Editor-In-Chief Dr. Shiv Kumar

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

Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India

Associate Editor-In-Chief Chair Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India

Dr. Anil Singh Yadav

Ph.D(ME), ME(ME), BE(ME)

Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), 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

Members of Associate Editor-In-Chief Chair Dr. Mayank Singh

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

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

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

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

Dr. Moinuddin Sarker

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

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

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

Dr. Shanmugha Priya. Pon

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

Africa, Tanzania

Dr. Veronica Mc Gowan

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

China.

Dr. Fadiya Samson Oluwaseun

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

Cyprus, Turkey.

Dr. Robert Brian Smith

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

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Prof. MPS Chawla

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

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

Dr. Vinod Kumar Singh

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

Dr. Rachana Dubey

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

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

(M.P.), India

Executive Editor Chair Dr. Deepak Garg

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

Members of Executive Editor Chair 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.

Members of Technical Program Committee Chair 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 Engineering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

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

Dr. Ch. Ravi Kumar

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

Pradesh), India.

Dr. Sanjay Pande MB

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

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

Belgaum (Karnataka), India.

Dr. Hany Elazab

Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.

Dr. M.Varatha Vijayan

Principal, Department of Mechanical Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai (Tamil Nadu)

India.

Dr. S. Balamurugan

Director, Research and Development, Intelligent Research Consultancy Services (IRCS), Coimbatore (Tamil Nadu), India.

Dr. Rajalakshmi Rahul

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

Founder and CEO Talaash Research Consultants, Chennai (Tamil Nadu), India.

Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Members of Editorial Chair Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

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

Dr. T. Sheela

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

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

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

Dr. Shivanna S.

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

Dr. H. Ravi Kumar

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

Dr. Pratik Gite

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

India

Dr. S. Murugan

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

Dr. S. Brilly Sangeetha

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

India

Dr. P. Malyadri

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

Dr. K. Prabha

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

Dr. Liladhar R. Rewatkar

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

Dr. Raja Praveen.N

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

Dr. Issa Atoum

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

University, Amman- Jordan

Dr. Balachander K

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

(Coimbatore), India

Dr. Sudhan M.B

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

Anna University, (Tamilnadu), India

Dr. T. Velumani

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

Dr. Subramanya.G.Bhagwath

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

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

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

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

Dr. P Venkata Subbareddy

Professor, Department of Computer Science and Engineering, Annamalai University (Tamil Nadu), India.

Dr. Muttipati Appala Srinuvasu

Professor, Department of Computer Science and Engineering, Gitam Deemed To Be University, Gandhi Nagar, Rushikonda

Visakhapatnam (Andhra Pradesh), India.

Dr. Namita Gupta

Professor, Department of Economics, MG Kashi Vidyapeeth, Varanasi (Uttar Pradesh), India.

Dr. Chandan Medatwal

Professor, Department of Management, University Of Kota, MBS Marg, Kota (Rajasthan), India.

Dr. Narasimhan D

Professor, Department of Mathematics, Srinivasa Ramanujan Centre Sastra Deemed University Kumbakonam (Tamil Nadu), India.

Dr. Yuriy Pyvovar

Professor, Department of Constitutional and Administrative Law, National Aviation University, Kiev, Ukraine.

Dr. Asim K. Mandal

Professor, Department of Agriculture, Bidhan Chandra Krishi Viswavidyalaya (BCKV), Mohanpur, Nadia (West Bengal), India.

Dr. Lokesh P Gagnani

Professor, Department of Computer Science and Engineering, C U Shah University, Nr. Kothariya Village, Dist. Surendranagar,

Wadhwan (Gujarat), India.

Dr. Trilochan Jena

Professor, Department of Civil Engineering, Siksha O Anusandhan (Deemed to be University), ITER, Bhubaneswar (Odisha), India.

Dr. S. Ismail Kalilulah

Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Avadi, Chennai (Tamil Nadu), India.

Dr. S Vijayakumar

Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.

Dr. Serhii Kozlovskyi

Professor, Department of Economics, Vasyl’ Stus Donetsk National University, Vinnytsia, Ukraine.

Dr. V. Jaiganesh

Professor, Department of Mechanical Engineering, Anna University Chennai (Tamil Nadu), India.

Dr. Mohankumar Namdeorao Bajad

Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.

Dr. G. Purushotham

Professor, Department of Mechanical Engineering Sciences, Visvesvaraya Technological University, Belagavi (Karnataka), India.

Dr. Rajendiran Muthusamy

Professor, Department of Computer Science and Engineering, Sathyabama University, Chennai (Tamil Nadu), India.

Dr. S Madhava Reddy

Professor, Department of Mechanical Engineering, Osmania University, Hyderabad (Telangana), India.

Dr. Siddhartha Choubey

Professor, Department of Computer Science and Engineering, MATS University, Aarang, Raipur (Chhattisgarh), India.

Dr. Ebissa

Professor, Department of Civil Engineering, IIT Roorkee, Roorkee (Uttarakhand), India.

Dr. R. Dhanasekaran

Professor, Department of Mechanical Engineering, Anna University, Chennai (Tamil Nadu), India.

Dr. Kajal Chaudhary

Professor, Department of Commerce, Chaudhary Charan Singh University, Meerut (Uttar Pradesh), India.

Dr. Sivasankari

Assistant Professor, Department of Chemistry, Cauvery College for Women, Tiruchirappalli (Tamil Nadu), India.

Dr. K. S. Meenakshisundaram

Former Director, Cresent School of Business, Crescent University, Chennai (Tamil Nadu), India.

S. NoVolume-8 Issue-2s12, September 2019, ISSN: 2278-3075 (Online)

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

1. Authors:Kumud Pant, Abhishek Semwal, Devvret Verma, Promila Sharma, Akansha Pal, Neetu Sharma,Akshara Pande, Somya Sinha, Neema Tufchi

Paper Title: Prediction of No Tropic Properties of Novel Drug Modafiendz using in-Silico Method

Abstract: The development and approval of new drug is a tedious, expensive and highly time-consuming task.The demand of new drugs is increasing, and the development of natural drugs and traditional drugs are re-emerging as a new strategic task. The in-silico techniques have boosted the development of potential drugcandidates. One important category of drugs is nootropes. It improves the cognitive function, memory,motivation and creativity in healthy individuals. The demand of the nootropic drugs has skyrocketed in past fewdecades. Modafiendz is a novel drug that is often used by the consumers as it is having similarity in structureand property with modafinil (Nootropic drug). But no major studies have been carried out on this molecule, soremains an investigatory molecule.There are several in-silico techniques that can be used to predict the likeness, metabolic activity andpharmacological property of a molecule. In the current study, realizing the importance of modafiendz, variousproperties of modafiendz have been predicted like metabolism site, ADME properties, metabolite prediction andDNA adduct formation tendencies.The properties of modafiendz were found to be similar to modafinil in variousregards of drug likeability, bioavailability, blood brain permeability (BBB), GI absorption and site ofmetabolism (SOM). The study suggests modafiendz as a better nootropic drug candidate, when compared tomodafinil.

Keywords: no tropics, modafinil, modafiendz, in-silico analysis, ADME, DNA adduct.

References:

1. N. A.Suliman, C. N. M. Taib, M. A.M. Moklas, M. I. Adenan, M. T. H. Baharuldin andR.Basir, “Establishing NaturalNootropics: Recent Molecular Enhancement Influenced by Natural Nootropic,”Evid. Based Complement. Alternat. Med., pp. 1-12, 2016.

2. Michael S.Gazzaniga, The Ethical Brain: The Science of Our Moral Dilemmas (P.S.). New York, N.Y: Harper Perennial., 2006,p. 75.

3. C. Giurgea,“Pharmacology of integrative activity of the brain. Attempt at nootropic concept in psychopharmacology,” Actual.Pharmacol., 25, pp. 115-116, 1972.

4. Zion Market Research “Global Nootropics Market Growth at 2019-2024” LP InformationInc, Jan 11, 2019. [Online]. Available”Global News Wire, http://www.lpinformationdata.com/reports/2466/global-nootropics-market. [Accessed May 10, 2019}

5. G. Norman, “Drugs, Devices, and the FDA: Part 1: An Overview of Approval Processes for Drugs,” JACC Basic Transl Sci.,1(3), pp. 170-179,Apr. 2006.

6. NCIthesaurus - modafinil , https://ncit.nci.nih.gov/

7. Provigil – Food and drug administration(FDA), https://www.fda.gov/downloads/drugs/drugsafety/ucm231722.pdf

8. G. Dowling, P. V. Kavanagh, B. Talbot, J. O’Brien, , G. Hessman, , G. McLaughlin andS. D.Brandt, “Outsmarted by nootropics?An investigation into the thermal degradation of modafinil, modafinic acid, adrafinil, CRL‐39,940 and CRL‐40,941 in the GCinjector: formation of 1, 1, 2, 2‐tetraphenylethane and its tetra fluoro analog,” Drug Test Anal., vol 9(3), pp. 518-528, Mar. 2017.

9. M. Billiard and R. Broughton, “Modafinil : its discovery, the early European and North American experience in the treatment ofnarcolepsy and idiopathic hypersomnia, and its subsequent use in other medical conditions,” Sleep medicine, 49, pp. 69-72, 2018.

10. Christoph Wittmann and Sang Yup Lee. Systems metabolic engineering, Springer, 2012, pp. 4-26.

11. S. A.Sheweita,“Drug-metabolizing enzymes mechanisms and functions,” Current drug metabolism, 1(2), pp. 107-132, 2000.

12. D. Bryson, P. L. Lim, A. Lawson, A. Lawson, S. Manjunath and G. M. Raner, “Isotopic labelling of the heme cofactor incytochrome p450 and other heme proteins,” Biotechnol. Lett., 33(10), pp. 2019-2026, 2011.

13. J. D. Maréchal, C. A. Kemp, G. C. Roberts, M. J. Paine, C. R. Wolf, and M. J. Sutcliffe, “Insights into drug metabolism bycytochromes P450 from modelling studies of CYP2D6-drug interactions,” Br. J. Pharmacol., 153 Suppl 1(Suppl 1), pp. S82-S89,2007.

14. F. P. Guengerich, “Cytochrome p450 and chemical toxicology” Chem.Res. Toxicol., 21(1), pp. 70-83, 2007.

15. Y. Djoumbou-Feunang, J.Fiamoncini, A. Gil-de-la-Fuente, R.Greiner, C. Manachand D. S. Wishart, “BioTransformer: acomprehensive computational tool for small molecule metabolism prediction and metabolite identification,” J. cheminform.,11(1), pp. 1-25, 2019.

16. T. B. Hughes, N. L. Dang, G. P. Millerand and S. J. Swamidass, “Modeling reactivity to biological macromolecules with a deepmultitask network,” ACS Cent. Sci., 2(8), pp. 529-537, 2016

17. P. P.Massion, andD. P. Carbone, “The molecular basis of lung cancer: molecular abnormalities and therapeutic implications,”Respir. Res., 4(1), p. 12, 2003.

18. Patrik Rydberg, David E. Gloriam and Lars Olsen, “The SMARTCyp cytochrome P450 metabolism prediction server,”Bioinformatics, Volume 26, Issue 23, pp. 2988–2989, 1 Dec. 2010.

19. S. Jamuna, A. Rathinavel, S. S. Mohammed Sadullah, and S. N. Devaraj, “In silico approach to study the metabolism andbiological activities of oligomeric proanthocyanidin complexes,” Indian J.Pharmacol., 50(5), pp. 242-250, 2018.

20. J. Kirchmair, M. J. Williamson, J. D. Tyzack, L.Tan, P. J.Bond, A. Bender and R. C. Glen, “Computational prediction ofmetabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms,” J. Chem. Inf. Model., 52(3), pp. 617-648, 2012.

21. A.Rudik, A. Dmitriev, A.Lagunin, D. Filimonov and V. Poroikov, “SOMP: web-service for in silico prediction of sites ofmetabolism for drug-like compounds,” Bioinformatics, 31 (12), pp. 2046-2048, 2015.

22. A. Daina, O. Michielin and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinalchemistry friendliness of small molecules,” Sci. Rep., 7, 42717, 2017 [Online] Available:https://www.nature.com/articles/srep42717

23. C.A. Lipinski, F. Lombardo, B. W. Dominy and P. J. Feeney, “Experimental and computational approaches to estimate solubility

1-8

and permeability in drug discovery and development settings,” Adv. Drug Deliv. Rev., 46, pp. 3-26, 2001.

24. A. K. Ghose, V. N. Viswanadhan and J. J. Wendoloski, “A knowledge-based approach in designing combinatorial or medicinalchemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases,” J. Comb.Chem., 1(1), pp. 55-68, 1999.

25. D. F. Veber, S. R. Johnson, H. Y. Cheng, B. R. Smith, K. W. Ward, and K. D. Kopple, “Molecular properties that influence theoral bioavailability of drug candidates,” J. Med. Chem., 45(12), pp. 2615-2623, 2002.

26. W. J. Egan, K. M. Merz and J. J. Baldwin, “Prediction of drug absorption using multivariate statistics,” J. Med. Chem., 43(21),pp. 3867-3877, 2000.

27. I. Muegge, S. L. Heald and D. Brittelli, “Simple selection criteria for drug-like chemical matter,” J. Med. Chem., 44(12), pp.1841-1846, 2001.

28. V. Delannée, S. Langouët, A. Siegel and N. Theret, “In silico prediction of Heterocyclic Aromatic Amines metabolismsusceptible to form DNA adducts in humans,” Toxico. Lett., 300, pp. 18-30, 2019.

29. T. B. Hughes, G. P. Miller and S. J. Swamidass, “Site of reactivity models predict molecular reactivity of diverse chemicalswithglutathione,” Chem. Res. Toxicol., 28(4), pp. 797-809, 2015

30. M. J. Waring, “Lipophilicity in drug discovery,” Expert Opinion on Drug Discovery, 5(3), pp. 235-248, 2010. 31. A.D. Mcnaught and A. Wilkinson, IUPAC Compendium of Chemical Terminology, 2nd ed, Oxford , 1997.

32. J. S. Delaney, “ESOL: estimating aqueous solubility directly from molecular structure,” J. Chem. Inf. Comput. Sci., 44(3), pp.1000-1005, 2004.

33. S. Kotta, A. W. Khan, K. Pramod, S. H. Ansari, R. K. Sharma and J. Ali, “Exploring oral nanoemulsions for bioavailabilityenhancement of poorly water-soluble drugs,” Expert Opin. Drug Deliv., 9(5), pp. 585-598, 2012.

34. J. L. Dahlin, J. W. M. Nissink, J. M. Strasser, S. Francis, L. Higgins, H. Zhou, and M. A. Walters, “PAINS in the assay: chemicalmechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS,” J. Med.Chem., 58(5), pp. 2091-2113, 2015.

35. J. Baell and M. A. Walters, “Chemistry: Chemical con artists foil drug discovery,” Nature News, 513(7519), pp. 481-483, 2014.

36. W. M. Pardridge, “The blood-brain barrier: bottleneck in brain drug development,” NeuroRx, 2(1), pp. 3-14, 2005.

37. J. A. Arnott and S. L. Planey, “The influence of lipophilicity in drug discovery and design,” Expert opinion on drug discovery,7(10), pp. 863-875, 2012.

2.

Authors: Anupriya Sharma Ghai, Kapil Ghai, Divya Kapil, Neetu Sharma

Paper Title: E-Waste Generation and Management Practices in Dehradun, India

Abstract: The advancement in the electrical and electronic equipment and change in technology increases thedemand of electronics appliances in the developing countries. People purchase electronic goods due to newfeatures and capabilities. A lot more people sold or discard the older equipment’s without the knowledge ofuseful life which leads to enormous used electronic equipment called electronic waste (E-waste). In thedeveloping countries like India which ranked fifth in producing e-waste globally, due to the lack of enoughinfrastructure and improper management practice. The present study evaluates the knowledge, management,dispose practices of e-waste and hazardous substances generated by the citizens of Dehradun, India in year 2019.An online questionnaire on 300 respondents based on calculating the sample size requirement was done in midof year 2019.The outcome of the study show case the effect of e-waste generated by different income basegroups with the inventory used by citizens in one year. Total number of heavy metals and plastics generated byhousehold appliances and ICT and consumer electronics were also measured. The result showed up the need ofawareness and urgent requirement of the serious issues of E-waste. The state government must involve withoutwaiting for the upcoming adverse effects on environment and health risks to human lives.

Keywords: E-waste, inventory assessment, heavy metals, income based category, electrical and electronicequipment.

References:

1. Nnorom, I. C., &Osibanjo, O. (2008). Electronic waste (e-waste): Material flows and management practices in Nigeria. WasteManagement, 28(8), 1472-1479.

2. Qu, Y., Wang, W., Liu, Y., & Zhu, Q. (2019). Understanding residents’ preferences for e-waste collection in China-A case studyof waste mobile phones. Journal of Cleaner Production, 228, 52-62.

3. Bhasker, B. (2013). Electronic commerce: framework, technologies and applications. Tata McGraw-Hill Education. 4. Bhuie, A. K., Ogunseitan, O. A., Saphores, J. D., & Shapiro, A. A. (2004, May). Environmental and economic trade-offs in

consumer electronic products recycling: a case study of cell phones and computers. In IEEE International Symposium onElectronics and the Environment, 2004. Conference Record. 2004 (pp. 74-79). IEEE.

5. Misra, V., & Pandey, S. D. (2005). Hazardous waste, impact on health and environment for development of better wastemanagement strategies in future in India. Environment international, 31(3), 417-431.

6. Baldé, C. P., Forti, V., Gray, V., Kuehr, R., & Stegmann, P. (2017). The Global E-waste Monitor–2017, United NationsUniversity (UNU), International Telecommunication Union (ITU) & International Solid Waste Association (ISWA),Bonn/Geneva/Vienna. ISBN Electronic Version, 978-92.

7. Kumar, A., Holuszko, M., & Espinosa, D. C. R. (2017). E-waste: an overview on generation, collection, legislation and recyclingpractices. Resources, Conservation and Recycling, 122, 32-42.

8. Sahu, G. (2013). Environmental Regulatory Authorities in India: An Assessment of State Pollution Control Boards. Centre forScience, Technology & Society School of Habitat Studies.

9. Kuppuswamy, B. (1981). Manual of socioeconomic status (urban)[M] New Delhi: Manasayan, 28. 10. Nunnally, J.C. (1978). Psychometric Theory. New York: McGraw-Hill. 11. 11. Appliance., 2016. 23rd Annual Portrait of the U.S. appliance industry. Saturation, share-of market, and life expectancy figures

are given along with a comprehensive listing of Who's Who in the Appliance Industry. Dana Chase Publication. 57, 83-110.

9-15

3. Authors: Atika Gupta, Bhaskar Pant, Nidhi Mehra, Divya Kapil

Paper Title: Machine Learning for Detecting Credit Card Frauds

Abstract: Credit card frauds has been a threat that has evolved as a major source of loss for the financialsectors. It has been seen in the different parts of world causing loss of billions of dollars. It is also a area whichneeds attention from the researchers as the task of fraud detection can be automated using the different machinelearning classifiers and data science. If the frauds model encounter the fraudulent transactions it will raise analarm to the system administrator. The paper proposes a model which uses the machine learning classifiers todetect the fraudulent transactions. The classifiers used in the paper are SVM (Support Vectore Machine ),Isolation Forest and Local Outlier. The focus of the research is to detect the fraudulent transactions to 100% andalso we emphasise on the fact that no normal transaction should be detected as fraud wrongly. The process startswith preprocessing the data and then the classifers are applied. The results from each classifers is evaluated tocheck the one with the better performance. The performance can be increased with use of deep learningalgorithms but with the rise in expennses.

Keywords: Credit card fraud, machine learning,isolation forest, local outlier.

References:

1. S. Aihua, T. Rencheng, and D. Yaochen, “Application of classification models on credit card fraud detection,” Proc. -ICSSSM’07 2007 Int. Conf. Serv. Syst. Serv. Manag., no. 1997, pp. 2–5, 2007.

2. A. Roy, J. Sun, R. Mahoney, L. Alonzi, S. Adams, and P. Beling, “Deep learning detecting fraud in credit card transactions,”2018 Syst. Inf. Eng. Des. Symp. SIEDS 2018, pp. 129–134, 2018.

3. T. P. Bhatla, V. Prabhu, and A. Dua, “Understanding Credit Card Frauds,” Cards Bus. Rev., vol. 1, no. 6, pp. 1–15, 2003. 4. S. Ghosh and D. L. Reilly, “Credit card fraud detection with a neural-network,” Proc. Hawaii Int. Conf. Syst. Sci., vol. 3, pp.

621–630, 1994. 5. I. Sadgali, N. Sael, and F. Benabbou, “Performance of machine learning techniques in the detection of financial frauds,” Procedia

Comput. Sci., vol. 148, no. Icds 2018, pp. 45–54, 2019. 6. P. Save, P. Tiwarekar, K. N., and N. Mahyavanshi, “A Novel Idea for Credit Card Fraud Detection using Decision Tree,” Int. J.

Comput. Appl., vol. 161, no. 13, pp. 6–9, 2017. 7. A. O. Adewumi and A. A. Akinyelu, “A survey of machine-learning and nature-inspired based credit card fraud detection

techniques,” Int. J. Syst. Assur. Eng. Manag., vol. 8, pp. 937–953, 2017. 8. Q. Lu and C. Ju, “Research on credit card fraud detection model based on class weighted support vector machine,” J. Converg.

Inf. Technol., vol. 6, no. 1, pp. 62–68, 2011. 9. S. Jha, M. Guillen, and J. Christopher Westland, “Employing transaction aggregation strategy to detect credit card fraud,” Expert

Syst. Appl., vol. 39, no. 16, pp. 12650–12657, 2012. 10. J. T. S. Quah and M. Sriganesh, “Real-time credit card fraud detection using computational intelligence,” Expert Syst. Appl., vol.

35, no. 4, pp. 1721–1732, 2008. 11. A. Srivastava, A. Kundu, S. Sural, and A. K. Majumdar, “Credit card fraud detection using Hidden Markov Model,” IEEE Trans.

Dependable Secur. Comput., vol. 5, no. 1, pp. 37–48, 2008. 12. R. Brause, T. Langsdorf, and M. Hepp, “Neural data mining for credit card fraud detection,” Proc. Int. Conf. Tools with Artif.

Intell., pp. 103–106, 1999. 13. Ravelin, “Machine learning for fraud detection,” no. May, 2002. 14. C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams, “Transaction aggregation as a strategy for credit card fraud

detection,” Data Min. Knowl. Discov., vol. 18, no. 1, pp. 30–55, 2009. 15. H. Ali Shukur and S. Kurnaz, “International Journal of Computer Science and Mobile Computing Credit Card Fraud Detection

using Machine Learning Methodology,” Int. J. Comput. Sci. Mob. Comput., vol. 8, no. 3, pp. 257–260, 2019. 16. O. S. Yee, S. Sagadevan, and N. H. A. H. Malim, “Credit card fraud detection using machine learning as data mining technique,”

J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1–4, pp. 23–27, 2018. 17. A. Pumsirirat and L. Yan, “Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann

machine,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 1, pp. 18–25, 2018. 18. V. N. Dornadula and S. Geetha, “Credit Card Fraud Detection using Machine Learning Algorithms,” Procedia Comput. Sci., vol.

165, pp. 631–641, 2019. 19. S P Maniraj, Aditya Saini, Shadab Ahmed, and Swarna Deep Sarkar, “Credit Card Fraud Detection using Machine Learning and

Data Science,” Int. J. Eng. Res., vol. 08, no. 09, pp. 110–115, 2019. 20. F. T. Liu, K. M. Ting, and Z. H. Zhou, “Isolation forest,” Proc. - IEEE Int. Conf. Data Mining, ICDM, pp. 413–422, 2008. 21. L. Sun, S. Versteeg, S. Boztas, and A. Rao, “Detecting Anomalous User Behavior Using an Extended Isolation Forest Algorithm:

An Enterprise Case Study,” 2016. 22. S. Hariri, M. Carrasco Kind, and R. J. Brunner, “Extended Isolation Forest,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2019. 23. M. M. Breunig, H. P. Kriegel, R. T. Ng, and J. Sander, “OPTICS-OF: Identifying local outliers,” Lect. Notes Comput. Sci.

(including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1704, pp. 262–270, 1999. 24. Z. He, X. Xu, and S. Deng, “Discovering cluster-based local outliers,” Pattern Recognit. Lett., vol. 24, no. 9–10, pp. 1641–1650,

2003. 25. D. Pokrajac, A. Lazarevic, and L. J. Latecki, “Incremental local outlier detection for data streams,” Proc. 2007 IEEE Symp.

Comput. Intell. Data Mining, CIDM 2007, no. Cidm, pp. 504–515, 2007. 26. H. C. Kim, S. Pang, H. M. Je, D. Kim, and S. Y. Bang, “Constructing support vector machine ensemble,” Pattern Recognit., vol.

36, no. 12, pp. 2757–2767, 2003. 27. M. E. Mavroforakis and S. Theodoridis, “A geometric approach to support vector machine (SVM) classification,” IEEE Trans.

Neural Networks, vol. 17, no. 3, pp. 671–682, 2006. 28. L. Auria and R. A. Moro, “Support Vector Machines (SVM) as a Technique for Solvency Analysis,” SSRN Electron. J., no.

August, 2011.

16-23

4. Authors: Vartika Gupta, Arunima Nayak, Brij Bhushan, Vijay Kumar

Paper Title:Assessment of Biosorption Potential of Poplar Sawdust for Removal of Dyes from Wastewater underSingle and Binary System

Abstract: In the present study, the performance of raw sawdust (RSD) as a biosorbent was assessed for theremoval of model dyes (MB-Methylene blue and CR-Congo red) in single as well as binary systems undervarious wastewater conditions. Biosorption studies in single system under simulated wastewater conditionsshowed highest uptake of MB and CR taking place at pH 6 and 2, respectively. pH and FTIR studies revealed

24-33

the binding to be electrostatic in nature, while a inter-particle diffusion mechanism was found to be operative.Irrespective of the nature of the dye, equilibrium was found to be achieved within 60 mins. Biosorption studiescarried out in binary systems under similar experimental conditions in simulated wastewater showed nosignificant difference in the removal efficiency. This could be attributed to the fact that there is no competitiverelationship between the cationic (MB) and anionic (CR) dyes when present simultaneously in the wastewater.On the other hand, the results as obtained under real wastewater binary system reveal lower removal capacity forthe removal of the dyes which could be due to competitive adsorption of organic pollutants as verified by a 50 %reduction in COD in the real wastewater. Irrespective of the wastewater conditions, isotherm studies showed thatat lower adsorbate concentrations, the Langmuir model was operative while the Freundlich model showed highercorrelation at higher adsorbate concentrations. Experimental results thus verify the usefulness of RSD as aneconomic, cost effective and potential biosorbent for the removal of dyes from diverse wastewater conditions.

Keywords: Sawdust; biosorbent; binary dye sorption; real wastewater; isotherm model; kinetic

References:

1. A.A. Giwa, M.A. Oladipo, K.A. Abdulsalam, Adsorption of Rhodamine B from single, binary and ternary dye systems usingsawdust of parkiabiglobosa as adsorbent: Isotherm, kinetics and thermodynamics studies, J. Chem. Pharm. Res. 7 (2015) 454-475.

2. A. Kurniawan, H. Sutiono, N. Indraswati, S. Ismadji, Removal of basic dyes in binary system by adsorption using rarasaponin–bentonite: Revisited of extended Langmuir model, Chem. Eng. J. 189 (2012) 264– 274.

3. V.K. Gupta, A. Nayak, S. Agarwal, I. Tyagi, Potential of activated carbon from waste rubber tire for the adsorption of phenolics:effect of pre-treatment conditions, J. Colloid Interface Sci. 417 (2014) 420- 430.

4. A. Nayak, B. Bhushan, V. Gupta, L. Rodriguez-Turienzo, Development of a green and sustainable clean up system from grapepomace for heavy metal remediation, J. Environ. Chem. Eng. 4 (2016) 4342–4353.

5. C.M. Castilla, J.R. Utrilla, Carbon materials as adsorbents for the removal of pollutants from the aqueous phase, Mater. Res. Soc.Bull. 26 (2001) 890-894.

6. M. Ghasemi, M. Naushad, N. Ghasemi, Y. Khosravi-fard, A novel agricultural waste based adsorbent for the removal of Pb(II)from aqueous solution: kinetic, equilibrium and thermodynamic studies, J. Ind. Eng. Chem. 20 (2014) 454-460.

7. J.J. Salazar-Rabago, R. Leyva-Ramos, J. River-Utrilla, R. Ocampo- Perez, F.J. Cerino-Cordova, Biosorption mechanism ofmethylene blue from aqueous solution onto white pine (Pinus durangensis) sawdust: effect of operating conditions, SustainableEnviron. Res. 27 (2017) 32-40.

8. U. Farooq, J.A. Kozinski, M.A. Khan, M. Athar, Biosorption of heavy metal ions using wheat based biosorbents-a review of therecent literature, Bioresour. Technol. 101 (2010) 5043–5053.

9. R.B. Garcia-Reyes, J.R. Rangel-Mendez, Adsorption kinetics of chromium(III) ions on agro-waste materials, Bioresour. Technol.101 (2010) 8099–8108.

10. K.S. Bharathi, S.T. Ramesh, Removal of dyes using agricultural waste as low-cost adsorbents: a review, Appl. Water Sci. 3(2013) 773–790.

11. M.A. Mohammed, A. Shitu, M.A. Tadda, M. Ngabura, Utilization of various agricultural waste materials in the treatment ofindustrial wastewater containing heavy metals: a review, Int. Res. J. Environ. Sci. 3 (2014) 62-71.

12. F. Ferrero, Dye removal by low cost adsorbents: hazelnut shells in comparison with wood sawdust, J. Hazard. Mater. 142 (2007)144- 152.

13. D. Mohan, H. Kumar, A. Saraswat, M. Alexandre-Franco, C.U. Pittman, Cadmium and lead remediation using magnetic oakwood and oak bark fast pyrolysis bio-chars, Chem. Eng. J. 236 (2014) 513– 528.

14. M. Rafatullah, O. Sulaiman, R. Hashim, A. Ahmad, Adsorption of copper (II), chromium (III), nickel (II) and lead (II) ions fromaqueous solutions by meranti sawdust, J. Hazard. Mater. 170 (2009) 969–977.

15. V.K. Garg, M. Amita, R. Kumar, R. Gupta, Basic dye (methylene blue) removal from simulated wastewater by adsorption usingIndian rosewood sawdust: a timber industry waste, Dyes Pigm. 63 (2004) 243-250.

16. T.A. Khan, S. Sharma, E.A. Khan, A.A. Mukhlif, Removal of congo red and basic violet 1 by chir pine (Pinus roxburghii)sawdust, a saw mill waste: batch and column studies, Toxicol. Environ. Chem. 96 (2014) 555-568.

17. B. Kakoi, J.W. Kaluli, G. Thumbi, A. Gachanja, Performance of activated carbon prepared from sawdust as an adsorbent forendosulfan pesticide, J. Sustainable Res. Eng. 2 (2015) 1-10.

18. R. Ansari, Z. Mosayebzadeh, Removal of basic dye methylene blue from aqueous solutions using sawdust and sawdust coatedwith polypyrrole, J. Iran. Chem. Soc. 7 (2010) 339-350.

19. G. Cheng, L. Sun, L. Jiao, L.X. Peng, Zhi-hong Lei, Y.X. Wang, J. Lin, Adsorption of methylene blue by residue biochar fromcopyrolysis of dewatered sewage sludge and pine sawdust, Desalin. Water Treat. 51 (2013) 7081–7087.

20. S.D. Khattri, M.K. Singh,Colour removal from synthetic dye wastewater using a bioadsorbent, Water Air Soil Pollut. 120 (2000)283-294.

21. O. Hamdaoui, Batch study of liquid-phase adsorption of methylene blue using cedar sawdust and crushed brick, J. Hazard. Mater.135 (2006) 264–273.

22. M.M.A. El-Latif, A.M. Ibrahim, M.F. El-Kady, Adsorption equilibrium, kinetics and thermodynamics of methylene blue fromaqueous solutions using biopolymer oak sawdust composite, J. Am. Sci. 6 (2010) 267-283.

23. B.H. Hameed, A.L. Ahmad, K.N.A. Latiff, Adsorption of basic dye (methylene blue) onto activated carbon prepared from rattansawdust, Dyes Pigm. 75 (2007) 143-149.

24. F.A. Batzias, D.K. Sidiras, Dye adsorption by calcium chloride treated beech sawdust in batch and fixed-bed systems, J. Hazard.Mater. 114 (2004) 167–174.

25. B.H. Hameed, A.T.M. Din, A.L. Ahmad, Adsorption of methylene blue onto bamboo-based activated carbon: kinetics andequilibrium studies, J. Hazard. Mater. 141 (2007) 819–825.

26. A. Ahmad, M. Rafatullah, O. Sulaiman, M.H. Ibrahim, R. Hashim, Scavenging behavior of meranti sawdust in the removal ofmethylene blue from aqueous solution, J. Hazard. Mater. 170 (2009) 357–365.

27. K. Kadirvelu, M. Kavipriya, C. Karthika, M. Radhika, N. Vennilamani, S. Pattabhi, Utilization of various agricultural wastes foractivated carbon preparation and application for the removal of dyes and metal ions from aqueous solutions, Bioresour. Technol.87 (2003) 129–132.

28. V.S. Mane, P.V.V. Babu, Studies on the adsorption of brilliant green dye from aqueous solution onto low-cost NaOH treated sawdust, Desalin. 273 (2011) 321–329.

29. S. Babel, T.A. Kurniawan, Cr(VI) removal from synthetic wastewater using coconut shell charcoal and commercial activatedcarbon modified with oxidizing agents and/or chitosan, Chemosphere 54 (2004) 951–967.

30. R. Gong, J. Ye, W. Dai, X. Yan, J. Hu, X. Hu, S. Li, H. Huang, Adsorptive removal of methyl orange and methylene blue fromaqueous solution with finger citron residue-based activated carbon, Ind. Eng. Chem. Res. 52 (2013) 14297-14303

31. J.E. Aguiar, B.T.C. Bezerra, A.C.A. Siqueira, D. Barrera, K. Sapag, D.C.S. Azevedo, S.M. P. Lucena, I.J. Silva, Improvement inthe adsorption of anionic and cationic dyes from aqueous solutions: a comparative study using aluminium pillared clays andactivated carbon, Sep. Sci. Technol. 49 (2014) 741–751.

32. I. Langmuir, The adsorption of gases on plane surfaces of glass, mica and platinum, J. Am. Chem. Soc. 40 (1918) 1361-1403. 33. H.M.F. Freundlich, Over the adsorption in solution, J. Phys. Chem. 57 (1906) 385-470. 34. M.M. Dubinin, E.D. Zaverina, L.V. Redushkevich, Sorption and structure of active carbons. I. adsorption of organic vapors, Zh.

Fiz. Khim. 21 (1947) 1351-1362. 35. M.K. Raman, G. Muthuraman, Removal of binary mixture of textile dyes on prosopisjuliflora pods – equilibrium, kinetics and

thermodynamics studies, Iran. J. Energy Environ. 8 (2017) 48-55. 36. L. Liu, B. Zhang, Y. Zhang, Y. He, L. Huang, S. Tan, X. Cai, Simultaneous removal of cationic and anionic dyes from

environmental water using montmorillonite-pillared graphene oxide, J. Chem. Eng. Data 60 (2015) 1270–1278. 37. M. Turabik, Adsorption of basic dyes from single and binary component systems onto bentonite: simultaneous analysis of basic

red 46 and basic yellow 28 by first order derivative spectrophotometric analysis method, J. Hazard. Mater. 158 (2008) 52–64.

5.

Authors: Chetan Pandey, Amit Juyal, Neeraj Panwar, Aditya Joshi

Paper Title: HBASE Data Security with AES Algorithm

Abstract: Since last decade almost every organization is focusing more on collecting their data (big data) andmaking analysis of it also applying the concluded valuable outcomes over their organization. The use ofsmartphones and smart gadgets fasten the gathering of data and enhances the three basic Vs (Volume/ Velocity/Variety) of big data. This paper focuses on big data security but without fourth V i.e. Value within data, there isno need of securing big data. Perhaps this may be the reason why Hadoop have no security mechanism within itsarchitecture since initially the focus of big data is on the basis of three basic Vs only. With this paper, hereauthors’ try to provide security to big data by using AES algorithm over HBase database. Authors just giving anidea of big data security methodology and for that the main focus of data security is only on valuable contents ofthe database.

Keywords: Big Data, AES, Hadoop, HDFS, HBase, NoSQL database.

References:

1. L. Wang, J. Tao, H. Marten, A. Streit, S. U. Khan, J. Kołodziej and D. Chen, “MapReduce Across Distributed Clusters for Data-intensive Applications”, IEEE IPDPSW, Shanghai, China, pp 2004 - 2011, 21-25 May 2012\

2. Chao YANG, Weiwei LIN, Mingqi LIU, “A Novel Triple Encryption Scheme for Hadoop-based Cloud Data Security”, IEEEEIDWT, 9-11 Sept. 2013, Xi'an, China, pp 437 - 442

3. Atif Mohammad, Hamid Mcheick, Emanuel Grant, “Big Data Architecture Evolution: 2014 and Beyond”, ACM MSWiM, Sept21-26, 2014, Montreal, QC, Canada, pp 139-144 Atif Mohammad, Hamid Mcheick, Emanuel Grant, “Big Data ArchitectureEvolution: 2014 and Beyond”, ACM MSWiM, Sept 21-26, 2014, Montreal, QC, Canada, pp 139-144

4. Karim Abouelmehdi, Abderrahim Beni-Hssane, Hayat Khaloufi, Mostafa Saadi, “Big Data Emerging Issues: Hadoop Securityand Privacy”, IEEE ICMCS, 29 Sept.-1 Oct. 2016, Marrakech, Morocco, pp 731 - 736

5. Arunima Dubey, Satyajee Srivastava, “A Major Threat To Big Data - Data Security”, IEEE ICCCA, 29-30 April 2016, Noida,India, pp 60 - 64

6. Ibtissam Ennajjar, Youness Tabii, Abdelhamid Benkaddour, “Securing Data in Cloud Computing by Classification”, ACMBDCA, March 29-30, 2017, Tetouan, Morocco, pp 493-498

7. Shagu.a Mehnaz, Gowtham Bellala, Elisa Bertino, “A Secure Sum Protocol and Its Application to Privacy-preserving Multi-partyAnalytics”, ACM SACMAT, June 7 2017, NY, USA, pp 219-230

8. John Carlo Bertot, Heeyoon Choi, “Big Data and e-Government: Issues, Policies, and Recommendations”, ACM DGR, June 17,2017, NY USA, pp 1-10

9. Guowen Xu, Yan Ren, Hongwei Li, Dongxiao Liu, Yuanshun Dai, Kan Yang, " CryptMDB: A Practical Encrypted MongoDBover Big Data", IEEE ICC, 21-25 May, 2017, Paris, France, pp 1-6

10. Elisa Bertino, Elena Ferrari (2018). Big Data Security and Privacy (vol. 31), Springer. 11. About Advanced Encryption Standard [Online]. Available: https://en.wikipedia.org/wiki/ Advanced_Encryption_Standard 12. About and Working of AES [Online]. Available: http://searchsecurity.techtarget.com/ definition/Advanced-Encryption-Standard 13. Cryptograhy Tutorial [Online]. Available: https://www.tutorialspoint.com/cryptography/ advanced_encryption_standard.htm 14. Encryption Process [Online]. Available: http://etutorials.org/Networking/ 802.11 + security.+ wi-fi + protected + access + and +

802.11i / Appendixes/Appendix + A .+ Overview+of+the+ AES + Block + Cipher / Steps +in+the+AES+Encryption+Process/ 15. HBase Overview [Online]. Available: http://moi.vonos.net/bigdata/hbase/ 16. HDFS in HBase [Online]. Available: http://www.aosabook.org/en/hdfs.html 17. DOI and ISBN [Online]. Available: https://www.doi.org/factsheets/ISBN-A.html

34-37

6. Authors: Anshul Kamboj, Devvret Verma, Damini Sharma, Kumud Pant, Bhasker Pant, Vijay Kumar

Paper Title:A Molecular Docking Study towards Finding Herbal Treatment against Polycystic Ovary Syndrome(PCOS)

Abstract: Polycystic ovary syndrome (PCOS) is one of the which has affected reproductive-age women, it ischaracterized by hyperinsulinemia, hyperandrogenism, menstrual irregularities, and long-term metabolicdisturbances. CYP 17 (P450c 17α) is an enzyme that plays an essential role in the biosynthesis of adrenal andgonadal steroids. Due to overexpression of the CYP17 encoding gene androgen is converted more efficiently totestosterone causing hyperandrogenism. By inhibiting this enzyme activity androgen synthesis can be preventedin the ovary. In this study, virtual screening of the phytochemicals of fruit from plant Terminalia chebula,Terminaliabellirica and Emblica officinalis were used as a ligand to identify a potent inhibitor of CYP17enzyme. The binding affinity of phytochemicals with the target protein CYP17 with the aid of AutoDockVinawere explored. Metformin, spironolactone and clomiphene were used as control and binding energy ofphytochemicals was compared with the docking score of control. All the phytocompounds shows inhibition ofthe CYP17 enzyme with a docking score of -3.7 to -9.5. Chebulanin, corilagin, neochebulinic acid, ellagic acid,

38-41

chebulinic acid,1,6-di-O-galloyl-d-glucose, 3,4,6-tri-O-glloyl-d-glucose, terchebulin, terflavin A, maslinic acid,arjunin, Isoquercitrin, rutin and chebulagic acid shows properties of potent inhibitor. This study reveals that thephytochemicals of fruit from plant Terminalia chebula, T.bellirica and Emblica officinalis can be used as apotential de novo drug to treat infertility in PCOS.

Keywords: Emblica officinalis , arjunin, Isoquercitrin, Terminalia, Chebulanin.

References:

1. Kamel, H. H. (2013). Role of phyto-oestrogens in ovulation induction in women with polycystic ovarian syndrome. EuropeanJournal of Obstetrics &Gynecology and Reproductive Biology, 168(1), 60-63.

2. Amudha, M., & Rani, S. (2016). In silico molecular docking studies on the phytoconstituents of cadabafruticosa (l.) Druce for itsfertility activity. Asian J Pharm Clin Res, 9(2), 48-50.

3. Apridonidze, T., Essah, P. A., Iuorno, M. J., &Nestler, J. E. (2005). Prevalence and characteristics of the metabolic syndrome inwomen with polycystic ovary syndrome. The Journal of Clinical Endocrinology & Metabolism, 90(4), 1929-1935.

4. Akhtar, M. K., Kelly, S. L., &Kaderbhai, M. A. (2005). Cytochrome b5 modulation of 17α hydroxylase and 17–20 lyase(CYP17) activities in steroidogenesis. Journal of Endocrinology, 187(2), 267-274.

5. Yoshimoto, F. K., Gonzalez, E., Auchus, R. J., &Guengerich, F. P. (2016). Mechanism of 17α, 20-Lyase and New HydroxylationReactions of Human Cytochrome P450 17A1 18O LABELING AND OXYGEN SURROGATE EVIDENCE FOR A ROLE OFA PERFERRYL OXYGEN. Journal of Biological Chemistry, 291(33), 17143-17164.

6. Chouhan, B., Kumawat, R. C., Kotecha, M., Ramamurthy, A., &Nathani, S. (2013). Triphala: A comprehensive ayurvedicreview. Int J Res Ayurveda Pharm, 4(4), 612-617.

7. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., ... & Bourne, P. E. (2000). The protein data bank.Nucleic acids research, 28(1), 235-242.

8. The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, LLC. https://pymol.org/2/ 9. Butkiewicz, M., Lowe, E. W., Mueller, R., Mendenhall, J. L., Teixeira, P. L., Weaver, C. D., &Meiler, J. (2013). Benchmarking

ligand-based virtual High-Throughput Screening with the PubChem database. Molecules, 18(1), 735-756. 10. Dallakyan, S., & Olson, A. J. (2015). Small-molecule library screening by docking with PyRx. In Chemical biology (pp. 243-

250). Humana Press, New York, NY. 11. Pandey, M. M., Rastogi, S., & Rawat, A. K. S. (2013). Indian traditional ayurvedic system of medicine and nutritional

supplementation. Evidence-Based Complementary and Alternative Medicine, 2013.

7. Authors: Rupa Khanna, Gunjan Awal, Shipra Gupta

Paper Title: Determinants of Online Trust: An Exploratory Study of University Students of Uttrakhand

Abstract: Inferable from the quick improvement of the Internet and data innovation in India, the development ofInternet shopping has been really wonderful as of late. But In spite of such phenomenal growth in onlineshopping in India, a large majority of online shoppers abandon the shopping cart, at a rate of over 70%, whichleads to trillions of dollars in lost sales. The principle motivation behind why online customers desert theirshopping cart is trust. Thus, the motivation behind this paper is to recognize and create an understanding aboutdifferent determinants of trust in an online environment. The information for this examination was accumulatedutilizing direct overview with the assistance of an organized survey. The investigation included those undergraduate and also post graduate students of various universities of Uttrakhand who purchase online. A 350example outline was picked for cooperation, however just 300 respondents restored the filled poll inside 4months of discharging. All factors for the survey were distinguished utilizing the writing on internet shopping.The information was investigated utilizing SPSS. The measurable methods of investigation that were utilized forthe given examination incorporate Factor Analysis for recognizing the components influencing on the webtrust .The results of this study indicate that consumer’s online trust is affected by various factors such asperceived reputation, Perceived security and privacy, Website Design, propensity to trust, brand/websiterecognition, proficiency and experience in Internet usage. Thus, this study aims to provide useful implications toonline marketers related to online trust.

Keywords: Web based business Trust, Online Trust, Trust, Trust Factors, Trust Determinants.

References:

1. Alam Syed Shah, Way Siew Shir, Ahsan Nil far, “Assessing the Determinants of Online Brand trust: An Empirical Study”, pp. 1-11.

2. Bauman Antonina, Bachmann Reinhard (2017), “Online Consumer Trust: Trends in Research” Journal of TechnologyManagement & Innovation, Volume 12, Issue 2,pp. 68-79.

3. Beldad Ardion, Steehouder Michaël (2010) “How shall I trust the faceless and the intangible? A literature review on theantecedents of online trust”, Computers In human Behavior, pp. 857-869.

4. Bojang Ismaila (2018) “Determinants of Trust in b2c e-commerce and their relationship with consumer online trust: a case ofYekaterinburg, Russian Federation” Journal of Internet Banking and Commerce, May 2017, vol. 22, no. S8, pp. 1-59.

5. Chan Mei-Jane (2009) “An Empirical Study for Factors that Affect Undergraduate Students’ Trust in Online Shopping inTaiwan,” Journal of Information Systems and Technology Management,Vol.4,.No.2,pp. 58-64.

6. Chang Yong-Sheng, Fang Shyh-Rong (2013) “Antecedents and Distinctions between Online Trust and Distrust: Predicting Highand Low-Risk Internet Behaviors”, Journal of Electronic Commerce Research, Vol.14, No.2, pp. 149-168.

7. Chao-Jung Hsu (2008) “Dominant Factors for Online Trust”, Proceedings of “International Conference on Cyber worlds 2008”,pp. 165-172.

8. Fard Sharifi Saeideh (2017) “Determinants of Online Purchase Intention and Moderating Role of Trust in Social NetworkWebsites in Malaysia”, World Applied Sciences Journal, Vol. 35, Issue 9, pp. 2060-2070.

9. Hwang Amber C, Ow Terence T., Hinton-Hudson Veronica D. (2007) “Antecedents of Online Trust and Acceptance of E-Commerce.”, Proceedings of IRMA International Conference, pp. 1345-1348.

42-48

10. Karimov Farhod P., Brengman Malaika, Leo Van Hove (2011) “The Effect of Website Design Dimensions on Initial Trust: aSynthesis of the Empirical Literature”, Journal of Electronic Commerce Research, Vol 12, NO 4, pp 272-301.

11. Kaur Baljeet, Madan Sushila. (2013) “Factors Influencing Trust in Online Shopping: An Indian Consumer’s Perspective”,European journal of Business and Management, Vol.5, No.29, pp. 132-138.

12. Khan F., Rasli A. M, Yusoff R. M., Isa K. (2015) “Impact of Trust on Online Shopping: A Systematic Review of Literature”,Journal of Advanced Review on Scientific Research,Vol 8,No.1,pp. 1-8.

13. Kim Hee-Woong, Xu Yunjie, Koh Joon (2004) “A Comparison of Online Trust Building Factors between Potential Customersand Repeat Customers”, Journal of Association for Information Systems, Vol. 5 No. 10, pp.392-420.

14. Kooli Kaouher, Mansour Kaouther Ben, Utama Rizky (2014),” Determinants of online trust and their impact on online purchaseintention”, International Journal of Technology Marketing, Vol.9, No.3, pp.305– 319.

15. Li Rong, Kim JaeJon, Park JaeSung (2007) “The Effects of Internet Shoppers’ Trust on their Purchasing Intention in China”,Journal of Information Systems and Technology Management Vol. 4, No. 3, pp. 269-286.

16. Prompongsatorn Chanidapa, Sakthong Nattapong, Chaipoopirutana Sirion, Combs Howard (2012) “The Factors InfluencingConsumer Trust of Internet Shopping in Thailand”, Proceedings of ASBBS, Vol.19, No.1, pp. 736-745.

17. Radwan M. Al-Dwairi (2013) “E-Commerce Web Sites Trust Factors: An Empirical Approach”, Contemporary EngineeringSciences, Vol. 6, no.1, pp. 1 – 7.

18. Sathiyavany N, Shivany S (2018) “E-Banking Service Qualities, E-Customer Satisfaction, and e-Loyalty: A Conceptual Model”The International Journal of Social Sciences and Humanities Invention, Vol. 5,Issue 6, pp. 4808-4820.

19. Teo, T and Liu, J. (2005) “Consumer Trust in E–Commerce in the United States, Singapore and China”, International Journal ofManagement Science, pp. 22-38.

20. Thakur Anand, Shabnam Narula, Chahal Puneet, “Examining Antecedents of Trust in Online Shopping: A Review Based Study”.21. Van der Werff, L., Real, C., & Lynn, T. (2018). Individual trust and the internet. In R. Searle, A. Nienaber, & S. Sitkin (eds.),

Trust. Oxford, UK: Routledge 22. Wang Ye Diana, Emurian Henry H. (2005) “An overview of online trust: Concepts, elements, and implications”, Computers in

Human Behavior, Vol.21, pp.105-125. 23. Zhong LI Wen, Gang DU Jian, “Online Trust in E-tailing: A Conceptual Framework, Implications, and Future Directions”, pp.

1132-1137.

8.

Authors: Meenu Singh, Vijay Kumar, Sidhartha Gupta, P. P. Pathak

Paper Title: Magnetic Field due to the Radiation of High Altitude Lightning

Abstract: High altitude optical discharges generated by extreme cloud-to-ground lightning strokes, which occurin the middle region of the atmosphere known as sprites. Streamer formation in sprites has been well stated to beexisting by several previous workers. These streamers are not only responsible for the initiation of sprites butalso they are composed of these streamers. It causes the production of electromagnetic radiation upto or belowthe ELF (very low frequency) region which have been reported earlier through various research theories. Thus,we are reporting out for the formulation of the model by using an earlier model used to estimate higherfrequency radiation from cloud and ground lightning discharges through these positive corona streamers. Takingit into account, other terms like radiation magnetic field has been evaluated with the studied observations.

Keywords: Altitude, ELF, magnetic, Streamer.

References:

1. Cummer, S.A., U.S. Inan, T.F. Bell, C.P. Barring-Leigh: ELF radiation produced by electric currents in sprites, Geophys. Res.Lett., 25, 1281–1284, 1998

2. Kosar, B: Luminosity and propagation characteristics of sprite streamers initiated from small ionospheric disturbances atsubbreakdown conditions, Journal of Geophysical Research: Space Physics, 117(A08328),2012.

3. Farges, T.,E. Blanc: Lightning and TLE electric fields and their impact on the ionosphere, C.R. Physique, 12,171–179, 2011 4. Qin[2013]- Qin, J., S. Celestin and V. P. Pasko: Dependence of positive and negative sprite morphology on lightning

characteristics and upper atmospheric ambient conditions, Journal of Geophysical Research: Space Physics, 118, 2623–2638,2013.

5. Paras, M. K. and J. Rai:Electric and Magnetic Fields from Return Stroke-Lateral Corona System and Red Sprites, Journal ofElectromagnetic Analysis and Applications, 3, 479-489, 2011.

6. Pathak, P. P. : Positive corona streamer as a source of high-frequency radiation, Journal Of Geophysical Research, 99(D5), 843-845,1994.

7. Singh, M., Kumar, A. and Pathak, P. P.: Review of various findings about sprites, Journal of Environmental and Biosciences, 31(2), 485-488, 2017

49-50

9. Authors: J S Kalra, Rajesh Pant, Pankaj Negi, Vijay Kumar

Paper Title: Designing of Autonomous Wheelchair for Movement in Standing Position

Abstract: Driving a manual wheelchair on any surface is a difficult task and past invented automaticwheelchairs are available at a very high cost in the market. The purpose of this paper, work is to propose anautonomous wheelchair that will reduce human effort while moving on a surface with inclination or declinationand is available at a comparatively lower cost. One of the basic problems of user on manual wheelchair is toovercome barriers like kerb, rough surfaces, inaccessible surfaces etc. Though many research have been done inthis field, but the question of overcoming these barriers always remains as a topic of discussion for manyresearchers. In our paper a motor operated & electronically hand controlled autonomous wheelchair conceptwhich can overcome the architectural barriers to a considerable extent has been developed. This paper involvesthe design of an ergonomically designed battery powered wheelchair for multipurpose use. All the designparameters of wheelchair's are based on the standard design of stair in India. Major part of the paper focuses onthe proposed design concept and mechanism and concludes by focusing upon the physical working modeldeveloped for the proposed design solution.

51-57

Keywords: electronically, physical working model, ergonomically designed.

References:

1. M. A. (2006, Oct. 01). Professor Ernesto Blanco: A Lesson in Creative Engineering. Available:http://mitadmissions.org/blogs/entry/

2. Manuscale.(2013,0ct.01).'United Spinal' Techguide. Available: htto://www .usatechguide.org/itemreview.php?itemid=1612 3. S. Sharma. (2012, 0ct.01). Vardaan: stair climbing wheelchair. Available: http://www.techped ia.in/award/proj ect-

detailNARDAAN-A-Convertible-Manual- Stair-Climb ing-Wheelchair 4. I. Nayar. (2012,oct.02). Wherethe pedal meets the mettle.Available:

http://photo.outlookindia.com/imnes/callerv/20120612/wheelchair iit k 20120625.ipg 5. S. S. f. E. B. a. Innovation. (2008, Oct. 01). The first manual stair-climbing wheelchair inthe world. Available:

http://www.enterpriseeuropenetwork. ch/marketplace/index.php?file=bbsshow.php&bbsref=08°/020CZ%20 0746 %200I RD 6. T. S. d. Liberta. (Oct. 14). Scoiattolo 2000. Available: http://www.tgr.it/ 7. Y. Sugahara, N. Yonezawa, and K. Kosuge, "A novel stair-climbing wheelchair with transformable wheeled four-bar linkages,"

in IntelligentRobots and Systems (IROS), IEEE/ IEEE/ RSJ International Conference on, 2010, pp. 3333-3339. 8. TGR. (2009, Oct. 01). Scoiattolo 2000/E. Available: https://www.youtube.com/watch?v=Pm0695001Y8

10. Authors: Nidhi Mehra, Atika Bansal, Divya Kapil, Shivashish Dhondiyal

Paper Title:An Inclusive Examination and Comparison of Machine Learning Techniques in the Domain ofEbola Virus Disease

Abstract: Diseases generated by viruses area unit transmitted, directly and indirectly will cause epidemics andpandemics. Despite the advances in medication and drugs , virus generated infectious diseases are one of themain reason behind death worldwide, particularly in low-income countries .Machine learning and computing arewidely utilized in diagnose certain types of cancer from imaging knowledge/data and also in other clinicalimaging data based diseases. This paper aims to investigate and compare machine learning classifiers for EbolaVirus Disease. The Kaggle data set for Ebola Virus diseases, containing 2486 instances, has been used as thedatabase for the training and testing. For experimental analysis, we use Naïve Bayes, Random forest, and J 48classification algorithms and show the results for TPR, precision FPR, F-measure, recall and ROC curve.

Keywords: Virus generated Disease, Machine learning, Classifiers,

References:

1. Morens, D. M., Folkers, G. K. & Fauci, A. S. The challenge of emerging and reemerging infectious diseases. Nature 430, 242–249 (2004).

2. Smolinski, M. S., Hamburg, M. A. & Lederberg, J. Microbial Threats to Health: Emergence, Detection, and Response (NationalAcademies Press, Washington DC, 2003).

3. Binder, S., Levitt, A. M., Sacks, J. J. & Hughes, J. M. Emerging infectious diseases: Public health issues for the 21st century.Science 284, 1311–1313 (1999)

4. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P. Global trends in emerging infectious diseases.Nature 2008; 451 (7181): 990 DOI: 10.1038/nature06536.

5. Wolfe, N., Dunavan, C. & Diamond, J. Origins of major human infectious diseases. Nature 447, 279–283(2007).https://doi.org/10.1038/nature05775

6. Report of a WHO/International Study Team. Ebola haemorrhagic fever in Sudan, 1976. Bull World Health Org. 1978;56:247–70.7. Johnson KM, Lange JV, Webb PA, Murphy FA. Isolation and partial characterisation of a new virus causing acute haemorrhagic

fever in Zaire. Lancet. 1977;1:569–71 8. Baize S, Pannetier D, Oestereich L, Rieger T, Koivogui L, Magassouba N, et al. Emergence of zaire ebola virus disease in

guinea: preliminary report. N Engl J Med. 2014;371(15):1418–25 9. 9 .Ebola Virus Disease. World Health Organization; Fact sheet Available at

https://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease 10. 10.Schmidt JP, Maher S, Drake JM, Huang T, Farrell MJ, Han BA. 2019. Ecological indicators of mammal exposure to

Ebolavirus. Phil. Trans. R. Soc. B 374: 20180337. http://dx.doi.org/10.1098/rstb.2018.0337 11. 11. Silver D, Schrittwieser J, Simonyan K et al. Mastering the game of Go without human knowledge. Nature 550, 354–359

(2017). https://doi.org/10.1038/nature24270 12. 12. Sun G., Matsui T., Hakozaki Y., Abe S.An infectious disease/fever screening radar system which stratifies higher-risk

patients within ten seconds using a neural network and the fuzzy grouping method J. Infect., 70 (3) (2015), pp. 230- 13. 13. Long JS, Mistry B, Haslam SM, Barclay WS. 2018 Host and viral determinants of influenza A virus species specificity. Nat.

Rev. Microbiol. 17, 67 – 81. (doi:10.1038/s41579-018-0115-z) 14. 14 . Emanuel J, Marzi A, Feldmann H. 2018. Filoviruses: ecology, molecular biology, and evolution. Adv. Virus Res. 100, 189–

221. (doi:10.1016/bs.aivir. 2017.12.002) 15. 15. Cui J, Li F, Shi Z-L. 2018 Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 17, 181– 192.

(doi:10.1038/s41579-018-0118-9) 16. 16. Wang L-F, Anderson DE. 2019 Viruses in bats and potential spillover to animals and humans. Curr. Opin. Virol. 34, 79–89.

(doi:10.1016/j.coviro.2018.12.007) 17. 17. Thibault PA, Watkinson RE, Moreira-Soto A, Drexler JF, Lee B. 2017 Zoonotic potential of emerging

royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20190017 8 paramyxoviruses: knowns and unknowns. Adv.Virus Res. 98, 1 – 55. (doi:10.1016/bs.aivir.2016.12.001)

18. 18. Hoelzer K, Parrish CR. 2010 The emergence of parvoviruses of carnivores. Vet. Res. 41, 39. (doi:10. 1051/vetres/2010011) 19. 19.Gutie´rrez-Bugallo G, Piedra LA, Rodriguez M, Bisset JA, Lourenc¸o-de-Oliveira R, Weaver SC, Vasilakis N, Vega-Ru´a A.

2019 Vector-borne transmission and evolution of Zika virus. Nat. Ecol. Evol. 3, 561– 569. (doi:10.1038/s41559-019-0836-z) 20. 20. LeCun Y, Bengio Y, Hinton G Deep learning. Nature 521, 436–444 (2015) 21. 21. Chen J.H., Asch S.M Machine learning and prediction in medicine—beyond the peak of inflated expectations N. Engl. J.

Med., 376 (26) (2017), pp. 2507-2509 22. 22.Colubri A., Silver T., Fradet T., Retzepi K., Fry B., Sabeti P.Transforming clinical data into actionable prognosis models:

machine-learning framework and field-deployable app to predict outcome of Ebola patients PLoS Negl. Trop. Dis., 10 (3)(2016), Article e0004549

23. 23. Ahmed W, Saeed .A, Salah.A , and Abdala.E, A Comparative Study for Machine Learning Tools Using WEKA and Rapid

58-64

Miner with Classifier Algorithms Random Tree and Random Forest for Network Intrusion Detection, vol. 4, no. 4, pp. 749–752,2019.

24. Chen J.H., Asch S.M.Machine learning and prediction in medicine—beyond the peak of inflated expectationsN. Engl. J.Med., 376 (26) (2017), pp. 2507-2509

11.

Authors: Atika Gupta, Sudhanshu Maurya, Divya Kapil, Nidhi Mehra, Harendra Singh Negi

Paper Title: Android Malware Detection using Machine Learning

Abstract: Machine Learning is empowering many aspects of day-to-day lives from filtering the content onsocial networks to suggestions of products that we may be looking for. This technology focuses on takingobjects as image input to find new observations or show items based on user interest. The major discussion hereis the Machine Learning techniques where we use supervised learning where the computer learns by the inputdata/training data and predict result based on experience. We also discuss the machine learning algorithms:Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support VectorMachine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset.Android is an operating system that is gaining popularity these days and with a rise in demand of these devicesthe rise in Android Malware. The traditional techniques methods which were used to detect malware was unableto detect unknown applications. We have run this dataset on different machine learning classifiers and haverecorded the results. The experiment result provides a comparative analysis that is based on performance,accuracy, and cost.

Keywords: Android, Malware, Machine learning, Classifiers.

References:

1. J. Sahs and L. Khan, “A machine learning approach to android malware detection,” Proc. - 2012 Eur. Intell. Secur. InformaticsConf. EISIC 2012, pp. 141–147, 2012.

2. J. Li, L. Sun, Q. Yan, Z. Li, W. Srisa-An, and H. Ye, “Significant Permission Identification for Machine-Learning-Based AndroidMalware Detection,” IEEE Trans. Ind. Informatics, vol. 14, no. 7, pp. 3216–3225, 2018.

3. J. Qiu, W. Luo, L. Pan, Y. Tai, J. Zhang, and Y. Xiang, “Predicting the Impact of Android Malicious Samples via MachineLearning,” IEEE Access, vol. 7, pp. 66304–66316, 2019.

4. Y. Zhou and X. Jiang, “Dissecting Android malware: Characterization and evolution,” Proc. - IEEE Symp. Secur. Priv., no. 4, pp.95–109, 2012.

5. F. Musumeci et al., “An Overview on Application of Machine Learning Techniques in Optical Networks,” IEEE Commun. Surv.Tutorials, vol. 21, no. 2, pp. 1383–1408, 2019.

6. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on EnablingTechnologies, Protocols, and Applications,” IEEE Commun. Surv. Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.

7. H. S. Ham and M. J. Choi, “Analysis of Android malware detection performance using machine learning classifiers,” Int. Conf.ICT Converg., pp. 490–495, 2013.

8. B. Amos, H. Turner, and J. White, “Applying machine learning classifiers to dynamic android malware detection at scale,” 20139th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2013, pp. 1666–1671, 2013.

9. A. Shabtai, U. Kanonov, Y. Elovici, C. Glezer, and Y. Weiss, “‘Andromaly’: A behavioral malware detection framework forandroid devices,” J. Intell. Inf. Syst., vol. 38, no. 1, pp. 161–190, 2012.

10. R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, “Machine Learning Classification over Encrypted Data,” no. February, pp. 8–11,2015.

11. M. Ghorbanzadeh, Y. Chen, Z. Ma, T. C. Clancy, and R. McGwier, “A neural network approach to category validation ofAndroid applications,” 2013 Int. Conf. Comput. Netw. Commun. ICNC 2013, pp. 740–744, 2013.

12. S. Y. Yerima, S. Sezer, and I. Muttik, “High accuracy android malware detection using ensemble learning,” IET Inf. Secur., vol.9, no. 6, pp. 313–320, 2015.

13. A. A. A. Samra, K. Yim, and O. A. Ghanem, “Analysis of clustering technique in android malware detection,” Proc. - 7th Int.Conf. Innov. Mob. Internet Serv. Ubiquitous Comput. IMIS 2013, pp. 729–733, 2013.

14. T. Chen, Q. Mao, Y. Yang, M. Lv, and J. Zhu, “TinyDroid: A lightweight and efficient model for android malware detection andclassification,” Mob. Inf. Syst., vol. 2018, 2018.

15. L. Yu, Z. Pan, J. Liu, and Y. Shen, “Android malware detection technology based on improved Bayesian classification,” Proc. -3rd Int. Conf. Instrum. Meas. Comput. Commun. Control. IMCCC 2013, pp. 1338–1341, 2013.

16. L. Tenenboim-Chekina et al., “Detecting application update attack on mobile devices through network featur,” pp. 91–92, 2014. 17. H. H. Kim and M. J. Choi, “Linux kernel-based feature selection for Android malware detection,” APNOMS 2014 - 16th Asia-

Pacific Netw. Oper. Manag. Symp., 2014. 18. et al., “Analysis of Android Vulnerabilities and Modern Exploitation Techniques,” ICTACT J. Commun. Technol., vol. 05, no.

01, pp. 863–867, 2014. 19. W. Ahmed, A. Saeed, A. Salah, and E. Abdala, “A Comparative Study for Machine Learning Tools Using WEKA and Rapid

Miner with Classifier Algorithms Random Tree and Random Forest for Network Intrusion Detection,” vol. 4, no. 4, pp. 749–752,2019.

20. E. P. F. Lee et al., “An ab initio study of RbO, CsO and FrO (X2Σ+; A2Π) and their cations (X3Σ-; A3Π),” Phys. Chem. Chem.Phys., vol. 3, no. 22, pp. 4863–4869, 2001.

21. G. Kaur, “Improved J48 Classification Algorithm for the Prediction of Diabetes,” vol. 98, no. 22, pp. 13–17, 2014. 22. F. Livingston, “Implementation of Breiman’s Random Forest Machine Learning Algorithm,” Mach. Learn. J. Pap., pp. 1–13,

2005.

65-70

12. Authors: Rajesh Pant, Jasmeet Kalra, Pankaj Negi, Vijay Kumar

Paper Title: Designing and Optimising the Parameters of Micro Channels

Abstract: This paper documents the optimization of different parameters of micro channel heat sink whichenhance the heat transfer. The objective is to find the major thermal resistance in micro channel and its effect onother parameters. Water is used as a coolant and the initial values of convective heat transfer coefficient andvolume flow rate are 30000 W/m2K and 1 lpm respectively. Different graph are plotted between pressuredrop,heat transfer co-efficient, pressure drop,thermal resistance and flow rate to finally achieve the optimized

71-76

valus of channel width and height, hydraulic diameter, thermal resistance and pressure drop. The result achievedare in good agreement with the previous researches.

Keywords: Micro channels,heat sink, MCC

References:

1. Wei X.J, Joshi Yogendra(2003) Optimization study of stacked microchannel heat sinks for micro-electronic cooling, IEEE

trasaction on Component and Packaging Technologies,Vol26, No-1, Pages 55–61. 2. Qu, W. and Mudawar, I. (2002), Experimental and numerical study of pressure drop and heat transfer in a single-phase micro-

channel heat sink. International Journal of Heat and Mass Transfer. Vol-45, Pages 2549 – 2565. 3. Fedorov, A. G. and Raymond, V. (2000), Three-dimensional conjugate heat transfer in the micro channel heat sink for electronic

packaging. International Journal of Heat and Mass Transfer. Vol-43, Pages 399-415. 4. H. H. Bau, (1998) Optimization of conduits shape in micro heat exchangers, Int. J. Heat Mass Transfer, vol. 41, Pages 2717–23. 5. Kawano, K., Minakami, K., Iwasaki, H. and Ishizuka, M. (1998). Micro channel heat Exchanger for cooling electrical equipment.

Appl. Heat Transfer Equip., Syst. Educ. ASME HTD-361-3/PID-3, Pages-173-180. 6. C. Gillot, C. Schaeffer, and A. Bricard,(1998) “Integrated micro heat sink forpower multichip module,” IEEE Trans. Ind.

Applicat., Vol-36, No-1, Pages 217–221. 7. R. W. Knight, D. J. Hall, J. S. Goodling, and R. C. Jaeger, (1992) Heat sinkoptimization with application to microchannels, IEEE

Trans. Comp.,Hydrids, Manufact. Technol., vol. 15, Pages 832–42. 8. Phillips, Richard J.,(1988), Microchannel heat sinks. The Lincoln Laboratory Journal, No.-1 Vol-1, Pages 31- 47. Designing and

Optimising the Parameters of Micro Channels 76 Retrieval Number: B10130982S1219/2020©BEIESPDOI:10.35940/ijrte.B1013.0982S1219 Published By: Blue Eyes Intelligence Engineering & Sciences Publication

9. Tuckerman, D.B. and Pease, R.F. (1981), High-performance heat sinking for VLSI, IEEE Electronic Devices Letters. EDL2, Vol-5, Pages 126-129

13.

Authors: Shipra Gupta, Rupa Khanna

Paper Title: Comparison of Selected Public Sector Banks in Different Aspects by using CAGR Method

Abstract: Banking sector has a vital role in Indian economy and a great change came in it after nationalization.Nationwide, there are a number of branches of banks and financial institutions have opened. Presently bankingsector is facing a high level competition. Banks or financial Institutions which have maximum profit areshowing maximum growth rate. By optimization of the resources of banks, cost becomes minimum and profitbecomes maximum. This manuscript is an effort to make a comparative study between SBI, PNB and OBC fortotal income, expenses, net profit, share capital, operating expenses, share holder funds, total reserves, earningper share, total liabilities, total assets and total investments from 2014-19. Year over Year (YOY) andCompound annual growth rate (CAGR) analytical methods are used. The main parameter of this study belongsto P&L and Balance-Sheet statement of the selected banks. This research paper will be very fruitful for banks,research scholars, investors (public), and society to understand about the above given parameters.

Keywords: Earning per share (EPS), Shareholders funds, Total Operating expenses, Net profit. CAGR, YOY.

References:

1. Chuag C C and Liang Hu, (2011). An Empirical study of customers Perception of E-Banking Service Based on Time Usage,Journal of Internet Banking and Commerce, 16 (2), 11-20.

2. Gupta, S, (2012). Comparative analysis of per share ratio of some selected Indian public sector banks, International Journal ofResearch in Commerce, Economics and Management, 2 (4), 89-96.

3. Gupta, S. (2012). Analysis of leverage ratio in selected Indian public sector banks, Asian Journal of Management Research, 2 (4),111-120.

4. F S A (U. K. Financial Srvices Authority) (2009), The Turner Review: A regularity response to the global banking crists. London.5. Hanson. S, Kashyap A and Stein J., A (2011). Macroprudential approach to financial regulation, Journal of Economic

perspectives, 1, 3-28. 6. https:/www.sbi.com. 7. Marugan. V G, (2012). Customer satisfaction with service quality: An empirical study of public and private sector banks in

Triputi region. International Journal of Research in Commerce & Management, 3 (1), 106-109. 8. Washington. D C Joint Forum, Credit Risk Transfer. Basel Committee on banking supervision (2005). 9. Ally Z (2013). Comparative Analysis of Financial Performance of Commercial Banks in Tanzania. Research Journal of Finance

and Accounting 4, 133-143. 10. Usman A, Khan MK (2012),Evaluating the financial performance of Islamic and conventional banks of Pakistan: A comparative

analysis. International Journal of Business and Social Science 3, 253-257. 11. Gupta S, Verma R (2008). Comparative Analysis of Financial Performance of Private Sector Banks in India: Application of

CAMEL Model. Journal of Global Economy 4, 139-147. 12. Aspal PK, Sanjeev D (2014). Financial performance assessment of banking sector in India: A case study of old private sector

banks. The Business & Management Review, 5, 1-196. 13. Nimalathasan B (2008). A Comparative Study of Financial Performance of Banking Sector in Bangladesh-An Application of

CAMELS Rating System, Annals of University of Bucharest, Economic and Administrative Series 2, 141-152. 14. Mohiuddin G (2014). Use of CAMEL Model: A Study on Financial Performance of Selected Commercial Banks in Bangladesh,

Universal Journal of Accounting and Finance 2, 151-160. 15. Thaddeus EO, Chigbu EE (2012) Analysis of Effect of Financing Leverage on Bank Performance: Evidence from Nigeria.

Journal of Public Administration and Governance 2, 178-187. 16. Sangmi M, Nazir T (2010). Analyzing Financial Performance of Commercial Banks in India: Application of CAMEL Model.

Pakistan Journal of Commerce & Social Sciences 4, 40-55. 17. https:/www.pnb.com. 18. https:/www.obc.com.

77-83

Authors: Devyani Rawat, Vijay Singh, Shiv Ashish Dhondiyal, Sumeshwar Singh

14.

Paper Title: Time Series Forecasting Models: A Comprehensive Review

Abstract: This comprehensive review provides an extensive overview of the existing Time Series Forecastingtechnique. This survey is not restricted to any single time series analysis; it provides forecasting of time series indifferent areas like marketing prediction, weather forecasting, technology prediction, financial forecasting etc. Inthis paper, we have analyzed forecasting in some areas namely, load forecasting, wind speed forecasting,prediction of energy consumption and short-term traffic flow prediction. Various models are available forprediction among them Autoregressive Integrated Moving Average model (ARIMA) is seen as a universalmechanism, these discussed forecasting areas utilizes different models that are combined with ARIMA. Hybridmodels are the combination of classical models and modern methods, like ARIMA (classical method) combineswith Artificial Neural Network (ANN) as well as with Support Vector Machine (SVM) (modern models).Hybrid model’s performance is depending on the variety of data that are taken for forecasting.

Keywords: ARIMA, ANN, SVM, Time series, ARMA.

References:

1. Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175, 2003. 2. Nichiforov, C., Stamatescu, I., Făgărăşan, I., &Stamatescu, G. Energy consumption forecasting using ARIMA and neural

network models. 5th ISEEE (pp. 1-4). IEEE, October 2017. 3. Zhu, B., &Chevallier, J., Carbon price forecasting with a hybrid Arima and least squares support vector machines methodology.

In Pricing and Forecasting Carbon Markets (pp. 87-107). Springer, Cham., 2017. 4. Cadenas, E., & Rivera, W., Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model.

Renewable Energy, 35(12), 2732-2738, 2010. 5. Kumar, S. V., &Vanajakshi, L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data.

European Transport Research Review, 7(3), 21, 2015. 6. Khashei, M., &Bijari, M., An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with

applications, 37(1), 479-489, 2010. 7. Wang, P., Zhang, H., Qin, Z., & Zhang, G. A novel hybrid-Garch model based on ARIMA and SVM for PM2. 5 concentrations

forecasting. Atmospheric Pollution Research, 8(5), 850-860, 2017. 8. Karthika, S., Margaret, V., &Balaraman, K., Hybrid short term load forecasting using ARIMA-SVM. i-PACT (pp. 1-7). IEEE,

April 2017. 9. Xuemei, L., Lixing, D., Yuyuan, D., &Lanlan, L., Hybrid support vector machine and ARIMA model in building cooling

prediction. International Symposium on Computer, Communication, Control and Automation (3CA) (Vol. 1, pp. 533-536). IEEE,May 2010.

10. Bedi, J., &Toshniwal, D., Deep learning framework to forecast electricity demand. Applied energy, 238, 1312-1326, 2019. 11. Wang, B., Huang, H., & Wang, X., A novel text mining approach to financial time series forecasting. Neurocomputing, vol. 83,

pp. 136-145, 2012. 12. Ediger, V. Ş., &Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy policy, 35(3), 1701-

1708.

84-86

15. Authors: Sulekha Varma, Raina Singh

Paper Title:Diaspora and Culture: A Study of Indian Immigration and Integumentary Anxiety in JhumpaLahiri’s The Namesake

Abstract: This research aims at capturing the sense of identity, loneliness and untold anxiety among theimmigrants from the writing of one of the prominent authors writing in English from Indian subcontinent. TheNamesake, a well-knit novel by the author Jhumpa Lahiri. The novel “The Namesake” depicts it the best kind ofreference to classify Diaspora as the word ‘Diaspora’ as well as its prime role in this present era, the first andsecond generation who are struggling for identity, loneliness and the most prominent one is integumentryanxiety among them. It is that untold anxiety which the people can’t disclose to anyone. It remains in the veryheart of them untold and unexpressed. In fact Jhumpa Lahiri the novelist is child of Indian immigrates and she isalso migrated from her birthplace England to America. The effect of both made her Diaspora writer and amigrant one. She mirrored the life of the Indian Diaspora, who are struggling for identity and the integumentaryanxiety. They construct unhomely home in the foreign land.

Keywords: Diaspora, Original, identity, transnational, multiculturalism, Indian Immigration, IntegumentaryAnxiety.

References:

1. Aditya Sinha: “Review of , The Namesake‟” The Malady of Naming”, Hindustan Times, September 28,2003 2. Bala, Suman, ed. Jhumpa Lahiri: The Master Story teller A Critical Response to Intereter of Maladies. New Delhi: KPH., 2002 3. Bhardwaj Ritu: Identity and Diaspora in Jhumpa Lahiri‟s The Namesake The English Literature Journal .Vol. 1, No. 1 (2014):

11-14 4. Emmanuel S. Nelson:“Writers of the Indian Diaspora: A Bio-Bibliographical Critical Sourcebook.” 5. Ghosh, A. (n.d.): The Diaspora in Indian Culture. In The Imam and The Indian (p. 98). Delhi, India: Ravi Dayal and Permanent

Books. 6. Hall, Stuart:“ Cultural Identity and Diaspora” Contemporary Postcolonial Theory: A Reader, ed. 7. Jain, Jasber: Critical Spectrum: Essays in Literary culture. Jaipur: Rawat publications, 2003. 8. Jhumpa Lahiri: The Namesake. New Delhi: Harper Collins, 2003. 9. Rushdie, Salman. “Imaginary Homelands” from Imaginary Homelands: Essays and Criticism 1981 - 1991, London: Granta

Books,1991. 10. Spivak, Gayatri Chakravorty. “The Post Colonial Critic: Interview, Strategies, Dialouges ” ed. Sarah Harasym. 11. Monaco, Angelo. "Jhumpa Lahiri. The Interpreter of the New Indian Diaspora." (2015): 73-90. Print.

87-91

12. Macwan, Hiral. "Struggle for Identity and Diaspora in Jhumpa Lahiri's the Namesake." International Journal of Humanities andSocial Science Invention 3.2319-7722 (2014): 45-49. Print.

13. 13.Said, Edward. "Reflections on Exile." Reflections on Exile and Other Essays. Harvard UP, 2000. Print. 14. “Maladies of Belonging” interview with Vibhuti patel, NewsweekInternational (20th September 1999) Web. 9 Oct 15. Singh, Shaleen. "Diaspora Literature : A Testimony of Realism." Ezine Articles. Google, 28 July 2008. Web. 7 Aug. 2015. 16. S, Sujaritha. "A Reading of Diaspora Literature." Muse India 64. Muse India. Web. 13 July 2015.

16.

Authors: Tarun Kumar Dhiman, Pankaj Negi, Kiran Sharma, Gagan Bansal

Paper Title: Optimization of Integration Plate for LASER Based Range Finding System using FEM

Abstract: The design and analysis of an integration plate for Laser Based Range Finding System (LBRFS) isbased on three subsystems which are going to be (payload) mounted on different locations. FEM modeling andsimulation of three different configurations have been considered for integration plate in assembled payloadconditions. Structural analysis of the plate under the simulated boundary conditions was carried out. Platedeflection at critical point was worked out. Depending upon the results obtained optimum plate thickness withstiffeners at the various locations was incorporated on the integration plate to meet the system requirements.

Keywords: Laser Based Range Finding System, payload.

References:

1. Keer LM, Stahl B. Eigen value problems of rectangular plates with mixed Boundary conditions. Journal of Applied Mechanics1972; 39:513–20

2. Narita Y. Application of a series-type method to vibration of orthotropic Rectangular plates with mixed boundary conditions.Journal of Sound and Vibration 1981; 77:345–55

3. CheungMS. Finite Strip analysis of structures. Ph.D. thesis, University of Calgary, 1971 4. Mizusawa T, Kaijita T. Vibration and buckling of rectangular plates with non-uniform elastic constraints in rotation. International

Journal of Solid Structures 1986; 23:45–55 5. Rao GV, Raju IS, Murthy TVGK. Vibration of rectangular plates with mixed boundary conditions. Journal of Sound and

Vibration 1973; 30:257–60 6. Mizusawa T, Leonard JW. Vibration and buckling of plates with mixed boundary conditions. Engineering Structures 1990;

12:285–90 7. Chia CY. Non linear vibration in anisotropic rectangular plates of non-uniform edge constraints. Journal: Sound and Vibration of

1985; 101:539–50 8. WangX, Bert CW. New approach by applying deferential quadrature in Static and free vibration analysis in beams & plates.

Journal :Sound and Vibration of 1992; 162:566–72 9. Laura PAA, Gutierrez RH. Analysis of vibrating rectangular plates with non-uniform boundary conditions by using the

deferential quadrature method. Journal of Sound and Vibration 1994; 173:702–6 10. Shu C, WangCM. Solution of mixed & non-uniform boundary conditions of GDQ vibration analysis in rectangular plates.

Engineering Structures of year 1999; 21:125–34 11. Warburton GB. Vibration in rectangular plates: The Institute of Mechanical Engineering of 1954; 168:371–84 12. Leissa AW. The free vibration of rectangular plates. Journal of Sound and Vibration 1973; 31:257–93 13. Wei GW. Solution to quantum eigen value problems through discrete singular convolution. Journal of Physics B 2000; 33:343–5214. Liew K M, Hung K C, Lam KY. Substructure method in Vibration analysis for rectangular plates along with noncontinuous

boundary Conditions. Journal of Sound and Vibration 1993; 163:451–62 15. Nowacki W, Free vibrations and buckling of a rectangular plate with discontinuous boundary conditions. Bulletin de

l’AcadXemie Polonaise des Sciences 1955; 3:159– 67 16. Gorman DJ. An exact analytical approach to the free vibration analysis of rectangular plates with mixed boundary conditions.

Journal of Sound and Vibration 1984; 93:235–47 17. Piskunov VH. Determination of the frequencies of the natural oscillations of rectangular plates with mixed boundary conditions.

Prikladnaya Mekhanika 1964; 10:72–6 [in Ukrainian] 18. Experimental Advanced Research Lidar', NASA.org. Retrieved 8 August 2007 19. Tom Paulson. 'LIDAR shows where earthquake risks are highest, Seattle Post (Wednesday, April 18, 2001)

92-99

17. Authors: Ambica Prakash Mani, Vinayendra Mani Tripathi

Paper Title: Does Education Affect the Online Impulse Buying in Millennials?

Abstract: The popularity and acceptance of online medium for buying and selling has increased both in termsof popular acceptance and widespread reach in every nook and corner of the country. A similar rise has beenobserved in the tendency of online impulsive buying behaviour too. Impulsive or impulse buying is unplannedand often done in a fraction of second over some human stimulus that is usually uncontrollable. There is nodoubt to the fact that the exposure and inclination towards online medium as a buying platform has increasedsignificantly over the last few years amongst all and its rise is particularly noteworthy in the generation Y.Today this young and well educated group qualifies to be an important segment for marketers. This researchpaper explores the impact of education over online impulsive buying behaviour in millennials of today.

Keywords: Impulsive buying, online, millennials, SPSS, ANOVA

References:

1. Luo, X. (2005). How Does shopping with Others Influence Impulsive Purchasing. Journal of Consumer Psychology. 15(4), 288-294.

2. Park,E. J., Kim. E.Y., Forney, J. C. (2006). A Structural Model of Fashion Oriented Impulse Buying Behaviour. Journal ofFashion Marketing and Management. 10(4), 433-446.

3. Sneath, J. Z., Lacey, R., Kennett-Hensel., P.A. (2009). Coping with Natural Disaster: Losses, Emotions and Impulsive andCompulsive Buying, Marketing Letters. 20 (1). 45-60.

100-103

4. Sharma, P., Sivakumaran, B., Marshal, R. (2010). Impulse Buying and Variety Seeking: A Trait correlates Perspective. Journal ofProduct & Brand Management.17 (1), 25-36.

5. Weinber, Peter, Gottwald, Wolfgang (1982) .Impulsive consumer buying as a result of emotions. Journal of Business Research.10 (1), 43-57.

6. Andrade, E. (2000). Identifying discriminating variables of online and offline buyers: A perceived-risk approach, Proceedings ofthe 6th Americas Conference on Information Systems, pp. 1386-1392

7. Bellman, S., Lohse, G., and Johnson, E. (1999). Predictors of online buying behavior, Communications of the ACM (42:12), pp.32-38.

18.

Authors: Vijay Kumar, J. S. Kalra, Devvret Verma, Shipra Gupta

Paper Title: Process and Environmental Benefit of Recycling of Waste Papers

Abstract: Paper is a fundamental part of most aspects of society; worldwide a total of approximately threehundred million tons of paper are produced each day and approximately 90% of this paper is produced frommature pulp wood. In addition, the demand of paper is expected to increase. Today the finest of paper areproduced all over the world. But one dismaying fact is that millions of trees are fell in a day to make paper.Increase demands of paper production and limited wood resources have directed researchers to look forappropriate additional resources of non-wood material (waste papers) for pulp and paper manufacturing. Settingup of handmade paper unit has the capability of recycling waste paper and cotton rags into fine qualityhandmade paper. Not only does this initiative conserve environmental resources but also helps in providingemployment to pupil from the unskilled and marginalized strata of society.

Keywords: Recycling, waste papers, Environmental benefit, Pollution control.

References:

1. Effluents from Pulp Mills using Bleaching - PSL1. Ottawa, ON: Health Canada and Environment Canada. 1991. ISBN 0-662-18734-2. Retrieved 2010-07-26.Catalog no. En40-215/2E.

2. Tarkpea, Maria; et al. (1999). "toxicity of conventional, elemental chlorine–free, and totally chlorine–free kraft-pulp bleachingeffluents assessed by shortterm lethal and sublethal bioassays". Environmental Toxicology and Chemistry 18 (11): 2487–2496.doi:10.1002/etc.5620181115.

3. Sonnenfeld, David A. (1999). "Social Movements and Ecological Modernization: The Transformation of Pulp and PaperManufacturing, Paper: WP00-6-Sonnenfeld". Berkeley Workshop on Environmental Politics. Berkeley, CA: Institute ofInternational Studies (University of California, Berkeley). Retrieved 2007-09-20.

4. Auer, Matthew R. (1996). "Negotiating toxic risks: A case from the Nordic countries," Environmental Politics 5: 687-699. 5. Bennis, H., Benslimane, R., Vicini, S., Mairani, A. &Princi, E. (2010). Fibre width measurement and quantification of filler size

distribution in paper-based materials byS EM and image analysis. Journal of Electron Microscopy 59 (2), 2010, pp.91-102. 6. Khantayanuwong, S. (2003). Determination of the Effect of Recycling Treatment on Pulp Fiber Properties by Principal

Component Analysis. Kasetsart J. (Nat. Sci.) 37, pp. 219 – 223. 7. Kučerová, V. & Halajová, L. (2009). Evaluation of changes of the recycled pulps by method

thegelpermeationchromatography.ActaFacultatisXylologiaeZvolen,51(2),2009, pp. 87-92. 8. Malesic,J.,Kolar,J.,Strlic,M.,Kocar,D.,Fromageot,D.,Lemaire,J.&Haillant,O.(2005). Photo-induced degradation of cellulose.

Polymer Degradation and Stability89(1),pp. 64-69. 9. Nazhad, M. M. (2005). Recycled fibre quality – A review', Journal of industrial and engineeringchemistry, In: Korean

Journal,11(3),314. 10. Pati, R.K., Vrat, P. & Kumar, P. (2008). A goal programming model for paper recycling system. Omega 36, 2008, pp. 405 – 417. 11. Song, X. & Law, K.N. (2010). Kraft pulp oxidation and its influence of recycling characteristics of fibres. Cellulose Chemistry

and Technology 44 (7-8),pp.265-270. 12. Zanuttini, M. A., McDonough, T. J., Courchene, C. E. & Mocchiutti, P. (2007). Upgrading OCC and recycled liner pulps by

medium-consistency ozone treatment. Tappi Journal 6(2), pp. 3-8. 13. Zervos,S.&Moropoulov,A.(2005).Cottoncelluloseageinginsealedvessels.Kineticmodel

ofautocatalyticdepolymerization.Cellulose12,2005,pp.485-496. 14. Čabalová, I., Kačík, F. &Sivák, J. (2009). Changes of molecular weight distribution of

celluloseduringpulprecycling.ActaFacultatisXylologiaeZvolen51(1),2009,pp.11- 17, ISSN1336-3824 15. Čabalová,I.,Kačík,F.&Sivák,J.(2011).Thechangesofpolymerizationdegreeofsoftwood fibers by recycling and ageing process. Acta

Facult at is Xylologiae Zvolen53 (1), 2011, pp. 61-64, ISSN1336-3824.

104-106

19. Authors: Rajesh Pant, Jasmeet Kalra, Jagdish Singh Mehta, Pankaj Negi

Paper Title: Fin Efficiency Design of Micro-Channel for Nd: YAG Slab Laser

Abstract: The heat generated by a Slab Lasers can exceed 1,000 watts but the area available for cooling is toosmall. This results in localization of heat flux which makes heat dissipation a challenge in slab lasers. The Heattransfer coefficient can increase up to a very high range, which can’t be efficiently achieved by the conventionalwater cooling. Micro-channel coolers address this problem competently. These channels contain liquid, whichtransfers heat to the sink with high efficiency.The objective of this paper is to design the micro-channel coolersthat will be efficiently capable of removing the heat from the slab laser without causing any thermal distortion inthe laser. The dimension of the slab is given as follows:-Length(l) =50mm, Width(w) =8mm andthickness(t)=2mm.

Keywords: MCC, Micro-channel, slab laser, heat sink.

References:

1. Walter Koencher, Solid state laser engineering.

107-110

2. D.B. Tuckerman and RF.W. Pease, "OptimizedConvectiveCooling Using Micromachined Structures," ElectrochemicalSoc.Extended Abstracts 82.197 (1982).

3. Qu, W., and Mudawar, I. Measurement and correlation of critical heat flux in two-phase microchannel heat sink. Int. J. Heat MassTransfer 47 (2004), 2045–2059.

4. Oh, C. H., and Englert, A. B. Critical heat flux for low flow boiling in vertical uniformly heated thin rectangular channels. Int. J.Heat Mass Transfer 36 (1993), 325–335.

5. Lienhart-V, J. H., and Lienhart-IV, J. H. A heat transfer text book, third ed. Phlogistonpress,2008. 6. Katto, Y. Critical heat flux. Int. J. Multiphase Flow 20 (1994), 53–90. 7. White, F. M. Fluid Mechanics, fourth ed. McGraw-Hill, 1999. 8. Zhang, W., Hibiki, T., Mishima, K., and Mi, Y. Correlation of critical heat flux for flowboiling of water in mini-channels. Int. J.

Heat Mass Transfer 49 (2006), 1058–1072. 9. Richard J. Phillips Micro-channel heat sink Lincoln laboratory journal volume 1 number 1 (1988), 31-48.

20.

Authors: SulekhaVarma, Raina Singh

Paper Title:A Profound Study of Women’s Pathetic Condition in Indian Society, the Selected Novels of MulkRajanand

Abstract: The subordinates are always suppressed, they can be known in terms of caste, class, gender, age. Thecurrent paper has attempted to analyze and uncover the most suppressed gender has been of the society in theselectednovels of Mulk Raj Anand. It would be an exploration of the concept or perception especially thewomen characters. The novel are Untouchable (1936), Coolie (1937) and The Road (1961).In these novels, he,indisputably, has established the reality of depreciating the women is the work of primordialpower of theapostolic people, who have finalized the future of them.

Keywords: Class, Caste, apostolic, Power, Subordinate

References:

1. GuhaRanajit (Ed.), Subaltern Studies, Vol.1, India, OUP, 1982, p.vii. 2. AnandMulk Raj and Huthessing Krishna, The Bride’s Book of Beauty, Bombay, Kutub Publication,1946,p.16. 3. Anand Mulk Raj, The Road, Bombay, Kutub Publication,1961,p.79. 4. AnandMulkRaj,Untouchable, Arnold Associates, New Delhi, 1935,p.74. 5. Ashcroft Bill et al.Postcolonial Studies: The key Concepts, London, Routledge, 2007,p.207. 6. KakarSudhir,Feminine Identity in India,inGhadiallyRehana(ed),Women in Indian Society, New York, Sage

Publication,1988,p.52. 7. AnandMulkRaj,The Road, Bombay,Kutub Publication,1961,p.87. 8. Beteille Andre, Caste in Routledge Encyclopedia of Social and Cultural Anthropology,(ed) Alan Bernard and Jonathan

Spencer,London,Routledge,2009,p.112. 9. AnandMulk Raj, Apology For Heroism, Bombay,Kutub Publication,1946,p.81.

111-114