is human dignity?. Maynooth philosophical papers, 2, 59-69.

26. Mason, R. A. (Ed.). (2018). John Knox and the British Reformations. Routledge. 27. Mason, R. A. (Ed.). (2018). John Knox and the British Reformations. Routledge.

28. Moses, O. M. Inter-religious dialogue.

29. Motwani, K. (1934). Manu: A study in Hindu social theory. Ganesh and Company, Madras.

17-20

30. Motwani, K. (1934). Manu: A study in Hindu social theory. Ganesh and Company, Madras. 31. Nussbaum, M. C. (2001). Women and human development: The capabilities approach (Vol. 3). Cambridge University Press.

32. Osler, A., & Starkey, H. (2017). Teacher education and human rights. Routledge.

33. Parekh, B. C. (1989). Gandhi’s political philosophy: A critical examination. Springer. 34. Radhakrishnan, S. (1926). Hindu view of life. George Allen And Unwin Ltd, London.

35. Rorty, R. (1993). Human rights, rationality, and sentimentality. Wronging Rights?: Philosophical Challenges for Human Rights, 1-34.

36. Satpathy, G. Value Education: Relevance of Gandhian Concept. DDCE, UTKAL UNIVERSITY, BHUBANESWAR, INDIA. 37. Sharma, M. (2017). Caste and nature: Dalits and Indian Environmental Policies. Oxford University Press.

38. Singer, P. (Ed.). (2013). A companion to ethics. John Wiley & Sons.

39. Sinha, M. K. (2005). Hinduism and international humanitarian law. International Review of the Red Cross, 87(858), 285-294. 40. Stern, J. (2018). Teaching religious education: Researchers in the classroom. Bloomsbury Publishing.

41. Subedi, S. P. (1999). Are the Principles of Human Rights Western Ideas-An Analysis of the Claim of the Asian Concept of Human

Rights from the Perspectives of Hinduism. Cal. W. Int'l LJ, 30, 45. 42. Taylor, C. (1999). Conditions of an unforced consensus on human rights. The politics of human rights, 101-119.

43. Varma, P. K. (2005). Being Indian: the truth about why the twenty-first century will be India's. Penguin Books India.

5

Authors: Kagolanu Trishul, Srinath R. Naidu

Paper Title: Helpfulness Prediction of Product Assessments using Machine Learning

Abstract: Paper Customers express their opinion on products through reviews. Since there will be a lot of

reviews that will be posted, only those reviews which are helpful should be made accessible to the customer.

Hence, helpfulness of review needs to be predicted. This work categorizes the features into reviewer, review

text and review metadata. Machine Learning algorithms Linear Regression and Random Forests are used for

prediction of helpfulness using these features. It is observed that rating of a review has the highest influence on

predicting helpfulness followed by user average rating deviation, difficult words and positive words. This work

defines the features such as stem sim length and lem sim length which are derived from the product description

which have performed reasonably well. Using all the features with Random Forests algorithm for prediction

gave the best performance in automatically predicting helpfulness.

Keywords: helpfulness prediction, lem sim length, machine learning, random forests, stem sim length.

References: 1. Malik, M. S. I. and Ayyaz Hussain. “An analysis of review content and reviewer variables that contribute to review helpfulness.” Inf.

Process. Manage. 54: 88-104, 2018.

2. Saumya, Sunil, Jyoti Prakash Singh, Abdullah M. Baabdullah, Nripendra P. Rana and Yogesh Kumar Dwivedi. “Ranking online consumer reviews.” Electronic Commerce Research and Applications 29: 78-89, 2018.

3. Park, Yoon-Joo “Predicting the helpfulness of online customer reviews across different product types”, Sustainability, 10. 1735, 2018. 4. Y.K. Chua, Alton & Banerjee, Snehasish “Helpfulness of user-generated reviews as a function of review sentiment, product type and

information quality”, Computers in Human Behavior, 54. 547-554, 2016.

5. Y. Zhang and D. Zhang, "Automatically predicting the helpfulness of online reviews," Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014), Redwood City, CA, pp. 662-668, 2014.

6. Mudambi, S. M., & Schuff, D. “What makes a helpful review? A study of customer reviews on Amazon. com.”, MIS Quarterly, 34(1),

185–200, 2010. 7. Y. Liu, X. Huang, A. An and X. Yu, "HelpMeter: a nonlinear model for predicting the helpfulness of online reviews," 2008

IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, NSW, pp. 793-796, 2008.

8. R. He, J. McAuley, “Modeling the visual evolution of fashion trends with one-class collaborative filtering.”, WWW, 2016 9. J. McAuley, C. Targett, J. Shi, A. van den Hengel, “Image-based recommendations on styles and substitutes.”, SIGIR, 2015

10. Merton, R.K., The Matthew effect in science: the reward and communication systems of science are considered. Science 159 (3810),

56–63, 1968. 11. Kim, S.M.; Pantel, P.; Chklovski, T.; Pennacchiotti, M. Automatically assessing review helpfulness.In Proceedings of the 2006

Conference on Empirical Methods in Natural Language Processing, Sydney, Australia; pp. 423–430, 22–23 July 2006 .

12. A. Mukherjee, V. Venkataraman, B. Liu, and N. Glance, “What yelp fake review filter might be doing?”, 2014. 13. X. Li, L. Xie, F. Zhang and H. Wang, "Online Deceptive Product Review Detection Leveraging Word Embedding," 2017 IEEE 15th

Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf

on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), Orlando, FL, pp. 867-870, 2017.

14. Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." Proceedings of the ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining (KDD-2004), Seattle, Washington, USA, Aug 22-25, 2004. 15. Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing and Comparing Opinions on the Web." Proceedings of the

14th International World Wide Web conference (WWW-2005), Chiba, Japan, May 10-14, 2005.

16. Pedregosa F. , Varoquaux G. , Gramfort A. , Michel V., Thirion B. , Grisel O. , Blondel M. , Prettenhofer P. , Weiss R. , Dubourg V. , V,erplas J. , Passos A. , Cournapeau D. , Brucher M. , Perrot M. , Duchesnay E.,"Scikit-learn: Machine Learning in Python", Journal

of Machine Learning Research, Volume 12, 2825-2830, 2011

17. Eltorai, Adam & S Naqvi, Syed & Ghanian, Soha & Eberson, Craig & Weiss, Arnold Peter & Born, Christopher & Daniels, Alan, "Readability of Invasive Procedure Consent Forms", Clinical and translational science,2015.

18. Pandey S., M S., Shrivastava A., “Data classification using machine learning approach”, In: Thampi S., Mitra S., Mukhopadhyay J., Li

KC., James A., Berretti S. (eds) Intelligent Systems Technologies and Applications. ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. Springer, Cham, 2018.

19. V. Vishagini and A. K. Rajan, "An improved spam detection method with Weighted Support Vector Machine," 2018 International

Conference on Data Science and Engineering (ICDSE), Kochi, pp. 1-5, 2018.

21-28

6

Authors: Shalini R, Dr. Mahua Biswas

Paper Title: CAPITAL STRUCTURE DETERMINANTS OF S&P BSE 500: A PANEL DATA RESEARCH

Abstract: The paper identifies the most important factors specific to companies which impacts on the capital

structure of 416 companies belonging to 14 industrial sectors listed in S&P BSE 500 for a duration of 19 years

which is from 2000 to 2018. Multi regression model is used to understand the influence of select variables on

capital structure. The study finds that 4 explanatory variables like firm size, tax paid, depreciation to total

assets ratio and profitability ratio are statistically significant capital structure determinants.

Keywords: Capital structure, financial leverage, firm size, tax paid, profitability

References: 1. Abe de Jong, Rezaul Kabir, Thuy Thu Nguyen, Capital structure around the world: The roles of firm- and country-specific

determinants, Journal of Banking & finance, vol.32, No.9, 2007.

2. Brealey, R.A., & Myers, S.C, Principles of Corporate Finance. McGraw-Hill, New York,1991

3. Graham J. and Harvey C. ‘How do the CFO’s make Capital Budgeting and the Capital Structure Decisions? The Journal of Applied

Corporate Finance, Volume 15, No 1, p 8-23, 2002

4. Joshua Abor, Determinants of the Capital Structure of Ghanaian Firms, AERC Research Paper 176, AERC, Nairobi, ISBN 9966-778-23-3, 2008

5. Joy Pathak, What Determines the Capital Structure of Listed Firms in India? Some Empirical Evidences from Indian Capital Market,

http://ssrn.com/abstract=1561145. 6. Kavitha R, Determinants of Capital Structure: Empirical Evidence from India, IJAR, Volume : 4, Issue : 7, ISSN - 2249-555X, 2014

7. Keshar J. Baral, Determinants of Capital Structure: A Case Study of the Listed Companies of Nepal, The Journal of Nepalese Business

Studies Vol. I No. 1,2004 8. Laurence Booth, VaroujAivazian, AsliDemirguc –Kunt& Vojislav Maksimovic, Capital structure in the Developing Countries, The

Journal of Finance, Vol LVI, No. 1,2001

9. Michael Angelo Cortez, Stevie Susanto, Determinants of corporate capital structure: Evidence from Japanese manufacturing companies, Journal of International Business Research, Volume 11, Special Issue, Number 3, 2012

10. Miller, Merton and Modigliani, Franco The Cost of Capital, Corporation Finance, and the theory of Investment, American Economic

Review, 48:261–297. 15, .1958 11. Miller, M. H. and Modigliani, F. Dividend Policy, Growth and the Valuation of Shares, Journal of Business, 34, pp 411-33, 1961

12. Mouna Amraoui, Ye Jianmu, Kenza Bouarara, Firm’s Capital Structure Determinants and Financing Choice by Industry in Morocco,

International Journal of Management Science and Business Administration, vol. 4, issue 3, pp. 41-51, 2018 13. Murray Z. Frank and Vidhan K. Goyal Capital Structure Decisions: Which Factors are Reliably Important?, Financial Management,

38-1, 1-37, 2007

14. Pandey IM, Capital structure and Market Power Interaction: Evidence from Malaysia, Capital Market Review, Vol. 10, No.1, pp.23-40, 2002

15. Ram kumarkakani and V N Reddy, Econometric analysis of the capital structure determinants, IIM, Calcutta, working paper series.

WPS No.333, ,1998 16. Raghuram G. Rajan & Luigi Zingales, What do we know about capital structure? Some evidence from International data, The Journal

of Finance, Vol. L, No. 5, 1995

17. Rasoolpur Gurnam Singh, An empirical analysis of capital structure determinants: Evidence from the Indian Corporate sector, International Journal of Management and Information Technology, vol.1, No. 3, 2012

18. Riyazahmed K, Determinants of Capital structure: A case study of automobile manufacturing companies listed in NSE, International

Journal of Marketing, Financial service and marketing research, Vol.1, No. 4, 2012 19. Sheltunkova Maria, Capital structure of private pharmaceutical companies in Russia, International Journal of Economic and

Management, 8 (2): 315 – 325, 2014

20. Sheridan Titman & Roberto Wessels, The Determinants of Capital Structure Choice, The Journal of Finance, Vol.XLIII, No.1, 1988 21. Sinha Pankaj & Bansal VishakaOnline at http://mpra.ub.uni-muenchen.de/49878, 2013

22. Singh, Priyanka and Kumar Brajesh Singh, Priyanka and Kumar, Brajesh, Trade Off Theory or Pecking Order Theory: What Explains the Behavior of the Indian Firms 2008

23. Soumitra N Bhaduri, Determinants of Capital structure choice: A study of the Indian corporate sector, Journal of Applied Financial

economics, Volume12, issue 9, 2002 24. Turki SF Alzomaia,, Capital structure determinants of publicly listed companies in Saudi Arabia, The International journal of

Business and Finance Research, Vol.8, No.2, 2014.

25. Shalini R, Dr. Mahua Biswas, Empirical study on the capital structure decisions of select pharmaceutical companies in India, IOSR-JBM, e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 19, Issue 5. Ver. II (May. 2017), PP 26-30

26. Shalini R, Dr. Mahua Biswas, Impact of Capital Structure Decisions on the Operating Performance of Select Companies in Power

Sector of India – A Correlation Analysis, IJEMR, vol.6, issue 3, 2016 27. Shalini R, Dr. Mahua Biswas, Capital structure determinants of FMCG companies listed in S&P BSE 500: A Panel data analysis,

published in International Journal of Research and Analytical Reviews, special issue, Feb 2019

29-32

7

Authors: Marinal Gupta, Sarang Narula

Paper Title: Consumer-Complainant’s Contentment with Reference to Performance of Consumer Dispute

Redressal Machinery

Abstract: Recognizing the significance of customer retention and creation, customer centric firms are

continuously and systematically measuring customer satisfaction in terms of how well their customers are

being treated and what are the factors influencing customer satisfaction level. Even when conscious efforts are

being made by the business to keep its customers satisfied, there may be an instance where a consumer instead

of getting his/her grievance resolved with the vendor / service provider approaches the consumer forum i.e.

third party for resolution of grievance. An important question that arises in such scenario is that whether such

aggrieved persons are satisfied with the offering of the forum or not. Moreover, in today’s dynamic and digital

world, third party complaints are on continuous rise. The present study focuses on identifying various factors

which influence the satisfaction level of consumer complainants. Through well designed questionnaire,

involving likert scale statements related to distributive, procedural and interactional justice, responses have

been collected from 300 respondents from the district of Ludhiana from the State of Punjab. Analysis of

responses, using factor analysis technique, has enabled the identification of factors such as convenience and

cost, behavioural aspect and credentials of the personnel working with consumer dispute redressal forums,

established under Consumer Protection Act 1986.

Keywords: customer, customer satisfaction, consumer complainant, Complainant satisfaction, consumer

dispute redressal forum.

References: 1. Halstead Diane, Jones Michael A and Cox April N, 'Satisfaction and the disadvantaged consumer, 'Journal of consumer satisfaction,

dissatisfaction and complaining behaviour’, Vol. 20, 2013, p.p. 15-35.

2. Jeffrey G. Blodgett, Donald H. Granbois, Rockney G. Walters, The effects of perceived justice on complainants' negative word-of-

33-37

mouth behavior and repatronage intentions, Journal of Retailing, Volume 69, Issue 4, Winter 1993, Page 399-428. 3. Orsingher, Chiara & Valentini, Sara & Deangelis, Matteo. (2009). A meta-analysis of satisfaction with complaint handling in services.

Journal of the Academy of Marketing Science. 38. 2009, 169-186.

4. Shannon Andetson, Lisa Klein Pearo and Sally K. Widener, 'Drivers of service satisfaction: linking customer satisfaction to the service concept and customer characteristics, 'Journal of service research’, Vol. 10, no.4, may 2014, pp.365-381.

5. Warsame Bashir A., “The impact of consumer complaints handling on complainant satisfaction and behavioral outcomes: A study

among the telephone service providers in mogadishu, Somalia”, Research report, 2008. 6. Zussman D. (1983) “Consumer Complaint Behavior and Third Party Meditation” Canadian Public Policy, Vol. 9, No.2 (Jun 1983),

pp.223-235.

8

Authors: Banuprakash. R, Hariprasad. S.A

Paper Title: A Metamaterial Inspired, Slotted Multiband Patch Antenna with Reconfigurability

Abstract: In this letter the antenna is designed for achieving the multiband frequency configuration with the

dimension of 26*26*1.6 mm3 with the use of substrate of dielectric constant of 4.4. It is capable of operating

at the frequency of 3.9 GHz,5.8GHz and 6.7GHz, with a gain of 2.9dB,4.6dB,-1.5dB respectively. By using

the method like DGS, Slots and SSRR structure, the design is able to generate and operate at the above

mentioned frequencies. Furthermore by placing a metallic switch on the rectangular shaped slot the proposed

antenna can also be used as reconfigurable antenna to produce different frequency.

Keywords: Slots, DGS, Reconfigurability, HFSS.

References: 1. Ali,Tanweer, K. Durga Prasad, and Rajashekhar C. Biradar. "A miniaturized slotted multiband antenna for wireless

applications." Journal of Computational Electronics (2018): 1-15.

2. Boukarkar, Abdelheq, Xian Qi Lin, Yuan Jiang, and Yi Qiang Yu. "Miniaturized single-feed multiband patch antennas." IEEE

Transactions on Antennas and Propagation 65, no. 2 (2017): 850-854. 3. Saraswat, Ritesh Kumar, and Mithilesh Kumar. "Miniaturized slotted ground UWB antenna loaded with metamaterial for WLAN and

WiMAX applications." Progress In Electromagnetics Research 65 (2016): 65-80.

4. Park, Seong-Ook, Viet-Anh Nguyen, and Rao Shahid Aziz. "Multi-band, dual polarization, dual antennas for beam reconfigurable antenna system for small cell base Station." In Antenna Technology:" Small Antennas, Novel EM Structures and Materials, and

Applications"(iWAT), 2014 International Workshop on, pp. 159-160. IEEE, 2014.

5. Ali, Tanweer, Mohammad Saadh Aw, and Rajashekhar C. Biradar. "A fractal quad-band antenna loaded with L-shaped slot and metamaterial for wireless applications." International Journal of Microwave and Wireless Technologies (2018): 1-9.

6. Banuprakash. R, Hariprasad. S.A.”A Compact Multi-band Rectangular Slot Microstrip Antenna for Wi-MAX, WLAN and X-band

Applications. “International Journal of Engineering &Technology(IJET),2018

38-42

9

Authors: Mr. Souvik Roy, Dr. N.K. Chakrabarti, Dr. Bhupal Bhattacharya

Paper Title: Inchoate aspects of Attempted Crimes: Revisiting Criminal Law for its effective Management

Abstract: Actus Reus is known as the external element of the objective component of Criminal Law. Mens

Rea, the guilty intention, determines the criminal responsibility. Mens Rea and Actus Reus both are the

components of a criminal activity that determines the liability of the accused person.

An action carried out in furtherance of criminal activity doesn’t become an attempted crime unless it is

confirmed by the illegality for which it was conducted. An attempted crime is an action that reveals the illegal

intention on its face.

The aspects of a crime such as the Mens Rea, Actus Reus, intentional crime, unintentional act caused as a

result of carelessness, motivates to indulge in violating the provisions of law. The four theories of law such as

the rule of proximity, the test of unequivocally, the indispensable element approach and the test of social

danger are the elements of a crime.

Keywords: Attempt; inchoate; criminal law; Mens Rea; deterrence; culpability

References: 1. Ashworth, A. (1987). Criminal Attempts and the Role of Resulting Harm under the Code, and in the Common Law.Rutgers LJ, 19,

725. 2. Ashworth, A. (1987). Criminal Attempts and the Role of Resulting Harm under the Code, and in the Common Law.Rutgers LJ, 19,

725. 3. Ashworth, A. (1987). Criminal Attempts and the Role of Resulting Harm under the Code, and in the Common Law.Rutgers LJ, 19,

725.

4. Ballantine, H. W. (1918). Criminal Responsibility of the Insane and Feeble Minded. J. Am. Inst. Crim. L. & Criminology, 9, 485. 5. Ben-Shahar, O., &Harel, A. (1996). Economics of the law of criminal attempts: A victim-centered perspective. U. Pa. L. Rev., 145,

299.

6. Ben-Shahar, O., &Harel, A. (1996). Economics of the law of criminal attempts: A victim-centered perspective. U. Pa. L. Rev., 145, 299.

7. Braithwaite, J. (1989). Crime, shame and reintegration.Cambridge University Press.

8. Brennan Jr, W. J. (1963). The Criminal Prosecution: Sporting Event or Quest for Truth. Wash. ULQ, 279. 9. Buell, S. W. (2006). The blaming function of entity criminal liability. Ind. LJ, 81, 473.

10. Cook, P. J. (1980). Research in criminal deterrence: Laying the groundwork for the second decade. Crime and justice, 2, 211-268.

11. Cornish, D. B., & Clarke, R. V. (1987). Understanding crime displacement: An application of rational choice theory. Criminology, 25(4), 933-948.

12. Cornish, D. B., & Clarke, R. V. (2003). Opportunities, precipitators and criminal decisions: A reply to Wortley's critique of situational

crime prevention. Crime prevention studies, 16, 41-96. 13. Damaska, M. R. (1986). The faces of justice and state authority: a comparative approach to the legal process. Yale University Press.

14. Duff, R. A. (2013).Criminal attempts.International Encyclopedia of Ethics.

15. Duff, R. A. (2013).Criminal attempts.International Encyclopedia of Ethics. 16. Enker, A. N. (1968). Impossibility in Criminal Attempts--Legality and the Legal Process. Minn. L. Rev, 53, 665.

43-47

17. Evans, G. (2009). The responsibility to protect: ending mass atrocity crimes once and for all. Brookings Institution Press. 18. Feinberg, K. R. (1980). Toward a new approach to proving culpability: Mens rea and the proposed Federal criminal code. Am. Crim.

L. Rev., 18, 123.

19. Hall, S., Critcher, C., Jefferson, T., Clarke, J., & Roberts, B. (2013).Policing the crisis: Mugging, the state and law and order.Macmillan International Higher Education.

20. Hoeber, P. R. (1986). The Abandonment Defense to Criminal Attempt and Other Problems of Temporal Individuation. Calif. L. Rev.,

74, 377. 21. Holmes Jr, O. W. (2009).The path of the law.The Floating Press.

22. Kadish, S. H. (1993). The criminal law and the luck of the draw.J. Crim. L. & Criminology, 84, 679.

23. Katz, L. (2012). Bad acts and guilty minds: Conundrums of the criminal law. University of Chicago Press. 24. Keedy, E. R. (1908). Ignorance and mistake in the criminal law.Harv. L. Rev., 22, 75.

25. Kenny, C. S. (1922). Outlines of Criminal Law: Based on Lectures Delivered in the University of Cambridge. University Press.

26. Miller, J. M. (2001). Mens rea quagmire: The conscience or consciousness of the criminal law. W. St. UL Rev., 29, 21. 27. Mitsilegas, V. (2003).from Empirical to Legal: The Ambivalent Concept of Transnational Organized Crime. Critical reflections on

transnational organized crime, money laundering and corruption, 55.

28. Mooney, C. (2014). Misdemeanor Prosecution: Your Legal Rights. The Rosen Publishing Group, Inc. 29. Mueller, G. O. (1957). On Common Law Mens Rea. Minn. L. Rev., 42, 1043.

30. Packer, H. (1968).The limits of the criminal sanction.Stanford University Press.

31. Robinson, P. H., & Darley, J. M. (1995). Justice, liability, and blame: Community views and the criminal law. 32. Smith, J. C. (1957). Two problems in criminal attempts. Harvard Law Review, 70(3), 422-448.

33. Strahorn, J. S. (1930). The Effect of Impossibility on Criminal Attempts. University of Pennsylvania Law Review and American Law

Register, 78(8), 962-998.

34. Warr, M. (2002). Companions in crime: The social aspects of criminal conduct. Cambridge University Press.

35. Wechsler, H., Jones, W. K., &Korn, H. L. (1961). The Treatment of InchoateCrimes in the Model Penal Code of the American Law

Institute: Attempt, Solicitation, and Conspiracy. Colum. L. Rev., 61, 957.

10

Authors: Geetha R, Bindhu A R

Paper Title: Implications of Perceived Benefits and Snags on Consumer Attitude towards Insurance Policies

Abstract: Insurance industry in India is relied upon to contribute US $280 billion by 2020.This portends a

significant leap for Insurance companies in India. The increased awareness, ever expanding distribution

channels, improved service quality and innovative products are all contributing to the robustness of Insurance

business in India. Besides these, insurance companies in India have an enormous potential that is under tapped.

This is significantly evident in the existing penetration rate which is meagre 3.69%. Insurance industry in India

has witnessed tremendous changes in the past decade, attributed to the dynamic changes in micro and macro

factors such as- booming competition, changing demographic profile of the customers, government regulations,

product innovation and technology interface. In the ensuing study an effort is made to identify and discuss the

factors fostering or encumbering consumers from purchasing policies of Insurance companies. By adopting a

diagnostic research design and survey technique, data was collected from around 124 sample elements that

were chosen randomly using probability sampling method. Among the ten perceived benefits, high rate of

return, tax benefits and policy tenure were found to be the key determinants positively influencing the buying

behaviour of consumers. Similarly cumbersome terms and conditions, lack of transparency and procedural

delays were found to be the factors that impede purchase action of consumers.

Keywords: Policy Purchase Behaviour, Perceived Benefits, Snags, Consumer Attitude

References:

1. Aaker, D. A., & Shansby, J. G. (1982). Positioning your product. Business horizons, 25(3), 56-62.

2. Augustine, R., & Chandrasekar, K. S. (2011). An empirical study on marketing orientation employed by

life insurance companies in Kerala, India. Journal of Marketing and Management, 2(2), 91- 102,104-107.

3. Ann E. Schlosser, Tiffany Barnett White, and Susan M. Lloyd (2006) Converting Web Site Visitors into

Buyers: How Web Site Investment Increases Consumer Trusting Beliefs and Online Purchase

Intentions.Journal of Marketing: April 2006, Vol. 70, No. 2, pp. 133-148.

4. Bellman, S., Lohse, G. L., & Johnson, E. J. (1999). Predictors of online buying behavior. Communications

of the ACM, 42(12), 32-38.

5. Bala, H. S. N. (2011). Customers' perception towards service quality of lifeInsurance Corporation of India:

A Factor Analytic Approach. International Journal of Business and Social Science, 2(18)

6. Brown, J. R., & Goolsbee, A. (2002). Does the Internet make markets more competitive? Evidence from

the life insurance industry. Journal of political economy, 110(3), 481-507.

7. Bridges, E., & Florsheim, R. (2008). Hedonic and utilitarian shopping goals:The online experience. Journal

of Business Research, 61(4), 309-314.

8. Berry, L. L., Wall, E. A., & Carbone, L. P. (2006). Service clues and customer assessment of the service

experience: Lessons from marketing. The Academy of Management Perspectives, 20(2), 43- 57.

9. Crosby, Lawrence. A., & Stephens, N. (1987). Effects of relationship marketing on satisfaction, retention,

and prices in the life insurance industry Journal of marketing research, 404-411

10. Choudhuri, P. S., & Parida, B. B. (2014). Evaluation of Customers Expectation-Perception Score on

Service Quality in Life Insurance Corporation of India. International Journal of Marketing & Business

Communication, 3(3).

48-52

11. Dutta, G., Basu, S., & John, J. (2010). Development of utility function for life insurance buyers in the

Indian market. Journal of the Operational Research Society, 61(4), 585-593

12. Devasenathipathi, T., Saleendran, P. T., & Shanmugasundaram, A. (2007). A Study on Consumer

Preference and Comparative Analysis of All Life Insurance Companies. Icfai Journal of Consumer

Behavior, 2(4).

13. Durvasula, S., Lysonski, S., Mehta, S. C., & Tang, B. P. (2004). Forging relationships with services: the

antecedents that have an impact on behavioural outcomes in the life insurance industry. Journal of Financial

Services Marketing, 8(4), 314-326.

14. Evans, F. B. (1963). Selling as a dyadic relationship-A new approach. The American Behavioral

Scientist, 6(9), 76.

15. Gayathri, H., Vinaya, M. C., & Lakshmisha, K. (2006). A pilot study on the service quality of insurance

companies. Journal of Services Research, 5(2),123-138 conducted quantitative study the levels of the

dimensions of service quality and its relation to the level of customer satisfaction in Mysore city

16. Ganguly, B., Dash, S. B., & Cyr, D. (2009). Website characteristics, trust and purchase intention in

online stores: an empirical study in the Indian context. Journal of Information Science and Technology,

6(2), 22-44.

17. Ghosh, A. (2013). Does life insurance activity promote economic development in India: an empirical

analysis? Journal of Asia Business Studies, 7(1), 31-43.

11

Authors: Geetha R, Divya Rekha

Paper Title: Organizational Elements Contributing to the Marketing Agility of a Firm

Abstract: Business establishments are enormously spending on competency intensification; more so for

enhancement of marketing worth. There is a need to rationalize the lucidity and significance of such huge

investments. Organizational resources play a fundamental role in boosting the marketing proficiency. But there

are very few empirical studies to corroborate the significance of organizational attributes in increasing

marketing dexterity of firms. Extant research is an effort to bridge this gap. It explores the role of

organizational characteristics and human resource proficiencies contributing to enhancing marketing agility of

the firm. Around 120 employees of a well-established manufacturing frim involved in the manufacture of

sustainable products were surveyed using a structured questionnaire. Through an exploratory analysis the

causative factors were identified and subsequently the opinion of the sample elements regarding the role of

identified factors in enriching the marketing efficiency of their firm was sought. The data was analyzed using

linear regression method and the results reflect that there is a significant correlation between organizational

characteristics and the marketing dexterity but surprisingly the contribution of human resource capabilities to

marketing adroitness is not considered as essential as organizational characteristics by the respondents.

Keywords: Organizational Characteristics, Human Resource Capabilities, Marketing agility.

References:

1. Gainer, B., and Padanyi, P. (2001). The utilization of the promoting idea to social associations: An

observational investigation of belief system and practice. In Proceedings AIMAC 2001, sixth International

Conference on Arts and Cultural Management (pp. 78-79).

2. Layton, R. A. (2011). Advertising: is the executives all that there is?. Diary of Historical Research in

Marketing, 3(2), 194-213.

3. Langerak, F., Hultink, E. J., and Robben, H. S. (2004). The effect of market introduction, item favorable

position, and dispatch capability on new item execution and authoritative execution. Diary of item

development the executives, 21(2), 79-94.

4. R., Geetha, Explicating the Interaction between Marketing Dexterity and Constitutive Elements of an

Enterprise (March 1, 2015). TIJARCM, Vol.1 Issue 1, March 2015, ISSN 2395-0854

53-57

12

Authors: Priya Samant, Anurupa B. Singh, Ritesh Dwivedi

Paper Title: Problems Faced by Microcredit Borrowers in Joint Liability Groups (JLGS): Empirical Evidence

of NBFC-MFIS in Uttarakhand

Abstract: Microcredit is proved to be an effective tool for socio-economic development of its borrowers. In

the past various models took shape in developing nations and made credit/loan available at the doorstep of the

borrowers. The framework for delivering credit is called credit delivery model. Joint Liability Groups (JLGs)

were widely adopted by various microfinance institutions in providing credit to the borrower groups. Many

studies talked about the success of JLGs but very little literature was available on the problems faced by

borrowers in JLGs. Data collected from Haridwar district is used to study the problems faced by borrowers in

JLGs. Relation between Group association and problems faced by borrowers was also studied. The findings

show that the major problems which borrowers faced include-strict repayment schedule, Non repayment/late

repayment of loan, lack of loan information, peer pressure among group members etc. There also exist a

relationship between group association and problems faced by borrowers.

Keywords: Microcredit, Joint Liability Groups, Microfinance Institutions (MFIs), Group Association, Credit

delivery models

References: 1. Ahlin, C. (2013). The role of group size in group lending. Journal of Development Economics, 1-51.

2. Alessandra Cassar, L. C. (2007). The Effect of Social Capital on Group Loan repayment:Evidence from Feild Experiments. The

Economic Journal, 85-106. 3. Bharat Bhole, S. O. (2009). Group lending and individual lending with strategic default. Journal of Development Economics, 348-363.

4. Determinants of Loan Defaults in Some Selected Credit Unions in Kumasi Metropolis of Ghana. (2018). Open Journal of Business and

Management, 778-795. 5. Edward Yeboah, I. M. (2018). Determinants of Loan Defaults in Some Selected Credit Unions in Kumasi Metropoils of Ghana. Open

Journal of Business and Management, 778-795.

6. Fikadu Gutu, W. M. (2017). Determinant Factors Affecting Loan Repayment Performance of Women Borrowers from Micro Finance Institutions in Southwest Ethiopia: Evidence from Four Woredas around Gilgel Gibe Hydroelectric Power Dam. Global Journal of

Management and Business Research, 43-51. 7. Ghosh, M. (2012). MIcrofinance and Rural Poverty in India: SHG Bank Linkage programme. Journal of Rural Development, 347-363.

8. Jean Marie Baland, R. S. (2013). Repayment incentives and distribution of gainsfrom group lending. Journal of Development

Economics, 1-25. 9. L., V. (2015). Microfinance Livelihood Initiatives and Women Empowerment in Selected Villages of Andhra Pradesh. Journal of

Rural Development, 31-48.

10. Maitreesh Ghatak, T. W. (1999). Economics of lending with joint liabilty:theory and practice. Journal of Development Economics, 195-228.

11. Peter, W. (2010). The Dynamics f Cooperation in Group Lending-A Microfinance Experiment. (pp. 1-25). Ecostor.

12. Priya Srivastava, D. S. (2015). Impact of Joint Liability Group on Sustainable Livelihoods and Social capital Promotion: A Study in Context of Bihar. International Conference on Global Economic Growth and Sustainability: Challenges and Prospects, (pp. 1-11).

Mysuru, Patna.

13. Sushanta Kumar Sarma, M. H. (2014). The Best Model for Microlending:Self Help Group or Joint Liability Group? Journal of Rural Development, 247-260.

14. The Economics of Lending with Joint Liability: Theory and Practice. (1999). Journal of Development Economics, 195-228.

15. Xavier Gine, D. S. (2014). Group versus individual liability:short and long term evidence from Philippine microcredit lending groups. Journal of Development Economics, 65-83.

16. Chowbey, B. M. (2013). Study of Joint Liability Groups Problems and Prospects. Patna: Centre for Microfinance Reserach Banker's

Institute of Rural development and Chandragupt Insitute of Management. 17. Deepak Chawla, N. S. (2014). Descriptive Analysis of Bivariate Data. In N. S. Deepak Chawla, Research Methodolody:Concepts and

Cases (pp. 303-304). Noida: Vikas Publishing House.

18. J.Senthilvel Murugan, S. (2014). Problems of Self Help Group Members in Hill Station:Evidence through Valparai. Summer Internship Society, 12-17.

19. Padmalochan, H. (2016, January). SHG Bank Linkage Programme: A Study in Nagaon district of Assam. Assam, Assam, India.

20. Pareek, P. (2015). “Gaon Badhe toh Desh Badhe”: A Study on Joint Liability Groups (JLGs) IN North GUujarat Initiative By NABARD. ELK Asia Pacific Journals of Social Sciences, 1-8.

21. Ramesh, P. K. (2013). Joint Liability Groups : The Savior of Urban Poor. IOSR Journals of Humanities and Social Sciences.

22. Satish, P. (2018). Excluding the poor from Credit:Lessons from Andhra Pradesh and Telangana. Economic and Political weekly.

58-64

13

Authors: Rajesh Kumar Maurya, Sanjay Kumar Yadav, Shwata Agrawal

Paper Title: Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer(BC) Dataset

Abstract: Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women

worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming

to economically developing country like India. Government of India made a lot of effort to make aware the

women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still

a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like

Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy

which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but

their performance varies with the kind of data available. In this study we, apply four different Kernels such

as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel

(RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC

dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with

respect to accuracy, runtime, specificity and precision. The investigations outcome displays

that RBFK provides greater accuracy with minimal errors.

Keywords: BC Causes, BC Problems, Challenges, ML Techniques, SVM-K, Efficiency, precision, accuracy,

run time, specificity, Confusion Matrix

References: 1. Prince, M. J., Wu, F., Guo, Y., Robledo, L. M. G., O'Donnell, M., Sullivan, R., & Yusuf, S. (2015). The burden of disease in

older people and implications for health policy and practice. The Lancet, 385(9967), 549-562.

2. Bouchard, C., Blair, S. N., & Haskell, W. L. (2018). Physical activity and health. Human Kinetics.

3. Takiar, R., Nadayil, D., & Nandakumar, A. (2010). Projections of number of cancer cases in India (2010-2020) by cancer groups. Asian Pac J Cancer Prev, 11(4), 1045-9.

4. Unger-Saldaña, K. (2014). Challenges to the early diagnosis and treatment of breast cancer in developing countries. World journal of clinical oncology, 5(3), 465.

5. Gooch, J. C., & Schnabel, F. (2019). Locoregional Recurrence of Breast Cancer. In Clinical Algorithms in General Surgery(pp. 97-100). Springer, Cham.

6. Yue, W., Wang, Z., Chen, H., Payne, A., & Liu, X. (2018). Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2(2), 13.

7. Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., ... & Mak, R. H. (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: a cancer journal for clinicians.

8. Sahu, B., Mohanty, S. N., & Rout, S. K. (2019). A Hybrid Approach for Breast Cancer Classification and Diagnosis.

9. Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer informatics,

65-70

2, 117693510600200030.

10. Fisher, R., Pusztai, L., & Swanton, C. (2013). Cancer heterogeneity: implications for targeted therapeutics. British journal of cancer, 108(3), 479.

11. Ing, E., Su, W., Schonlau, M., & Torun, N. (2019). Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes. Canadian journal of ophthalmology. Journal canadien d'ophtalmologie, 54(1), 116-118.

12. Satyananda, V., Ozao-Choy, J., Dauphine, C., & Chen, K. T. (2019). Effect of the Affordable Care Act on Breast Cancer Presentation at a Safety Net Hospital. The American Journal of Surgery.

13. Ahmad, L. G., Eshlaghy, A. T., Poorebrahimi, A., Ebrahimi, M., & Razavi, A. R. (2013). Using three machine learning techniques for predicting breast cancer recurrence. J Health Med Inform, 4(124), 3.

14. Vanaja, S., & Kumar, K. R. (2014). Analysis of feature selection algorithms on classification: a survey. International Journal of Computer Applications, 96(17)

15. Bejnordi, B. E., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., ... & Geessink, O. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama, 318(22), 2199-2210.

16. Gbenga, D. E., Christopher, N., & Yetunde, D. C. (2017). Performance Comparison of Machine Learning Techniques for Breast Cancer Detection. Nova, 6(1), 1-8

17. Huang, S., Cai, N., Pacheco, P. P., Narandes, S., Wang, Y., & Xu, W. (2018). Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics-Proteomics, 15(1), 41-51.

18. Dubey, U. (2017). Epidemiology of breast cancer in Indian women. Asia-Pacific Journal of Clinical Oncology.

19. Goel, P., & Padole, M. (2019). Bioinformatics: An Application in Information Science. In First International Conference on Artificial Intelligence and Cognitive Computing (pp. 223-238). Springer, Singapore.

20. Dammann, O., & Smart, B. (2019). Health Data Science. In Causation in Population Health Informatics and Data Science(pp. 15-26). Springer, Cham.

21. David, S. K., Saeb, A. T., Rafiullah, M., & Rubeaan, K. (2019). Classification Techniques and Data Mining Tools Used in Medical Bioinformatics. In Big Data Governance and Perspectives in Knowledge Management (pp. 105-126). IGI Global.

22. Shepherd, J., & Perou, C. (2019). Abstract B185: Epithelial cancer cell-expressed genes contribute to clinically relevant immune-based classifications of breast cancer.

23. Lu, Y., & Han, J. (2003). Cancer classification using gene expression data. Information Systems, 28(4), 243-268.

24. Kriegeskorte, N., & Golan, T. (2019). Neural network models and deep learning-a primer for biologists. arXiv preprint arXiv:1902.04704.

25. Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using.

26. Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer informatics, 2, 117693510600200030.

27. Karthiga, R., & Narasimhan, K. (2018, March). Automated Diagnosis of Breast Cancer Using Wavelet Based Entropy Features. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 274-279). IEEE.

28. Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]

14

Authors: Pravin Kumar Singh, Dr Mandeep Kaur

Paper Title: IoT and AI Based Emotion Detection and Face Recognition System

Abstract: : Human facial emotion detection is a prime goal in the current technical world. Robotic

applications are being applied in almost all domains. To enable successful human-robotic interaction, emotion

recognition is crucial. This project aims to develop and implement a novel, automatic emotion detection system

and facial recognition system based on AI (Artificial Intelligence) and IoT (Internet of Things).

Keywords: Face Recognition, Emotion Detection, Artificial Intelligence, Internet of Things,

References: 1. Greg Allen, Taniel Chan, “Artificial Intelligence and National Security”, July 2017

2. James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, Jacques Bughin, Dan Aharon, “The Internet of Things: Mapping the Value Beyond the Hype”, June 2015

3. I.Yugashini, S.Vidhyasri, K.Gayathri Devi, “Design and Implementation of Automated Door Accessing System With Face

Recognition”, International Journal of Science and Modern Engineering (IJISME), Volume 12, November 2013. 4. Shrikrushna Khedkar, Dr. G.M. Malwatkr. 2016. Survey on Home Automation using Raspberry Pi through GSM. IJSR. 5(1),

ISSN: 2319-7064.

5. SHaik Anwar, D. Kishore,”IoT based Home security system with alert and door access control using Smart Phone”, December 2016, IJERT.

6. Anagha S. Dhalvikar, Dr R.K.Kulkarni “Face detection and facial expression recognition System”, 2014 International conference

Mumbai. 7. Leandro y. manoa, bruno s. faical, luis h. v. nakamuraa, pedro h. gomese, giampaolo l. libralonc, rodolfo i. menegueteb, geraldo

p. r. filhoa, gabriel t. giancristofaroa, gustavo pessin, bhaskar krishnamachari, j´o ueyama, “Exploiting IoT Technologies for

Enhancing Health Smart Homes through patient identification and emotion recognition”, March 2016 8. Prof Archana Gaikwad, Prof. Paresh D. Sonawane “An efficient video surveillance system using video-based face recognition on

Real world Data”, IJSETR, Volume 5, Issue 4.

9. Anuradha Savadi, Chandrakala V Patil, “Face Based Automatic Human Emotion Recognition”, IJCSNS International Journal of

Computer Science and Network Security, VOL.14 No.7, July 2014

10. Bharati A.Dixit and Dr. A.N.Gaikwad ”Statistical Moments Based Facial Expression Analysis” IEEE International Advance

Computing Conference (IACC), 2015

11. Hteik Htar Lwin, Aung Soe Khaing, Hla Myo Tun, “Automatic Door Access System Using Face Recognition”, International

Research Journal of Engineering and Technology (IRJET), Volume 4, Issue 06, June 2015

12. Monika Dubey, Prof. Lokesh Singh, “Automatic Emotion Recognition Using Facial Expression”, International Research Journal

71-75

of Engineering and Technology (IRJET), Volume 3, Issue 02, February 2016

13. Prof. Neelum Dave, Narendra Patil, Rohit Pawar, Digambar Pople, “Emotion Detection Using Face Recognition”, IJESC, Volume

7, Issue No.4, 2017 14. D. Yanga, Abeer Alsadoona, P.W.C. Prasad, A. K. Singhb, A. Elchouemic, “An Emotion Recognition Model Based on Facial

Recognition in Virtual Learning Environment”, 6th International Conference on Smart Computing and Communications, ICSCC

2017, 7-8 December 2017 15. Biswanath saha, “IoT Platform and its significances”, https://www.mobiloitte.com/blog/iot-platform-and-its-significance/, july 4,

2017

16. Ravi Kishore Kodali, Vishal Jain, Suvadeep Bose and Lakshmi Boppana” IoT Based Smart Security and Home Automation System”, IEEE 2016

17. S.V. Thate, A.S. Narote, S.P. Narote, “Human face Detection and Recognition in Videos”, 21-24 September 2016, Jaipur, India.

18. Sandesh Kulkarni, Minakshee Bagul, Akanksha Dukare, Prof. Archana Gaikwad, “Face Recognition using IoT”, International Journal of Innovations & Advancement in Computer Science (IJIACS), ISSN 2347 – 8616, Volume 7, Issue 3, March 2018

19. Martinez, A., Du, S., “A model of the perception of facial expressions of emotion by humans: Research overview and

perspectives”, Journal of Machine Learning Research 13, 1589–1608, 2012. 20. John A. Stankovic” Research Direction for the Internet of Things”, February 2014, University of Virginia

21. S.V. Thate, A.S. Narote, S.P. Narote, “Human face Detection and Recognition in Videos”, 21-24 September 2016, Jaipur, India.

15

Authors: Arunesh Kumar Singh, Abhinav Saxena

Paper Title: Implementation of Fuzzy Logic Controller in Solar PV Array Based AC Drives

Abstract: This paper shows the real implementation of fuzzy logic controller in an AC drive system

(Induction motor Drive) under Solar PV array-based system. The switching of boost converter is controlled

with help of fuzzy logic by taking inputs from solar PV array while For Inverter switching is done by using

fuzzy logic controller by taking inputs from induction motor drive. Initially, 20 kW solar PV array is designed

for feeding the 10Kw Induction motor drive with the help MATLAB/SIMULINK. The complete system gives

reliable, smooth, efficient, lesser harmonic content level in the output.

Keywords: solar PV array, fuzzy, Induction motor, switching.

References: 1. Saurabh Shukla and Bhim Singh,'Single Stage PV Array Fed Speed Sensorless Vector Control of Induction Motor Drive for Water

Pumping',DOI 10.1109/TIA.2018.2810263, IEEE Transactions on Industry Applications,2018.

2. Manasa. M. Shetty, Girish Joshi, "Simulation of MPPT using Fuzzy Logic Controller for AC Drive" International journal of Innovative

research in electrical, electronics, instrumentation and control engineering, Vol. 3, Special issue 1, April 2015. 3. Nurul Afiqah Zainal, Chan Sooi Tat and Ajisman "Fuzzy Logic Controlled Solar Module for Driving ThreePhase Induction Motor" IOP

Conf. Series: Materials Science and Engineering 114 (2016).

4. Max Savio, Jayavelu “Drive Applications of Fuzzy Logic Controlled Interleaved Boost Converter for Maximum Power Point Tracking in Solar PV" 2016 IJEDR | Volume 4, Issue 4,2016.

5. M. A. Elgendy, D. J. Atkinson and B. Zahawi, “Experimental investigation of the incremental conductance maximum power point

tracking algorithm at high perturbation rates,” IET Renewable Power Generation, vol. 10, no. 2, pp. 133-139, Feb. 2016. 6. J. Titus, J. Teja, K. Hatua and K. Vasudevan, “An Improved Scheme for Extended Power Loss Ride-Through in a Voltage-Source-

Inverter-Fed Vector-Controlled Induction Motor Drive Using a Loss Minimization Technique,” IEEE Trans. Ind. Appl., vol. 52, no. 2,

pp. 1500-1508, March-April 2016. 7. S. A. Odhano, R. Bojoi, A. Boglietti, Ş. G. Roşu and G. Griva, "Maximum Efficiency per Torque Direct Flux Vector Control of

Induction Motor Drives," IEEE Trans. Ind. Appl., vol. 51, no. 6, pp. 4415-4424, Nov.-Dec. 2015.

8. L. An and D. D. C. Lu, “Design of a single-switch DC/DC converter for a PV-battery-powered pump system with PFM+PWM control,” in IEEE Trans. Ind. Electron., vol. 62, no. 2, pp. 910-921, Feb. 2015.

9. D. Stojić, M. Milinković, S. Veinović and I. Klasnić, “Improved stator flux estimator for speed sensorless induction motor drives,”

IEEE Trans. Power Electron., vol. 30, no. 4, pp. 2363-2371, April 2015. 10. A. B. Raju, S. Kanik and R. Jyoti, “Maximum efficiency operation of a single stage inverter fed induction motor PV water pumping

system”, Emerging Trends in Eng. And Tech. (ICETET), pp.905-910, 2008.

11. C. Jain and B. Singh, “Single-phase single-stage multifunctional grid interfaced solar photo-voltaic system under abnormal grid conditions”, IET Genr., Trans. & Distr., vol. 9, no. 10, pp. 886-894, Feb.2015.

12. Bhavnesh Kumar, Yogesh K. Chauhan, and Vivek Shrivastava "Solar Powered Fuzzy Logic Controller based Vector Controlled

Induction Motor Drive" Journal of Automation and Control Engineering Vol. 1, No. 4, December 2013. 13. Abubakkar Siddik. A, Shangeetha. M "Implementation of Fuzzy Logic controller in Photovoltaic Power generation using Boost

Converter and Boost Inverter" International Journal of Power Electronics and Drive System (IJPEDS) Vol. 2, No. 3, September 2012,

pp. 249~256. 14. Satean Tunyasrirut, Tianchai Suksri, and Sompong Srilad “Fuzzy Logic Control for a Speed Control of Induction Motor using Space

Vector Pulse Width Modulation .“World Academy of Science, Engineering and Technology International Journal of Computer and

Information Engineering Vol:1, No:1, 2007. 15. V. Chitra, and R. S. Prabhakar “Induction Motor Speed Control using Fuzzy Logic Controller.” World Academy of Science,

Engineering and Technology 23, pp. 756-761, 2008.

76-81

16

Authors: Prashant, Anwar Shahzad Siddiqui, Abhinav Saxena, Satyam, Vidushi, Shikha

Paper Title: AN ADVANCE METHODOLOGY FOR HYBRID MODELLING AND SELECTION OF GRID INTEGRATED

RENEWABLE ENERGY [WIND/SOLAR] PROFILE THROUGH PROTEUS

Abstract: This paper shows the synchronization of grid integrated renewable energy and conventional

sources feeding a load centres using proteus. The certain factor like variation in solar radiation, wind and load

demand has been taken into consideration. The most challenging task during synchronization is selection of

particular generating sources. In this Paper, Experimental hybrid modelling of grid integrated renewable energy

projects (wind, solar) and conventional supply source has been performed. The experimental result verified on

simulation platform proteus software through Arduinouno microcontroller. The output of system confirms the

selectivity of particular energy source as per requirement of changing in the load demand and environmental

82-88

conditions.

Keywords: hybrid, modelling, proteus, modelling, solar, wind.

References: 1. for Solar Intensity Forecasting”,IEEE transactions on industrial informatics, vol.14, no.4, april, 2018.

2. Shekhanabi B Chalageri, Akash M Deshpande, Manjunath S Banad, Anoop S Pavate, Prof. SujataEresimi ”, Generation of Electricity by Wind Tree” ISSN: 2350-0328 International Journal of Advanced Research in Science, Engineering and Technology Vol. 4, Issue

5 , May 2017.

3. Ragunath L Senthilvel S. “Hybrid Energy Generation Through Vertical Axis Savonius Wind Turbine and Solar Panel” IJIRST –International Journal for Innovative Research in Science & Technology, Volume 2 , Issue 11 , April 2016 .

4. Arjun A. K., Athul S., Mohamed Ayub, Neethu Ramesh, and Anith Krishnan, Micro-Hybrid Power Systems – A Feasibility Study. Journal of Clean Energy Technologies, Vol. 1, No. 1, January 2013.

5. Ashish S. Ingole, Bhushan S. Rakhonde,” Hybrid Power Generation System Using Wind Energy and Solar Energy”.International

Journal of Scientific and Research Publications, Volume 5, Issue 3, ISSN 2250-3153, March 2015. 6. Mohammed Hadi Ali, “Experimental Comparison Study for Savonius Wind Turbine of Two & Three Blades At Low Wind Speed”,

International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol. 3, Issue. 5, pp-2978-2986 ISSN: 2249-6645,

Sep – Oct. 2013. 7. MagediMohM.Saad, NorzelawatiAsmuin, “Comparison of horizontal axis wind turbines and vertical axis wind turbines”in IOSR

journal of engineering, Volume 04, Issue 08,August 2014.

8. AshwinDhote,VaibhavBankar, “Design, Analysis and Fabrication of savonius vertical axis wind turbine” in International research

journal of engineering and technology. Volume:02 Issue:03,June 2015.

9. N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay, “Clustering based improvement of nonparametric functional time series

forecasting: Application to intra-day household-level load curves,” IEEE Power Energy Mag., vol. 5, no. 1, pp. 411–419, Sep. 2014. 10. X. Fang, S. Misra, G. Xue, D. Buccella, and C. Yang, “Smart grid - The new and improved power grid: A survey,” IEEE Commun.

Surveys Tuts., vol. 14, no. 4, pp. 944–980, Dec. 2012

11. .W.T.Chong,”Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application”, Renewable Energy 51(2013)388-397.

12. https://etap.com/product/photovoltaic-array-solar-panel

13. Ashish S. Ingole, Bhushan S. Rakhonde , “Hybrid Power Generation System Using Wind Energy and Solar Energy”.International Journal of Scientific and Research Publications, Volume 5, Issue 3, ISSN 2250-3153, March 2015.

14. T. Govindaraj , K. Bharanidharan,” Stability and Reliability Improvement in Solar Wind Hybrid Power System with Battery Energy

Storage Station”, International Journal of Emerging Trends in Electrical and Electronics ,IJETEE ,ISSN: 2320-9569, Vol. 10, Issue. 3, April-2014.

15. Mohamed Nfaoui Khalil El-Hami, “Extracting the maximum energy from solar panels “, Energy Reports, Vol 4, pp 536-

545,November 2018. 16. W.T. Chong, “Performance investigation of a power augmented vertical axis wind turbine for urban high-rise application”, Renewable

Energy 51 (2013) 388-397.

17. Wei Qi, Jinfeng Liu, Xianzhong Chen, and Panagiotis D. Christo fides, “Supervisory Predictive Control of Standalone Wind/Solar Energy Generation Systems”, IEEE Transactions on Control Systems Technology ,Volume: 19, Issue: 1, Jan. 2011.

18. X. Fang, S. Misra, G. Xue, D. Buccella, and C. Yang, “Smart grid - The new and improved power grid: A survey,” IEEE Commun.

Surveys Tuts., vol. 14, no. 4, pp. 944–980, Dec. 2012.

17

Authors: Manoj Kapil, Manish Sharma

Paper Title: Minitaurized Reconfigurable Multiband Antennas For GPS, UMTS, WiMAX & WLAN Wireless

Applications

Abstract: In this manuscript, compact multiband antenna for four different wireless applications is

presented. Two stubs which are embedded on radiating patch produces resonances for Global Positioning

System (GPS) and Universal Mobile Telecommunication system (UMTS). Also, the other two resonating

bands are obtained by etching two slots which produces resonances for WiMAX and WLAN bands. Antenna

offers good gain and radiation pattern at operating frequencies bands. Furthermore, to control resonating bands

which are obtained by means of stubs and slots, antenna is reconfigured by using 4 PIN diodes. Antenna is

designed on Rogers RT Duroid5870 with compact dimensions of 20×20 mm2

Keywords: GPS, UMTS, WiMAX, WLAN, RT Duroid, PIN Diode

References: 1. Y.I. Abdulraheem, G.A. Oguntala, A.S. Abdullah, H.J. Mohammed, R.A. Ali, R.A. Abd-Alhameed, and J.M. Noras, "Design of

frequency reconfigurable multiband compact antenna using Two PIN diodes for WLAN/WiMAX applications," IET Microwaves, Antennas & Propagation, vol. 11, no. 8, pp. 1098-1105, 2017.

2. T. Ali, M.M. Khaleeq, S. Pathan, and R.C. Biradar, "A multiband antenna loaded with metamaterial and slots for GPS/WLAN/WiMAX

applications," Microwave Optical Technology Letters, vol. 60, pp. 79-85, 2017. 3. M.A. Kenari, M.N. Moghadasi, R.A. Sadeghzadeh, and B.S. Virdee, "Hexa-Band planar antenna with assymetric fork-shaped radiators

for multiband and broadband communication applications," IET Microwaves, Antennas & Propagation, vol. 10, no. 5, pp. 471-478,

2016. 4. P.S. Bakariya, S. Dwari, M. Sarkar, and M.K. Mandal, "Proximity coupled multiband microstrip antenna for wireless applications,"

IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 646-649, 2015.

5. Boukarkar, X.Q. Lin, J.W. Yu, P. Mei, Y. Jiang, and Y.Q. Yu, "A highly integrated independently Tunable Triple-Band notch

antenna," IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 2216-2219, 2017.

6. R.S. Brar, K. Saurav, D. Sarkar, and K.V. Srivastava, "A quad-band dual-polarized monopole antenna for

GNSS/UMTS/WLAN/WiMAX applications," Microwave Optical Technology Letters, vol. 60, pp. 538-545, 2018. 7. S. Chen, M. Fang, D. Dong, M. Han, and G. Liu, "Compact multiband antenna for GPS/WiMAX/WLAN applications," Microwave

Optical Technology Letters, vol. 57, pp. 1769-1773, 2015.

8. H.R. Cheong, K.H. Yeap, K.C. Lai, P.C. Teh, and H. Nisar, "A compact CPW-fed antenna with fractal S-shaped patches for multiband applications," Microwave Optical Technology Letters, vol. 59, pp. 541-546, 2017.

9. L. Kumar, A. K. Gautam, B. K. Kanaujia, and K. Rambabu, "Design of Compact F-shaped slot triple band antenna for

WLAN/WiMAX applications," IEEE Transactions on Antennas and Propagation, pp. 1-6, 2015. 10. A.R. Jalali, J.A. Shokouh, and S.R. Emadian, "Compact multiband monopole antenna for UMTS, WiMAX and WLAN applications,"

Microwave Optical Technology Letters, vol. 58, pp. 844-847, 2016.

11. M. Moosazadeh, and S. Kharkovsky, "Compact and small planar monopole antenna with symmetrial L- and U-shaped slots for

89-93

WLAN/WiMAX applications," Microwave Optical Technology Letters, vol. 13, pp. 388-391, 2014. 12. Z. Yu, J. Yu, X. Ran, and C. Zhu, "A novel Koch and Sierpinski combined fractal antenna for 2G/3G/4G/5G/WLAN/Navigation

applications," Microwave Optical Technology Letters, vol. 59, pp. 2147-2155, 2017.

18

Authors: Abhinav Saxena, G.M Patil, Prashant, Parveen Poon Terang, Nirmal kumar Agarwal, Arun

Rawat

Paper Title: Optimal Load Distribution Of Thermal Generating Units Using Particle Swarm Optimization

(PSO)

Abstract: This paper shows load planning of two thermal generating units feeding a load of 200 MW using

Particle swarm optimization (PSO). The PSO involves selection of population size or number of particle,

fitness function. The advantages of PSO over conventional method are better and reliable solution, better

convergence rate. Initially fitness function, population size corresponding to each variable are decided,

thereafter PSO is used for finding most optimal solution of the generating units under different set of iteration.

The performance of system using PSO is compared with conventional method in terms of the tolerance band.

Keywords: PSO, fitness, population size, thermal

References: 1. Jichun Liu, Member, IEEE and Jie Li, Member, IEEE,’ Interactive Energy-Saving Dispatch Considering Generation and Demand Side

Uncertainties: A Chinese Study’, DOI 10.1109/TSG.2016.2623947,IEEE Transactions on Smart Grid,2018 2. Morteza Nojavan, Heresh Seyedi,’ Voltage stability margin improvement using hybrid non-linear programming and modified binary

particle swarm optimisation algorithm considering optimal transmission line switching’, IET Generation, Transmission &

Distribution,2018 3. Mahari, A., Seyedi, H.: ‘A wide area synchrophasor-based load shedding scheme to prevent voltage collapse’, Int. J. Electric. Power

Energy Syst.,78, (1), pp. 248–257,2016

4. Saffarian, A., Sanaye-Pasand, M.: ‘Enhancement of power system stability using adaptive combinational load shedding methods’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1010–1020

5. Arya, L.D., Singh, P., Titare, L.S.: ‘Differential evolution applied for anticipatory load shedding with voltage stability considerations’,

Int. J. Electric. Power Energy Syst.,42, (1), pp. 644–652,2012 6. Verbic, G., Gubina, F.: ‘A new concept of protection against voltage collapse based on local phasors’. Int. Conf. Power System

Technology, pp. 965– 970,2000

7. Šmon, I., Pantoš, M., Gubina, F.: ‘An improved voltage-collapse protection algorithm based on local phasors’, Electr. Power Syst. Res., 2008, 78, (3), pp. 434–440

8. Rabiee, A., Parvania, M., Vanouni, M., et al.: ‘Comprehensive control framework for ensuring loading margin of power systems considering demand-side participation’, IET. Gener. Transm. Distrib.6, (12), pp. 1189–1201,2012

9. Titare, L.S., Singh, P., Arya, L.D., et al.: ‘Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage

stability’, Int. J. Electric. Power Energy Syst.,63, (1), pp. 588–599,2014 10. Mohseni-Bonab, S.M., Rabiee, A., Mohammadi-Ivatloo, B.: ‘Voltage stability constrained multi-objective optimal reactive power

dispatch under load and wind power uncertainties: a stochastic approach’, Renew. Energy,85, (1), pp. 598–609,2016

11. Karbalaei, F., Kalantar, M., Kazemi, A.: ‘On line diagnosis of capacitor switching effect to prevent voltage collapse’, Energy Convers. Manage.,51, (11), pp. 2374–2382,2010

12. Raoufi, H., Kalantar, M.: ‘Reactive power rescheduling with generator ranking for voltage stability improvement’, Energy Convers.

Manage., 50, (4), pp. 1129–1135 ,2009 13. Rolim, J.G., Machado, L.J.B.: ‘A study of the use of corrective switching in transmission systems’, IEEE Trans. Power Syst., 1999,

14, (1), pp. 336–341

14. Wu, J., Cheung, K.W.: ‘On selection of transmission line candidates for optimal transmission switching in large power networks’. Power and Energy Society General Meeting, 2013

15. Shao, W., Vittal, V.: ‘Corrective switching algorithm for relieving overloads and voltage violations’, IEEE Trans. Power Syst., 2005,

20, (4), pp. 1877– 1885 16. Shao, W., Vittal, V.: ‘A new algorithm for relieving overloads and voltage violations by transmission line and bus-bar switching’.

Power Systems Conf. and Exposition, 2004

17. Wang, X., Shao, W., Vittal, V.: ‘Adaptive corrective control strategies for preventing power system blackouts’. 15th Power Systems

Computation Conf., Liège, Belgium, 2005

18. Shao, W., Vittal, V.: ‘BIP-based OPF for line and bus-bar switching to relieve overloads and voltage violations’. Power Systems

Conf. and Exposition, 2006 19. Gou, B., Zhang, H.: ‘Fast real-time corrective control strategy for overload relief in bulk power systems’, IET. Gener. Transm.

Distrib., 2013, 7, (12), pp. 1508–1515

20. Lobato, E., Echavarren, F., Rouco, L., et al.: ‘A mixed-integer lp based network topology optimization algorithm for overload alleviation’. Power Tech Conf. Proc., 2003

21. Arya, L.D., Choube, S.C., Kothari, D.P.: ‘Line switching for alleviating overloads under line outage condition taking bus voltage

limits into account’, Int. J. Electric. Power Energy Syst., 2000, 22, (3), pp. 213–221 22. Granelli, G., Montagna, M., Zanellini, F., et al.: ‘Optimal network reconfiguration for congestion management by deterministic and

genetic algorithms’, Electr. Power Syst. Res., 2006, 76, (6), pp. 549–556

23. Lalwani, S., Kumar, R., Gupta, N.: ‘A novel two-level particle swarm optimization approach to train the transformational grammar based Hidden Markov models for performing structural alignment of pseudoknotted RNA’, Swarm Evol. Comput., 2015, 20, (1), pp.

58–73

94-98

19

Authors: Vinish Kumar, Anuj Sharma

Paper Title: Human Activity Recognition Using Smart Phone Sensors

Abstract: In the new age technology, there exists many smart devices, which are using human activity data

in reshaping the modern world dynamics of every aspect of our life be it health trackers, smartphones,

intelligent systems. One futuristic concept is the connected devices that are way more efficient, adaptive,

responsive and flexible to any conditions and reacts according to the data. For some connected devices to work

more efficiently, human activity data is required. This data can be used to make devices smarter and using it

can be useful in solving many problems of healthcare, efficient surveillance. Our work is an effort in efficient

surveillance and using deep learning models, we detect the presence of human activities in different

environments and use the data to analyze better to have efficient and effective surveillance. Many different

99-104

models of deep learning model are used in our work from the likes of CNN (Convolutional Neural Networks)

to LSTM (Long Short-Term Memory Networks. The data collected is from sensors’ data which is present in

the mobile and can make the predictions about various activities like sitting, walking, jumping and some other

human activities. The prime focus here is to detect various canonical activities that are not given to the system.

Keywords: Ubiquitous Sensing, Machine Learning, CNN, Human Activity Recognition, Tensor flow, Deep-

Learning

References: 1. Wilde, Adriana G. "An overview of human activity detection technologies for pervasive systems." Department of Informatics

University of Fribourg, Switzerland 212 (2010): 72-112. 2. Sung, Jaeyong, et al. "Unstructured human activity detection from rgbd images." Robotics and Automation (ICRA), 2012 International

Conference on. IEEE, 2012.

3. Yu, Gang, Junsong Yuan, and Zicheng Liu. "Propagative hough voting for human activity detection and recognition." IEEE Transactions on Circuits and Systems for Video Technology 25.1 (2015): 87-98.

4. Lara, Oscar D., and Miguel A. Labrador. "A survey on human activity recognition using wearable sensors." IEEE Communications

Surveys and Tutorials 15.3 (2013): 1192-1209. 5. Bayat, Akram, Marc Pomplun, and Duc A. Tran. "A study on human activity recognition using accelerometer data from

smartphones." Procedia Computer Science 34 (2014): 450-457.

6. Zhao, Rui, Haider Ali, and Patrick van der Smagt. "Two-stream RNN/CNN for action recognition in 3D videos." Intelligent Robots

and Systems (IROS), IEEE/RSJ International Conference on. IEEE, 2017.

7. Yin, Xizhe, et al. "Human activity detection based on multiple smart phone sensors and machine learning algorithms." Computer

Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on. IEEE, 2015. 8. Kim, Youngwook, and Taesup Moon. "Human detection and activity classification based on micro-Doppler signatures using deep

convolutional neural networks." IEEE geoscience and remote sensing letters 13.1 (2016): 8-12.

9. Liu, Jun, et al. "Spatio-temporal lstm with trust gates for 3d human action recognition." European Conference on Computer Vision. Springer, Cham, 2016.

10. Yin, Jie, Qiang Yang, and Jeffrey Junfeng Pan. "Sensor-based abnormal human-activity detection." IEEE Transactions on Knowledge

& Data Engineering 8 (2007): 1082-1090. 11. Ryoo, Michael S., and Jake K. Aggarwal. "Recognition of composite human activities through context-free grammar based

representation." CVPR, IEEE computer society conference on. Vol. 2. IEEE, 2006.

12. Web-link of the Data-Set used in this paper “http://www.cis.fordham.edu/wisdm/dataset.php”

20

Authors: Sandeep Tayal, Kapil Sharma

Paper Title: The Recommender systems model for Smart Cities

Abstract: Recommender systems were introduced in the early 1990s. They did not get too much attention

and were limited to a narrow domain implemented by only a few companies until the outburst of E-commerce.

As online shopping became popular, the recommender system started becoming an integral part of an

organization marketing strategy and since then they have completely evolved a lot. This give an opportunity to

start with a recommendation System project by collecting information from news of users to provide a best

recommendation. The cities become smatter so, this paper review different methods of implementing

Recommender systems models for smart cities along with their drawbacks and possible improvements

Keywords: Content-Based System, Collaborative Filtering, Evaluation, Hybrid System, Recommender

System, smart cities

References: 1. D. Goldberg, D. Nichols, B. Oki, and D. Terry.Using collaborative filtering to weave an information tapestry. Communications of the

Association of Computing Machinery, 35(12):61–70, 1992.

2. F. J. Martin, “RecSys ’09 industrial keynote: Top 10 lessons learned to develop deploying and operating real-world recommender

systems,” in ACM RecSys’09, pp. 1–2, ACM, 2009. 3. Resnick P, Iacovou N, Suchak M, Bergstrom P, and Riedl J, GroupLens: An open architecture for collaborative filtering of netnews.

Proc. ACM Conf. on Computer-Supported Cooperative Work, 1994, Chapel Hill, pp. 175–186.

4. Shardanand U and Maes P, Social Information Filtering: Algorithms for Automating ‘Word of Mouth’. Proc. Conf. Human Factors in Computing Systems. Denver, 1995: 210–217.

5. Breese JS, Heckerman D, and Kadie C, Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proc. 14th Conf.

Uncertainty in Artificial Intelligence. Madison, 1998:43–52. 6. Sarwar B, Karypis G, Konstan J, and Riedl J, Item-Based Collaborative Filtering Recommendation Algorithms. Proc. 10th Int’l

WWW Conf., Hong Kong, 2001: 1–5. 7. Atsuyoshi Nakamura and Naoki Abe. Collaborative filtering using weighted majority prediction algorithms. In ICML ’98:

Proceedings of the Fifteenth International Conference on Machine Learning, pages 395–403, San Francisco, CA, USA, 1998. Morgan

Kaufmann Publishers Inc. 8. Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, and Russell Greiner. Imputation-boosted collaborative filtering using machine

learning classifiers. In SAC ’08: Proceedings of the 2008 ACM symposium on Applied computing, pages 949–950, New York, NY,

USA, 2008. ACM. 9. J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings

of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230–237, Berkeley,

CA, 1999. ACM Press. 10. Xiaoyuan Su and Taghi M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009:1–

20, 2009.

11. Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734–749, 2005.

12. Greg Linden, Brent Smith, and Jeremy York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet

Computing, 7(1):76–80, 2003. 13. Badrul Sarwar, George Karypis, Joseph Konstan, and John Reidl. Item-based collaborative filtering recommendation algorithms. In

WWW ’01: Proceedings of the 10th international conference on World Wide Web, pages 285–295, New York, NY, USA, 2001.

ACM. 14. Deshpande M and Karypis G, Item-Based Top-N Recommendation Algorithms. ACM Trans. Information Systems, 2004, 22(1):

143–177.

105-110

15. Robert Bell Yehuda Koren and Chris Volinsky. Matrix factorization techniques for recommender systems. In IEEE Computer, volume 42 (8), pages 30–37, 2009.

16. D.D. Lee and H.S. Seung. Learning the parts of objects by non-negative matrix factorization. In Nature, 401 (788), 1999.

17. Baeza-Yates R and Ribeiro-Neto B, Modern Information Retrieval. Addison-Wesley, Wesley Press, 1999. 18. Salton G, Automatic Text Processing. Addison-Wesley, 1989.

19. Rocchio, J.: Relevance Feedback Information Retrieval. In: G. Salton (ed.) The SMART retrieval system - experiments in automated

document processing, pp. 313–323. Prentice-Hall, Englewood Cliffs, NJ (1971) 20. Mooney RJ, Bennett PN, and Roy L, Book Recommending Using Text Categorization with Extracted Information. Proc.

Recommender Systems Papers from 1998 Workshop, Technical Report WS-98-08, 1998.

21. Robertson S and Walker S, Threshold Setting in Adaptive Filtering. J. Documentation, 2000, 56: 312–331. 22. Zhang Y and Callan J, Maximum Likelihood Estimation for Filtering Thresholds. Proc. 24th Ann. Intel ACM SIGIR Conf., New

Orleans, 2001: 294–302.

23. Zhang Y, Callan J, and Minka T, Novelty and Redundancy Detection in Adaptive Filtering. Proc. 25th Ann. Intel ACM SIGIR Conf., Tampere, 2002: 81–88.

24. Mart´ınez L, P´erez LG, and Barranco M, A multigranular linguistic content-based recommendation model: Research Articles.

International Journal of Intelligent Systems, 2007, 22(5): 419–434. 25. Claypool M, Gokhale A, Miranda T, Murnikov P, Notes D, and Sartin M, Combining Content-Based and Collaborative Filters in an

Online Newspaper. Proc. ACM SIGIR ’99 Workshop Recommender Systems: Algorithms and Evaluation, Berkeley 1999.

26. Pazzani M, A Framework for Collaborative, Content-Based, and Demographic Filtering. Artificial Intelligence Rev., 1999, 13(5-6): 393–408.

27. Schein AI, Popescul A, Ungar LH, and Pennock DM, Methods and Metrics for Cold-Start Recommendations. Proc. 25th Ann. Intel

ACM SIGIR Conf., Tampere, 2002: 253–260.

28. Billsus D and Pazzani M, User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction, 2000, 10(2-3):

147–180.

29. Getoor L and Sahami M, Using Probabilistic Relational Models for Collaborative Filtering. Proc. Workshop Web Usage Analysis and User Profiling, San Diego, 1999.

30. Basu C, Hirsh H, and Cohen W, Recommendation as Classification: Using Social and Content-Based Information in

Recommendation. Papers from 1998 Workshop, Technical Report WS-98-08, AAAI Press 1998: 714–720. 31. Marko Balabanovic and Yoav Shoham. Fab: Content-based, collaborative recommendation. Communications of the Association for

Computing Machinery, 40(3):66–72, 1997.

32. Christakou C, Vrettos S, and Stafylopatis A, A hybrid movie recommender system based on neural networks. International Journal on Artificial Intelligence Tools, 2007, 16(5): 771–792.

33. Yoshii K, Goto M, Komatani K, Ogata T, and Okuno HG, An efficient hybrid music recommender system using an incrementally

trainable probabilistic generative model. IEEE 34. Girardi R and Marinho LB, A domain model of Web recommender systems based on usage mining and collaborative filtering.

Requirements Engineering, 2007, 12(1): 23–40.

21

Authors: Supriya Khaitan, Rashi Agarwal, Mandeep Kaur

Paper Title: Novel Apporach of Secure Communication Using Logistic MAP

Abstract: Significant research efforts have been invested in recent years to export new concepts for secure

cryptographic methods. Many mathematicians are attracted by Chaos functions as it has sensitive nature toward

its initial conditions and their colossal suitability to problems in daily life. Inspired by new researches, a new

chaotic cryptography algorithm is proposed in this paper. The key feature of this approach is that instantaneous

key is generated at host independently that is used to determine the type of operations on each pixel. The

information available in images is 24 bit RGB these value are modified mathematically using eight reversible

operations. Also during encryption, the control parameter of the chaotic system is updated timely.

Keywords: Chaotic Map, Encryption, Decryption, Security, Logistic Map

References: 1. G. Alvarez, F. Montoya, M. Romera and G. Pastor, “Cryptanalysis of an ergodic chaotic cipher” in Phys. Lett. A, Vol. 31, Issue 2,

2003, pp. 172-179.

2. G. Alvarez, F. Montoya, M. Romera and G. Pastor, “Cryptanalysis of dynamic look-up table based chaotic cryptosystems”, in Phys.

Lett. A,Vol. 326, Issue 3, 2004, pp. 211-218. 3. G. Alvarez, F. Montoya, M. Romera and G. Pastor, “Cryptanalysis of a chaotic secure communication system”, in Phys. Lett. A,Vol.

306, Issue 4, 2003, pp. 200-205.

4. G. Alvarez, F. Montoya, M. Romera and G. Pastor, “Cryptanalysis of a discrete chaotic cryptosystem using external key” in Phys. Lett. A, Vol. 319 Issue, 2003, pp. 334-339.

5. M. Ausloos and M. Dirickx, “The logistic map and the route to chaos. From the beginnings to modern applications”, Springer-Verlag,

Berlin, 2006. MR2202732 Zbl pre05018776 6. M. S. Baptista, “Cryptography with chaos”, Vol. 240, Issue 1, Phys. Lett. A,1998, pp. 50-54.

7. Armin Bunde and Shlomo Havlin, “Fractals in science”, Springer-Verlag, Berlin, 1994. MR1290340 8. Richard M. Crownover, “Introlduction to fractals And chaos”, Jones & Barlett Publishers, 1995.

9. P. Garcia and J. Jimenez, “Communication through chaotic map systems” in Phys. Lett. A,Vol. 298, Issue, 2002, pp. 35-40.

10. Richard A. Holmgren, “A first course in discrete dynamical systems”, Springer-Verlag, 1996 MR1410752 11. Palacios and H. Juarez, “Cryptography with cycling chaos” in Phys. Lett. A,,Vol. 303, Issue 5,2002 , pp. 345-351.

12. L. M. Pecora and T.L. Crroll, “synchronization in chaotic systems”, Physical review letters Vol. 64,1997, pp. 821.

13. Heinz-Otto Peitgen, Hartmut Jürgens and Dietmar Saupe, “ Fractals for the classroom. Part 2: Complex systems and Mandelbrot set”,

Springer-Verlag, Berlin, 1992, Zbl 0785.58001

14. Peitgen, Jurgens and Saupe, “Chaos and Fractals” in Springer-Verlag, New York, Inc., 2006.

15. Mamta Rani and Rashi Agarwal, “A new experimental approach to study logistic map”, in Chaos Soliton and fractals, Vol. 41 Elsevier 2009, pp. 2062-2066.

16. Mamta Rani and Rashi Agarwal, “Generation of fractals from complex logistic map”, Chaos Soliton and fractals, Vol. 42 , Elsevier,

2009, pp. 447-452 17. Mamta Rani and Rashi Agarwal, “Effect of stochastic noise on superior Julia sets”, Journal of mathematical imaging and vision,

springer. DOI: 10.1007/s10851-009-0

18. Mamta Rani, Ph.D. Thesis “Iterative procedure in Fractals and Chaos”, Gurukala Kangri Vishwavidyalaya, Hardwar, India, 2002. 19. Mamta Rani and Vinod Kumar, “Superior Julia set”, J Korea Soc Math Edu Series D: Research in Math Edu, 2004, pp. 261–277.

20. Mamta Rani and Vinod Kumar, “Superior Mandelbrot set”, J Korea Soc Math Edu Series D: Research in Math Edu, , 2004, pp. 279–

111-116

291. 21. Mamta Rani and Vinod Kumar, “A new experiment with the logistic function”, J. Indian Acad. Math., 2005, pp. 143-156.

MR2224669

22. W. K. Wong, L. P. Lee and K. W. Wong, “Keystream cryptanalysis of a chaotic cryptographic method”, in Computer Physics Communications, Vol. 15, Issue 2 ,2004, pp. 208.

23. K. W. Wong, “A fast chaotic cryptographic scheme with dynamic look-up table” in Phys. Lett. A, Vol. 298 Issue 4, 2002, pp. 238-

242. MR1918066 24. Xie, G.B., Wang, T “A new bit-scrambling hyperchaotic image encryption algorithm”,in Microelectron. Comput, Vol. 33 Issue 7,

2016 pp. 28-32.

25. Xu, L., Gou, X., Li, Z., et al “A novel chaotic image encryption algorithm using block scrambling and dynamic index based diffusion” Opt. Lasers Eng. Vol. 91, 2017 pp: 41–52

22

Authors: Shashank Sahu, Rashi Agarwal, Rajesh Kumar Tyagi

Paper Title: Fuzzy Vehicle Control System for Single Intersection

Abstract: Traffic light at the intersection is an important part of the daily life of the urban cities. Traffic light

needs to be managed properly so that a large number of vehicles able to cross the intersection. Current traffic

light system is not able to allot efficient traffic light cycle length to vehicles. It is because of the traffic system

is based on the fixed cycle length. Today traffic is highly fluctuating. Number of vehicles in the traffic varies

with respect to time and day. To handle this situation, the paper proposes a vehicle control system using fuzzy

logic: Fuzzy Vehicle Control System (FVCS). FVCS allots dynamic cycle length signal duration to vehicles

based on the present traffic available at the intersection. Experiments have been performed on the proposed

FVCS architecture using MATLAB. Experiments show that a reduction of 28.92% can be achieved in the

number of vehicles that are waiting at the intersection to cross it.

Keywords: Cycle Length, Green Light Time, Intersection Fuzzy Logic, Vehicle Control System.

References: 1. Graham I, Jones PL. “Expert systems: knowledge, uncertainty, and decision”, Chapman & Hall, Ltd.; 1, pp. 117–118, (1988).

2. Hegyi A, De Schutter B, Hoogendoorn S, Babuska R, Van Zuylen H, Schuurman H., “A fuzzy decision support system for traffic control centers”, Intelligent Transportation Systems, pp. 358-363. IEEE (2001)

3. C. Taylor, D. Meldrum, “Fuzzy Ramp Implementation”, Research Project prepared by Department of Electrical Engineering of

University of Washington, at Seattle, Washington 98195, pp. 1, 6, (2000). 4. Day, Scoot Split, “Cycle & Offset Optimization Technique”, presented by Siemens AG, Traffic Control Systems Division, presented

to Transportation Research Board, TRB Committee A3A18 Traffic Signal Systems, TRB Mid- Year Meeting and Adaptive Traffic

Signal Control Workshop, pp. 6,9,12,13 (1998).

5. NIITTYMaki, J., and Marko Mäenpää. "Fuzzy public transport priority in traffic signal control." In Transport Systems Organisation

And Planning. Proceedings Of The 3rd KFB Research Conference, Held In Stockholm, Sweden, 13th To 14th June 2000.

6. Wei, Wu, and Mingjun Wang. "Traffic signal control using fuzzy and neural network." In 8th International Conference on Neural Information Processing, Shanghai, China. 2001.

7. Liu, H.X., Ma, W., Hu, H., Wu, X. and Yu, G., “SMART-SIGNAL: Systematic Monitoring of Arterial Road Traffic Signals”, In: 11th

International IEEE Conference on Intelligent Transportation Systems, Beijing, pp. 1061-1067 (2008). 8. Kulkarni, G.H. and Waingankar, P.G, “Fuzzy Logic Based Traffic Light Controller”, In: Second International Conference on

Industrial and Information Systems, pp. 107-110 (2007).

9. Wang, Q., Wang, L. and Guangcun, W., “Research on Traffic Light Adjustment Based on Compatibility Graph of Traffic”, In: Third International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 88-92 (2011).

10. Zhang, Lin, Honglong Li, and Panos D. Prevedouros. "Signal control for oversaturated intersections using fuzzy logic."

In Transportation Research Board Annual Meeting, Washington DC, USA. 2005. 11. Pappis, Costas P., and Ebrahim H. Mamdani. "A fuzzy logic controller for a trafc junction." IEEE Transactions on Systems, Man, and

Cybernetics 7, no. 10 (1977): 707-717.

12. L. A. Akanbi and E. A. Olajubu., “A fuzzy-Based Intelligent Traffic Control System for Managing VIP-Induced Chaos at Road Intersections”, African Journal of Computing & ICT, Vol 5. No. 4, June, (2012).

13. Leung, Ricky WK, Henry CW Lau, and C. K. Kwong. "On a responsive replenishment system: a fuzzy logic approach." Expert

Systems 20, no. 1 (2003): 20-32. 14. Hamed Homaei, S. R. Hejazi, Seyed Ali Mohamad Dehghan, “A New Traffic Light Controller Using Fuzzy Logic for a Full Single

Junction Involving Emergency Vehicle Preemption”, Journal of Uncertain Systems Vol.9, No.1, pp.49-61, (2015).

15. Asaithambi, Gowri, Venkatesan Kanagaraj, and Tomer Toledo. "Driving behaviors: Models and challenges for non-lane based mixed traffic." Transportation in Developing Economies 2, no. 2 (2016): 19.

16. Riaz, F. & Niazi, M.A., “Road collisions avoidance using vehicular cyber-physical systems: a taxonomy and review”, Complex

Adaptive Systems Modeling 4: 15. doi:10.1186/s40294-016-0025-8 (2016). 17. Jose E. Naranjo, Miguel A. Sotelo, Carlos Gonzalez, Ricardo Garcia, Teresa De Pedro, “Using Fuzzy Logic in Automated Vehicle

Control”, IEEE Intelligent Systems , Volume: 22, Issue: 1, Jan 2007, pp 36 – 45 (2007). 18. Yin, He, Wenhao Zhou, Mian Li, Chengbin Ma, and Chen Zhao. "An adaptive fuzzy logic-based energy management strategy on

battery/ultracapacitor hybrid electric vehicles." IEEE Transactions on Transportation Electrification 2, no. 3 (2016): 300-311.

19. Kisacikoglu, M. C., M. Uzunoglu, and M. S. Alam. "Fuzzy logic control of a fuel cell/battery/ultra-capacitor hybrid vehicular power system." In 2007 IEEE vehicle power and propulsion conference, pp. 591-596. IEEE, 2007.

20. Sordi Filho, Álvaro Luiz, Leonardo Presoto de Oliveira, André Schneider de Oliveira, João Alberto Fabro, and Marco Aurélio

Wehrmeister. "Simultaneous Navigation and Mapping in an Autonomous Vehicle Based on Fuzzy Logic." Designing with Computational Intelligence, pp. 53-68. Springer, Cham, 2017.

21. Shiri, MJ Shirvani, and Hamid Reza Maleki. "Maximum Green Time Settings for Traffic-Actuated Signal Control at Isolated

Intersections Using Fuzzy Logic." International Journal of Fuzzy Systems 19, no. 1 (2017): 247-256. 22. Luo, Quyuan, Xuelian Cai, Tom H. Luan, and Qiang Ye. "Fuzzy logic-based integrity-oriented file transfer for highway vehicular

communications." EURASIP Journal on Wireless Communications and Networking 2018, no. 1 (2018): 3.

117-122

23

Authors: Ajay Sharma, Rajendra Singh Kaler, Shamimul Qamar, Himanshu Monga, Navneet Sharma

Paper Title: Exploration of Hybrid FSO/RF Availabilities in Optical Wireless System

Abstract: In this paper a more practical solution has investigate by extend the range of availability through

FSO back-up link with a radio frequency (RF) link of lower data rate. This paper investigate the technical

advantages of integrating FSO and RF communication systems to get availability of 99.999% to expand a new

system called hybrid/dual RF/FSOF system. Analysis shows that a consequences of rain and fog have an effect

on the RF and FSO links severally however not at the same time and barely occur at the same time to grant the

choice to build up new hybrid FSO/RF system.

Keywords: Radio Frequency, Free Space Optics, Hybrid RF/FSO, Visibility, Availability

References:

1. J. M. Kahn, D. A. Miller, "Communications expands its space", Nature Photon., vol. 11, no. 1, pp. 5-8, Jan. 2017.

2. M. Alzenad, M. Z. Shakir, H. Yanikomeroglu, M. S. Alouini, "FSO-based vertical backhaul/fronthaul framework for 5G+ wireless

networks", IEEE Commun. Mag., vol. 56, no. 1, pp. 218-224, Jan. 2018.

3. I.Sousa, M. P. Queluz, A. Rodrigues, "An efficient visibility prediction framework for free-space optical systems", Wireless

Pers. Commun., vol. 96, no. 3, pp. 3483-3498, Oct. 2017.

4. A. K. Majumder and J. C. Ricklin, “Free-space laser communications”, New York, NY: Springer, 2008.

5. A. Mahdy and J. S. Deogun, “Wireless optical communications: a survey”, Proceedings of IEEE Wireless Communications and

Networking Conference (WCNC), 2004, Atlanta, USA, pp. 2399-2404. 6. A. AbdulHussein, A. Oka, T. T. Nguyen and L. Lampe, “Rateless coding for hybrid free-space optical and radio-frequency

communication”, IEEE Trans. Wireless Commun., vol. 9, pp. 907- 913, 2010.

7. S. Vangala and H. Pishro-Nik, “A highly reliable FSO/RF communication system using efficient codes”, in Proc. IEEE Global Telecommunications Conference (GLOBECOM), 2007,Washington DC, USA, pp. 2232-2236.

8. S. Bloom and W. Hartley, “The last-mile solution: hybrid FSO radio,” in Whitepaper, AirFiber Inc., May 2002.

9. I. I. Kim and E. Korevaar, “Availability of free space optics (FSO) and hybrid FSO/RF systems”, in Proc. SPIE, Optical Wireless Communications IV, 2001, Denver, CO, USA, vol. 4530, pp. 84-95.

10. I. I. Kim, B. McArthur, and E. Korevaar, “Comparison of laser beam propagation at 785 nm and 1550 nm in fog and haze for optical

wireless communications”, Proc. SPIE, 2001, vol. 4214, pp. 26- 37. 11. B. He and R. Schober, “Bit-interleaved coded modulation for hybrid RF/FSO systems”, IEEE Trans. Commun., vol. 57, no. 12, pp.

3753-3763, 2009.

12. H. Tapse and D. K. Borah, “Hybrid optical/RF channels: characterization and performance study using low density parity check codes”, IEEE Trans. Commun.,vol. 57, no. 11, pp. 3288- 3297, 2009.

13. W. Zhang, S. Hranilovic and C. Shi, “Soft-switching hybrid FSO/RF links using short-length Raptor codes: design and

implementation”, IEEE J. Sel. Areas Comm., vol. 27, pp. 1698-1708, 2009. 14. I. I. Kim, R. Stieger, J. Koontz, C. Moursund, M. Barclay, P. Adhikari, J. Schuster, and E. Korevaar, “Wireless optical transmission

of Fast Ethernet, FDDI, ATM, and ESCON protocol data using the TerraLink laser communication system,” Opt. Eng., vol. 37, pp.

3143-3155, 1998. 15. International station meteorological climate summary. Ver. 4.0, Federal Climate Complex Asheville, 1996

16. T. H. Carbonneau and D. R. Wisely, “Opportunities and challenges for optical wireless; the competitive advantage of free space

telecommunications links in today’s crowded marketplace,” Wireless Technologies and Systems: Millimeter Wave and Optical, Proc. SPIE, 1997, 3232, pp. 119-128

17. I. I. Kim, R. Stieger, J. Koontz, C. Moursund, M. Barclay, P. Adhikari, J. Schuster, and E. Korevaar, “Wireless optical transmission of Fast Ethernet, FDDI, ATM, and ESCON protocol data using the TerraLink laser communication system,” Opt. Eng., vol. 37, pp.

3143- 3155, 1998.

18. L. C. Andrews and R. L. Philips, “Laser beam propagation through random media”, Washington, DC: SPIE, 2005. 19. “AirebeamTM G60 Series datasheet”, http://www.lightpointe.com/downloads/datasheets/Airebeam_G60.pdf [online], last accessed:

Apr. 18, 2011.

20. J. Schothier, “WP3-study: the 60 GHz channel and its modeling,” Tech. Report, IST-2001-32686 Broadway, 2001.

123-126

24

Authors: Dr. Neelu Tiwari

Paper Title: Impact of crude prices on retail prices – A Time series analysis

Abstract: The UK has emerged as one of the largest producers of petroleum in the world. A significant

amount of petroleum is used for fulfilling the energy demand within the country. However, the country

witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to

meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil

prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which

are case specific and can comply with the research aim.

The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A

series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study

found that there was an asymmetric relationship between the movements of prices of crude oil with respect to

retail fuel prices in the long run. However, the study is not without limitations which are represented at the end

of the study following with its future scope.

Keywords: Autoregressive, Co-integration, Crude Prices, Regression, Stationarity,Unit root,

References: 1. Agrawal, A. &. (2011). An Introductory Study on Time Series Modeling and Forecasting.

2. Anene, B. D., Eloundou, F., Larach, T., & Lugo, M. (2011). Cointegration of Equity Returns in Brazil, Russia, India, China and South

Africa ( BRICS ), 392. 3. Arrow, K. J. (1959). Towards a Theory of Price Adjustment.

4. Atil, A., Lahiani, A., & Khuong Nguyen, D. (2014). Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural

gas prices. Energy Policy, Elsevier. https://doi.org/10.1016/j.enpol.2013.09.064> 5. Cook, G. (2004). 8 Week Speed Development Program.

6. Cottrell, A. (2004). Economics 215 The Error Correction Model 1 Setting up the EC model. Economics.

7. Deng, C., Jiang, Z., & Sun, C. (2018). Estimating the efficiency and impacts of petroleum product pricing reforms in China. Sustainability (Switzerland), 10(4), 1–17. https://doi.org/10.3390/su10041080

8. Dias, D. A., Marques, C. R., Martins, F., & Silva, J. M. C. S. (2009). PRICE ADJUSTMENT LAGS: EVIDENCE FROM FIRM-

LEVEL DATA. Winter. 9. Frey, G., & Manera, M. (2007). ECONOMETRIC MODELS OF ASYMMETRIC PRICE TRANSMISSION. Journal of Economic

Surveys, 21(2), 349–415. https://doi.org/10.1111/j.1467-6419.2007.00507.x

10. Gienko, S. (2009). Price Relationships Between Crude Oil And Retail Fuel In Ukraine. Retrieved from http://www.kse.org.ua/uploads/file/SGienko_Thesis2.pdf

127-133

11. Helm, T. W. (2012). Essays on the economics of price transmission. The University of Melbourne. 12. Ibrahim, A.-J. (2017). Why the Refineries in the UK perform so poorly? Research Gate.

13. Kanioura, A., & Turner, P.. “Sheffield Economic Research Paper Series SERP Number” : 2003001, (March)-2003.

14. Karagiannis, S., Panagopoulos, Y., & Vlamis, P. (2015). “Are unleaded gasoline and diesel price adjustments symmetric? A comparison of the four largest EU retail fuel markets”. Economic Modelling, 48, 281–291.

https://doi.org/10.1016/J.ECONMOD.2014.11.003

15. Kristoufek, L., & Lunackova, P. (2014). “Rockets and feathers meet Joseph: Reinvestigating the oil-gasoline asymmetry on the international markets”

16. LANZA, A. (1991). “The speed of Adjustment and Market Structure: A Study of the Gasoline Market in Germany. Oxford Institute

for Energy Studies”. 17. Lütkepohl, H. (2005). “Vector Error Correction Models. New Introduction to Multiple Time Series Analysis” 237–267.

https://doi.org/10.1007/978-3-540-27752-1_6

18. Meyer, J., & Von Cramon-Taubadel, S. (2002). “Asymmetric Price Transmission: A Survey. EAAE”. Monthly Business Survey. (2016). Fuel prices explained: A breakdown of the cost of petrol and diesel - Office for National Statistics.

19. Noel, D., David, H., & Cassie, B. (2016). “Petrol and diesel prices”, (4712), 25. https://doi.org/SN/SG/4712

20. Perdiguero-García, J. (2013). “Symmetric or asymmetric gasoline prices? A meta-analysis approach”; 21. Rosales, F., & Von-Cramon, S. (n.d.). “Analysis of Price Transmission using a Nonparametric Error Correction Model with Time-

Varying Cointegration”.

22. Sabnavis, M., Jagasheth, H. U., Khutal, A., & Kaushik, S. (2017). “Impact of Crude oil price hike. Care Rating”, (April), 1–5. 23. Shah, S. R., & Al-Bargi, A. (2016). “Research Paradigms: Researchers' Worldviews, Theoretical Frameworks, and Study Designs”.

Awe, 4(4), 252–264. Retrieved from http://www.awej.org/index.php?option=com_content&view=article&id=398:sayyed-rashid-

shah-abdullah-al-bargi&catid=44&Itemid=133.

24. Silva, A. S. da, Vasconcelos, C. R. F., Vasconcelos, S. P., & de Mattos, R. S. (2014). “Symmetric transmission of prices in the retail

gasoline market in Brazil. Energy Economics”, 43, 11–21. https://doi.org/10.1016/J.ENECO.2014.02.002

25. Statista. (2018). “Oil production and consumption in the United Kingdom (UK) | Statista.” 26. Waghmare, A. (2018). “Tax on tax gives states a high”.

27. Zakaria, M., & Zakaria, M. (2008). Mp r a, (11543).

28. Zivot, E., & Wang, J. (2006). “Unit Root Tests. Modeling Financial Time Series with S-PLUS”, (1), 111–139. https://doi.org/10.1016/0304-4076(92)90104-Y

29. Zlatcu, I., Kubinschi, M., & Barnea, D. (2015). “Fuel Price Volatility and Asymmetric Transmission of Crude Oil Price Changes to

Fuel Prices. Theoretical and Applied Economics”, XXII(4), 33–44.

25

Authors: Kalpana, Preeta Rajiv Sivaraman, Rishabh Kumar

Paper Title: Handling IDN Homograph Attack using Facial Expression Password

Abstract: With the advancement of technology and its applications in diverse areas, people are able to get

access of the desired information, exchange ideas and connect with the world. But one of the areas of concern

is that user shares personal information for example images, videos, sharing location etc. on various social

networking websites very frequently. Another important aspect is sharing financial information for various

activities such as payments and purchases which is misused using phishing attacks. There are different variety

of attacks that one may come across. The objective of this paper is to make people aware of the increasing

fishing attacks such as IDN and how using latest authentication technique of Facial Recognition system one

record various facial expressions to secure himself from such attacks since only the user is aware what facial

expression he/she has recorded for a particular website.

Keywords: Phishing, IDN, Facial Recognition System.

References: 1. Min Wu, Robert Miller and Simson Garfinkel, " Do Security Toolbars Actually Prevent Phishing Attacks? " MIT Computer Science

and Artificial Intelligence Lab,32 Vassar Street Cambridge, October ,2005.

2. J.D. Tygar and Rachna Dhamija ," The Battle Against Phishing: Dynamic Security Skins" , ACM Symposium on Usable Security and

Privacy, July 2005, pp. 77-88

3. .Kasturi More, Prajakta Kadam, Anjali Jadhav and Dilip Dalgade,”Face Authentication Application for Social Networking Site

“,International Journal of Computer Science and Mobile Computing Vol. 4, Issue. 3, March 2015, pg.430 – 433

4. Balamurali Kaliyaperumal and Rajasekaran.M ," Application Authentication:Facial Expression Password ",iieng conference proceedings,2015.

5. Pengqing Xie ,” Facial movement based human user authentication”, University Digital Repository Iowa State University Ames,

Iowa ,2014 6. Or Katz , “A New Era in Phishing — Games, Social, and Prizes”, May/2018.

7. Qian Cui, Guy-Vincent Jourdan, Gregor V. Bochmann,” Tracking Phishing Attacks Over Time, International World Wide Web

Conference Committee (IW3C2), ACM 978-1-4503-4913-0/17/04, Perth, Australia,April 2017,.

134-138

26

Authors: Priyanka Tyagi, Dr. S.K. Singh, Dr. Piyush Dua

Paper Title: Gate Diffusion Input Technique for Power Efficient Circuits and Its Applications

Abstract: In present scenario world become completely digital. In digital devices the speed and life of the

battery is the biggest issue .To resolve these problems there are my techniques for design the devices. A low

power design technique is Gate Diffusion input (GDI). This review has the study of GDI technique which is

most recent research in low power designing field. In this study many paper were reviewed. The review has

structure of THE GDI cell, modeling and application. This review also presented the comparison of GDI

technique with other technique of designing. The purpose of the study to find out most recent research in field

of GDI. From this study we find out this technique mostly used for digital circuits. This review provides the

current state of research and future scopes in this field.

Keywords: CMOS, CNTFET, GDI, MGDI, PDP

139-144

References: 1. R. J. Baker, H. W. Li, and D. E. Boyce, “CMOS circuit design, layout, and simulation,” IEEE Press Series on Microelectronic

Systems, pp.205–242.

2. N. Weste and K. Eshraghian, Principles of CMOS digital design. Reading, MA: Addison-Wesley, pp. 304–307.

3. A. P. Chandrakasan, S. Sheng, and R. W. Brodersen, “Low- power CMOS digital design,” IEEE J. Solid-State Circuits, Apr. 1992,

vol. 27, pp.473–484,.

4. W. Al-Assadi, A. P. Jayasumana, and Y. K. Malaiya, “Passtransistor logic design,” Int. J. Electron.,1991, vol. 70, pp. 739– 749.

5. I. S. Abu-Khater, A. Bellaouar, and M. I. Elmastry, “Circuit techniques for CMOS low-power high-performance multipliers,” IEEE J. Solid- State Circuits, Oct. 1996, vol. 31, pp. 1535–1546.

6. K. Yano, Y. Sasaki, K. Rikino, and K. Seki, “Top-down passtransistor logic design,” IEEE J. Solid-State Circuits, June1996, vol. 31,

pp. 792–803,. 7. R. Zimmermann and W. Fichtner, “Low-power logic styles: CMOS versus pass-transistor logic,” IEEE J. Solid-State Circuits, June

1997, vol. 32, pp. 1079–1090. 8. T. Sakurai, “Closed-form expressions for interconnection delay, coupling, and crosstalk in VLSI’s,” IEEE Trans. Electron Devices,

vol. 40, pp. 118–124, Jan. 1993.

9. V. Adler and E. G. Friedman, “Delay and power expressions for a CMOS inverter driving a resistive-capacitive load,” Analog Integrat. Circuits Signal Process, vol. 14, pp. 29–39, 1997.

10. A. Morgenshtein et al., “Gate- Diffusion Input (GDI) - A Power Efficient Method for Digital Combinatorial Circuits” IEEE trans.

VLSI, Vol. 10, no. 5, pp. 566-581, October 2002. 11. Divya Soniand Mihir V.Shah, “ Review On Modified Gate Diffusiobn Input Technique” , International Research Journal of

engineering And Technology(IRJET), , June 1997,vol.4 ,no.4,pp 874-78.

12. A. Morgenshtein et al., Gate-diffusion input (GDI) - A technique for low power design of digital circuits: Analysis and

characterization IEEE International Symposium on Circuits and Systems · February 2002.

13. A. Morgenshtein, A.Fish and I.A. Wagner., “Asynchronous gate-diffusion-input (GDI) circuits”, IEEE Trans. VLSI Syst. pp.847-856,

vol.12, no.8, 2004 . 14. A. Morgenshtein et al., “An Efficient Implementation of D- Flip Flop using GDI Technique” Proceedings of IEEE International

Symposium on Circuits and Systems (ISCAS), pp. 673-676, 2004.

15. Massimo Alioto et al. “High –Speed/ Low-Power Mixed Full Adder Chains: Analysis and Comparison versusTechnology” Proc. of ISCAS, pp. 2998-3001, 2007.

16. Adarsh Kumar Agrawal et al. , “A New Mixed Gate Diffusion Input Full Adder Topology for High Speed Low Power Digital Circuits”

,World Applied Sciences Journal 7( Special Issue of Computer & IT) pp.138-144, 2009 . 17. R.Uma, “ 4- Bit Fast Adder Design :Topology and Layout with Self-Resetting Logic for Low Power VLSI Circuit” ,International

Journal of Advanced Engineering Science and Technology,2011, Vol. 7, Issue no. 2, pp. 197-205,

18. Kunal et al.” GDI Technique: A Power –Efficient method For Digital Circuits”, International journal of Advance Electrical and Electronics Engineering, 2012,Vol.1,issue3 pp.87-93.

19. Mrs. K. Kalaiselvi, et. al.,” Design of Area Optimized High Speed Adder Circuit in Self Resetting Logic”, IOSR Journal of VLSI and

Signal , 2014, vol.4 , issue 2, pp31-38. 20. Dr.K.Nehru et al “Analysis of 16-Bit Counter Using GDI Technique and CMOS Logic” International Journal of applied Engineering

Research, January 2015, Vol 10, issue6, pp 1612116128 .

21. Mohan Shoba, “ GDI based Full Adder for Energy Efficient Arithmetic Applications” , Engineering Science and Technology an International Journal, 2016,vol.19 pp. 485-496.

22. P. sheel et al.” Comparitive Analysis Of Gate Diffusion Input Based Full Adder”, IOSR journal Of VLSI and signal processing, 2016,

Vol.6, issue 3, pp24-30. 23. P.A Irfan Khan,” Design of 2×2 vedic multiplier using GDI technique” , International Conference on Energy, Communication, Data

Analytics and Soft Computing (ICECDS)aug2017, DOI: 10.1109/ICECDS.2017.8389786.

24. R.Uma et al. “New Low Power Adders in Self Resetting Logic with Gate Diffusion Input Technique”, Journal of King Saud University, Engineering Sciences, Vol. 29, pp.118-134, 2017.

25. Munesh Tirpathi,” Low power based Manchester encoder by GDI” 2018 2nd International Conference on Inventive Systems and

Control (ICISC), Jan 2018, DOI: 10.1109/ICISC.2018.8398895 . 26. E V Nagalakshmi , K Kavya,” Design of 4 Bit Alu Using Modified Gdi Technology for Power Reduction”, International Journal of

Engineering Science Invention (IJESI), February 2018 ,Volume 7, Issue 2 ,PP. 38-45

27. Dr. N.Suresh ,” Design and Analysis of Low Power Full Adder using GDI- MUX”, International Journal of Electronics, Electrical and Computational System IJEECS April 2018 , Volume 7, Issue 4

28. .Padmanabhan Balasubramanian and Johince John, “ Low Power Digital design using modified GDI method”, International

Conference on Design and Test of Integrated 29. Systems in Nanoscale Technology, IEEE, pp.190-193, September 2006, DOI: 10.1109/DTIS.2006.1708713.

30. R.Uma et al., “Modified Gate Diffusion Input Technique: A new Technique for Enhancing Performance in Full Adder Circuits”,

International Conference on communication, Computing and Security, ICCS, Vol.6, pp.74-81,2012, https://doi.org/10.1016/j.protcy.2012.10.010 .

31. Uma ramadass et al., “New Low Power Delay Element in Self Resetting Logic with Modified Gate Diffusion Input Technique”, 10th

IEEE International Conference on Semiconductor Electronics (ICSE) , 2012, DOI: 10.1109/SMElec.2012.6417197 32. Pankaj Verma , Ruchi Singh and Y. K. Mishra, ”Modified GDI Technique - A Power Efficient Method For Digital Circuit Design”

International Journal of Electronics and Computer Science Engineering,2013, vol2, issue 4. 33. Krishnendu Dhar, “ Design of a Low Power, High Speed, Energy Efficient Full Adder Using Modified GDI and MVT Scheme in

45nm Technology”, IEEE, International Conference on Control, Instrumentation, Communication and Computational Technologies

(ICCICCT) pp.36-41, July 2014,DOI: 10.1109/ICCICCT.2014.6992926 34. Krishnendu Dhar, “Design of a High Speed, Low Power Synchronously Clocked NOR-based JK Flip-Flop using Modified GDI

Technique in 45nm Technology”, IEEE International Conference on Advances in Computing, Communications and Informatics

(ICACCI), September, 2014,DOI: 10.1109/ICACCI.2014.6968212. 35. Deepali Koppad et al,”Low Power 1-Bit Full Adder Circuit Using Modified Gate Diffusion Input (GDI)” 2016 First International

Conference on Micro and Nano Technologies, Modelling and Simulation, DOI 10.1109/MNTMSim.2016.21.

36. P. Prakasha,⁎, K. Mohana Sundarama, M. Anto Bennetb “A review on carbon nanotube field effect transistors (CNTFETs) for ultra-low power applications”, Renewable and Sustainable Energy Reviews, 2018, 194-203 .

37. Mehrabani YS, Mirzaee RF, Zareei Z, Daryabari SM. “A novel high-speed, low-power CNTFET-based inexact full adder cell for

image processing application of motion detecto”r. J Circuits System Computation 2017;26(05):1750082. [37] SoheliFarhana , ZahirulAlam, SherozKhan ,”High frequency CNTFET-based logic gate”. IEEE regional symposium on micro and nanoelectronics

(RSM). p. 1–4.

38. Somineni RajendraPrasad, Padma Sai Y, Naga Leela S. “Low leakage CNTFET full adders”, Global Conference on Communication Technologies (GCCT).p.174–9,2015,DOI: 10.1109/GCCT.2015.7342647

39. J. Appenzeller, “Carbon Nanotubes for High-Performance Electronics—Progress and Prospect,” Proc. IEEE, Volume 96, Issue 2, pp.

201 - 211, Feb. 2008 . 40. P. Reena Monica , V. T. Sreedevi ,” A Low Power And AreA Efficient CNTFET Based GDI Cell For Logic Circuits”, ARPN

Journal of Engineering and Applied Sciences, DECEMBER 2014 , VOL. 9, NO. 12

41. Nazirahmed , Mohammad Shafquatul Islam et al ”Performance Study of 12-CNTFET and GDI CNTFET based Full Adder in HSPICE”,IEEE International Conference on Advances in Engineering &Technology Research(ICAETR-2014),DOI:

10.1109/ICAETR.2014.7012895. 42. EbrahimAbiri1AbdolrezaDarabi2 Design of low power and high read stability 8T-SRAM memory based on the modified Gate

Diffusion Input (m-GDI) in 32 nm CNTFET technology, 2015,Microelectronics Journal Volume 46, Issue 12, Part A, December

2015, Pages 1351-1363. 43. P.Chanderashekar, RKarthik, ArdhiBhavana, ”Design of low threshold Full Adder Cell Using CNTFET” International journal of

Applied Engineering Research, 2017, vol 12,no.12.

44. Priya Kaushal Energy Efficient CNTFET Based Full Adder Using Hybrid Logic International Journal on Recent and Innovation Trends in Computing and Communication,2017,Volume 5, Issue 7, 98 – 10.

45. EbrahimAbiri,AbdolrezaDarabi,”CNTFET-based divide-byN/[N+1] DMFPs using m-GDI method for future generation

communication networks Nano Communication Networks” December Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.

27

Authors: Ms. Seema, Dr. Mahima Gupta

Paper Title: Collaboration as an Essential Key to Education for Sustainable Development

Abstract: Sustainable Development deals with issues like climate change, disaster risk, biodiversity, poverty,

environmental degradation, pollution, overconsumption, gender inequality, health, quality education and

economic growth. As evident from the 17 Sustainable Development Goals, every aspect of natural, social and

economic field has become an area of concern for sustainability at local as well as global level. Collaboration

promotes peace worldwide and brings effectiveness and accountability at institutional level. This would

facilitate their contribution towards sustainability as an underlying force towards sustainable development.

Human settlements with fast growing industrialization have to keep balance with other forms of life on the

planet and also with its natural resources. Education serves as a basis to sustainable development as it is

through education only that global citizens can be created who have the competencies to sustainable

development now and in the future. Education for sustainable development requires coordination and

collaboration among all these different fields and they all must support education. People and organizations

from diverse fields need to come together and make collaborative efforts in education for sustainable

development. In the present paper, an attempt has been made to throw light on the need and importance of

collaboration and interrelation of education sector with various other sectors of society such as economics,

business, health, environment and legal framework.

Keywords: Collaboration, Education for Sustainable Development (ESD), Global Citizens, Sustainability,

Sustainable Development Goals (SDGs).

References: 1. Schools in action, global citizens for sustainable development: a guide for students (PDF). Paris:UNESCO.2017.ISBN 978-92-3-

100179-6.

2. “Shaping the Future We Want. UN Decade of Education for Sustainable Development (2005-2014). Final Report” (PDF). UNESCO.

2014. 3. Fricke H., Cathryn, G., Skinner, A. 2015. Monitoring Education for Global Citizenship: A Contribution TO Debate. Brussels, DEEEP

– CONCORD DARE Forum. Available at:http://unesdoc.unesco.org/images/0023/002329/232003e.pdf

4. United Nations (UN).2015. Sustainable Development Goals. Available at: http://www.un.org/sustainabledevelopment/sustainable-development-goals

5. UNESCO. 2015a Education 2030. Incheon Declaration and Framework for Action, Towards Inclusive and equitable Quality Education

and Lifelong Learning. Paris, UNESCO. Available at: http://unesdoc.unesco.org/images/0023/002329/232993e.pdf 6. Education for Sustainable Development Goals: Learning Objectives (PDF). Paris:UNESCO.2017.

7. Rethinking Education: Towards a global common good (PDF). UNESCO. 2015.ISBN 978-92-3-100088-8

8. Dernback, J.C. (2002) Stumbling towards sustainability. 9. Huckle, J. and Sterling, S.R. (2006) Education for sustainability. Earthscan.

10. “The UN Decade of Education for Sustainable Development 2005-2014”. UNESCO.

11. “Global Citizenship Education: Topics and learning objectives” (PDF) UNESCO.2015.

12. “Transforming our world: the 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25

September 2015”. United Nations. 2015.

13. Woods (1 January 2003).” Environmental Education for a sustainable future: formal schooling”. Curriculum.edu.au 14. Schools in action, global citizens for sustainable development: a guide for students (PDF). Paris: UNESCO.2017. ISBN 978-92-3-

100179-6.

15. Teaching and Learning for a Sustainable future: A multimedia teacher education program.” UNESCO.2010. 16. Schools in action, global citizens for sustainable development: a guide for teachers. Paris: UNESCO.2016. ISBN 978-92-3-100180-2.

17. “Shaping the future, we want. UN Decade of Education for Sustainable Development (2005-2014). Final Report”. UNESCO. 2014.

18. NEP2016/ReportNEP.pdf 19. Singh H. “Education for sustainable development”. Vol. 1. ISSN 0975-1254.15 Jan 2010.

20. “CAT Education and Life Long Learning”. Learning.cat.org.uk.

21. Education for Sustainable Development and Global learning (PDF)

145-148

28

Authors: Sonia Saini, S. P. Singh, Ruchi Agarwal

Paper Title: Augmented Machine Learning Ensemble Extension Model for Social Media Health Trends

Predictions

Abstract: Social Networks are the source of rich, interactive, textual, and other media.Users of the social

media generate data at a tremendous pace.This data consisting of user opinions and attitudes is so large that it

has necessitated automated methods to analyze and extract knowledge from the same. Social networks have

been studied and analyzed using various graph-based analysis techniques. Prominent analysishas centered on

features like ego-networks, distance, centrality, sub-networks etc. The areas of study for social media analysis

have been centered around populations, boundaries, Cohesion, Centrality and Brokerage, Prestige and Ranking.

In the past several models have been propounded for various machine learning based analytics for the Social

Networks study but there is a perceived need for studying social networks for health data using Ensemble

Learning wherein an array of various Machine Learning techniques can be employed to achieve better

149-153

classification or clustering results. We introduce an Analytical Model which will identify most discussed terms/

topics of health/ healthcare on social networks to predict the emerging health trends. The model is to use

temporal datasets to deduce multi-label classification of health-related topics. The Model employs the

technique of Temporal Clustering (using Machine Learning) on the Topic Classification done on datasets using

Ensemble Machine Learning to deduce the most discussed topics. Using this model, we will see how

Ensemble Machine Learning based Analytical Model for analyzing social network data for health topics is

efficient than traditional Machine Learning technique(s).

Keywords: Augmentation Analytical Model, Ensemble Learning, Machine Learning, Social Media Data

References: 1. Dimitriadou E., Weingessel A., Hornik K,Voting-Merging: An Ensemble Method for Clustering. In: Dorffner G., Bischof H., Hornik K.

(eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg,2001,ch.31.

2. A. Strehl and J. Ghosh, “Cluster ensembles — a knowledge reuse framework for combining multiple partitions,” The Journal of Machine Learning., vol. 3, pp. 583–617, March.,2003. Available: http:/ https://dl.acm.org/citation.cfm?id=944935/

3. X. Z. Fern and C. E. Brodley, “Solving cluster ensemble problems by bipartite graph partitioning.” in ICML, C. E. Brodley, Ed., vol. 69. ACM, 2004. [Online]. Available: http://dblp.uni-trier.de/db/conf/icml/icml2004.html#FernB04.

4. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.

5. R. E. Schapire, “A brief introduction to boosting,” in IJCAI, Stockholm, Sweden, 1999, pp. 1401–1406.

6. T. Chakraborty, D. Chandhok, and V. S. Subrahmanian, “MC3: A multi-class consensus classification framework,” in PAKDD, Jeju, South Korea, 2017, pp. 343–355.

7. J. Friedman and B. Popescu, “Predictive learning via rule ensembles,” Annals of Applied Statistics, vol. 3, no. 2, pp. 916–954, 2008.

8. Tsoumakas, Grigorios; Vlahavas, Ioannis , “Random k-labelsets: An ensemble method for multilabel classification.”,2007

9. Lo SL, Chiong R, Cornforth D ,“Using Support Vector Machine Ensembles for Target Audience Classification on Twitter”. PLoS ONE 10(4): e0122855. doi: 10.1371/journal.pone.0122855, 2015

10. Scott Fortmann-Roe.(2012,month).Understanding the Bias-Variance Tradeoff.Available:http://scott.fortmann-roe.com/docs/BiasVariance.html

11. Zheng Fang and Zhongfei (Mark) Zhang. 2012. Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining (ICDM '12). IEEE Computer Society, Washington, DC, USA, 864-869.

12. Coletta, Luiz & Felix, Nadia & Hruschka, Eduardo & Hruschka, Estevam.” Combining Classification and Clustering for Tweet Sentiment Analysis” published in Brazilian Conference on Intelligent Systems. DOI: 10.1109/BRACIS.2014.46 .

13. Ana Stanescu and Doina Caragea, Ensemble-based semi-supervised learning approaches for imbalanced splice site datasets 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM),UKDOI: 10.1109/BIBM.2014.6999196

14. “Shuffling Paragraphs - using data augmentation in NLP to increase accuracy.” https://medium.com/bcggamma/ shuffling-paragraphs-using-data-augmentation-in-nlp-to-increase-accuracy. [Accessed: 2019-01-29].

29

Authors: Shruti Karkra, Priti Singh, Karamjit Kaur

Paper Title: Convolution Neural Network: A Shallow Dive in to Deep Neural Net Technology

Abstract: It is always beneficial to reassess the previously done work to create interest and develop

understanding about the subject in importance. In computer vision, to perform the task of feature extraction,

classification or segmentation, measurement and assessment of image structures (medical images, natural

images etc.) is to be done very efficiently. In the field of image processing numerous techniques are available,

but it is very difficult to perform these tasks due to noise and other variable artifacts. Various Deep machine

learning algorithms are used to perform complex task of recognition and computer vision. Recently

Convolutional Neural Networks (CNNs-back bone of numerous deep learning algorithms) have shown state of

the art performance in high level computer vision tasks, such as object detection, object recognition,

classification, machine translation, semantic segmentation, speech recognition, scene labelling, medical

imaging, robotics and control, , natural language processing (NLP), bio-informatics, cybersecurity, and many

others. Convolution neural networks is the attempt to combine mathematics to computer science with icing of

biology on it.

CNNs work in two parts. The first part is mathematics that supports feature extraction and second part is about

classification and prediction at pixel level. This review is intended for those who want to grab the complete

knowledge about CNN, their development form ancient age to modern state of art system of deep learning

system. This review paper is organized in three steps: in the first step introduction about the concept is given

along with necessary background information. In the second step other highlights and related work proposed

by various authors is explained. Third step is the complete layer wise architecture of convolution networks.

The last section is followed by detailed discussion on improvements, and challenges on these deep learning

techniques. Most papers consider for this review are later than 2012 from when the history of convolution

neural networks and deep learning begins.

Keywords: Convolution neural network (Covnet), Deep learning, Image Net, Neural Networks, Semantic

Segmentation

References: 1. Nicholson, C., & Gibson, A. (2014, November 27). A Beginner's Guide to Neural Networks and Deep Learning. Retrieved from

https://skymind.ai/wiki/neural-network

2. Anderson,D., & McNeill, G.,(1992,August 20).ARTIFICIAL NEURAL NETWORKS TECHNOLOGY.A DACS state-of-art report Griffiss AFB, NY

3. Maind S., & Wankar ,(2014,January). Research Paper on Basic of Artificial Neural Network , International Journal on Recent and

154-162

Innovation Trends in Computing and Communication,2(1),96-100. ISSN: 2321-8169 4. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological

Review, 65(6), 386-408. doi:10.1037/h0042519

5. Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018,February 1). Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, Hindawi 2018, 1-13. doi:10.1155/2018/7068349

6. Simonyan ,K., & Zisserman A.(2015 ,April 10).VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE

RECOGNITION, Visual Geometry Group, Published as a conference paper at ICLR 2015, University of Oxford. 7. Dickson, B. (2018, March 01). The limits and challenges of deep learning. Retrieved from https://bdtechtalks.com/2018/02/27/limits-

challenges-deep-learning-gary-marcus.

8. Nguyen, H. D., Le, A. D., & Nakagawa, M. (2015,Nov). Deep neural networks for recognizing online handwritten mathematical symbols. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). doi:10.1109/acpr.2015.7486478

9. Al-Saffar, A. A., Tao, H., & Talab, M. A. (2017,Oct). Review of deep convolution neural network in image classification. 2017

International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET). doi:10.1109/icramet.2017.8253139

10. Yamashita, R., Nishio, M., Do, R. K., & Togashi, K. (2018 June). Convolutional neural networks: An overview and application in

radiology. Insights into Imaging, 9(4), 611-629. doi:10.1007/s13244-018-0639-9 11. Paszke,A.,Chaurasia,A.,Kim,S.,Culurciello,E.,(2016 June 16). ENet: A Deep Neural Network Architecture for Real-Time Semantic

Segmentation, Computer Vision and Pattern Recognition (cs.CV), arXiv:1606.0016 June 162147v1

12. Sharma, A. (2017, December 05). Convolutional Neural Networks in Python.Retrievedfrom https://www.datacamp.com/community/tutorials/convolutional-neural-networks-python

13. Long, J., Shelhamer, E., & Darrell, T. (2016,May). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference

on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2015.7298965 UC Berkeley, arXiv:1605.06211v1

14. .Ardakani, A., Condo, C., Ahmadi, M., & Gross, W. J. (2017 Oct 17). An Architecture to Accelerate Convolution in Deep Neural

Networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(4), 1349-1362. doi:10.1109/tcsi.2017.2757036.

15. Rawat, W., & Wang, Z. (2017 Oct). Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Computation, 29(9), 2352-2449. doi:10.1162/neco_a_00990.

16. Zahangir, M., Taha, M., T., Christopher, Westberg, Stefan, . . . K., V. (2018, September 12). The History Began from AlexNet: A

Comprehensive Survey on Deep Learning Approaches. Computer Vision and Pattern Recognition, arXiv:1408.3264 17. Shea, K., & Nash , R.(2015 Dec 2).An Introduction to Convolutional Neural Networks, Neural and Evolutionary Computing,

arXiv:1511.08458v2.

18. Gupta, D., & Dishashree. (2018, July 13). Architecture of Convolutional Neural Networks (CNNs) demystified. Retrieved from https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/

19. Murphy,J.(2016).An Overview of Convolutional Neural Network Architectures for Deep Learning,IEEE Conference on Computer

Vision and Pattern Recognition (CVPR), Microway, Inc.Fall 2016. 20. Karpathy05, A. (n.d.). CS231n Convolutional Neural Networks for Visual Recognition. Retrieved from http://cs231n.github.io/

,Stanford university lecture notes.

21. Zhang, Q. (2018). Convolutional Neural Networks. 3rd International Conference on Electromechanical Control Technology and Transportation, 434-439. doi:10.5220/0006972204340439 © 2018 by SCITEPRESS – Science and Technology Publications.

22. Nair, V., & E. Hinton, G. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th

International Conference on Machine Learning, Haifa, Israel, 2010.

23. Ujjwalkarn, U. (2017, May 29). An Intuitive Explanation of Convolutional Neural Networks. Retrieved from

https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ 24. Deshpande, A. (2016,july). A Beginners Guide To Understanding Convolutional Neural Networks Part 2. Retrieved from

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginners-Guide-To-Understanding-Convolutional-Neural-Networks-Part-2/

25. Dev, D. (2017). Deep Learning with Hadoop. Birmingham: Packt Publishing. 26. Wei Ma,W.,Lu, J.(2017 Dec 4).An Equivalence of Fully Connected Layer and Convolutional Layer, Machine Learning (cs.LG)

Department of of Computer Science EPFL, Lausanne, arXiv:1712.01252v1 .

27. Gibson, A., & Patterson, J. (2017, August). Deep Learning. Retrieved from https://www.oreilly.com/library/view/deep-learning/9781491924570/ch04.html. Publisher: O'Reilly Media, Inc., ISBN: 9781491924570

28. Hinton, G. E., Osindero, S., & Teh, Y. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18(7), 1527-

1554. doi:10.1162/neco.2006.18.7.1527 29. Liu, T., Fang, S., Zhao, Y., Wang, P., & Zhang, J. (n.d.). Implementation of Training Convolutional Neural Networks. Retrieved

from https://arxiv.org/pdf/1506.01195

30. Kang, M., & Hong, K. (2018). Automatic Bird-Species Recognition using the Deep Learning and Web Data Mining. 2018 International Conference on Information and Communication Technology Convergence (ICTC). doi:10.1109/ictc.2018.8539463

31. Joost van Doorn, Van J..(2014 June 23).Analysis of Deep Convolutional Neural Network Architectures, 21th Twente Student

Conference on IT Enschede, The Netherlands. Copyright 2014, 32. Loffe, S., & Szegedy, C. (2015,March 2). Batch Normalization: Accelerating Deep Network Training by ... Retrieved from

https://arxiv.org/pdf/1502.03167v3.pdf,

33. Garcia, A. G., Escolano, S. O., Opera, S., Martinez, V. V., & Rodriguez, J. G. (2017, April 22). 1 A Review on Deep Learning Techniques Applied to Semantic Segmentation, Submitted to TPAMI, Computer Vision and pattern recognition,,

arXiv:1704.06857v1 [cs.CV] 22 Apr 2017

34. Dar, P., & Analytics Vidhya. (2018, April 04). 25 Open Datasets for Deep Learning Every Data Scientist Must Work With. Retrieved from https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/?cv=1

35. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998, November). Gradient-based learning applied to document recognition Proc.

IEEE 86(11): 2278–2324, 1998.). 36. Zeiler, M. D., & Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. Computer Vision – ECCV 2014 Lecture

Notes in Computer Science, 818-833. doi:10.1007/978-3-319-10590-1_53 Copyright Springer International Publishing Switzerland.

37. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017 May 12 ). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine

Intelligence, 40(4), 834-848. doi:10.1109/tpami.2017.2699184

38. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017). Deformable Convolutional Networks. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.89.

39. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015 Sep). Going deeper with convolutions.

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.201 40. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision.

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.308

41. Sergey, C., Vanhoucke, L., Vincent, V., & Alex, A. (2016, August 23). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Computer Vision and Pattern Recognition (cs.CV) Retrieved from https://arxiv.org/abs/1602.07261

42. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer

Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90 43. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity Mappings in Deep Residual Networks. Computer Vision – ECCV 2016 Lecture

Notes in Computer Science, 630-645. doi:10.1007/978-3-319-46493-0_38.

44. Chollet, F. (2017A pril 4). Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.195

Authors: Deepti Aggarwal, Sonu Mittal, Vikram Bali

Paper Title: Prediction Model for Classifying Students Based on Performance Using Machine Learning Techniques

30

Abstract: In today’s competitive world of educational organizations, the universities and colleges are using

various data mining tools and techniques to improve the students’ performance. Now a days, when the number

of drop out students is increasing every year, if we get to know the probability of a student whether he/she will

be able to cope up easily with the course, it is possible to take some preventive actions beforehand. In other

words, if we get to know that a student will clear his papers in the course or he will have reappear in papers, a

teacher/parent can focus more on such students. The data set of students has been taken from the UCI Machine

Learning repository where a sample of 131 students have been provided with twenty-two attributes. The results

of six classification algorithms have been compared in order to predict the most appropriate model for

classifying whether a student will have a reappear in a course or not.

Keywords: Classification, Multi-Layer Perceptron, Prediction, Random Forest

References: 1. Hussain S, Dahan N.A, Ba-Alwi F.M, Ribata N., ” Educational Data Mining and Analysis of Students Academic Performance Using

WEKA”, Indonesian Journal of Electrical Engineering and Computer Science, Vol. 9, Issue 2, pp. 447-459, 2018

2. Kumar N., Mishra B., and Bali V. , “A Novel Approach for Blastit-Induced Fly Rock Predication Based on Particle Swarm

Optimization and Artificial Neural Network”, B.Tiwari et al. (Eds.), Proceedings of International Conference on Recent Advancement in Computers and Communication, Lecture Notes in Networks and Systems 34, Springer Nature Singapore Pvt. Ltd, Chapter 3, Book

Id: 448040_1_En, ISBN: 978-981-10-8197-2, 2018

3. Shuja Mirza, Sonu Mittal, Majid Zaman, “Design and implementation of predictive model for prognosis of diabetes using data mining techniques", International Journal of Advanced Research in Computer Science. Vol. 9, Issue 2, pp-393-398, 2018

4. Shuja Mirza, Sonu Mittal, Majid Zaman, “Decision Support Predictive model for prognosis of diabetes using SMOTE and Decision

tree”, International Journal of Applied Engineering Research,Vol. 13, Issue 11, pp. 9277-9282,2018 5. Evandro B.Costa, Baldoino Fonseca, Fabrísia Ferreirade and Araújo, Joilson Regod , “Evaluating the effectiveness of educational data

mining techniques for early prediction of students' academic failure in introductory programming courses”, Computers in Human

Behavior, ELSEVIER, Vol. 73, pp. 247-256,2017 6. Almarabeh, H., “Analysis of Students’ Performance by Using Different Data Mining Classifiers” I.J. Modern Education and Computer

Science, Vol. 9, Issue 8, pp. 9-15, 2017

7. K. Govindaswamy and T. Velmurugan, “A Study on Classification and Clustering Data Mining Algorithms based on Students Academic Performance Prediction”, International Journal of Control Theory and Applications, Vol. 10, Issue 23, 2017

8. Anuradha, C. and T. Velmurugan, “ A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of

Students Performance”, Indian Journal of Science and Technology, Vol. 8, Issue 15, pp. 1-12,2015 9. Jha P.C., Bali V., Narula S. and Kalra M., “Optimal Component Selection Based on Cohesion and Coupling for Component based

Software System under Build-or-Buy Scheme”, Journal of computational sciences, ELSEVIER, Vol. 5, pp 233-242, ISSN: 1877-7503. ( SCI Impact Factor: 1.748, SCOPUS: 0.481), 2014

10. Patil, T. and S.S. Sherekar, “Performance Analysis of Naïve Bayes and J4.8 Classification Algorithm for data classification”,

International Journal of Computer Science and Applications, Vol. 6, Issue 2, pp. 256-261, 2013 11. Jha P.C. and Bali V. (2012), "Goal Programming Approach for Selection of COTS Components in Designing a Fault Tolerant

Modular Software System under Consensus Recovery Block Scheme", International Journal of Computer and Communication

Technology (IJCCT), Vol. 3, No. 1, pp. 1-8, ISSN (Online) 2231-0371, (Print) 0975-7449, 2012 12. Dekker, G.W., M. Pechenizkiy, and J.M. Vleeshouwers, “Predicting students drop out: A case study”, EDM ’09-Educational Data

Mining 2009: 2nd International Conference on Educational Data Mining, pp. 41-50, 2009

13. C. Romero and S. Ventura, ” Educational data mining: A survey from 1995 to 2005”, ELSEVIER, Expert Systems with Applications, Vol. 33, Issue 1, pp. 135–146, 2007

163-170

31

Authors: Garima Jain, Diksha Maurya

Paper Title: Extraction of Association Rule Mining using Apriori algorithm with Wolf Search Optimisation in R Programming

Abstract: Association rules mining (ARM) is a standout amongst the most essential Data Mining Systems.

Find attribute patterns as a binding rule in a data set. The discovery of these suggestion rules would result in a

mutual method. Firstly, regular elements are produced and therefore the association rules are extracted. In the

literature, different algorithms inspired by nature have been proposed as BCO, ACO, PSO, etc. to find

interesting association rules. This article presents the performance of the ARM hybrid approach with the

optimization of wolf research based on two different fitness functions. The goal is to discover the best

promising rules in the data set, avoiding optimal local solutions. The implementation is done in numerical data

that require data discretization as a preliminary phase and therefore the application of ARM with optimization

to generate the best rules.

Keywords: Association Rule Mining, Apriori algorithm, Fitness Function, Wolf search optimization

References:

1. GL. Tsay, R. S. Sreenivas, and R. larry W, “Scalable Association Rule Mining with Predication on Semantic Representations of

Data,” pp. 180–186, 2015. 2. Mayank Agrawal, M. Manuj, and P. S. S. Kushwah, “Association Rules Optimization using Improved PSO Algorithm,” pp. 395–398,

2015.

3. R. Tang, S. Fong, X. S. Yang, and S. Deb, “Wolf search algorithm with ephemeral memory,” 7th Int. Conf. Digit. Inf. Manag. ICDIM 2012, pp. 165–172, 2012.

4. D. S. Cunha, R. S. Xavier, D. G. Ferrari, L. N. De Castro, and S. Paulo, “Association Rule Mining using a Bacterial Colony Algorithm,”

2015. 5. K. S. Kumar and R. M. Chezian, “A Survey on Association Rule Mining using Apriori Algorithm,” Int. J. Comput. Appl., vol. 45, no.

5, pp. 47–50, 2012.

6. Manju and C. Kant, “Mining association rules directly using ACO without generating frequent itemsets,” Int. Conf. Energy Syst.

171-174

Appl. ICESA 2015, no. Icesa, pp. 390–395, 7. X. S. Yang, “A new metaheuristic Bat-inspired Algorithm,” Stud. Comput. Intell., vol. 284, pp. 65–74, 2010.

8. N. Optimisation, “Engineering optimisation by cuckoo search Xin-She Yang*,” vol. 1, no. 4, pp. 330–343, 2010.

9. I. E. Agbehadji, S. Fong, and R. Millham, “Wolf Search Algorithm for Numeric Association Rule Mining,” no. ICCKE, pp. 1–5, 2016.

10. W. Yamany, E. Emary, and A. E. Hassanien, “Wolf search algorithm for attribute reduction in classification,” IEEE SSCI 2014

- 2014 IEEE Symp. Ser. Comput. Intell. - CIDM 2014 2014 IEEE Symp. Comput. Intell. Data Mining, Proc., pp. 351–358, 2015. 11. R. J. Kuo, C. M. Chao, and Y. T. Chiu, “Application of particle swarm optimization to association rule mining,” Appl. Soft

Comput., vol. 11, no. 1, pp. 326–336, 2011.

12. T. Watanbe, A. Monden, Y. Kamei, and S. Morisaki, “Identifying Recurring Association Rules in Software Defect Prediction.,” 2016.

13. Hussain, Sadiq, et al. “Classification, clustering and association rule mining in educational datasets using data mining tools: A

case study.” Computer Science On-line Conference. Springer, Cham, 2018. 14. Vijayashree, J., and H. Parveen Sultana. "A Machine Learning Framework for Feature Selection in Heart Disease Classification

Using Improved Particle Swarm Optimization with Support Vector Machine Classifier." Programming and Computer Software 44.6

(2018): 388-397.

32

Authors: Dr Sheelesh Kumar Sharma, Mr Navel Kishor Sharma

Paper Title: Text Document Categorization using Modified K-Means Clustering Algorithm

Abstract: The volume of the information that is to be managed is increasing at exponential pace. The

challenge arises how to manage this large data effectively. There are many parameters on which the

performance of such a system can be measured such as time to retrieve the data, similarity of documents placed

in same cluster etc. The paper presents an approach for auto-document categorization using a modified k-

means. The proposed methodology has been tested on three different data sets. Experimental findings suggest

that proposed methodology is accurate and robust for creating accurate clusters of documents. The proposed

methodology uses cosine similarity measure and a fuzzy k-means clustering approach to yield the results very

fast and accurately.

Keywords: K-means, Text mining, Web mining

References: 1. Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saied Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut. 2017.

A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques . In Proceedings of KDD Bigdas, Halifax,

Canada, August 2017, 13-26. 2. Berry, M. W. (2004). Survey of text mining. Computing Reviews, 45(9), 548.

3. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.

4. M. K. Ng, J. Z. Huang and L. Jing, (2007) "An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1026-1041.

5. Huang, A. (2008, April). Similarity measures for text document clustering. In Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand (Vol. 4, pp. 9-56).

6. Neuhaus, J. M. and Kalbfleisch. J. D. (1998). Between- and within-cluster covariate effects in the analysis of clustered data.

Biometrics, 54(2), pp 638–645

7. 20 News Net Dataset: http://qwone.com/~jason/20Newsgroups/ (last visited on February 15, 2019)

8. Classic dataset

http://www.dataminingresearch.com/download/dataset/classicdocs.rar (last visited on February 15, 2019)

9. CMU Web Knowledgebase Dataset http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/ (last visited on February 15, 2019)

10. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the

Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. 11. Rasson, J. P., & Kubushishi, T. (1994). The gap test: an optimal method for determining the number of natural classes in cluster

analysis. In New approaches in classification and data analysis (pp. 186-193). Springer, Berlin, Heidelberg.

12. Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of

Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1), 103-119.

13. Revanasiddappa, M. B., & Harish, B. S. (2018). A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to

Categorize Text Documents. International Journal of Interactive Multimedia & Artificial Intelligence, 5(3). 14. Nasser, S., Sreejith, C., & Irshad, M. (2018, July). Convolutional Neural Network with Word Embedding Based Approach for

Resume Classification. In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological

Research (ICETIETR) (pp. 1-6). IEEE. 15. Lata, S., & Loar, M. R. (2018). Text Clustering and Classification Techniques-A Review. International Journal on Recent and

Innovation Trends in Computing and Communication, 6(3), 237-241.

16. S.Kumar, R. Bahsoon, T. Chen, and R. Buyya. Identifying and Estimating Technical Debt for Service Composition in SaaS Cloud.25th IEEE International Conference on Web Services, 2019.

17. S.Kumar, R. Bahsoon, T. Chen, K. Li, and R. Buyya. Multi-Tenant Cloud Service Composition Using Evolutionary

Optimization. 24th IEEE International Conference on Parallel and Distributed Systems, 2018. 18. A.Vashishtha, S. Kumar, P. Verma, and R. Porwal. A Self-Adaptive View on Resource Management in Cloud Data Center.8th

International Conference on Cloud Computing, Data Science & Engineering, 2018

175-178

33

Authors: Varsha Deb, Vasudha Vashisht, Nidhi Arora

Paper Title: Semantic Web Ontologies based Knowledge Management Framework for IT Service Management

Abstract: Technology driven organisations are investing hugely in training and knowledge enrichment of

their employees. This is due to the fact that knowledge is now considered as an asset by organisations.

Additionally, with emerging technologies, organisations are also spending heavily in Information and

Communication Technology (ICT) to enhance their internal operations and processes. Among the various

internal processes, Knowledge Management is an area which has been there since many years but when it is

about the application of latest technology and innovations for Knowledge Management practices, there are

huge opportunities. This paper presents an analysis of various KM frameworks available for different domains

and based on current state and limitation identified, it proposes a Semantic Web Ontology based Knowledge

179-184

Management System for IT Service Industry.

Keywords: Knowledge Base, Knowledge Management System, Knowledge Reusability, Ontologies, OWL,

Protégé, Semantic Web

References:

1. V. Deb, V. Vashisht and N. Arora, "An Analytical Approach to Improve the Effectiveness and to Assess

Current Technological Trends & Challenges of Knowledge Management System," 2018 8th International

Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, 2018, pp. 14-15.doi:

10.1109/CONFLUENCE.2018.8443037

2. Knowledge Management: The Missing Element in Business Continuity Planning Denise Johnson McManus

Wake Forest University, USA, Charles A. Snyder, Auburn University, USA, DOI: 10.4018/978-1-59904-

933-5.ch211

3. Gartner Report: 2018 strategic roadmap for 2018; Published: 5 April 2018; ID: G00346258; Chris Matchett,

Ed Holub, Colin Fletcher, Hank Marquis, Kenneth Gonzalez, Roger Williams, Siddharth Shetty, Robert

Naegle

4. Using Ontologies for Knowledge Management:An Information Systems Perspective;Igor Jurisica, John

Mylopoulos, Eric Yu;University of Toronto, Toronto, Ontario, Canada; Annual Conference of the

American Society for Information Science, Washington, D.C., November 1-4, 1999.

5. Vasudeva Varma; Chapter 2; USE OF ONTOLOGIES FOR ORGANIZATIONAL KNOWLEDGE

MANAGEMENT AND KNOWLEDGE MANAGEMENT SYSTEMS; Ontologies: A Handbook of

Principles, Concepts and Application in Information Systems; http://www.springer.com/978-0-387-37019-4

6. Agnieszka Konys / Procedia Computer Science 126 (2018) 2208–2218 2209

7. https://www.netowl.com/2017/08/11/80-worlds-data-unstructured-entity-extraction-must

8. Ploskas, Nikiforos & Berger, Michael & Zhang, Jiang & Wintterle, G.-J. (2008). A Knowledge

Management Framework for Software Configuration Management. 593 - 598.

10.1109/COMPSAC.2008.106.

9. Malik Nidhi, Sharan Aditi, Semantic Web Oriented framework for Knowledge Management in Agriculture

Domain, International Journal of Web Applications Volume 8 Number 3 September 2016

10. Zhiyang Jiaa, Yiyin Shia, Yuan Jiab, Ding Lia, A Framework of Knowledge Management Systems for

Tourism Crisis Management; International Workshop on Information and Electronics Engineering

(IWIEE); Procedia Engineering 29 (2012) 138-143; doi:10.1016/j.proeng.2011.12.683

11. Edgar Tello-Leal, Ana B. Rios-Alvarado, Alan Diaz-Manriquez “A Semantic Knowledge Management

System for Government Repositories”, in 2015 26th International Workshop on Database and Expert

Systems Applications

12. The impact of social media networks on healthcare process knowledge management (using of semantic web

platforms); Abid Ali Fareedi; Syed Hassan; 2014 14th International Conference on Control, Automation

and Systems (ICCAS 2014) Pages: 1514 - 1519

13. L.Y. Ding, B.T. Zhong, S. Wu, H.B. Luo, Construction risk knowledge management in BIM using ontology

and semantic web technology, Safety Science 87 (2016)

14. Simone Maccanti, Jameela Al-Jaroodi, Arif Sirinterlikci; Knowledge Management Framework for Software

Reuse; 2016 IEEE 40th Annual Computer Software and Applications Conference; DOI

10.1109/COMPSAC.2016.147

15. Raslapat Suteeca, Prompong Sugunnasil; A Knowledge Management Framework for Studying the Child

Obesity; 2016 International Conference on Industrial Engineering, Management Science and Application

(ICIMSA)

16. Sally M. El-Ghamrawy; A Knowledge Management Framework for imbalanced data using Frequent Pattern

Mining based on Bloom Filter; 2016 11th International Conference on Computer Engineering & Systems

(ICCES)

17. S. Mohapatra et al., Designing Knowledge Management-Enabled BusinessStrategies, Management for

Professionals, DOI 10.1007/978-3-319-33894-1_2

18. Zhang Dongmin, Hu Dachao, Xu Yuchun; A Framework for Ontology-based Product Design Knowledge

Management; 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD

2010)

19. R. K. Kavitha, M. S. Irfan Ahmed; A Knowledge Management Framework for Agile Software

Development Teams; 2011 International Conference on Process Automation, Control and Computing

20. Michal Sir, Zdenek Bradac, Petr Fiedler, Ontology versus Database, IFAC-PapersOnLine, Volume 48,

Issue 4, 2015, Pages 220-225, ISSN 2405-8963,https://doi.org/10.1016/j.ifacol.2015.07.036.

21. Leung, Nelson K. Y. & Lau, Sim & Fan, Joshua. (2007). An Ontology-Based Knowledge Network to Reuse

Inter-Organizational Knowledge.

22. G. Baioco, A. C. Monteiro Costa, C. Z. Calvi and A. S. Garcia, "IT service management and governance

modeling an ITSM Configuration process: A foundational ontology approach," 2009 IFIP/IEEE

International Symposium on Integrated Network Management-Workshops, New York, NY, 2009, pp. 24-

33. doi: 10.1109/INMW.2009.5195930

23. M. Valiente, E. García-Barriocanal and M. Sicilia, "Applying Ontology-Based Models for Supporting

Integrated Software Development and IT Service Management Processes," in IEEE Transactions on

Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 1, pp. 61-74, Jan. 2012.

doi: 10.1109/TSMCC.2011.2132717

24. F. Kleiner, A. Abecker and S. F. Brinkmann, "WiSyMon: Managing Systems Monitoring Information in

Semantic Wikis," 2009 Third International Conference on Advances in Semantic Processing, Sliema, 2009,

pp. 77-85. doi: 10.1109/SEMAPRO.2009.13

25. M. Sarnovsky and K. Furdik, "IT service management supported by semantic technologies," 2011 6th IEEE

International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, 2011,

pp. 205-208. doi: 10.1109/SACI.2011.5873000

26. C. Mendes, J. Ferreira and M. M. da Silva, "Using DEMO to Identify IT Services," 2012 Eighth

International Conference on the Quality of Information and Communications Technology, Lisbon, 2012,

pp. 166-171. doi: 10.1109/QUATIC.2012.67.

27. Xin Zhang, Xingyu Chen, Shaoyong Guo and Zhiqiang Zhan, "Ontology-based ITSM knowledge

representation research," 2010 International Conference on Advanced Intelligence and Awarenss Internet

(AIAI 2010), Beijing, China, 2010, pp. 230-235. doi: 10.1049/cp.2010.0759

28. G. Kim and D. Lee, "Intelligent Health Diagnosis Technique Exploiting Automatic Ontology Generation

and Web-Based Personal Health Record Services," in IEEE Access, vol. 7, pp. 9419-9444, 2019;.doi:

10.1109/ACCESS.2019.2891710

29. Applications of Ontologies in Software Engineering; Hans-Jörg Happel and Stefan Seedorf; Semantic

Scholar; Published 2006

30. Natalya F. Noy and Deborah L. McGuinness, Ontology Development 101: A Guide to Creating Your First

Ontology

34

Authors: Ujwal Chopra, Naman Thakur, Lavanya Sharma

Paper Title: Cloud Computing: Elementary Threats and Embellishing Countermeasures for data security

Abstract: Internet offers bunch of services and resources, one of them is cloud computing. The centers

which provide these services are located all around the world and this service has its own mainstream pros and

cons. Cloud computing consists of taking cloud services from the internet and taking them away from the

considerable firewall. This paper aims in enhancing specifications of cloud computing, review its security

threats and address security concerns along with the cloud operations that needs security. Also, this paper

addresses basic security models, qualities and prerequisites for cloud Computing.

Keywords: Cloud Services, Security model, Prerequisites

References: 1. M. Ahmed and M.A. Hossain “Cloud Computing And Security Issues In The Cloud”, International Journal of Network Security & Its

Applications (IJNSA), Vol.6, No.1, 2014.

2. S.L. Mewada1, U.K. Singh, P. Sharma “Security Enhancement in Cloud Computing (CC)” Section: Research Paper, Product Type: Isroset-Journal Vol.1, Issue.1, pp.31- 37, Jan-2013.

3. M. Miller, “Cloud Computing-Web Based Application that change the way you collaborate online”, Publishing of QUE, 2nd print

2009 [accessed on 28 December, 2018] 4. L. Youseff, S. Barbara “Toward a Unified Ontology of Cloud Computing” [accessed on 25January, 2019]

5. Z.M. Aljazzafa, M.A.M. Capretzb, M; accepted 22 December 2015. [ Accessed on 1 Feburary, 2019]

6. W. Jansen, T. Grance, “Guidelines on Security and Privacy in Public Cloud Computing” January 2011 [Accessed on 15th Feburary, 2019]

7. S. Subashini, and V. Kavitha “A survey on security issues in service delivery models of cloud computing.” J Network Computer

Application, Jul, 2010. [Accessed on 20 Feburary, 2019] 8. T. kraska “Building Database Applications in the Cloud”, 2010. [Accessed on 3rd March, 2019]

9. Challenges to cloud computing, Available at https://www.netspaceindia.com/what-is-cloud-computing- how-cloud-managed-services-

changing-the-face-of-cloud- hosting-in-india/ [Accessed on: 10-January-2019]

10. Features of Cloud computing, available at https://data- flair.training/blogs/features-of-cloud-computing/ [Accessed on 10- March-5

2019]

11. Types of cloud computing, available at https://www.researchgate.net/figure/Types-of-Cloud- Computing_fig1_306353636 [Accessed on 20- Feburary- 2019]

12. Types of Cloud computing services, Available at: http://talkcloudcomputing.com/types-of-cloud-computing- services/ [Accessed on

15- Feburary-2019] 13. Real time applications of cloud computing. Available at https://en.wikipedia.org/wiki/Cloud_computing[Accessed on 14-Feburary-

2019]

14. Data security lapse statics in different countries around the world, available at https://www.google.com/search?tbm=isch&q=cloud+compu ting&chips=q:cloud+computing,g_1:security:clrpWY3Vbk

U%3D&usg=AI4_- kSuDGHe8FFzvbJY_EnfAuekBhqn_A&sa=X&ved=0ahU

KEwjeyszNgJbhAhUGS48KHYT4D60Q4lYIKCgA&biw= 1242&bih=524&dpr=1.1 [ Accessed on 11- Feburary-2019] 15. ” Cloud Computing challenging issues” Available at: https://www.datamation.com/cloud-computing/top-10- cloud-computing-

challenges.html [Accessed on 12-march- 2019]

16. Security Threats and Countermeasures in Cloud Computing available at https://www.semanticscholar.org/paper/Security-Threats- 17. and-Countermeasures-in-Cloud-Ashktorab- Taghizadeh/4ce891731f0dc7352d329b1f2dcc5b56cb8f6190 [Accessed on 10-Feburary-

2019]

18. L.Sharma, N.Lohan, “ Internet of Things with Object detection: Challenges, Applications, and Solutions”, Handbook of Research on Big Data and the IoT, IGI Global, pp. 89-100, March 2019.

19. L. Sharma, D. Yadav, A. Singh, “Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video”,

Infrared Physics & Technology, Elsevier, vol. 78, pp. 118-128, Sept. 2016. 20. L. Sharma, D. Yadav, “Histogram-based adaptive learning for background modelling: moving object detection in video surveillance”,

International Journal of Telemedicine and Clinical Practices, Inderscience, vol. 2, no. 1, pp. 74-92, 2017.

21. Lavanya Sharma, Nirvikar Lohan, “Performance analysis of moving object detection using BGS techniques in visual surveillance”, International Journal of SpatioTemporal Data Science, Vol.1 No.1, pp.22 – 53, Jan. 2019.

22. BKSP kr. Raju; G. Geethakumari, “A model for trust enhancement in cloud computing”, Publisher: IEEE, INSPEC Accession

Number: 15022171 DOI: 10.1109/ICCCT2.2014.7066716; March, 2015 23. S Abdul S. Muhseen; A.S. Elameer; “A Review in Security Issues and Challenges on Mobile Cloud Computing” (MCC); 2018 1st

Annual International Conference on Information and Sciences (AiCIS)Year: 2018; Page s: 133 – 139; IEEE Conferences

185-190

24. S.K Gupta; S. Rawat; P. Kumar, “A novel based security architecture of cloud computing; Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization; Year: 2014; Page s: 1 – 6; Cited by: Papers (5); IEEE

Conferences

25. B. Gowrigolla; S. Sivaji; M. Roberts Masillamani, “Design and auditing of Cloud computing security” 2010 Fifth International Conference on Information and Automation for Sustainability; Year: 2010; Page s: 292 – 297; Cited by: Papers (8); IEEE Conferences

26. R. Gehlot; N. Sinha; “Enhancing security on cloud using additional encrypted parameter for public authentication”, 2016 Symposium

on Colossal Data Analysis and Networking (CDAN); Year: 2016; Page s: 1 – 5; Cited by: Papers (3); IEEE Conferences

35

Authors: Akshit Anand, Vikrant Jha, Lavanya Sharma

Paper Title: An Improved Local Binary Patterns Histograms Technique for face recognition for real time applications

Abstract: Recently, face recognition and its applications has been considered as one of the image analysis

most successful applications, especially over the past several years. Face Recognition is a unique system that

can be used by using unique facial features for identification or verification of a person from a digital image. In

a face recognition system, there are many technique that can be used. This paper provides an efficient of the

Local Binary Patterns Histograms (LBPH) based technique provided by OpenCV library which is implemented

in Python programming language which is well suitable for realistic scenarios. In this paper we also provide

visual qualitative outcome with existing algorithm (Haar-cascade classifier and Local Binary Patterns

Histograms (LBPH)). As a result, the proposed technique outperform better in terms of visual qualitative

analysis.

Keywords: Face Recognition, Face Detection, Local Binary Patterns Histograms, OpenCV, Haar-Cascade

Classifier, Python

References: 1. A. Ahmed, J. Guo, F. Ali, F. deeba. LBPH Based Improved Face recognition At Low Resolution,2018 Internation Conference on

Artificial Intelligence and Big Data (ICAIBD), At Chengdu, China.

2. J. CHAO W L, D. J J, L. J Z. Facial expression recognition based on improved local binary pattern and class-regularized locality

preserving projection. Signal Processing, 2015, 117:1-10. 3. J. HU Liqiao, Q. Runhe. Face recognition based on adaptive weighted HOG. Computer Enigeering and Applications, 2017, 53(3): 164-

168.

4. J. Yu yan JIANG, P. LI, Q. WANG. Labeled LDA model based on shared background topic. Acta Electronica Sinica, 2015, 2013, (9): 1794-1799.

5. J. WU Qi, W. Tang-hong, L. Zhan-li. Imporved face recognition algorithm based on Gabor feature and collaborative representation,

Computer Engineering and Design, 2016, 37(10): 2769-2774.

6. A. Singh, S. Kumar Singh, S. Tiwari, Comparison of face Recognition Algorithms on Dummy Faces, The International Journal of

Multimedia & Its Applications (IJMA) Vol.4, No.4, August 2012.

7. X. Zhao, C. Wei, A Real-time Face Recognition System Based on the Improved LBPH Algorithm, 2017 IEEE 2nd International Conference on Signal and Image Processing.

8. V. Garg, K. Garg, Face Recognition Using Haar Cascade Classifier, Journal of Emerging Technologies and Innovative Research

(JETIR) , December 2016, Volume 3, Issue 12. 9. H. Zhang, Z. Qu Liping, Y. GangLi, A Face Recognition Method Based on LBP Feature for CNN, 2017 IEEE 2nd Advanced

Information Technology, Electronic and Automation Control Conference (IAEAC).

10. T. Chen, Y. Wotao, S. Z. Xiang, D. Comaniciu, and T. S. Huang, “Total variation models for variable lighting face recognition” IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9):1519{1524, 2006

11. Z. and R. Chellappa “Robust face recognition using symmetric shape from-shading” Technical Report, Center for Automation

Research, University of Maryland, 1999 12. Z. Xiang, H. Tan, W. Ye. The excellent properties of dense gird-based HOG features on face recognition compare to gabor and LBP,

2018 volume issue 99.

13. W. Chao, “Face Recognition”,GICE,National Taiwan University. 14. L. Zhichao and Er. Meng Joo, “Face Recognition Under Varying Illumination”, Nanyang Technilogical University, Singapore.

15. X. Sun, Q. Zhang and Z. Wang “Face Recognition Based on NMF and SVM”, Wuhan University of Technology and Henan University

of Technology, China, 2009. 16. Y. Hu, B. Liu “Face Recognition Based on PLS and HMM”, Guizhou University, China, 2009.

17. A. Gubbi, Mohammad F. Azeem and N. Z H Nayakwadi “Face recognition using Local Ternary Pattern and Booth’s

Algorithm”,3rd International Conference on Eco-Friendly Computing and Communication Systems, 2014. 18. Q. Li, C. Sun,Jingao Liu “Illumination Invariant Face Recognition Based on ULBP and SVM”, JSNU, ECNU, China 5th International

Conference on BioMedical Engineering and Informatics (BMEI 2012).

19. S. Y.Raut, D. A.Doshi “A Face Recognition System by Hidden Markov Model and Discriminating Set Approach”, 2016 20. M. Rajapakse and L. Wyse “Face Recognition with Non-negative Matrix Factorization”,Institute for Infocomm Research, Singapore.

21. Markov Model. Available at: https://in.mathworks.com/help/stats/hidden-markov-models-hmm.html [accessed on 12 march 2019]

22. K. Srinivasa Reddy, V.Vijaya Kumar, B. Eswara Reddy “Face Recognition Based on Texture Features using Local Ternary Patterns”, Hyderabad, Hyderabad, A.P.,India.,2015

23. Proceedings of the International Conferences on Automatic Face and Gesture Recgonition, 1995-1998. 24. P.J. Phillips, P. Rauss, and S.Der, “FERET(Face Recognition Technology)

25. S. Lee and C. Lee, “Low complexity background subtraction based on spatial similarity”, Eurasip Journal on Image and video

processing, Springer, june, 2014. 26. E. Kermani and D. Asemani, “A robust adaptive algorithm of moving object detection for video surveillance”, Eurasip Journal on

Image and video processing, Springer, 2014.

27. M. Piccardi, “Background Subtraction Techniques: a review”, IEEE, International Conference on Systems, Man, Cybernatics, 2004. 28. K. Toyama, J. Krumm, B. Brumitt and B. Meyers, "Wallflower: Principles and Practice of Background Maintenance", IEEE Computer

Society Press, Seventh International Conference on Computer Vision, Kerkyra, Greece, pp. 255-261, 1999.

29. L. Sharma, D. Yadav, “Histogram-based adaptive learning for background modelling: moving object detection in video surveillance”, Internation Journal if Telemedicine and Clinical Practices, Inderscience, vol. 2, no.1, pp. 74-92,2017

30. L. Sharma, N. Lohan, “Performance analysis of moving object detection using BGS techniques in visual surveillance”, Internation

Journal of Spatiotemporal Data Science, Vol.1 No.1, pp.22-53,Jan.2019 31. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking”, IEEE Computer Society Conference

on Computer Vision and Pattern Recognition, june, 1999.

32. J. Dou and J. Li, “Moving object detection based on improved VIBE and graph cut optimization”, optic 124, pp. 6081-6088, Elsevier, 2013.

191-196

33. L. Lin, Y. Xu, X. Liang and J. Lai, “Complex background subtraction by pursuing dynamic spatio- temporal models”, IEEE Trans. On image processing, vol. 23, no. 7, july 2014.

36

Authors: Anubhav Kumar, Gaurav Jha, Lavanya Sharma, Sunil Kr. Khatri

Paper Title: Challenges Potential and Future of Internet of Things integrated with Blockchain

Abstract: In the Internet of Things (IoT) idea, regular gadgets end up savvy and self-governing. As we are

seeing idea behind this is transforming into a realism on account of achievement in innovation, but we still face

challenges, especially in some field like security e.g., information dependability. Taking account, the upcoming

advancement in the field of IoT, it seems very important to give trust in the field of enormous approaching data

foundation. Blockchain has given us the new way to share our data with others. Building such a trust in discrete

condition with is term as the key factor of blockchain in which we don’t need any specialists is a hi-tech

development that can possibly change numerous enterprises, the IoT among them. Troublesome advances, for

example, IoT used enormous information and distributed to beat its restrictions, and we blockchain can be one

of the accompanying ones. This paper centers around this correlation, explores difficulties in blockchain with

IoT implementation, and reviews recent significant work so as to investigate about how the existing blockchain

technology can change the way we work in IoT.

Keywords: Anonymity, application, blockchain, decentralization, difficulties, innovation, Internet of things,

security, smart environment, strength, trust.

References: 1. Ana Reyna, Cristian Martín, Jaime Chen, Enrique Soler and Manuel Díaz "On blockchain and its integration with IoT. Challenges and

opportunities” Volume 88, November 2018

2. Internet of Things, Blockchain and Shared Economy Applications by Steve Huckle , Rituparna Bhattacharya, Martin White and Natalia Beloff ,Volume 98, 2016, Pages 461-466

3. Ali Dorri, Salil S. Kanhere, and Raja Jurdak “Blockchain in Internet of Things: Challenges and Solutions”

4. J. Shen et.al., “Secure data uploading scheme for a smart home system,” Information Sciences, vol. 453, pp. 186–197, 2018. 5. K. Christidis and M. Devetsikiotis, “Blockchain and Smart Contracts for the Internet of Things,” IEEE Access, vol. 4, pp. 2292–2303,

2016.

6. S. Huh, S. Cho, and S. Kim, “Managing IoT devices using blockchain platform,” in Proceedings of the 19th International Conference on Advanced Communications Technology, ICACT 2017, pp. 464–467, kor, February 2017.

7. C. Wang et.al., “A Novel Security Scheme Based on Instant Encrypted Transmission for Internet of Things,” Security and

Communication Networks, vol. 2018, pp. 1–7, 2018. 8. Bitcoin assembles blockchain. Available at: https: www.investopedia.com/terms/b/blockchain.asp [accessed on 10 march 2019]

9. Barboutov, K.; Furuskär, A.; Inam, R.; Lindberg, P.; Öhman, K.; Sachs, J.; Sveningsson, R.; Torsner, J.; Wallstedt, K. Ericsson

Mobility Report. [accessed on 10 March 2019]. 10. https://www2.deloitte.com/de/de/pages/technology-media-and-telecommunications/articles/cyber-security-prevention-of-ddos-attacks-

with-blockchain-technology.html [accessed on 11-march-2019] 11. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements,and future directions.

Future Gener. Comput. Syst. 2013, 29, 1645–1660.

12. Kolias, C.; Kambourakis, G.; Stavrou, A.; Voas, J. DDoS in the IoT: Mirai and other botnets. Comput. 2017, 50, 80–84. 13. Block Structure. Available at: http://www.reseachgate.net/publication/326868072 [accessed on 12-March-2019]

14. Transaction block validation and addition flow. Available at: http://www.reseachgate.net/publication/326868072 [accessed on 19-

March-2019] 15. Trustless Available at: https://lisk.io/academy/blockchain-basics/benefits-of-blockchain/why-is-blockchain-trustless [accessed on 10-

march-2019]

16. Permission less Available at: https://www.blockchain-council.org/blockchain/advantages-and-disadvantages-of-permissionless-blockchain/ [accessed on 10-march-2019]

17. Oversight safe Available at: https://cryptodisrupt.com/blockchain-oversight-could-change-arms-trade/ [accessed on 10-march-2019]

18. Sicari, S.; Rizzardi, A.; Cappiello, C.; Miorandi, D.; Coen-Porisini, A. Toward data governance in the internet of things. In New Advances in the Internet of Things; Springer:Cham, Germany, 2018;pp. 59-74

19. Open & circulated record ,Available at: https://hackernoon.com/blockchain-technology-explained-introduction-meaning-and-

applications-edbd6759a2b2 [accessed on 10 march-2019] 20. Synchronization of Ledger copies, Available at :https://codeburst.io/distributed-ledger-technology-fundamentals-you-must-know-

2d0f82628258 [accessed on 10-march-2019] Paper scope distribution. Available at:

http://www.reseachgate.net/publication/326868072 [accessed on 10 March 2019] 21. Crucial component, Available at: https://medium.com/permissionio/democracy-on-the-blockchain-6bb37c2e893f [accessed on 11-

march-2019]

22. Challenging Issues Of Blockchain, Available at: https://www.coindesk.com/information/blockchains-issues-limitations [accessed on 11-march-2019]

23. Usage pattern Organization available at http://www.reseachgate.net/publication/326868072 [accessed on 10 March 2019]

24. https://www.ibm.com/blogs/blockchain/2017/09/three-features-of-blockchain-that-help-prevent-fraud/ [accessed on 12-march-2019] 25. https://medium.com/coinmonks/blockchain-is-self-regulation-sufficient-5bb68ac7e33f [accessed on 12-march-2019]

26. https://www.quora.com/Is-a-Bitcoin-transaction-truly-instant [accessed on 12-march-2019]

27. https://www.coindesk.com/blockchain-aid-efficiency [accessed on 12-march-2019] 28. https://www.investopedia.com/articles/investing/052014/why-bitcoins-value-so-volatile.asp [accessed on 13-march-2019]

29. http://www.police-foundation.org.uk/2018/09/is-blockchain-good-news-or-bad-when-it-comes-to-policing-and-crime/ [accessed on

13-march-2019]

30. TIAGO M. FERNÁNDEZ-CARAMÉS, (Senior Member, IEEE), and PAULA FRAGA-LAMAS, (Member, IEEE) “A Review on the

Use of Blockchain for the Internet of Things” Department of Computer Engineering, Faculty of Computer Science, Campus de Elviña,

s/n, Universidade da Coruña, April 11, 2018. 31. Dr. Xing Liu,Kwantlen Polytechnic University,Surrey, B.C., CANADA “Internet of Things Based on Blockchain” IEEE IEMCON

2018 - The 9 th IEEE Annual Information Technology, Electronics & Mobile Communication Conference 1-3 November, 2018

32. Francesco Restuccia, Member, IEEE, Salvatore D’Oro, Member, IEEE, Salil S. Kanhere, Senior Member, IEEE, Tommaso Melodia, Fellow, IEEE, and Sajal K. Das, Fellow, IEEE “Blockchain for the Internet of Things: Present and \Future” IEEE INTERNET OF

THINGS JOURNAL, VOL. 1, NO. 1, JANUARY 2018

33. D.Salma Faroze Department of Computer Science and Engineering CBIT College Proddatur, And Pallavolu Andhra Pradesh – India “Block Chain & Internet of Things: Security, Challenges, Research Issues” International Journal of Computer Science Trends and

Technology (IJCST) – Volume 6 Issue 5, Sep-Oct 2018

34. L. Sharma, N.Lohan, “ Internet of Things with Object detection: Challenges, Applications, and Solutions”, Handbook of Research on

197-203

Big Data and the IoT, IGI Global, pp. 89-100, March 2019. 35. L. Sharma, D. Yadav, A. Singh, “Fisher’s linear discriminant ratio-based threshold for moving human detection in thermal video”,

Infrared Physics & Technology, Elsevier, vol. 78, pp. 118-128, Sept. 2016.

36. L. Sharma, D. Yadav, “Histogram-based adaptive learning for background modelling: moving object detection in video surveillance”, International Journal of Telemedicine and Clinical Practices, Inderscience, vol. 2, no. 1, pp. 74-92, 2017.

37. L.Sharma, Nirvikar Lohan, “Performance analysis of moving object detection using BGS techniques in visual surveillance”,

International Journal of SpatioTemporal Data Science, Vol.1 No.1, pp.22 – 53, Jan. 2019

37

Authors: Gauri Jha, Pawan Singh, Lavanya Sharma

Paper Title: Recent Advancements of Augmented Reality in Real Time Applications

Abstract: Augmented Reality (AR) is an advanced technology that improves users view of interactivity with

the present reality by adding virtual objects in it and it is the most natural way to interface with your digital

world. This paper presents a survey about real-time applications, commonly used technologies, various open

challenges, possible set of solution provided by several researchers and academicians. This paper also provides

future of Augmented reality in various areas of artificial intelligence.

Keywords: Augmented Reality, Virtual reality, Artificial Intelligence, Head mounted display

References: 1. Dünser, R. Grasset and M. Billinghurst, “A Survey of Evaluation Techniques Used in Augmented Reality Studies,” Human Interface

Technology Laboratory New Zealand, 2008 2. M. Bulearca and D. Tamarjan, “Augmented Reality: A Sustainable Marketing Tool?” Economics of the Romanian Academy

Bournemouth University, Vol.2, No. 2, 2010

3. K. Lee, “Augmented Reality in Education and Training,” University of Northern Colorado, Vol. 56, No.2, 2012 4. E. Zhu et.al., “Augmented reality in healthcare education: an integrative review,” Hubei University, China, 2014

5. R. Hammady, M. Ma and N. Temple, “Augmented Reality and Gamification in Heritage museums,” University of Huddersfield,

Huddersfield, UK, 2016 6. D. Harborth, “Augmented Reality in Information Systems Research: A Systematic Literature Review,” Twenty-third Americas

Conference on Information Systems, Boston, 2017

7. R. Umeda et.al., “A medical training system using augmented reality,” Okinawa, Japan, 2017 8. D. Chatzopoulous et.al., “Mobile Augmented Reality Survey: From Where We Are to Where We Go,” The Hong Kong University of

Science and Technology, Hong Kong, Vol. 5, IEEE access, 2017

9. T. Williams et.al., “Virtual, Augmented, and Mixed Reality for Human-Robot Interaction,” HRI’18 Companion, , Chicago, IL, USA, 2018

10. K. Kim et.al., “Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008–2017),” Vol.24,

2018

11. L. Berkemeir et.al., “Engineering of Augmented Reality-Based Systems,” Osnabruck University, Vol. 61, No, 67, 2019

12. V. Interrante et.al, “Virtual and Augmented Reality,” University of California, Santa Barbara, Vol. 38, Issue 2, 2018

13. Future of Augmented Reality. Available at: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3UxNFLmqFTPEqns6Gc9Nppyb8pfvl7tfO95gBEVtblxt5sdolA [accessed on 06- march-

2019]

14. http://sevenmediainc.com/the-history-of-augmented-reality/ [accesed on 06-march-2019] 15. https://vydia.com/evolution-augmented-reality/ [accessed on 06-march-2019]

16. https://hcipioneers.wordpress.com/portfolio/feiner-steve/ [accessed on 06 -march-2019]

17. https://ultimatehistoryvideogames.jimdo.com/arquake/ [accessed on 06-march 2019] 18. https://medium.com/@argoproject/a-brief-history-of-augmented-reality-infographic-af040a4fd86f [accessed on 06-march-2019]

19. Evolution of Augmented Reality. Available at:

https://www.researchgate.net/profile/Gallayanee_Yaoyuneyong/publication/228841030/figure/fig2/AS:300775898140673@1448721970472/History-of-AR-a-brief-timeline.png [accessed on 09 -march-2019]

20. https://www.archer-soft.com/en/blog/how-augmented-reality-used-medicine [accessed on 09-march-2019]

21. Microsoft HoloLens in Medical Imaging. Available at: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTD9dAH2nYEIzZmXsACKVXw_4lgW3v0-1BRtI1154qqZKKD0o2L [accessed on 09-

march-2019]

22. Augmented Reality in Defence Available at: https://jasoren.com/augmented-reality-military/ [accessed on 09-march-2019] 23. Military training using AR technology. Available at: https://encrypted-

bn0.gstatic.com/images?q=tbn:ANd9GcSeV3htqpAGeMsbZUIbjKjuUsHU_PYn4cz0azdUOWow- j9-A-Ie [accessed on 09-march-

2019] 24. Augmented Reality in Education. Available at: https://elearningindustry.com/augmented-reality-in-education-impact [accessed on 09-

march-2019]

25. Head Mounted Display for easy learning. Available at: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPbMRxkqQ2DMhtaJdJ0M59- 5wiecMzVK05y9foOiyi5Q4uSSoEtg [accessed on 10-march-

2019]

26. Augmented Reality in Entertainment. Available at: https://augray.com/blog/augmented-reality-entertainment/ [accessed on 10- march-2019]

27. Game Pokemon Go. Available at: https://encrypted- tbn0.gstatic.com/images?q=tbn:ANd9GcRRVHTUgmtOicyOJyPm8VeRosZ3mYUveQJK63vgS034 KeAHoUR-hQ [accessed on 11-

march-2019]

28. Augmented Reality in Industries. Available at: https://dzone.com/articles/using-augmented-reality-in-manufacturing-industry [accessed on 10-march-2019]

29. Use of AR in Automobile Industry. Available at: https://encrypted-

tbn0.gstatic.com/images?q=tbn:ANd9GcSo4_PmYhuisLBM2G_PSuIa5HAOI0UVqaJH3uhu3HDuwVG0HkjQxA [accessed on 11-march-2019]

30. Augmented Reality in Advertising media. Available at: https://rubygarage.org/blog/augmented-reality-in-advertising [accessed on 11-

march-2019] 31. Virtual Fitting Room. Available at: https://encrypted-

tbn0.gstatic.com/images?q=tbn:ANd9GcSuhWCOppsYrqE7uHoHz5retrCsgRjI_x7sYokkjqW0GvgABJpU [accessed on 11-march-

2019] 32. Augmented Reality challenges. Available at: https://www.upwork.com/hiring/for-clients/biggest-challenges-augmented-reality/

[accessed on 12-march-2019]

33. Augmented Reality challenges. Available at: https://www.weforum.org/agenda/2017/09/augmented-and-virtual-reality-will-change-how-we-create-and-consume-and-bring-new-risks/ [accessed on 12-march-2019]

34. Augmented Reality challenges. Available at: https://documoto.com/problem-with-augmented-reality/ [accessed on 12-march-2019].

204-209

35. Augmented Reality challenges. Available at: https://www.csoonline.com/article/3101644/real-world-risks-in-an-augmented-reality.html [accessed on 12-march-2019]

36. Augmented Reality challenges. Available at: https://datafloq.com/read/4-technical-challenges-ar-vr-need-to-solve/4148 [accessed on

12-march-2019] 37. Augmented Reality challenges. Available at: https://becominghuman.ai/six-ethical-problems-for-augmented-reality-6a8dad27122

[accessed on 12- march-2019]

38. L.Sharma, N.Lohan, “ Internet of Things with Object detection: Challenges, Applications, and Solutions”, Handbook of Research on Big Data and the IoT, IGI Global, pp. 89-100, March 2019.

39. L. Sharma, D. Yadav, A. Singh, “Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video”,

Infrared Physics & Technology, Elsevier, vol. 78, pp. 118-128, Sept. 2016. 40. L. Sharma, D. Yadav, “Histogram-based adaptive learning for background modelling: moving object detection in video surveillance”,

International Journal of Telemedicine and Clinical Practices, Inderscience, vol. 2, no. 1, pp. 74-92, 2017.

41. Lavanya Sharma, Nirvikar Lohan, “Performance analysis of moving object detection using BGS techniques in visual surveillance”, International Journal of Spatio- Temporal Data Science, Vol.1 No.1, pp.22 – 53, Jan. 2019.

38

Authors: Unnati Jadaun, Saurabh Kumar

Paper Title: Impact of Performance Appraisal on Employee Performance in private sector banks of district Aligarh

Abstract: As we all know Human Resources are the most vital component of an organization. To succeed in

this competitive era, an organization must possess effective and efficient human resource in every manner.

Hence after the selection employee, he has been trained and motivated. For this evaluation of the performance

of an employee is needed. This process of evaluation is known as Performance Appraisal. With the help of

systematic appraisal methods, corporate performance can be enhanced. It is basically to know the value of an

employee qualitatively. It is indispensable tool for every organization for measuring the effectiveness of the

employee. This research study has been done to understand the impact of Performance Appraisal on Employee

performance in Private sector banks of district Aligarh. In this study, researcher selected two private sector

banks ICICI and Axis bank with the sample size of 100. Researcher used Linear Regression as statistical tool

for data analysis.

Keywords: Employee Performance, Linear Regression, Performance Appraisal.

References: 1. Yaseen, A. (2015) “ Performance Management Practices and its impact on bank’s performance in Pakistan”, International Journal of

Human Resources, 5, pp- 110-127.

2. Asrar, H., Rizwan, M., Pasha S., and Usmani, W.U. (2015), “Analysis of Performance Management System in Pakistan Banking

Industry”, International Journal of Management Sciences and Business Research, 4, pp- 1-5.

3. Mukulu,E.(2014)“Effect of reward and compensation strategies on the performance of commercial banks in Kenya”, 2, pp- 1-20.

4. Showkat, S. (2013) “Performance Appraisal in Banking Organizations”, International Refereed Research Journal, Volume 4, pp- 66-79.

5. Shrivastava, P. and Rai U.K. (2012) “Performance Appraisal Practices In Indian Banks”, Integral Review - A Journal of

Management, 5, pp 46-52. 6. Shrivastava, A. and Purang P. (2011) “Employee perceptions of performance appraisals: a comparative study on Indian banks”, The

International Journal of Human Resource Management, 22, pp- 632-647.

210-212

39

Authors: Abhishek Jha, Dr. Suneel Arora

Paper Title: Risk Incorporation into the Capital Budgeting process of Solar Power Plants

Abstract: The following article deals with a new approach of incorporating risk profile of a solar power

project into the Capital Budgeting process. As revealed in the literature review, the current capital budgeting

practices being followed in the industry suffers from practice of non sophisticated methods of risk assessment.

These include sensitivity and scenario analyses only.

Keywords: — Risk, Monte Carlo, Capital, Budgeting, Solar, Plant

References: 1. D. Clancy, and D. Collins, “Capital budgeting research and practice: The state of the art”, Advances in Management Accounting, vol.

24, pp. 117-161, September 2014.

2. F. Verbeeten, “Do organizations adopt sophisticated capital budgeting practices to deal with uncertainty in the investment decision?: A

research note”, Management Accounting Research vol. 17, no. 1, pp. 106-120, March 2006. 3. G.C. Arnold, P.D. Hatzopoulos, “The theory practice gap in capital budgeting: evidence from the United Kingdom”, Journal of

business finance & Accounting, vol. 27, no. 5&6, pp. 603-26, June 2000.

4. G. Kester and G. Robbins, “The capital budgeting practices of listed Irish companies: insights from CFOs on their investment appraisal techniques”, Accountancy Ireland, vol. 43, no. 1, pp.28-30, February. 2011.

5. H. Chan, K. Haddad, and W. Sterk, “Capital budgeting practices of Chinese firms”, Journal of Global Business Management, vol. 4,

no. 2, 2008. 6. K. Bennouna, G.G. Meredith, T. Marchant, “Improved capital budgeting decision making: evidence from Canada”, Management

decision, vol. 48, no. 2, pp. 225-247, March, 2010.

7. K.C. Lam, D. Wang, and M. C. K. Lam, “The capital budgeting evaluation practices (2004) of building contractors in Hong Kong”,

International Journal of Project Management , vol. 25, pp. 824-834, 2007.

8. L. Kengatharan, “Moderating Effect of Social Uncertainty between Capital Budgeting Practices and Performance”, International

Journal of Accounting and Financial Reporting, vol. 7, pp. 79-95, 2017. 9. M. Anand, “Corporate finance practices in India: a survey.”, Vikalpa, vol. 27, pp. 29-56, 2002.

10. M. Nurullah and L. Kengatharan, “Capital budgeting practices: evidence from Sri Lanka”, Journal of Advances in Management

Research, vol. 12, pp. 55-82, May 2015. 11. M. Rossi, "The use of capital budgeting techniques: an outlook from Italy", International Journal of Management Practice, vol. 8, no.

1, pp. 43-56, 2015.

12. P.K. Jain, and S. Yadav, “Financial management practices in public sector enterprises: a study of capital budgeting decisions”, Journal of advances in management research, vol. 2, pp. 32-46, Jan 2005.

13. R. Batra, and S. Verma, “Capital budgeting practices in Indian companies”, IIMB Management Review, vol. 29, no. 1, pp. 29-44,

March 2017.

213-219

14. S. Haka, “A review of the literature on capital budgeting and investment appraisal: past, present, and future musings”, Handbooks of Management Accounting Research, vol. 2, pp. 697-728, 2006.

15. S.S. Jape, T. Korde, “Study of established financial management tools and techniques and their application by business houses (with

reference to Mumbai based Companies period-2002-2012)” , Aweshkar Research Journal, vol. 18, no. 2, pp. 71-89, September 2014. 16. S. Verma, S. Gupta and R. Batra, “A Survey of Capital Budgeting Practices in Corporate India. Vision”, The Journal of Business

Perspective, vol. 13, no. 3, pp. 1-17, July 2009.

17. Y.D. Arthur, K.B. Gyamfi, S.K. Appiah, “Probability distributional analysis of hourly solar irradiation in Kumasi-Ghana”, International Journal of Business and Social Research, vol. 3, no. 3, pp. 63-75, 2013.

18. Kenbrooksolar.com, ‘1kW-1mW Solar Power Plant Detail, Return On Invesment & Price Details 2019’, 2019. [Online].

Available:https://solarenergypanels.in/solar-power-plants/mw-solar-power-grid. [Accessed: 25-Feb-2019]

40

Authors: Chanchal Chauhan, Hem Shweta Rathore, Satish Kumar Matta

Paper Title: An Empirical Research on FMCG Sector

Abstract: Equity analysis is simply the research or study of stocks for the purpose of investment. Every

investor wants more and more profit on their investment. For this share of profit he always tries to select right

security and portfolio for investment. In general term, more profit is directly related to more risk. FMCG sector

played a vital role in economic development of country. The relationship between risk and return can easily be

measured by some statistical tools like standard deviation, beta, correlation and variance.

Keywords: Equity Analysis, Portfolio, Economic Development, Risk and Return

References: 1. Nagarajan, S., & Prabhakaran, K. (2013). A study on equity analysis of selected FMCG companies listed on NSE. International Journal

of Management Focus,1-7. Retrieved January 3, 2019.

2. Satyanarayana, I., Sidhu, N. B., & Srinivas, S. (2015). Equity analysis of banking stocks. International Journal of Advance Research in

Computer Science and Management Studies,3(2), 344-348. Retrieved January 5, 2019. 3. Naveen, S., & Mallikarjunappa, T. (2016). A study on comparative analysis of risk and return with reference to stocks of CNX Bank

Nifty. International Journal of Scientific Research and Modern Education,1(1), 737-743. Retrieved January 9, 2019.

4. Gopalakrishnan, M. M., & K, A. P. (2017). Equity analysis of automobile industry in Indian Stock Market. International Journal of Advance Research and Development,2(5), 166-171. Retrieved January 9, 2019.

5. Subramanyam, P., & Kalyan, N. B. (2018). A study of risk and return analysis of selected securities in India. International Journal of

Engineering Technologies and Management Research,5(4), 79-86. Retrieved January 10, 2019. 6. Navya, A., & Reddy, B. K. (2018). A report on equity analysis of telecom sector”. International Journal of Engineering Technology

Science and Research,5(3), 1396-1402. Retrieved January 10, 2019.

7. https://in.finance.yahoo.com/quote/^CNXFMCG/history/. (n.d.). Retrieved January 3, 2019. 8. Https://in.finance.yahoo.com/quote/HINDUNILVR.NS/history/. (n.d.). Retrieved January 3, 2019.

9. Https://in.finance.yahoo.com/quote/PG/history/. (n.d.). Retrieved January 3, 2019.

10. Https://in.finance.yahoo.com/quote/BRITANNIA.NS/history/. (n.d.). Retrieved January 4, 2019. 11. Https://in.finance.yahoo.com/quote/DABUR.NS/history/. (n.d.). Retrieved January 4, 2019.

12. Https://in.finance.yahoo.com/quote/NESTLEIND.NS/history/. (n.d.). Retrieved January 5, 2019.

13. Https://www.moneycontrol.com/. (n.d.). Retrieved January 3, 2019. 14. Https://en.wikipedia.org/wiki/Hindustan_Unilever. (2015, November 12). Retrieved February 10, 2019.

15. Https://en.wikipedia.org/wiki/Procter_&_Gamble. (2016, March 3). Retrieved February 12, 2019.

16. Https://en.wikipedia.org/wiki/Procter_&_Gamble. (2018, November 7). Retrieved February 12, 2019. 17. Http://britannia.co.in/. (2018, December 5). Retrieved February 12, 2019.

18. Http://www.capitalmarket.com/Company-Information/Information/About-Company/Dabur-India-Ltd/3392. (n.d.). Retrieved February

15, 2019. 19. Https://www.nestle.com/aboutus/history/nestle-company-history. (2019). Retrieved February 15, 2019.

20. Https://www.statisticshowto.datasciencecentral.com/sample-mean/. (2015, July 16). Retrieved March 6, 2019.

21. Kothari, C. R., & Garg, G. (2018). Research Methodology (4th ed.). New age international. 22. Avadhani, V. A. (2019). Securities Analysis and portfolio management(12th ed.). Himalaya Publishing House.

220-224

41

Authors: Vikram Kumar Sharma, Arun Kumar Singh

Paper Title: An Empirical evaluation of key factors affecting Mobile Shopping in India

Abstract: The word ‘Shopping’ was originated from the French word eschoppe which means leaning in front

of a booth. Since 16th century the word is going circles in the society. Now a days Shopping means to explore

and buy goods in the exchange of money. For years it’s like a buzzword in the households. In the era of mobile

technology revolution and the advent of regulatory framework, the way we shop has changed dramatically.

Mobile technology has created a new paradigm in retail industry. The customized approach and the

convenience of shopping through handsets are making the overall experience effective. The Purpose of this

paper is to study the imperatives of Indian consumers’ to avail mobile shopping services and to develop a

Mobile Shopping Acceptance Model. Three constructs i.e Attitude towards Behaviour, Subjective Norms and

Perceived Behavioural Control are used for the purpose. Theory of Planned Behaviour (TPB) and Innovation

Diffusion Theory (IDT) are used to develop the Model. The Model was validated using Factor Analysis and

Hypothesis Testing.

Keywords: Acceptance Model, Consumer Behaviour, Decision preferences Mobile Shopping, TRA, TPB,

Technology Cluster

References:

1. Mort, G.S. and Drennan, J. (2005), “Marketing m-services: Establishing a usage benefit typology related to mobile user

characteristics”, Journal of Database Marketing & Customer Strategy Management, Vol. 12 No. 4, pp. 327-41.

2. Kiseol Yang, (2010),"Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping

services", Journal of Consumer Marketing, Vol. 27 Iss 3 pp. 262 – 270 3. Alastair Holmes Angela Byrne Jennifer Rowley , (2013),"Mobile shopping behaviour: insights into attitudes, shopping process

involvement and location", International Journal of Retail & Distribution Management, Vol. 42 Iss 1 pp. 25 - 39

225-228

4. Albaum, G., Roster, C.A., Wiley, J., Rossiter, J. and Smith, S.M. (2010), “Developing web surveys in marketing research: does use of forced answering affect completion rates?”, Journal of Marketing Theory and Practice, Vol. 18 No. 3, pp. 285-293.

5. Aldas-Manzano, J., Ruiz-Mafe, C. and Sanz-Blas, S. (2009), “Exploring individual personality factors as drivers of M-shopping

acceptance”, Industrial Management & Data Systems, Vol. 109 No. 6, pp. 739-757. 6. Barwise, P. and Strong, C. (2002), “Permission-based mobile advertising”, Journal of Interactive Marketing, Vol. 16 No. 1, pp. 14-24.

7. Barkhuus, L. and Polichar, V.E. (2011), “Empowerment though seamfulness: smart phones”, Personal Ubiquitous Computing, Vol. 15,

pp. 629-639. 8. William C. McDowell, Rachel C. Wilson, Charles Owen Kile. 2016. An examination of retail website design and conversion rate.

Journal of Business Research 69:11, 4837-4842

9. Michael Groß. 2015. Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. The International Review of Retail, Distribution and Consumer Research 25:3, 215-235

10. Patrick Hille, Gianfranco Walsh, Mark Cleveland. 2015. Consumer Fear of Online Identity Theft: Scale Development and Validation.

Journal of Interactive Marketing 30, 1-19 11. Kiseol Yang, (2010),"Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping

services", Journal of Consumer Marketing, Vol. 27 Iss 3 pp. 262 – 270

12. Lu, H.-P. and Su, P.Y-J. (2009), “Factors affecting purchase intention on mobile shopping websites”, Internet Research, Vol. 19 No. 4, pp. 442-458

13. Persaud, A. and Azhar, I. (2012), “Innovative mobile marketing via smartphones: are consumers ready?”, Marketing Intelligence and

Planning, Vol. 30 No. 4, pp. 408-443 14. Kuo, Y. and Yen, S. (2009), “Towards an understanding of the behavioral intention to use 3G mobile value-added services”,

Computers in Human Behavior, Vol. 25, pp. 103-10

15. Hsi-Peng Lu Philip Yu-Jen Su, (2009),"Factors affecting purchase intention on mobile shopping web sites", Internet Research, Vol. 19

Iss 4 pp. 442 - 458

42

Authors: Dr. S. Ramachandran, Ms. Mandeep Kaur, Ms. Deepa Sharma

Paper Title: Impact of Technological Innovation on Quality of Management

Abstract: The main purpose of this paper is getting knowledge how technology innovation has impact on the

quality of management in its execution. Execution of new technological innovation is not an easy task for the

management. Management must fully equipped for handling the new technology in an organization our society

always look for new invention for full filling their needs it is a duty for the manager to help organization to

adopt and to execute new technological innovation in better way for achieving their goals and for gaining

goodwill in the market. Technical innovation considers an important step towards the growth of economy and

also helpful for improving the standard of living. Technical innovation is requirement of our society by these

types of innovation we can increase our customers by giving what they want but this is possible only when

organization has a effective management and qualified managers those who are able to understand how new

technological innovations will execute by optimum utilization of resources

Keywords: Technological, Innovation, Quality management

References: 1. Innovation and R&D in the upper Echelon: The association between the CTO’s power Depth and Breath and TMT’s commitment to

innovation – Florian Peter Garms and Andreas Engelen 05 March 2018

2. Innovation: The new Face of quality- Praveen Gupta 31 March 2009

3. Executive Education – Whart on university of Pennsylvania- By Nicolaj Siggel 4. 5 key points to consider when developing on innovation strategy- by Wouter Koetzier and Christapper Schorling

5. Innovation indicators throughout the innovation process: An extensive literature analysis- By Marisa Dziallas and Knut Blind

Technovation volumes 80-81 February- March pages 3-29. 6. Measuring Innovation part 1: Frequently used indicators- By Leif Denti

7. 10 new innovations that could change the world- by Joshua Bleiberg and Hillary Schaub 10 June 2014

8. Effects of Innovation Types on firm performance- by Gurhan Gunday, Gunduz Ulusoy, Kemal Kilic and Lutfihal Alpkan 9. Impact of technology on management and organization – by Alfonzo Venturi 8 dec 2014

10. The impact of technological innovation on building a sustainable city- by Chai-le-goi Innovation is about execution-by Martin

zuilling

229-232

43

Authors: Ms. Deepa Sharma, Dr. S. Ramachandran, Ms. Mandeep Kaur

Paper Title: Evaluation Framework of Human Resource Management Effectiveness in Organizations

Abstract: Human Resource Management (HRM) Play a vital role in today era.HRM Evaluation based on

what they do for the Companies that employ them and how they relate to those on the Organization operation

side. Human Resource Management takes the decisions for the Organization’s beneficial if the Organization

gain the profit that means HRM takes the effective and accurate decisions in favor of Organization’s .HRM are

the decisions makers like the Operating function, information and Finance decisions etc. Human Resource

Management that role changed with the advent of the technological age. HRM is a play very vital role between

employees and management .In other words we can say that Organizations are not mere bricks, mortar,

machineries or inventories. .HRM refers to core person of the organization HRM department take the effective

decisions in favor of employees and organizations. .HRM takes the effective decisions on the behalf of the

organizations.

Keywords: Human Resource Management, Strategic Management

References: 1. Aswathappa,k; Human Resource Management ,Himalaya Publishing House, Bombay 1990, p.626

2. www.questia.com-HRM articles ,Research studies 3. www.hrmguide.net-HumanResourceManagement

4. International Studies of Management and Organization, 30(1), 63-92. 5. Anderson, S. (2004). Internationalization in different industrial contexts. Journal of Business Venturing, 19, 851-875. An, P., and

233-235

Qiao.B., (2008). 6. Askewer, K. (1996). External-internal linkages and overseas autonomy-control tension: the management.

44

Authors: Pradhuman Singh, Capt (Retd) Pierre Memheld, Maj. Arthur Cooke, Dr Neeraj Anand

Paper Title: Dual use technology from prehistoric era to modern age: Utilisation of crossbow as a lethal weapon and an agricultural

cum research device

Abstract: Designed by the Vedic God Shiva, the crossbow has gone beyond mythical lore and became a tool

of strategic warfare in helping decimate knights, paladins and armored cavalry by arbalests or men who shoot

crossbows. This ancient design has stood the test of time and has still found favor with not only action cinema,

but also the hunting community and the special forces across the world. It is interesting to note that the Far

eastern as well as Nordic texts term it as a favorite tool of dragon slayers, it had found major applications in

hunting whales, sharks and other edible marine harvests. Certain countries in the northern hemisphere issue

crossbow licenses to farmers in ensuring crop protection from vermin as well as harvesting organic meat from

any overpopulating species in a given area. Additionally, it has been extensively used to build rope based

connectivity using hooks when a huge gap needs to be crossed. It has also evolved as a tool to track oceanic

activities by pegging sensors on its tips to etch it on whales and thus gather crucial data as they navigate the

sea. This evolution from an armor piercing weapon to a civilian application device goes to the next level when

a crossbow bolt is used to gain atmospheric and aerial data without fear of gaseous exhaust or electro-magnetic

fields of drones. The next progression may be of ‘Cold launch missiles’ that are nearly undetectable and may

change the course of military technology in the times to come.

Keywords: Aeronautics, Armor, Aviation, Avionics, Ballistics, Flight path, Force, Kinetic Energy,

Penetration, Projectile, Trajectory.

References: 1. Peers, C. J. (1996), Imperial Chinese Armies (2): 590-1260AD, Osprey 2. Hart, V. G., & Lewis, M. J. T. (2010). The Hatra ballista: a secret weapon of the past? Journal of Engineering Mathematics, 67(3),

261-273.

3. Brodie, B., & Brodie, F. M. (1973). From Crossbow to H-bomb (No. 161). Indiana University Press. 4. Nishioka, J. Z. (1988). U.S. Patent No. 4,766,874. Washington, DC: U.S. Patent and Trademark Office.

5. Bednar, W. J. (1997). U.S. Patent No. 5,649,520. Washington, DC: U.S. Patent and Trademark Office.

6. Shea, J., Davis, Z., & Brown, K. (2001). Experimental tests of Middle Palaeolithic spear points using a calibrated crossbow. Journal of Archaeological Science, 28(8), 807-816.

7. Rogers, C., Dowell, S., Choi, J. H., & Sathyavagiswaran, L. (1990). Crossbow injuries. Journal of Forensic Science, 35(4), 886-890.

8. Foley, V., Palmer, G., & Soedel, W. (1985). The crossbow. Scientific American, 252(1), 104-111. 9. Payne-Gallwey, R. (1903). The Crossbow, Mediæval and Modern, Military and Sporting: Its Construction, History and Management,

with a Treatise on the Balista and Catapult of the Ancients. Longmans, Green and Company.

10. Lopez, R. S., Slessarev, V., & Lane, F. C. (1969). The crossbow in the nautical revolution of the middle ages. Explorations in Economic History, 7(1), 161.

11. Bliujienė, A. U. D. R. O. N. Ė. (2002). The main stylistic features of the Baltic crossbow brooches in the Migration

period. Archaeologia Baltica.-Vilnius, 5, 145-161. 12. Smith, W. H. (2006). Crossbow Hunting. Stackpole Books.

13. Payne-Gallwey, R. (2007). The crossbow: its military and sporting history, construction and use. Skyhorse Publishing Inc.

14. Wright, D. C. (2005). Nomadic power, sedentary security, and the crossbow. Acta Orientalia, 58(1), 15-31. 15. Bachrach, D. S. (2004). Crossbows for the king: the crossbow during the Reigns of John and Henry III of England. Technology and

culture, 45(1), 102-119.

236-241

45

Authors: Katembo Kituta Ezéchiel, Shri Kant, Ruchi Agarwal

Paper Title: A New Non-Blocking Validation Protocol for Eager Replication of Databases over a Decentralized P2P Architecture

Abstract: Replicating a database on a decentralized P2P network using the eager approach is a difficult

problem, especially since participants (peers) are dynamic on such kinds of networks. A second defect is

conflicting transactions executed concurrently by different peers to update the same data. These problems

cause the perpetual abortions of transactions so that replicas remain always inconsistent. Thus, this article

introduces a new Four-Phase-Commit (4PC) validation protocol that allows the completion of transactions with

available peers and recovers unavailable ones when they re-join the network. Nested transactions and

distributed voting technique were used to arrive at an algorithm that was implemented with C#. An

experimentation scenario has made it possible to measure its performance and has finally revealed that the new

algorithm is effective because in real-time it can replicate a large number of records; it can queue the records of

the absent peers in order to distribute these updates to them when they become present again.

Keywords: Eager replication, Two-Phase-Commit (2PC) protocol, Read-One Write-All (ROWA), Peer-to-

Peer (P2P).

References: 1. Nicoleta-Magdalena, I. C. (2011). The replication technology in e-learning systems, Procedia-Social and Behavioral Sciences,

28(2011), pp. 231-235.

2. Maarten, V. S. and Andrew, T. (2016). A brief introduction to distributed systems, In Distributed Systems, Principles and Paradigms

(2nd edition), Netherlands, Publisher: Springer.

3. Özsu, M., T., and Valduriez, P. (2011). Principles of Distributed Database Systems (3rd Ed.), New York, United State: Springer

Science+Business Media, LLC.

4. Fatos, X., Kolici, V., Potlog, A., Spaho, E., Barolli, L., and Takizawa, M. (2012). Data Replication in P2P Collaborative Systems,

IEEE Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing Victoria, BC, Canada, pp. 49-57.

242-258

5. Spaho, E., Barolli, A., Fatos, X., and Barolli, L. (2015). P2P Data Replication: Techniques and Applications, In: Xhafa F., Barolli L.,

Barolli A., Papajorgji P. (eds) Modeling and Processing for Next-Generation Big-Data Technologies. Modeling and Optimization in

Science and Technologies, Publisher: Springer, Vol. 4, pp. 145-166.

6. Vu, Q. H., Lupu, M., and Ooi, B. C. (2009). Peer-to-peer computing: Principles and applications. Berlin, Heidelberg, Germany:

Springer Science & Business Media.

7. Kituta, K., Kant, S., Agarwal, R. (2018). Analysis of database replication protocols, International Journal of Latest Trends in

Engineering and Technology, Special Issue ICRMR-2018, pp. 075-083.

8. Zhou, T. and Wei. Y. (2013). Database Replication Technology having high Consistency Requirements, IEEE 3th International

Conference on Information Science and Technology March 23-25, 2013: Yangzhou, Jiangsu, China, pp. 793- 797.

9. Kituta, K., Kant, S. Agarwal, R. (2019). A systematic review on distributed databases systems and their techniques, Journal of

Theoretical and Applied Information Technology, 96(1), pp. 236-266.

10. Souri, A., Pashazadeh, S., and Navin, A. H. (2014). Consistency of data replication protocols in database systems: A review,

International Journal on Information Theory (IJIT), 3(4), pp. 19-32.

11. Srivastava, A., Shankar, U. and Tiwari, S. K. (2012). Transaction management in homogenous distributed real-time replicated

database systems, International Journal of Advanced Research in Computer Science and Software Engineering. 2(6), pp. 190-196.

12. Meroufela, B. and Belalem, G. (2013). Managing Data Replication and Placement Based on Availability, Procedia - AASRI

Conference on Parallel and Distributed Computing Systems: Elsivier. Vol. 5, pp. 147 – 155.

13. Sinde, V. and Aware, P. (2016). Concurrency control in distributed database systems, International Journal for Research in

Engineering Application & Management (IJREAM), 1(10), pp. 1-5.

14. Kaur, M. and Kaur, H. (2013). Concurrency Control in Distributed Database System, International Journal of Advanced Research in

Computer Science and Software Engineering, 3(7), pp. 1443-1447.

15. Kumar, S., Sharma, A. and Swaroop, V. (2011). Replication: Analysis & Tackle in Distributed Real Time Database System,

International Journal of Recent Trends in Electrical & Electronics Engg., 1(2), pp. 43-48.

16. Wiesmann, M., Pedone, F., Schiper, A., Kemme, B. and Alonso, G. (2000). Understanding replication in databases and distributed

systems, Proceedings 20th IEEE International Conference on Distributed Computing Systems, pp. 464-474.

17. Thomas, H. C., Charles, E. L., Ronald, L. R., and Clifford, S. (2012). Introduction to Algorithms (4th Ed.). London, England: The MIT

Press.

18. Kothari, C., R., and Garg, G. (2014). Research methodology methods and techniques (3rd Ed.). New-Delhi, India: House, Ed., M.P

Printers.

19. Sheikh, H. J., Rimiru, R. M. and Kimwele, M. W. (2017). Alternative Model to Overcoming Two Phase Commit Blocking Problem,

International Journal of Computer (IJC), 26(1), pp. 71-88.

20. Abuya, T. K., Rimiru, R. M. and Cheruiyot, W. K. (2015). Clustering Algorithm in Two-Phase Commit Protocol for Optimizing

Distributed Transaction Failure, International Journal of Computer Science and Mobile Computing, 4(3), pp. 97-106.

21. Kumar, N., Sahoo, L. and Kumar, A. (2014). Design and implementation of Three Phase Commit Protocol (3PC) algorithm, IEEE

International Conference on Reliability Optimization and Information Technology (ICROIT), pp. 116-120.

22. Santana, M., Enrique, J. and Francesc, D. (2015). Evaluation of database replication techniques for cloud systems. Computing and

Informatics, Vol. 34, pp. 973-995.

23. Hababeh, I. (2010). Improving network systems performance by clustering distributed database sites, Journal of Supercomputing,

Publisher: Springer Science+Business Media, LLC, 59(2012), pp. 249–267.

46

Authors: Dr Nidhi Arora, Dr Priti Verma

Paper Title: Customer Relationship Management in Banks: Convenience vs. Human Touch

Abstract: The banking landscape is changing rapidly with digital platforms becoming a dominant form of

interaction between banks and their clients; but isn’t this advancement a big challenge to human touch? Will

this advancement render the human element unnecessary? Today, Consumers, particularly millennials, expect

banking services to be faster and seamless on the back of smart digital interfaces. Technology has improved

productivity. However, it has made the banking operations more mechanical and devoid of human touch. The

paper explores the young generation banking customer’s attitude towards Customer Relationship Management

strategies adopted by different banks and discovers the alarming signal of vanishing human touch; as the study

finds that both banks and customers prefer technological convenience over human touch. Thus, in the evolution

of banking, the invisible wall is hindering the relationship between banks and customers which would be

showing the impact in the near future. The paper suggests that in the process of aligning the futuristic goals and

current practices the major ignorance to human touch is an invisible challenge, which should be addressed at

the right time. A service industry like banking cannot breathe without Emotional Connection. In times to

come, the fading human touch phenomenon needs to be reserved.

Keywords: Customer Relationship Management, convenience, technology, human touch

References: 1. Sangita Mehta. Tech apart, human touch still important in banking: Arundhati Bhattacharya.ET Bureau| Updated: Oct 13, 2017. Link:

//economictimes.indiatimes.com/articleshow/61061517.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst (Accessed Feb 15,2019).

2. Anoop Roy. Bankers divided over tech vs human touch. November 07, 2017 https://www.rediff.com/business/report/bankers-divided-

over-tech-vs-human-touch/20171107.htm (Accessed Feb15,2019) 3. Lawrence David Herbert (2017)General English Course Book Anthology of Poetry “work” Prasaramga Bangalore University

Bangalore.(Coppyright The Academy of American Poets for Work by D. H, Lawrence)

4. Injazz J. Chen, Karen Popovich, (2003) "Understanding customer relationship management (CRM): People, process and technology", Business Process Management Journal, Vol. 9 Issue: 5, pp.672- 688, https://doi.org/10.1108/14637150310496758

5. FUTURE NOW YES BANK-corporate overview (yes bank online annual report 2017) https://www.yesbank.in/investor-

relations/annual-reports.html 6. Kunkalienkar Manoj (2003). “IT in Banking 2003 (Special) Perspectives of CEO’s: Banking Software Companies”. Cited in Dr.

Munish Sabharwal ,“Indian Banks: The adoption of CRM program to encourage use of ebanking & obtain customer feedback

regarding Quality of IT related services”, Research Journal of Science & IT Management. RJSITM: Volume: 03, Number: 5, March-2014. ISSN:2251- 1563.

259-264

7. vyas P2004. Measurement of Customer Satisfaction on Information Technology adoption in banking Services, 81 (1-2): 8-16. 8. IDRBT (2013). Green Banking (Online report), retrieved online at

http://www.idrbt.ac.in/publications/Frameworks/Green%20Banking%20Framewor k%20(2013).pdf, March 2015.

9. Rautenstrauch, C. (1999). Environmental information systems for business. Berlin, Heidelberg: Springer. 10. Sakalauskas, V., Kriksciuniene, D., Kiss, F., &Horváth, A. (2009). Factors for sustainable development of E-banking in Lithuania and

Hungary. 5th International Vilnius Conference, Vilnius, Lithuania, 30 September – 3 October.

11. ENVISION – International Journal of Commerce and Management ISSN: 0973- 5976 (P); 2456-4575 (E) VOL-10, 2016. 12. Green Banking Adoption: A Comparative Study of Indian Public and Private Sector Banks Tejinder Pal Singh Brar.

13. Shanlax International Journal of Arts, Science & Humanities a. Vol. 3 No. 3 January 2016 ISSN: 2321 – 788X 11.Press release of

Standard Chartered Bank Mumbai, on 23 April, 2018 https://www.sc.com/in/. 14. Press release of Standard Chartered Bank Mumbai, on 23 April, 2018 https://www.sc.com/in/.

47

Authors: R. Venkatamuni Reddy, Raghavendra Nayak, Nagendra S, Mr Ashwith

Paper Title: Impact of Macro - Economic Factors on Indian Stock Market- A Research of BSE Sectoral Indices

Abstract: Macro-Economic factors plays a major role in decision making. Evaluation of macroeconomic

environment is required to examine the behaviour of stock prices, which further influences the investor’s

investment behaviour. Even though some macro-economic factors are not directly related to the company or

industry, but those factors has an impact on stock prices, further economic activity in the domestic and global

level has its own impact on stock market. When economy of the country grows hastily, it leads to faster growth

in the industry and vice versa. Financial market plays a central role in the performance of financial system of

an economy. Stock market is a market where securities of listed companies are exchanged between different

investors, it is very responsive market which, gives a stage to investors to invest their money in various

securities. Market indices are the tools to measure the performance of various securities of stock market and

Investors make use of those market indices to analyse performance of those industries in which, they prefer to

invest. This study takes into account six macro-economic factors (Crude oil Price, Gold Price, Silver Price,

Exchange Rate, Inflation and Interest Rate) to study & analyse the impact of these variables on selected

sectoral indices at BSE, SENSEX, S&P BSE BANKEX, S&P BSE Oil and Gas, S&P BSE Capital Goods,

S&P BSE Consumer Durables, S&P BSE Reality, S&P BSE PSU and S&P BSE Power. The study shows that

gold price, exchange rate, consumer price index and interest rate are positively correlated with four indices but

crude oil price and silver price have positively correlated with 3 indices. So from the result it is clear that

investor need to take of all the variables for their investment decision and the investment banker also take care

of these indicators before giving suggestion to their clients.

Keywords: Macro -economic, Stock Market, Market Indices, SENSEX, BSE, Inflation, Interest rate

References:

1. Pandian punithavathy, security analysis and portfolio management, vikas publishing house pvt ltd, 2009.

2. Avadhani V.A, security analysis and portfolio management, Himalaya publishing house, 10th edition, 2010.

3. Dwivedi DN, macroeconomics, tata McGraw-Hill publishing company limited, second edition, 2005.

4. Natarajan k, gordon E, financial markets and services, himalaya publishing house, 6th edition, 2010.

5. Kothari CR and Garg Gaurav, research methodology methods and techiniques, new age international (P) limited, publishers, third

edition, 2014.

6. Patel Samveg, “The effect of macroeconomic determinants on the performance of the Indian stock market”, NMIMS management

review, volume 22, 2012.

7. Singh Pooja,“Indian stock market an macro-economic factors in current scenario”, international journal of reasearch in business

management, volume 2, issue 11, 2014, pp 43-54.

8. Kotha Kumar Kiran and Sahu Bhawna, “Macro economic factors and Indian stock market: exploring long and short run relationships”,

international journals of economics and finance issues, volume 6, issues 3, 2016.

9. K Gurloveleen and BS Bhaitia, “An impact of macroeconomic variables on the functioning of Indian stock market: A study of manufacturing firms of BSE 500”, journal stock and forex trading, volume 6, issue 1, 2015.

265-270