International Journal of Recent Technology and Engineering · 7. Open PHACTS: semantic...

ISSN : 2277 - 3878Website: www.ijrte.org

Technology and EngineeringTechnology and EngineeringInternational Journal of Recent International Journal of Recent

Volume-7 Issue-5, JANUARY 2019Volume-7 Issue-5, JANUARY 2019

Published by: Blue Eyes Intelligence Engineering and Sciences Publication

Published by: Blue Eyes Intelligence Engineering and Sciences Publication

n E d a n g i y n g e o e l r o i n n g h c e T t n e c e R I n f t o e l r na n at r i u o o n J l a

IjrteIjrte

Exploring Innovation

www.ijrte.org

EXPLORING INNO

VATI

ON

Editor-In-Chief Chair Dr. Shiv Kumar

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

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

(M.P.), India

Associated Editor-In-Chief Chair Dr. Dinesh Varshney

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

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

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

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

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

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

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

Transport, Egypt

Dr. Mayank Singh

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

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

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

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

Dr. Moinuddin Sarker

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

Stamford, USA.

Dr. Shanmugha Priya. Pon

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

Africa, Tanzania

Dr. Veronica Mc Gowan

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

China.

Dr. Fadiya Samson Oluwaseun

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

Cyprus, Turkey.

Dr. Robert Brian Smith

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

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

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

Executive Editor Chair Dr. Deepak Garg

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

Executive Editor Members Dr. Vahid Nourani

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

Dr. Saber Mohamed Abd-Allah

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

Dr. Xiaoguang Yue

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

Dr. Labib Francis Gergis Rofaiel

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

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

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

Dr. Sunandan Bhunia

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

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

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

Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail

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

Technical Program Committee Members Dr. Haw Su Cheng

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

Dr. Hasan. A. M Al Dabbas

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

Dr. Gabil Adilov

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

Dr. Ch.V. Raghavendran

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

Dr. Thanhtrung Dang

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

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

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

Convener Chair Mr. Jitendra Kumar Sen

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

Editorial Chair Dr. Sameh Ghanem Salem Zaghloul

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

Editorial Members Dr. K. Priya

Professor & Head, Department of Commerce, Vivekanandha College of Arts & Sciences for Women (Autonomous, Elayampalayam,

Namakkal (Tamil Nadu), India.

Dr. Pushpender Sarao

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

(Telangana), India.

Dr. Nitasha Soni

Assistant Professor, Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad

(Haryana), India.

Dr. Siva Reddy Sheri

Associate Professor, Department of Mathematics, School of Technology Hyderabad Campus, GITAM University, Visakhapatnam

(Andhra Pradesh), India.

Dr. Nihar Ranjan Panda

Associate Professor, Department of Electronics and Communication Engineering, Sanketika Vidya Parishad Engineering College,

Visakhapatnam (Andhra Pradesh), India.

S. No

Volume-7 Issue-5, January 2019, ISSN: 2277-3878 (Online) Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page

No.

1.

Authors: Chirag Madan, Aayushi Sinha, Kamlesh Sharma

Paper Title: Success of Blockchain and Bitcoin

Abstract: Block chain technology in today’s time changing the world of transactions and documentations. Mainly it gives a transparency to the numerous fields like electronic voting, cost analysis of product manufacturing,

paying employees, cloud storage and smart contracts (the economist). Or we can say that these applications are the

major pillors of a country which can be handled very efficiently with the help of blockchain technology. This paper

explains the role of blockchain in bitcoin and will give the applications of blockchain In the field of transactions,

governance, productions and documentation. The use of blockchain will reduce the use of third party and third party

databases. The paper will give the relation of bitcoin and blockchain technology for improving the political aspects

of country and reducing the dominating behaviour of the powerful persons and frauds in various fields by giving

the transparency.

Keywords: Transactions; Governance; Productions Component; Blockchain.

References: 1. Pilkington, M ,Blockchain technology: principles and applications. Browser Download This Paper, 2015.

2. Atzori, M ,Blockchain technology and decentralized governance: Is the state still necessary?,2015.

3. https://www.finextra.com/blogposting/13068/5-ways-blockchain-will- transform-financial-services

4. Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—a systematic

review. PloS one, 11(10), e0163477.

5. Mattila, J. (2016). The blockchain phenomenon–the disruptive potential of distributed consensus architectures (No. 38). The Research

Institute of the Finnish Economy.

6. Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016, August). A Case Study for Blockchain in Healthcare:“MedRec” prototype

for electronic health records and medical research data. In Proceedings of IEEE open & big data conference (Vol. 13, p. 13).

1-7

2.

Authors: Dipesh Jain, Vivek Kumar, Darpan Khanduja, Kamlesh Sharma, Ritika Bateja

Paper Title: A Detailed study of Big Data in Healthcare: Case study of Brenda and IBM Watson

Abstract: Big data analytics will revolutionize the health care sector. It provides us the power to assemble, handle, analyze, and understand massive amount of different, organized and unorganized data generated by the

health care sector regularly. Consultants have known the requirement for analytics to enhance the standard of health

care and improve care coordination for patients. It will improve operational efficiencies, facilitate predict and

arrange responses to malady epidemics, improve the standard of observance of clinical trials, and optimize health

care defrayment in the least levels from patients to hospital systems to governments. This paper, provides a

summary of massive knowledge, relevancy of it in health care, a number of the add progress and a future outlook on

however huge data analytics will improve overall quality in health care systems.

Keywords: Big Data Analytics, Assemble, Handle, Analyze, and Understand Massive Amount of Different,

References: 1. http://www.nature.com/nature/journal/v498/n 7453/full/498255a.html 2. Brenda software information is taken from https://en.wikipedia.org/wiki/BRENDA 3. http://www.futurescience.com/doi/full/10.415 5/fmc-2016-0264 4. http://www.intelligentpharma.com/blog.php?i d=65 5. All images are taken from http://www.google.co.in 6. Data-driven medicinal chemistry in the era of big data by Scott J. Lusher , Ross McGuire , Rene´ C. van Schaik , C. David Nicholson and

Jacob de Vlieg

7. Open PHACTS: semantic interoperability for drug discovery by Antony J. Williams, Lee Harland, Paul Groth, Stephen Pettifer, Christine Chichester, Egon L. Willighagen ,volume 17, 2012

8. The Journal of Antibiotics: Where we are now and where we are heading by Jason Berdy, 2012 Japan Antibiotics Research Association 9. Role of open chemical data in aiding drug discovery and design by Anna Gaulton and John P Overington , 2010 10. IBM Watson https://www-01.ibm.com/common/ssi/cgi- bin/ssialias?htmlfid=HLW03045USEN& 11. Uses of big data https://www.sas.com/en_us/insights/analytics/bi g-data-analytics.html

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3.

Authors: Saher Manaseer, Oroba M. Al-Nahar, Abdallah S. Hyassat

Paper Title: Network Traffic Modeling, Case Study: The University of Jordan

Abstract: Network traffic modelling is the process of describing the dynamic behavior of network by random processes. The issue that it is hard to fully predict the demand on any network from service provider point of

view, so it is important to find an accurate traffic model to maintain the quality of service. This paper focus on

analyzing the internet traffic in the University of Jordan network as a case study. The reading and monitoring of

the traffic was done with appreciated support from the service provider (JU Net Co.).

Keywords: Network traffic, Traffic model Quality of Service (QoS), Internet Service Provider (ISP), Markov Traffic Models, Poisson Traffic Model, Long-tail traffic models.

References: 1. Lee, H., Jeon, D. (2015). A Mobile Ad-Hoc network multipath routing protocol based on biological attractor selection for disaster

recovery communication, The Korean Institute of Communications Information Sciences. ICT Express 1 (2015) 86–89. 2. https://doi.org/10.1016/j.icte.2015.10.001 3. Manaseer, S., Alawneh, A. (2017). A New Mobility Model for Ad Hoc Networks in Disaster Recovery Areas. International

13-16

Association of Online Engineering. V. 13, No.3. 4. Chen, T. (2007). Network Traffic Modeling. Hossein Bidgoli (ed.), Wiley. 5. S. Floyd and V. Jacobson, Link-sharing and resource management models for packet networks, IEEE Trans. Networking, vol. 3, 1995. 6. S. Keshav. (1997). An engineering approach to computer networking: ATM networks, the Internet, and the telephone network.

Addison-Wesley Longman Publishing Co.

7. SB .A. Mohammed, S.M Sani, D.D. DAJAB. (2013). Network Traffic Analysis: A Case Study of ABU Network. Computer engineering and intelligent systems journal, Vol 4, No 4, 2013.

8. Thompson, K., Miller, G., and Wilder, R. (1997). Wide-area Internet traffic patterns and characteristics. IEEE Network, 11. 9. Flickenger, R.; Belcher, M.; Canessa, E.; Zennaro, M. How To Accelerate Your Internet: A practical guide to Bandwidth Management

and Optimisation using Open Source Software. (2006) ISBN 0-9778093-1-5. Accessed in May/2018. 10. Chandrasekaran, B. (2009). “Survey of Network Traffic Models”. Technical report accessed in May/2018, available online: <

http://www.cse.wustl.edu/~jain/cse567-06/traffic_models3.htm >.

4.

Authors: Sivaram Rajeyyagari, Gopatoti Anand Babu, Mohebbanaaz, G. Bhavana

Paper Title: Analysis of Image Segmentation of Magnetic Reso-nance Image in the Presence of Inhomongeneties

Abstract: The present work proposes the Image processing plays a vital role in medical diagnosis system. Out of various processing tools, image segmentation is very crucial in identifying the exact reason of disease. Image

segmentation clusters the pixels into silent image regions i.e. regions corresponding to individual surfaces,

objects or any part of objects. Various algorithms have been proposed for image segmentation. We have analyzed

the various systems that have been developed to medical diagnosis analysis. Reviewing of these frameworks will

be dependent upon level set strategies from claiming segmenting pictures. The theme, merits, faults from

claiming Different frameworks will be talked about in this paper. Dependent upon that, another framework need

been suggested to segmenting those MRI picture utilizing variety level situated calculation without

reinitialisation for MRI image. Those framework could be used both to recreated and also genuine im-ages.

Keywords: MRI, Segmentation Level set, Image Processing

References: 1. G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of

Variations. New York: Springer-Verlag, 2002.

2. V. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numer. Math., vol. 66, no. 1, pp. 1–31, Dec. 1993.

3. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb. 1997. 4. T. Chan and L. Vese, “Active contours without edges,” IEEE Trans. Image. Process, vol. 10, no. 2, pp. 266–277, Feb. 2001. 5. D. Cremers, “A multiphase levelset framework for variational motion segmentation,” in Proc. Scale Space Meth. Comput. Vis., Isle of

Skye, U.K., Jun. 2003, pp. 599–614.

6. Bhavana Godavarthi , M Lakshmi Raviteja, Paparao Nalajala,” Pressure Monitoring by capturing IR Image,” International Journal of Emerging Trends in Engineering Research, Vol. No.4, Issue IV, pp.32-35, Jan 2016. ISSN: 2347 - 3983, (Impact Factor: 0.987)

7. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Gradient flows and geometric active contour models,” in Proc. 5th Int. Conf. Comput. Vis., 1995, pp. 810–815.

8. R. Kimmel, A. Amir, and A. Bruckstein, “Finding shortest paths on surfaces using level set propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 6, pp. 635–640, Jun. 1995.

9. C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, “A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity,” in Proc. Med. Image Comput. Comput. Aided Intervention, 2008, vol. LNCS 5242, pp. 1083–1091, Part II.

10. C. Li, C. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1940–1949, Oct. 2008.

11. C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243–3254, Dec. 2010.

12. R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 2, pp. 158–175, Feb. 1995.

13. D. Mumford and J. Shah, “Optimal approximations by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math., vol. 42, no. 5, pp. 577–685, 1989.

14. S. Osher and J. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. Comp. Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988.

15. N. Paragios and R. Deriche, “Geodesic active contours and level sets for detection and tracking of moving objects,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 3, pp. 266–280, Mar. 2000.

16. N. Paragios and R. Deriche, “Geodesic active regions and level set methods for supervised texture segmentation,” Int. J. Comput. Vis., vol. 46, no. 3, pp. 223–247, Feb. 2002.

17. R. Ronfard, “Region-based strategies for active contour models,” Int. J. Comput. Vis., vol. 13, no. 2, pp. 229–251, Oct. 1994. 18. C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans.

Pattern Anal. Mach. Intell., vol. 22, no. 5, pp. 460–472, May 2000.

19. An efficient algorithm for image compression" paper published in IJCIET journal with V-8,I-8, Aug-2017. 2."An efficient motion estimation multiple reference frames algorithm" paper published in IJMET journal with V-8, I-8, Aug, 2017

20. P.Bachan,Samit Kumar Ghosh, Shelesh Krishna Saraswat, "Comparative Error Rate Analysis of Cooperative Spectrum Sensing in Non-Fading and Fading Environment”, IEEE International Conference on Communication Control and Intelligent Systems, GLA University.

Mathura.Pages:124-127, ISBN: 978-1-4673-7540-5, DOI: 10.1109/CCIntelS.2015.7437891, 2015. (IEEE Xplore)

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5.

Authors: Abdullah S. Alotaibi

Paper Title: Automation and Refuge of Fault Tolerance Approaches using Cloud Computing Platform

Abstract: This research paper proposes the Cloud computing platforms would spread very quickly the standout amongst the principle aspects of cloud computing will be the Part under a number layers. Starting with

specialized fault tolerance a large portion cloud computing platforms misuse virtualization, this intimates that

they need a part under 3 layers such as hosts, virtual machines and requisitions. Starting with an organization

purpose from claiming view, they need aid part under 2 layers: the cloud supplier who manages those facilitating

focal point and the client who manages as much provision in the cloud. This structuring for cloud makes it

challenging to actualize all the viable management arrangements. This paper concentrates for deficiency

tolerance over cloud Computing platforms for more that's only the tip of the iceberg decisively once autonomic

22-24

https://dl.acm.org/author_page.cfm?id=81331496270&coll=DL&dl=ACM&trk=0https://www.cse.wustl.edu/~jain/cse567-06/traffic_models3.htm

repair shed in the event that about faults. It examines the meanings from claiming this Part in the usage about

issue tolerance. Clinched alongside The majority for current approaches, faults line tolerance will be only took

care of toward the supplier alternately that customer, which prompts fractional or wasteful results. Solutions,

which include a coordinated effort the middle of the supplier and the client, need aid substantially guaranteeing.

We show this talk for analyses the place elite Also community oriented deficiency tolerance results are actualized

to an autonomic cloud foundation that we prototyped.

Keywords: Cloud Computing, Fault Tolerance, Faulty Node, Proactive Technique.

References: 1. Jangjaimon. Design and Implementation of Effective Check pointing for Multithreaded Applications on Future Clouds. IEEE-Cloud

Computing, 2013. 2. Jiajun Cao, Matthieu Simonin. Checkpointing as a Service in Heterogeneous Cloud Environments. HAL Archives-2014. 3. D. Ghoshal and L. Ramakrishnan, “Frieda: Flexible robust intelligent elastic data managementin cloud environments,” in High

Performance Computing, Networking, Storage andAnalysis (SCC), 2012 SC Companion:. IEEE, 2012, pp. 1096–1105. 4. Singh. D, Singh, J., "High Availability of Clouds: Failover Strategies for Cloud Computing Using Integrated Checkpointing Algorithms"

IEEE-CSNT, 2012.

5. Jangjaimon, "Design and Implementation of Effective Checkpointing for Multithreaded Applications on Future Clouds", IEEE-Cloud Computing (CLOUD), 2013

6. D. Ghoshal and L. Ramakrishnan, “Frieda: Flexible robust intelligent elastic data managementin cloud environments,” in High Performance Computing, Networking, Storage andAnalysis (SCC), 2012 SC Companion:. IEEE, 2012, pp. 1096–1105.

7. S. Di, Y. Robert, F. Vivien, D. Kondo, C.-L. Wang, and F. Cappello, “Optimization of cloudtask processing with checkpoint-restart mechanism,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, ser.

SC’13. New York, NY, USA: ACM, 2013, pp. 64:1–64:12.

6.

Authors: Mohammad Rashid Hussain, Mohammed Qayyum, Mohammad Equebal Hussain

Paper Title: Statistical Approach to Analyze Student Learning Outcomes

Abstract: to improve students’ learning outcomes (SLO’s), it requires efforts on many aspects, out of which effective learning techniques helps and motivate students to achieve their learning goals. Learning conditions

depends on prior knowledge, learning environments and the nature of the area in which the techniques are

implemented. There are certain rubrics which have been decided to measure SLO’s. To achieve the target, it is

important to meet the introduced methodology in all respect of Course Learning Outcomes(CLO’s) in National

Qualification Framework (NQF) Domains of Learning and Alignment with Assessment Methods and Teaching

Strategies: Knowledge, Cognitive Skills, Interpersonal Skills & Responsibility, Communication, Information

Technology, Numerical and Psycho-motor.

Keywords: National Qualification Framework (NQF), Course Learning Outcomes (CLO’s), Students Learning outcomes (SLO’s).

References: 1. NILOA (The National Institute for Learning Outcomes Assessment, 2015). Measuring Quality in Higher Education: An Inventory of

Instruments, Tools and Resources. 2015-12-12.

2. Jianxin Zhang, Research on the Assessment of Student Learning Outcomes, 2017 Council for Higher Education Accreditation/CHEA International Quality Group.

3. Dr. Jennifer E. R. “Methods for Assessing Student Learning Outcomes,” Coordinator of Academic Assessment Office of Institutional Research, Planning, and Assessment Northern Virginia Community College 2008.

4. Yusuf A. Al-Turki, Anwar L. Bilgrami, “A case study of key performance indicators in scienctific research in a middle eastern university, International journal of latest research in science and technology. Volume 4, issue 6: Page No.21-28, Nov-Dec 2015

5. Dick M. Carpenter II Æ Lindy Crawford Æ Ron Walden, “Testing the efficacy of team teaching” Springer Science+Business Media B.V. 2007, Learning Environ Res (2007) 10:53–65 DOI 10.1007/s10984-007-9019.

6. Tomasz NIEDOBA* , Paulina PIĘTA, “Application of Anova in mineral Processing” Mining Science, vol. 23, 2016, 43−54, ISSN 2084-4735

7. Kwaku F. Darkwah, Richard Tawiah1 , Maxwell Adu-Gyamfi, “Two-way ANOVA for the Study of Revenue Mobilization Inequalities” Lithuanian Statistical Association, Statistics Lithuania Lietuvos statistikų sąjunga, Lietuvos statistikos departamentas ISSN 2029-7262, 2015, vol. 54, No 1, pp. 45–51 2015, 54 t., Nr. 1, 45–51 p,

8. Jean Ashby,” Comparing student success between developmental math courses offered online, blended, and face-to-face” Volume 10, Number 3, Winter 2011 ISSN: 1541-4914, Journal of Interactive Online Learning

9. Ramona Lile, Camelia Bran CESC 2013, “The assessment of learning outcomes”, Procedia - Social and Behavioral Sciences 163 ( 2014 ) 125 – 131. Published by Elsevier Ltd.

10. Berger, J. B., & Milem, J. F. (1999). The role of student involvement and perceptions of integration in a causal model of student persistence. Research in Higher Education, 40, 641–664.

11. Sylvia Encheva, Evaluation of Learning Outcomes, ICWL 2010: Advances in Web-Based Learning – ICWL 2010 pp 72-80. SpringerLink

25-33

7.

Authors: Mohammad Rashid Hussain, Mohammed Qayyum, Mohammad Equebal Hussain

Paper Title: Effect of Seven Steps Approach on Simplex Method to Optimize the Mathematical Manipulation

Abstract: the Simplex method is the most popular and successful method for solving linear programs. The objective function of linear programming problem (LPP) involves in the maximization and minimization problem

with the set of linear equalities and inequalities constraints. There are different methods to solve LPP, such as

simplex, dual-simplex, Big-M and two phase method. In this paper, an approach is presented to solve LPP with

new seven steps process by choosing “key element rule” which is still widely used and remains important in

practice. We propose a new technique i.e. seven step process in LPP for the simplex, dual-simplex, Big-M and two

phase methods to get the solution with complexity reduction. The complexity reduction is done by eliminating the

number of elementary row transformation operation in simplex tableau of identity matrix. By the proposed

technique elementary transformation of operation has completely avoided and we can achieve the results in

considerable duration.

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Keywords: Linear programming problem (LPP), Key element (KE), Key column (KC), Key row (KR), Profit per unit (PPU), Random variables (RV), Linear Gaussian Random variables (LGRV), standard deviation (SD),

Probability Density Function (PDF)

References: 1. T. Kitahara and S. Mizuno: A Bound for the Number of Different Basic Solutions Generated by the Simplex Method, Technical Report,

October 15, 2010, to appear in Mathematical Programming (available online at http://www.springerlink.com/content/103081/)

2. T. Kitahara and S. Mizuno: An Upper Bound for the Number of Different Solutions Generated by the Primal Simplex Method with Any Selection Rule of Entering Variables.

3. T. Kitahara and S. Mizuno: New Evaluation of Computational Amount of the Simplex Method (Japanese), Technical Report No. 2011-8, August, 2011.

4. Divya K.Nadar : Some Applications of Simplex Method, International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 4, Issue 1, pp: (60-63), Month: January - March 2016, Available at: www.researchpublish.com

5. Dr. R.G. Kedia: A New Variant of Simplex Method, Volume-3, Issue-6, December-2013, ISSN No.: 2250-0758 International Journal of Engineering and Management Research Available at: www.ijemr.net Page Number: 73-75.

6. Barry Cipra: The Best of the 20th Century: Editors Name Top 10 Algorithms SIAM News, Volume 33, Number 4, page 1, 2000. 7. Benichou, M., J. M. Gauthier, G. Hentges, G. Ribi`ere. 1977. The efficient solution of large scale linear programming problems. Some

algorithmic techniques and computational results. Math. Programming 13 280–322. 8. Achterberg, T. 2009. SCIP: solving constraint integer programs. Math. Programming Computation, 1 (1) 1–41. 9. Bartels, R. H., G. H. Golub. 1969. The simplex method of linear programming using LU decomposition. Communications of the

Association for Computing Machinery 12 266–268. 10. Harris, P. J. J. 1974. Pivot selection methods of the devex LP code. Math. Programming 5 1–28. 11. Hoffman, A., A. Mannos, D. Sokolowsky, D. Wiegmann. 1953. Computational experience in solving linear prog. SIAM J.11–33. 12. Karmarkar, N. 1984. A new polynomial-time algorithm for linear programming, Combinatorica 4 373–395. 13. Van Roy, T. J., L. A., Wolsey. 1987. Solving mixed integer programming problems with automatic reformulation. Operations Research 35

(1) 45– 57.

14. Mihai Banciu: Dual Simplex, School of Management Bucknell Univ. Lewisburg, PA 17837 [email protected] Jan 2010. 15. Dimitris Bertsimas and John Tsitsiklis. Introduction to Linear Optimization. Athena Scientific, Belmont, MA, 1st edition, 1997. 16. George B. Dantzig. Maximization of a linear function of variables subject to linear inequalities. In T. C. Koopmans, editor, Activity

Analysis of Production and Allocation, pages 359–373. John Wiley and Sons, Inc., New York, NY, 1951. 17. V. Klee and G. J. Minty. How good is the simplex algorithm? In O. Shisha, editor, Inequalities - III, pages 159–175. Academic Press, New

York, NY, 1972.

18. William S. Jewell: A Primal-Dual Multi-Commodity Flow Algorithm, Operations Research Center University of California, Berkeley, September 1966, ORC 66-24

19. Beale, E.M.L., "An Alternative Method for Linear Programming," Proo. Cambridge Philos. Soc. 50 (l954), 513-523. 20. Charnes, A., Cooper, W.W., and Farr, D. and Staff, "Linear Programming and Profit Preference Scheduling for a Manufacturing Firm,"

Jour. Oper. Res. Soc. Amer., _1, no. 3 (1953), 114-129.

21. Dautzig, G.B., "Maximizing of a Linear Function of Variables Subject to Linear Inequalities," Chapter XXI of Activity Analysis of Production and Allocation, edited by Koopmans, T-C, Wiley, N.Y., 1951.

22. Dantzig, G.B., "Computational Algorithm of the Simplex Method", the RAND Corporation, Paper 394, 10 April 1953. (RM-1266, Part Xll, 26 October, 1953)

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24. Ford, L.R., and Fulkerson, D.R., "A Primal Dual Algorithm for the Capacitated Hitchcock Problem," The RAITO Corporation, RI.i-1798 Part XXXIV, 25 Sept. 1956. (Naval Res. Legist. Quarto, 4, no. 1 (l957), 47-54, RAND Conference on Computational Aspects of Linear

Programming, 30-31 Aug. 1956.)

25. Goldman, A. J., and Tucker, A.W. , "Theory of Linear programming," Paper 4 in Linear Inequalities and Related Systems, Princeton Univ. Press, Princeton, 1956, 53-98.

26. S.Mittal,APande, LWang, PKumar (2011) Design Exploration and Implementation ofSimplex Algorithm over Reconfigurable Computing Platforms In: IEEE International Conference on Digital Convergence 204-209

27. Cui, T. Ho, T. Tellambura, C. Linear Programming Detection and Decoding for MIMO Systems IEEE International Symposium on Information Theory, pp. 1783-1787, July 2006.

28. Majumdar, A. FPGA Implementation of Integer Linear Programming Accelerator International Conference on Systemics, Cybernetics and Informatics, (ICSCI), Jan 2006.

29. R.G. Bland, D. Goldfarb, and M.J. Todd, “The ellipsoid method: a survey,” Operations Research 29, 1039-1091 (1981 30. Karmarkar, N. A new polynomial-time algorithm for linear programming Combinatorica 4, 373−395 (1984) 31. Borgwardt, K. H. Some distribution independent results about the asymptotic order of the average number of pivot steps in the simplex

method Mathematics of Operations Research, vol.7, no.3, pp.441-462, 1982.

8.

Authors: Mohd Mursleen, Ankur Singh Bist, Jaydeep Kishore

Paper Title: A Support Vector Machine Water Wave Optimization Algorithm Based Prediction Model for

Metamorphic Malware Detection

Abstract: In this paper, we proposed a novel method based on coupling of SVM (Support Vector Machine) and WWO (Water Wave Optimization) for detection of metamorphic malware. The working of SVM model depends

upon the proper selection of SVM parameters. Malware signatures have been taken from G2, MWOR, MPCGEN

and NGVCK (Next Generation Virus Creation Kit).Benign signatures have been taken from Gygwin, GCC, TASM,

MingW and Clang .ClustalW and T-Coffee are used for signature alignment during primary pairwise alignment and

secondary multiple alignment in order to avoid the problem of variable length of code. In this study WWO has been

employed for determining the parameters of SVM. The performance of SVM-WWO method has been compared

with LAD Tree, Naive Bayes, SVM and ANN(Artificial Neural Network). Furthermore, The results obtained show

that the newly proposed approach provides significant accuracy. Satisfactory experimental results show the

efficiency of proposed method for metamorphic malware detection. Further, it has been recommended that this

method can be used to facilitate commercial antivirus engines.

Keywords: metamorphic malware detection, support vector machine (SVM), water wave optimization (WWO).

References: 1. U. Bayer, A. Moser, C. Kruegel and E. Kirda, " Dynamic Analysis of Malicious Codes," Journal of Computer Virology and Hacking

44-50

http://www.springerlink.com/content/103081/http://www.me.titech.ac.jp/technicalreport/h23/2011-8.pdfhttp://www.researchpublish.com/javascript:visitAuthor(%22A_Pande%22)javascript:visitAuthor(%22L_Wang%22)javascript:visitAuthor(%22P_Kumar%22)

Techniques, vol. 2, no.1, pp. 67-77, 2006. 2. F. Cohen, "Computer Viruses: Theory and Experiments." Computers & Security," vol. 6, no. 1, pp. 22-35, 1985. 3. K. Fu, and J. Blum, "Controlling for Cybersecurity Risks of Medical Device Software," Communications of the ACM," vol. 56, no. 10,

pp.35-37, 2013. 4. Z. Zhou, Q. Zhu and M. Zhou," On The Time Complexity of Computer Viruses," IEEE Transaction of Information Theory," vol. 51, no. 8,

pp. 2962-2966, 2005.

5. L. Bilge and T. Dumitras, " Before We Knew it: An Empirical Study of Zero-Day Attacks in The Real World,"In: Proceedings of The ACM Conference on Computer and Communication Security," pp. 833-844, 2012.

6. B. Bilar," Opcodes are Predictor for Malware," IJESDF, vol. 1, no. 2, pp. 156-168, 2007. 7. P. Szor," The Art of Computer Virus Research and Defense,"Pearson Education, 2005. 8. A. S. Bist, "Detection of Metamorphic Viruses: A Survey,"In Advances in Computing, Communications and Informatics (ICACCI,

International Conference on, pp. 1559-1565, 2014.

9. V. P. Nair, H. Jain, Y. K. Golecha, M. S.Gaur, & V. Laxmi, " MEDUSA: MEtamorphic Malware Dynamic Analysis Using Signature from API," In Proceedings of the 3rd International Conference on Security of Information and Networks, pp. 263-269.

10. C. Annachhatre, T. H. Austin, and M. Stamp, "Hidden Markov models for malware classification," Journal of Computer Virology and Hacking Techniques, vol. 11, no. 2, pp.59-73, 2015.

11. W. Wong, and M. Stamp, "Hunting for metamorphic engines," Journal in Computer Virology, vol. 2, no. 3 , pp. 211-229, 2006. 12. S. Srinivasan, "SSCT Score for Malware Detection," SJSU Master Thesis, pp. 10-40, 2015. 13. D. Rajeswaran, "Function Call Graph Score for Malware Detection." SJSU Master Thesis, pp. 11-47, 2015. 14. R. K. Jidigam, T. H. Austin & M. Stamp, "Singular value decomposition and metamorphic detection," Journal of Computer Virology and

Hacking Techniques, vol. 11, no. 4, pp. 203-216, 2015.

15. U. Narra, F. D. Troia, V. A. Corrado, T.H. Austin, and M. Stamp, "Clustering versus SVM for malware detection," Journal of Computer Virology and Hacking Techniques, pp.1-12. 2015.

16. M. Mangesh, T. H. Austin, and M. Stamp. "Hunting for Metamorphic JavaScript malware," Journal of Computer Virology and Hacking Techniques , vol. 11, no. 2, pp. 89-102, 2015.

17. D. Sayali, Y. Park, and M. Stamp. "Eigenvalue analysis for metamorphic detection," Journal of computer virology and hacking techniques, vol. 10, no. 1 , pp. 53-65, 2014.

18. S. Gayathri, R. M. Low, and M. Stamp, "Simple substitution distance and metamorphic detection," Journal of Computer Virology and Hacking Techniques, vol. 9, no. 3 , pp. 159-170, 2013.

19. B. Donabelle, R. M. Low, and M. Stamp. "Structural entropy and metamorphic malware," Journal of computer virology and hacking techniques, vol. 9, no. 4 , pp. 179-192, 2013.

20. D. Sayali, Y. Park, and M. Stamp. "Eigenvalue analysis for metamorphic detection," Journal of computer virology and hacking techniques 10, no. 1, pp. 53-65, 2014.

21. R. Neha, R. M. Low, and M. Stamp. "Opcode graph similarity and metamorphic detection," Journal in Computer Virology , vol. 8, no. 1-2 , pp. 37-52, 2012.

22. S. Ronak. "METAMORPHIC VIRUSES BUFFER OVER." PhD diss., San Jose State University, 2010. 23. D. P., Mila, R. Giacobazzi, A. Lakhotia, and I. Mastroeni, "Abstract symbolic automata: mixed syntactic/semantic similarity analysis of

executables," In ACM SIGPLAN Notices, vol. 50, no. 1, pp. 329-341, 2015.

24. A. Lakhotia, A. Walenstein, C. Miles, and A. Singh, "VILO: a rapid learning nearest-neighbor classifier for malware triage," Journal of Computer Virology and Hacking Techniques , vol. 9, no. 3, pp.109-123, 2013.

25. A. Shahid, R. Nigel Horspool, and I. Traore, "MAIL: Malware Analysis Intermediate Language: a step towards automating and optimizing malware detection," In Proceedings of the 6th International Conference on Security of Information and Networks, pp. 233-240, 2013.

26. F. Parvez, V. Laxmi, M. S. Gaur, and P. Vinod, "Mining control flow graph as API call-grams to detect portable executable malware," In Proceedings of the Fifth International Conference on Security of Information and Networks, pp. 130-137, 2012.

27. F. Ivan, A. Erwin, and A. S. Nugroho. "Analysis of machine learning techniques used in behavior-based malware detection," In Advances in Computing, Control and Telecommunication Technologies (ACT), Second International Conference on, pp. 201-203, 2010.

28. P. V. Shijo, and A. Salim. "Integrated static and dynamic analysis for malware detection, " Procedia Computer Science , vol. 46. pp. 804-811, 2015.

29. R. Neha, R. M. Low, and M. Stamp. "Opcode graph similarity and metamorphic detection," Journal in Computer Virology, vol. 8, no. 1, pp.37-52, 2012.

30. T. H. Annie, and M. Stamp. "Chi-squared distance and metamorphic virus detection," Journal of Computer Virology and Hacking Techniques , vol. 9, no. 1 ,pp. 1-14, 2013.

31. R. Hardikkumar, and M. Stamp "Hunting for Pirated Software Using Metamorphic Analysis." Information Security Journal: A Global Perspective, vol. 23, no. 3 ,pp. 68-85, 204.

32. Y. J. Zheng, "Water wave optimization: a new nature-inspired metaheuristic. Computers & Operations Research", 55, 1-11,2015. 33. S. M. Sridhara, & M. Stamp, (2013). "Metamorphic worm that carries its own morphing engine". Journal of Computer Virology and

Hacking Techniques, 9(2), 49-58. 34. NGVCK. VX Heavens, Retrieved from: http://vxheaven.org/vx.php?id=tn02 35. MPCGEN. VX Heavens, Retrieved from: http://vxheaven.org/vx.php?id=tn02 36. G2. VX Heavens. Retrieved from:

http://download.adamas.ai/dlbase/Stuff/VX%20Heavens%20Library/static/vdat/creatrs1.htm

37. Clang. Retrieved from http://clang.llvm.org/. 38. Cygwin. Retrieved from: http://www.cygwin.com/ 39. GCC. Retrieved from http://gcc.gnu.org/. 40. MinGW. Taken from: http://www.mingw.org/. 41. TASM. Retrieved from: 42. http://trimtab.ca/2010/tech/tasm-5-intel-8086-turbo-assemblerdownload 43. O. Kisi , & K. S. Parmar, "Application of least square support vector machine and multivariate adaptive regression spline models in long

term prediction of river water pollution". Journal of Hydrology, 534, 104-112. 44. J. C. Mojumder, J. C. Ong, W. T. Chong, & S. Shamshirband, "Application of support vector machine for prediction of electrical and

thermal performance in PV/T system". Energy and Buildings, 111, 267-277. 2016.

45. W. D. Fisher, T. K. Camp & V. V. Krzhizhanovskaya, "Crack detection in earth dam and levee passive seismic data using support vector machines". Procedia Computer Science, 80, 577-586.2016.

46. S. Ch, S. K. Sohani, D. Kumar, A. Malik, B. R. Chahar, A. K.Nema, ... & R. C. Dhiman, "A support vector machine-firefly algorithm based forecasting model to determine malaria transmission". Neurocomputing, 129, 279-288.2014.

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50. Q. K. A. Mirza, I. Awan, & M. Younas, "CloudIntell: An intelligent malware detection system" Future Generation Computer Systems.2017. 51. S. Peddabachigari, A. Abraham, C. Grosan, & J. Thomas, "Modeling intrusion detection system using hybrid intelligent systems" Journal of

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876—880.Available:http://www.halcyon.com/pub/journals/21ps03-vidmar

9.

Authors: C.Ravichandran, C.Kalaiselvan

Paper Title: A Structural Patch Decomposition Approach for MME- Image Fusion Technique using Video

Abstract: Removal of shadows from one image could be a difficult drawback. Manufacturing a high-quality shadow-free image that is indistinguishable from a replica of a real shadow-free scene is even tougher Shadows in

pictures area unit generally full of many phenomena within the scene, as well as physical phenomena like lighting

conditions, kind and behavior of shadowy surfaces, occluding objects, etc. Additionally, shadow regions might

endure post acquisition, image process transformations, e.g., distinction sweetening, which can introduce noticeable

artifacts within the shadow-free pictures. We dispute that the assumptions introduced in most studies arise from the

quality of the matter of shadow removal from one image and limit the category of shadow pictures which might be

handled by employing a Modified Multi-exposure image fusion (MMEF) technique. Experimental results showing

definitively the capabilities of our algorithmic rule are given. The difference is that HDR reconstruction works in

the radiance domain where the value is linear w.r.t. the exposure, while MMEF works in the intensity based

domain. Compared with object motion, camera motion is relatively easy to tackle via either setting a tripod or

employing some registration techniques.

Keywords: Index Terms - SPD-MMEF, Image fusion, Ghost Removal Algorithm, Pixel - level based image Fusion. Image enhancement..

References: 1. Kede Ma, Hui Li, Hongwei Yong &Zhou Wang (2017) ‘Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition

Approach’IEEE Transactions On Image Processing, Vol. 26, No. 5,pp.2519-2532.

2. K. Ma and Z. Wang, “Multi-exposure image fusion: A patch-wise approach,” in Proc. IEEE Int. Conf. Imag. Process., Sep. 2015, 3. pp. 1717–1721. 4. S. B. Kang, M. S. Winder, and R. Szeliski, “High dynamic range video,” ACM Trans. Graph., vol. 22, no. 3, pp. 319–325, 2009. 5. Gu, B. Li, W. Wong, J. Zhu, M. and Wang, M. (2012) ‘Gradient field Multi-exposure images fusion for high dynamic range image

Visualization’,J.Vis.Comm.image vol. 23, no. 4, pp. 604–610. 6. 5. Hu, J. Gallo, O. and Pulli, K.(2012) ‘Exposure stacks of live scenes with Cameras’, in Proc. Eur. Conf. Compute. Vis., 2012, pp.

499– 512.

7. Hu, J. Gallo, O. Pulli, K. and Sun, X. (2013) ‘HDR deghosting How 8. to deal with saturation?’, in Proc. IEEE Conf. Compute. Vis. Pattern Recognit. pp. 1163–1170. 9. Lee, J-Y. Matsushita, Y. Shi, B. Kweon, I. S. (2013) Mach 10. Radiometric calibration by rank minimization’, IEEE Trans. Pattern Anal. In tell. vol. 35, no. 1, pp. 144–156. 11. 8. Li, S. Kang, X. and Hu, J. (2013) ‘Image fusion with guided filtering’, 12. IEEE Trans. Image Process, vol. 22, no. 7, pp. 2864–2875. 13. 9. Zitová, B. and Flusser, J. (2003) ‘Image registration methods: A 14. survey’, Image Vis. Compute., vol. 21, pp. 977–1000. 15. 10. Li, S. and Kang, X. (2012) ‘Fast multi- exposure image fusion with 16. median Filter and recursive filter’, IEEE Trans. Consume. Electron. vol. 58, no. 2, pp. 626–632. 17. 11. Lowe, D. G. (2004) ‘Distinctive image features from scale-invariant 18. key points’ Int. J.Comp,Vis. vol. 60, no.2,pp.91-110. 19. 12. Ma, K. and Wang, Z. (2015) ‘Multi-Exposure image fusion: 20. Apatch-wise Approach’, in .Proc. IEEE Int. Conf. Image. Process, pp.1717–1721. 21. 13. Ma, K. Zeng, K. and Wang, Z. (2015) ‘Objective quality assessment 22. for Color to-gray image conversion’, IEEE Trans.Image Pro.vol.11,pp 3345-3356. 23. 14. Ma, K. Yeganeh, H. Zeng, K. and Wang, Z.(2015) ‘High dynamic 24. Range image compression by optimizing tone mapped image quality Index’, IEEE Trans. Image Process. vol. 24, no. 10, pp. 3086–3097. 25. 15. Mertens, T. Kautz, J. and Van Reeth, F. (2009) ‘Exposure fusion: A 26. Simple and practical alternative to high dynamic range photography’, Compute. Graph. Forum, vol. 28, no. 1, pp. 161–171 27. 16. Raman, S. and Chaudhuri, S. (2009) ‘Bilateral filter based 28. ompositing For variable exposure photography’, in Proc. Euro graphics, pp. 1–4. 29. 17. Sen, P. Kalantari, N. K. Yaesoubi, M. Goldman, D. B. & Shechtman, 30. E (2012)‘Robust patch-based HDR reconstruction of Dynamic scenes’, ACM Trans. Graph., vol. 31, no. 6, pp. 203-213 31. 18. Wang, Z. Bovik, A. C. Sheikh, H. R. and Simon cell, E. Pi, (2004) 32. ‘Image quality assessment: From error visibility to structural milarity’, IEEE Trans. Image Process. vol. 13, no. 4, pp. 600–612. 33. 19. A. A. Goshtasby, “Fusion of multi-exposure images,” Image Vis. 34. Comput.,vol. 23, no.6 pp. 611–618, Jun. 2005. 35. 20. S. Li, X. Kang, and J. Hu,(2013) “Image Fusion with guided 36. filtering,” IEEE Trans. Image.Proc.,vol. 22, no. 7, pp. 37. 2864–2875. 38. 21. Enfuse HDR webpage. [Online]. Available: 39. http://www.photographerstoolbox. com/products/lrenfuse.php, 2016. 40. 22. J. Wang, S. Wang, K. and Z. Wang,(2017) “Perceptual depth quality 41. in distorted stereoscopic images,” IEEE Trans. Image Process., vol. 26, no. 3, pp. 1202–1215

51-59

10.

Authors: Motilal Lakavat, Pankaj Kumar Sharma, Mukesh Saxena, Parag Diwan

Paper Title: Effect of Electroless Ni-P Coatings Containing Nano Additives on Surface Topography of

Magnesium Alloy

Abstract: In order to improve the wear and corrosion behavior for the alloys, coating is found as the most suitable method. Mg base alloys have a wide range of industrial application. These alloys shows a high specific

strength but poor wear and corrosion resistance. An ordinary coating of Cu, Ni & Zn etc. provide a physical barrier

against the wear rate and corrosion attack of magnesium substrate. In the present investigation, Ni-P plating was

done on AZ91 composite by immersing samples into Nickel sulphate bath in presence of surfactants. The

mechanism of Ni-P deposits was studied by using SEM. Ni-P coating was coated uniformly in the presence of

surfactants. Effect of surfactant and Effect of Nano-additives Al2O3, ZnO, and SiO with different quantities were

studied. 0.5 g/l Nano Al2O3 additive enhanced the deposition of Ni-P on AZ91 magnesium composite and the same

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results have been observed in case of SiO addition. Influence of ZnO was also observed. So is very clear that Ni-P

coating is very effective to reduce the corrosion and increase the wear behaviour if it is used along with Nano

additive and the surfactants.

Keywords: Coating, Nano-additives, Scanning Electron Microscope, surfactants.

References: 1. A. Yamashita, Z. Horita, and T. G. Langdon, "Improving the mechanical properties of magnesium and a magnesium alloy through severe

plastic deformation," Materials Science and Engineering: A, vol. 300, pp. 142-147, 2001.

2. A. Singh and S. P. Harimkar, "Laser surface engineering of magnesium alloys: a review," Jom, vol. 64, pp. 716-733, 2012. 3. W. Kasprzak, F. Czerwinski, M. Niewczas, and D. Chen, "Correlating hardness retention and phase transformations of Al and Mg cast

alloys for aerospace applications," Journal of Materials Engineering and Performance, vol. 24, pp. 1365-1378, 2015.

4. L. Cisar, Y. Yoshida, S. Kamado, Y. Kojima, and F. Watanabe, "Development of High Strength and Ductile Magnesium Alloys for Automobile Applications," Materials Science Forum, vol. 419-422, pp. 249-254, 2003.

5. J. Tan and M. Tan, "Dynamic continuous recrystallization characteristics in two stage deformation of Mg–3Al–1Zn alloy sheet," Materials Science and Engineering: A, vol. 339, pp. 124-132, 2003.

6. P. J. Blau and M. Walukas, "Sliding friction and wear of magnesium alloy AZ91D produced by two different methods," Tribology International, vol. 33, pp. 573-579, 2000.

7. J. K. Pancrecious, S. B. Ulaeto, R. Ramya, T. P. D. Rajan, and B. C. Pai, "Metallic composite coatings by electroless technique – a critical review," International Materials Reviews, pp. 1-25, 2018.

8. S. Xu, S. Kamado, N. Matsumoto, T. Honma, and Y. Kojima, "Recrystallization mechanism of as-cast AZ91 magnesium alloy during hot compressive deformation," Materials Science and Engineering: A, vol. 527, pp. 52-60, 2009.

9. Y.-h. Sun, R.-c. Wang, C.-q. Peng, Y. Feng, and M. Yang, "Corrosion behavior and surface treatment of superlight Mg–Li alloys," Transactions of Nonferrous Metals Society of China, vol. 27, pp. 1455-1475, 2017/07/01/ 2017.

10. C. K. Lee, "Corrosion and wear-corrosion resistance properties of electroless Ni–P coatings on GFRP composite in wind turbine blades," Surface and Coatings Technology, vol. 202, pp. 4868-4874, 2008/06/25/ 2008.

11. M. Sribalaji, P. Arunkumar, K. S. Babu, and A. K. Keshri, "Crystallization mechanism and corrosion property of electroless nickel phosphorus coating during intermediate temperature oxidation," Applied Surface Science, vol. 355, pp. 112-120, 2015/11/15/ 2015.

12. A. Araghi and M. H. Paydar, "Wear and corrosion characteristics of electroless Ni–W–P–B4C and Ni–P–B4C coatings," Tribology - Materials, Surfaces & Interfaces, vol. 8, pp. 146-153, 2014/09/01 2014.

13. T. Mimani and S. M. Mayanna, "The effect of microstructure on the corrosion behaviour of electroless Ni P alloys in acidic media," Surface and Coatings Technology, vol. 79, pp. 246-251, 1996/02/01/ 1996.

14. X. L. Ge, D. Wei, C. J. Wang, B. Zeng, and Z. C. Chen, "A study on wear resistance of the Ni-P-SiC coating of Magnesium Alloy," in Applied Mechanics and Materials, 2011, pp. 1078-1083.

15. Y. Choi, C. Lee, Y. Hwang, M. Park, J. Lee, C. Choi, et al., "Tribological behavior of copper nanoparticles as additives in oil," Current Applied Physics, vol. 9, pp. e124-e127, 2009/03/01/ 2009.

16. M. Saeedi Heydari, H. R. Baharvandi, and S. R. Allahkaram, "Electroless nickel-boron coating on B4C-Nano TiB2 composite powders," International Journal of Refractory Metals and Hard Materials, vol. 76, pp. 58-71, 2018/11/01/ 2018.

17. M. Gholizadeh-Gheshlaghi, D. Seifzadeh, P. Shoghi, and A. Habibi-Yangjeh, "Electroless Ni-P/nano-WO3 coating and its mechanical and corrosion protection properties," Journal of Alloys and Compounds, vol. 769, pp. 149-160, 2018/11/15/ 2018.

18. L. Bonin, V. Vitry, and F. Delaunois, "The tin stabilization effect on the microstructure, corrosion and wear resistance of electroless NiB coatings," Surface and Coatings Technology, vol. 357, pp. 353-363, 2019/01/15/ 2019.

11.

Authors: Mahendra Vucha, K Jyothi, Kiran Kumari, R Karthik

Paper Title: Cost Effective Autonomous Plant Watering Robot

Abstract: This paper presents a solution to those who forget to water the indoor potted plants because of the busy schedule. It presents a system that is fully autonomous and cost-effective.This autonomous system consists of

a mobile robot with RFID detector and a temperature-humidity sensor and uses wireless communication between

the mobile robot and sensing module. Thisautonomous system is adaptive to any kind of weather condition and

addresses the watering needs of the plants with the help of temperature-humidity sensor. The gardening robot used

is portable and contains an RFID module, Controller, awater reservoir and a water pump. Without human

intervention thisautonomousrobot can sense the watering need of a plantlocatesthe plant following a predefined

path and then waters the plant.An RFID tag attached to the potted plant helps for identification. In addition this

paper describes the implementation of the system in detail along with the complete circuitry. The paper is

concluded with the analysis of water carrying capacity and time needed to water a set of potted plants.

Keywords: Atmega16 micro controller, RFID reader, Mobile robot, RFID Tag, motor drivers, DC motors.

References: 1. B.C. Wolverton, Anne Johnson, and Keith Bounds, “Interior Landscape Plants for Indoor Air Pollution Abatement: Final Report”,

National Aeronautics and Space Administration ( NASA-TM-101768) Science and TechnologyLaboratory, Stennis Space Center, 1989.

2. E.J. Van Henten, J. Hemming, B.A.J. Van Tuijl, J.G. Kornet, J. Meuleman, J. Bontsema and E.A. Van Os; “An Autonomous Robot for Harvesting Cucumbers in Greenhouses”; Autonomous Robots; Volume 13 Issue 3, November 2002.

3. Kevin Sikorski, “Thesis- A Robotic PlantCare System”, University of Washington, Intel Research, 2003. 4. Ayumi Kawakami, Koji Tsukada, Keisuke Kambara and ItiroSiio, “Pot Pet: Pet-like Flowerpot Robot”, Tangible and Embedded

Interaction 2011, Pages 263-264 ACM New York, NY, USA, 2011. 5. ConstantinosMarios Angelopoulos, Sotiris Nikoletseas, GeorgiosConstantinosTheofanopoulos, “A Smart System for Garden Watering

using Wireless Sensor Networks”, MobiWac '11 Proceedings of the 9th ACM internationalsymposium on Mobility management and

wireless access Pages 167-170 ACM New York, NY, USA, 2011. 6. T.C.Manjunath, Ph.D. ( IIT Bombay ) & Fellow IETE, Ashok Kusagur , Shruthi Sanjay, SarithaSindushree, C. Ardil, “Design,

Development & Implementation of a Temperature Sensor using Zigbee Concepts”, InternationalJournal of Electrical and Computer

Engineering 3:12 2008. 7. Rafael Muñoz-Carpena and Michael D. Dukes, “Automatic Irrigation Based on Soil Moisture for Vegetable Crops”, Applied

Engineering in Agriculture (2005).

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12.

Authors: Santhosh B Panjagal, V.Harinath, G.N.Kodanda Ramaiah, R Karthik

Paper Title: Design of Farmer Friendly Intelligent System to Monitor and Control the Parameters in Precision

Agriculture

Abstract: The principle objective of the proposed framework is to outline a convenient, versatile and low cost 70-73

farmer friendly intelligent system to accomplish efficient use of water supply and motor control. It senses on field

data like climate temperature and soil moisture level, rainfall, with the assistance of sensors used in the system. And

also check for 3-ϕ supply availability, no load condition of water pump, intruder detection (humans, animals etc.).

farmer receives all the parameters data sensed on field, for further decision making about the need for watering.

Also need for motor Turn ON/OFF based on sensing rain fall and the same will be sent to the farmer, who might be

away from the field. If any intruder is detected alarm gets enabled and the same is notified to the farmer via SMS.

In the proposed system “SMS on demand service” is provided to get the status of all parameters like water resource

availability, soil’s moisture content, 3-ϕ power supply availability and the intruder detection. The system helps the

farmer in switching the motor according to his need i.e., whether the water is required for the crop or not. A user

friendly mobile application and normal SMS service will enable the farmer to monitor and control the land

parameters from the remote place efficiently. Hence the proposed method shows satisfactory performance to

measure and monitor the land parameters and moreover 3- ϕ supply availability based motor operation saves from

motor failure.

Keywords: Ultra-Low power MSP430, 3-Phase system, GSM, Sensors, Intruder detector, Motor etc..

References: 1. Roy, Sanku Kumar, Arijit Roy, Sudip Misra,Narendra S. Raghuwanshi, and Mohammad S.Obaidat. "AID: A prototype for Agricultural

Intrusion Detection using Wireless Sensor Network", 2015 IEEE International Conference on Communications (ICC), 2015.

2. “Design of Remote Monitoring and Control System with Automatic Irrigation System using GSM-Bluetooth” International Journal of Computer Applications (0975 – 888) Volume 47– No.12, June 2012.

3. Natural Capitalism Solutions, prepared for boulder country parks and open spaces by: “Sustainable Agriculture Literature Review”. March 2011.

4. India Country Overview 2008". World Bank. 2008 5. S. Siebert et al. (2010), Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, pp. 1863–1880. 6. Global map of irrigated areas: India FAO-United Nations and Bonn University, Germany (2013) 7. Agricultural irrigated land (% of total agricultural land) The World Bank (2013) 8. Bush, E. D. (2010). An overview of the estimation of kimberlite diamond deposits. Southern African Institute of Mining and Metallurgy:

Diamonds—source to use 2010 (pp. 73–84). Johannesburg, S Africa: The Southern African Institute of Mining and Metallurgy.

9. Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., et al. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in precision agriculture. Catena, 167, 293–304.

10. Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., et al. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17(12), 2794.

https://doi.org/10.3390/s17122794. 11. Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among some soil physico-chemical

properties of a field in central Italy using multivariate geostatistics. Geoderma, 97(1–2), 39–60. https://doi.org/10.1016/S0016-

7061(00)00025-2. 12. Corwin, D. L., & Lesch, S. M. (2010). Geostatistical applications for precision agriculture. In M. A. Oliver (Ed.), Geostatistical

applications for precision agriculture (pp. 139–165). Berlin, Heidelberg, Germany: Springer. https://doi.org/10.1007/978-

90-481-9133-8. 13. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.

https://doi.org/10.1126/science.1183899. 14. Mulla, D. J. (2017). Spatial variability in precision agriculture. In S. Shashi, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2118–

2125). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-23519-6_1652-1. 15. McBratney, A. B., Minasny, B., & Whelan, B. (2011). Defining proximal soil sensing. In V. I. Adamchuk & R. A. ViscarraRossel (Eds.),

The second global workshop on proximal soil sensing (pp. 144–146). Montreal, Canada: McGill University.

16. Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., & MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal, 69(5), 1572–1579. https://doi.org/10.2136/sssaj2005.0062.

17. R Karthik, Dharma Reddy Tetali, Susmitha Valli Gogula, G Manisha - Enhancement of Disciples Cognition levels using Bloom's Taxonomy in Data Mining, Journal of Advanced Research in Dynamical and Control Systems, Vol. 3S, pp. 1225-1237, (2018).

1. 18. Design of low threshold Full Adder cell using CNTFET – P Chandrashekar, R Karthik, O Koteswara Sai Krishna, Ardhi Bhavana, International Journal of Applied Engineering Research, Vol 12, No 1, pp. 3411-3415, (2017).

18. Samit Kumar Ghosh, P.B. Natarajan, Tapan Kumar Dey, J. Nagaraju, R. Karthik and T.S. Arulananth, “Energy Aware Multi-hop Routing Protocol for Internet of Things based Wireless Sensor Network”, Journal of Engineering and Applied Sciences, Vol. 12, pp. 5307-5311,

(2017).

13.

Authors: T Ravinder, T Vijetha, P Chandra Shaker, Ch Neelima, R Karthik

Paper Title: Design of 8T SRAM using FINFET Technology

Abstract: Retrieving the data is the major aspect of concern in CMOS technology. At present lower power consumption is the primary objective. The lower power consumption the SRAM cells will be used in the near future

extensively. The existing models do not give stability in reading operation because of which a correct logic decision

at the output cannot be made. In this paper SRAM cell is designed using FinFET technology and is compared with

existing CMOS 45nm technology, and a new SRAM cell structure is proposed which enhances the read stability

and write stability with reduction in noise. The transient analysis is done for both CMOS 45nm and FinFET

technology based SRAM cell. This proposed model is designed with 8 transistors where 6 transistors are used for

data writing and another two are for data reading. The present design increases the read stability.

Keywords: Read stability, 8T SRAM, CMOS, FinFET.

References: 1. ParidhiAthe, S. Dasgupta “A Comparative Study of 6T, 8T and 9T Decanano SRAM cell”, 2009 IEEE Symposium on Industrial

Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia.

2. NahidRahman, B. P. Singh “Design and Verification of Low Power SRAM using 8T SRAM Cell Approach”, International Journal of Computer Applications (0975 – 8887) Volume 67– No.18, April 2013.

3. E. Grossar, “Read Stability and Write-Ability Analysis of SRAM Cells for Nanometer Technologies”, IEEE Journal of Solid-State

74-76

https://doi.org/10.3390/s17122794https://doi.org/10.1007/978-90-481-9133-8https://doi.org/10.1007/978-90-481-9133-8https://doi.org/10.1126/science.1183899https://doi.org/10.1007/978-3-319-23519-6_1652-1https://doi.org/10.2136/sssaj2005.0062

Circuits, vol.41, no.11, pp. 2577-2588, Nov.2006. 4. Budhaditya Majumdar, Sumana Basu, “Low Power Single Bit line 6T SRAM Cell With High Read Stability”, IEEE 2011 International

Conference on Recent Trends in Information Systems.

5. K. Takeda et al., “A Read-Static-Noise-Margin- Free SRAM Cell for Low-VDD and High-Speed Applications,” IEEE Journal of Solid-State Circuits, vol.41, no.1 pp.113-121, Jan., 2006.

6. S. Birlaeta., “Static Noise Margin Analysis of Various SRAM Topologies”, IACSIT, pp.304309, vol.3, No.3, June2011. 7. Aly, R. E.,Bayoumi, M. A., “Low-Power Cache Design Using 7T SRAM Cell ”, IEEE Transaction on Circuit and Systems II, April 2007,

pp. 318-322.

8. Sil, S. Ghosh and M. Bayoumi, “A novel 8T SRAM cell with improved read-SNM,” IEEE Northeast workshop on circuit and system, 2007, pp.1289-1292.

9. K. Khare, N. Khare, V. Kulhade and P. Deshpande, “VLSI Design And Analysis Of Low Power 6T SRAM Cell Using Cadence Tool”, leSE, lohorBahru, Malaysia, 2008.

10. Premalatha, “A Comparative Analysis of 6T, 7T, 8T and 9T SRAM Cells in 90nm Technology” 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

11. Kirti Bushan Bawa, “A Comparative Study of 6T, 8T and 9T SRAM Cell” Volume 3 Issue 6, June 2015.

14.

Authors: Yojna Arora, Dinesh Goyal

Paper Title: Performance Comparison ofHive, Pig & Map Reduce overVariety of Big Data

Abstract: Big Data refers to that huge amount of data which cannot be analyzed by using traditional analytics methods. With the increase of web content at a rapid rate, only analyzing data is not enough rather managing it with

that great pace and efficiency is needed. A new framework Hadoop was implemented in order to perform parallel

distributed computing. Hadoop is supported by various frameworks. In this paper, a performance comparison of

Pig, Hive and Map Reduce over Big Data is analyzed.

Keywords: Pig, Hive, Map Reduce, Hadoop, Big Data

References: 1. Prof R A Fadnavis & Sannudhi Tabhane, "Big Data Processing using Hadoop", in IJCSIT, Vol I, 2015 2. Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu and Raghotham

Murthy, “Hive – A Petabyte Scale Data warehouse using Hadoop”, IEEE, 2010 3. Alan F. Gates, Olga Natkovich, Shubham Chopra, Pradeep Kamath, “Building a high level data flow system on top of Map Reduce : the

Pig Experience”, Proceedings of VLDB Endowment, Vol 2, Issue 2, August, 2009

4. Jeffery Dean & Sanjay Ghemawat, “Map Reduce : Simplified Data Processing on Large Clusters”, 6th Symposium on Operating System Design and Implementation, Dec 2004

5. E. Laxmi Lydia & Dr. M. Ben Swarup, “ Big Data analysis using Hadoop components like Flume, Map Reduce, Pig and Hive”, IJCSET, Vol 5, Issue 11, Nov 2015

6. Bichitra Mandai, Ramesh Kumar Sahoo and Srinivas Sethi "Architecture of efficient word processing using Hadoop for Big Data Applications", in International Conference on Man and Machine Interfaccing",IEEE 2015

7. Poonam Vashisht and Vishal Gupta, “Big Data Analytics Techniques: A survey” , in IEEE 2015

77-81

15.

Authors: Pooja Singh, Nasib Singh Gill

Paper Title: WCA-DGVC: A Weight Clustering Algorithm for Decentralized Group Key Management with

Variable size Cluster

Abstract: Wireless Ad hoc networks are experiencing a rapid increase in its applicability as well as in security threats. The wireless communication medium makes them highly prone to security attacks. Key management plays

a vital role in secured communication. Power efficient and secure key management is one of its major requirements.

Group key management is a promising approach for efficient cryptographic key management for MANETs. In this

paper, we proposed a weight clustering algorithm for a decentralized group key management. The whole network is

divided into smaller subgroups called clusters. The cluster is locally managed by the cluster head (CH). The CHs

mutually manage the security key process. All nodes have equal opportunities to take part in CH selection. The CHs

are selected by a weight clustering algorithm based on the computational power and the neighbor count of the node.

The elected CH selects next CH from its neighbor by comparing their computational power, neighbor nodes and

their distance from it. This eliminates the need of gateway nodes for inter-cluster communications. The size of the

cluster is directly proportional to the weight of the cluster head that is the cluster head with high weight will

manage the large cluster. Therefore the group key management activities are proportionally divided among the

cluster heads according to their power. This eliminates the risk of frequent drowning of cluster heads. The

performance of our algorithm is assessed through stimulation and compare with two popular weight clustering

algorithms.

Keywords: Cluster, Decentralized group key management, weight clustering algorithm, Wireless ad hoc ntwork.

References: 1. Basagni, S., Conti, M., Giordano, S., Stojmenovic, I.: Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition, Chap-1,

John Wiley and Sons (2013) 2. Zhang, Y., Lee, W.: Security in Mobile Ad-Hoc Networks. In: Ad Hoc Networks Techologies and Protocols, Springer (2005) 3. Rafaeli, S., Hutchison, D.: A survey of Key Management for Secure Group Communication, ACM Computing Surveys, pp. 309-329, Vol.

35, No. 3, September (2003) 4. Kuroiwa, J.,Yamauchi,Y., Sun,W., Ito,M.: A self-stabilizing algorithm for stable clustering in mobile ad-hoc networks. IEEE (2011) 5. Tao, Y., Wang, J., Wang, Y. L., Sun, T.,: An enhanced maximum stability weighted clustering algorithm in ad hoc network. In: Proc. 4th

Int. Conf. Wireless Commun. Netw. Mobile Comput. Pp. 1-4 (2008) 6. Anitha, V. S., Seastian, M. P.,: (k,r)-dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks, IET

Commun., vol. 5, no. 13, pp. 1836-1853 (2011)

7. Wang, X., Cheng, H., Huang, H.,: Constructing a MANET based on clusters. Wireless Pers. Commun., vol. 75, no. 2, pp. 1489-1510 (2014)

8. Sathiamoorthy, J., Ramakrishnan, B.,: Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs. Wireless Netw., vol. 23, no. 2, pp. 371-385 (2017)

9. Cai, M., Rui, L., Liu, D., Huang, H., Qiu, X.,: Group mobility based clustering algorithm for mobile ad hoc networks. In: proc. APNOMS, pp. 340-343, August (2015)

82-87

10. Maragatham, T., Karthik, S., Bhavadharini, R. M.,: TCACWCA: transmission and collusion aware clustering with enhanced weight clustering algorithm for mobile ad hoc networks, Cluster Computing, https://doi.org/10.1007/s10586-017-1574-0 (2018)

11. Salma, B. U., Lawrence, A. A.,: Improved group key management region based cluster protocol in cloud. Cluster Computing. https://doi.org/10.1007/s10586-017-1455-6 (2017)

12. Aftab, F., Zhang, Z., Ahmad, A.,: Self-Organization Based Clustering in MANETs Using Zone Based Group Mobility. IEEE Access https://doi.org/10.1109/ACCESS.2017.2778019 (2017)

13. Farkas, K., Hossmann, T., Plattner, B., Ruf, L.,: NWC: node weight computation in MANETs. In: Int. Conf. on Computer Commun. And Netw, pp. 1059-1064 (2007

16.

Authors: Bakhtiar Affandy Othman, Aminaton Marto, Nor Zurairahetty Mohd Yunus, Tan Choy Soon, Faizal

Pakir

Paper Title: The Grading Effect of Coarse Sand on Consolidated Undrained Strength Behaviour of Sand Matrix

Soils

Abstract: In geotechnical engineering field, the behaviour of soil does rely much on the shear strength for design purpose. Previously, findings show that the change of grained size in soil will change the structure

(microstructure) and behaviour of the soil; consequently, affected the strength. To date, limited study focused on

the effect of grading on the behaviour of sand fine mixtures. This study aims to investigate the effect of coarse sand

on undrained strength behaviour of sand matrix soils in comparison with clean sand. A series of test on

reconstituted sand matrix soils had been carried out by conducting consolidated undrained (CU) triaxial test using

GDS ELDYN® triaxial machine. Coarse sand (retain within 2.0 mm to 0.6 mm) was mixed with 0%, 10 %, 20%,

30%, and 40% of fine particles (kaolin) independently by weight to prepare reconstituted samples. Triaxial samples

of 50 mm diameter and 100 mm height were prepared using wet tamping technique (5% of moisture content) with

targeted relative density at 15% (loose state). Each reconstituted sample was sheared at two effective confining

pressures of 100 kPa and 200 kPa, respectively. Results show that the gradation contributed to the behaviour of the

sand matrix soils. Increasing percentage of coarse sand in sand matrix soils exhibited higher effective friction angle.

Similar trends were also observed on the angularity effect on undrained shear strength parameters.

Keywords: Sand Matrix Soils, Coarse Sand, Consolidated Undrained, Cohesion, Friction Angles. .

References: 1. H. Yokoi, 1968. Relationship between soil cohesion and shear strength. Soil Science and Plant Nutrition, Vol. 14, No. 3. 2. M.M. Rahman, S.R. Lo, 2008. Effect of Sand Gradation and Fines Type on Liquefaction Behaviour of Sand-finesMixture. Geotechnical

Earthquake and Engineering and Soil Dynamics IV Congress 2008. Page 1-11.

3. S.V. Dinesh, G. Mahesh Kumar, Muttana S. Balreddy, B.C. Swamy, 2011. Liquefaction Potential of Sabarmati-River Sand. ISET Journal of Earthquake Technology, Paper No. 516, Vol. 48, No. 2-4, June-Dec. 2011, pp. 61–71.

4. V.T. Phan, D. Hsiao, P.T. Nguyen, 2016. Critical State Line and State Parameter of Sand-Fines Mixtures. Procedia Engineering 142 (2016) 299-306.

5. R.J.N. Azeiteiro, P.A.L.F. Coelho, D.M.G. Taborda, J.C.D. Grazina. J., 2017. Critical State–Based Interpretation of the Monotonic Behavior of Hostun Sand. Geotech. Geoenviron. Eng., (2017), 143(5):04017004.

6. N.D. Nik Norsyahariati, K.R. Hui, A.G.A. Juliana, 2016. The Effect of Soil Particle Arrangement on Shear Strength Behavior of Silty Sand. MATEC Web of Conferences 47, 03022.

7. Marto, C.S. Tan, A.M. Makhtar, N.Z. Mohd Yunus, A. Amaluddin, 2013. Undrained Shear Strength of Sand With Plastic Fines Mixtures. Malaysian Journal of Civil Engineering 25(2) :189-199

8. C.S. Tan, 2015. Effect of Fines Content and Plasticity on Liquefaction Susceptibility of Sand Matrix Soils. PhD Thesis, Universiti Teknologi Malaysia.

9. R.W. Boulanger, M.W. Meyers, L.H. Mejia, I.M. Idriss, 1998. Behavior of a fine-grained soil during Loma Prieta earthquake. Canadian Geotechnical Journal, 35(1), 146-158.

10. Batilas, P. Pelekis, V. Vlachakis, G. Athanasopoulos, 2013. International Journal of Geoengineering Case Histories, 2(4), 270-287. 11. R.P. Orense, T. Kiyota, S. Yamada, M. Cubrinovski, Y. Hosono, M. Okamura, S. Yasuda, 2011. Comparison of liquefaction features

observed during the 2010 and 2011 Canterbury earthquakes. Seismological Research Letters, 82(6), 905-918.

12. D. Fontana, S. Lugli, S.Marchetti Dori, R. Caputo, M. Stefani, 2015. Sedimentology and composition of sands injected during the seismic crisis ofMay 2012 (Emilia, Italy): clues for source layer identification and liquefaction regime. Sedimentary Geology 325 (2015) 158–167.

13. D. Gautam, F. Santucci de Magistris, G. Fabbrocino, 2017. Soil liquefaction in Kathmandu valley due to 25 April 2015 Gorkha, Nepal earthquake. Soil Dynamics and Earthquake Engineering 97 (2017) 37–47

14. B.A. Othman, A. Marto, 2018. Laboratory test on maximum and minimum void ratio of tropical sand matrix soils. IOP Conf. Ser.: Earth Environ. Sci. 140 012084

15. Marto, C.S. Tan, A.M. Makhtar, N.J. Jusoh, 2016. Cyclic Behaviour of Johor Sand. International Journal of GEOMATE. Vol. 10, Issue 21, pp, 1891-1898

16. J.A. Yamamuro, P.V. Lade, 1997. Static liquefaction of very loose sands. Can. Geotech. J. 34:905-917. 17. W. Chang, M. Hong, 2008. Effects of Clay Content on Liquefaction Characteristics of Gap-Graded Clayey Sands. SOILS AND

FOUNDATIONS Vol. 48, No. 1, 101–114. 18. Y. Yilmaz, M. Mollamahmutoglu, V.Ozaydin, K.Kayabali, 2008. Experimental investigation of the effect of grading characteristics on the

liquefaction resistance of various graded sands. Engineering Geology 100 (2008) 91-100.

19. Juneja, M.E. Raghunandan, 2010. Effect of Sample Preparation on Strength of Sands. Indian Geotechnical Conference – 2010, GEOtrendz, 327-330.

20. E. Ibraim, A. Diambra, D. Muir Wood, A.R. Russell, 2010. Static liquefaction of fibre reinforced sand under monotonic loading. Geotextiles and Geomembranes 28 (2010) 374–385.

21. Y. Jafarian, R. Vakili, A. Sadeghi Abdollahi, 2013. Prediction of cyclic resistance ratio for silty sands and its applications in the simplified liquefaction analysis. Computers and Geotechnics 52 (2013) 54–62.

22. Mohammadi, A. Qadimi, 2015. Characterizing the process of liquefaction initiation in Anzali shore sand through critical state soil mechanics. Soil Dynamics and Earthquake Engineering 77 (2015) 152–163.

23. A.E. Takch, A. Sadrekarimi, H.E. Naggar, 2016. Cyclic resistance and liquefaction behavior of silt and sandy silt soils. Soil Dynamics and Earthquake Engineering 83 (2016) 98–109

24. S. Rees, 2013.What is Triaxial Testing? Part 1of 3. Published on the GDS website w ww.gdsinstruments.com (2013). 25. K.H. Head, R.J. Epps, 2014. Manual of Soil Laboratory Testing, Volume 3 : Effective Stress Tests, ISBN 978-184995-054-1. 26. C.A. Bareither, T.B. Edil, C.H. Benson, D.M. Mickelson, 2008. Geological and Physical Factors Affecting the Friction Angle of

Compacted Sands. Journal of Geotechnical and Geoenvironmental Engineering, Vol. 134, No. 10, October 1.

27. BS 1377-2: 1990, 1990. Methods of test for soils for civil engineering purposes - Part 2: Classification tests. 28. BS1377-8: 1990, 1990. Methods of test for soils for civil engineering purposes – Part 8 : Shear strength tests (effective stress).

88-92

17. Authors: A. Mary OdilyaTeena M. Aaramuthan

https://doi.org/10.1007/s10586-017-1574-0https://doi.org/10.1007/s10586-017-1455-6

Paper Title: An Uncertain Trust and Prediction Model in Federated Cloud using Machine Learning Approach

Abstract: Federated Cloud Model referred as the interconnection of two or more providers with some guidelines prescribed in Service Level Agreement to address the uncertainty such as SLA Violation for the specific

service. Most well-known models use the concept of either probability or fuzzy set theory in managing the Quality

of Service (QoS) required by the Cloud user, application and tool. In this paper, Deep Learning is applied to predict

the SLA Violation and manage the uncertainty. SLA violation is defined as the failure to meet the requirement

prescribed for the user and application. In addition to that, banker’s algorithm is modified and used as prediction

algorithm to find the possible safe state computation of the tasks and avoid wastage of resources in federated cloud.

Random forest data mining technique is applied to rank the trust based provider and top provider may be considered

for the service. The simulation results reveal that the proposed model helps to avoid uncertainty to about 78% and

recognized that it is one of the most appropriate model needed in federated cloud architecture.

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

References:

1. Rodrigo N. Calheiros, Rajiv Ranjan , Anton Beloglazov , César A. F. De Rose and RajkumarBuyya, “CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, 24 August 2010, https://doi.org/10.1002/spe.995.

2. RajkumarBuyya ;Saurabh Kumar Garg ; Rodrigo N. Calheiros, ”SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions” in: 2011 IEEE International Conference on Cloud and Service Computing, 12-14 Dec. 2011

3. 3.Saurabh Kumar Garg, Steve Versteeg and RajkumarBuyya,” SMICloud: A Framework for Comparing and Ranking Cloud Services” in 2011 Fourth IEEE International Conference on Utility and Cloud Computing, 5-8 Dec 2011.

4. J. Udayakumar, M. Manikkam, and A. Arun, "Cloud-SLA: Service Level Agreement for Cloud Computing." 5. Mohammed, T. Dillon, E. Chang, SLA-based trust model for cloud computing, in: Proceedings of 2010 13th International Conference on

Network-Based Information Systems (NBiS), 2010, pp. 321–324.

6. Maheswari, R. Sanjana, S. Sowmiya, SudhirShenai& G. Prabhakaran, “An Efficient Cloud Security System Using Double Secret Key Decryption Process for Secure Cloud Environments”, International Journal of Advanced Scientific Research & Development (IJASRD), 3

(1/II), pp. 134 – 139.

7. Sudip Chakraborty, Krishnendu Roy, An SLA-based framework for estimating trustworthiness of a cloud, in: Proceedings of 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2012, pp. 937–942.

8. D. Serrano, S. Bouchenak, Y. Kouki, T. Ledoux, J. Lejeune, J. Sopena, L. Arantes, and P. Sens, "Towards QoS-oriented SLA guarantees for online cloud services," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013, pp.

50-57.

9. L. Wu, S. K. Garg, and R. Buyya, "SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments," in Cluster, Cloud and Grid Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, 2011, pp. 195-204.

10. Y. Xiaoyong, L. Ying, J. Tong, L. Tiancheng, and W. Zhonghai, "An Analysis on Availability Commitment and Penalty in Cloud SLA," in Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual, 2015, pp. 914-919.

11. J. Abawajy, Determining service trustworthiness in inter-cloud computing environments, in: Proceedings of 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks (ISPAN), 2009, pp. 784–788.

12. J. Abawajy, Establishing trust in hybrid cloud computing environments, in: Proceedings of 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2011, pp. 118–125.

13. D. Bernstein, D. Vij, Inter-cloud security considerations, in: Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 2010, pp.

14. MukeshSinghal, Chandrasekhar Santosh, GeTingjian, Sandhu Ravi, Krishnan Ram, Ahn Gail-Joon, Bertino Elisa, Collaboration in multi cloud computing environments: framework and security issues, Computer 46 (2) (2013).

15. F. Lu, H.Z. Wu, “Research of Trust Valuation and Decision-making Based on Cloud Model in Grid Environment,” Journal of System Simulation, Vol. 21, Jan. 2009, pp. 421 – 426.

16. J.Y.J. Hsu, K.J. Lin, T.H. Chang, C.J. Ho, H.S. Huang, W.R. Jih, Parameter learning of personalized trust models in broker-based distributed trust management, Inform. Syst. Front. 8 (4) (2010) 321–333.

17. K.J. Lin, H. Lu, T. Yu, C.E. Tai, A reputation and trust management broker framework for web applications, in: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service, 2005, pp. 262–269.

93-97

18.

Authors: Muthunoori Naresh, P Munaswamy

Paper Title: Smart Agriculture System using IoT Technology

Abstract: In olden Days Farmers used to figure the ripeness of soil and influenced suspicions to develop which to kind of yield. They didn't think about the humidity, level of water and especially climate condition which terrible a

farmer increasingly The Internet of things (IOT) is remodeling the agribusiness empowering the agriculturists

through the extensive range of strategies, for example, accuracy as well as practical farming to deal with

challenges in the field. IOT modernization helps in assembly information on circumstances like climate, dampness,

temperature and fruitfulness of soil, Crop web based examination empowers discovery of wild plant, level of

water, bug location, creature interruption in to the field, trim development, horticulture. IOT utilize farmers to get

related with his residence from wherever and at whatever point. Remote sensor structures are utilized for watching

the homestead conditions and tinier scale controllers are utilized to control and mechanize the home shapes. To see

remotely the conditions as picture and video, remote cameras have been used. IOT development can diminish the

cost and update the productivity of standard developing.

Keywords: Soil moisture sensor, Water level sensor, Humidity sensor, Temperature sensor, IOT

References: 1. k.lakshmisudha, swathi hegde, neha cole, shruti iyer, " good particularity most stationed cultivation spinning sensors", state-of-the-art

weekly going from microcomputer applications (0975-8887), number 146-no.11, july 2011

2. nikesh gondchawar, dr. r.complexion.kawitkar, "iot based agriculture", all-embracing almanac consisting of contemporary analysis smart

minicomputer additionally conversation planning (ijarcce), vol.5, affair 6, june 2016. Overall Journal on Recent and Innovation Trends in

98-102

Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 2 177 – 181

3. M.K.Gayatri, J.Jayasakthi, Dr.G.S.Anandhamala, "Giving Smart Agriculture Solutions to Farmers for Better Yielding Using IoT", IEEE

International Conference on Technological Innovations in ICT for Agriculture and Rural

4. Lustiness. r. nandurkar, slant. r. thool, r. tumor. thool, "plan together with situation coming from rigor horticulture technique executing

trans-missions sensor network", ieee world consultation toward telemechanics, regulate, intensity also wiring (aces), 2014. Development

(TIAR 2015).

5. Paparao Nalajala, D. Hemanth Kumar, P. Ramesh and Bhavana Godavarthi, 2017. Design and Implementation of Modern Automated

Real Time Monitoring System for Agriculture using Internet of Things (IoT). Journal of Engineering and Applied Sciences, 12: 9389-

9393.

6. Joaquín Gutiérrez, Juan Francisco Villa-Medina, Alejandra Nieto-Garibay, and Miguel Ángel PortaGándara, "Computerized Irrigation

System Using a Wireless Sensor Network and GPRS Module", IEEE Transactions on Instrumentation and Measurements, 0018-

9456,2013

7. Paparao Nalajala, P Sambasiva Ra

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