International Journal of Recent Technology and Engineering · Alok Kumar, “Improvements in efficiency of solar parabolic trough”, IOSR Journal of Mechanical and Civil Engineering

S. No

Volume-8 Issue-1S2, May 2019, ISSN: 2277-3878 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Prem Kumar T, Naveen C, Avinash Kumar R, N. Praveen Kumar

Paper Title: Effect of Thermal Stratification and Insulation on the Performance of Parabolic Trough Collector

Abstract: Parabolic trough collector (PTC) is a two dimensional solar focusing collector which collects beam

radiation on tracking. In this research, closed parabolic trough solar collector of aperture area 0.6979 m2, copper

tube receiver with black powder coating of diameter 22.6 mm, surface area of 0.05996 m2 is used. The

concentration ratio (CR=Aa/Ar) of the PTC is 11.3. PTC is provided with top glass cover to induce greenhouse

effect and to protect the reflector surface from dirt, weather, oxidation. The significance of insulation & thermal

stratification and its effect on the performance of parabolic trough collector is studied. Non insulated storage

tank led to high collection efficiency till 14:00 pm whereas it lead significant drop in system efficiency after

that. Because of stirring the effect of thermal stratification was collapsed which reduces the thermo symphonic

effect. This led to reduction in the system efficiency (difference of 16 %) of parabolic trough collector and also

reduced the final temperature attained by water (difference of 4oC) in the storage tank. Hence insulation of

storage tank without stirring is preferred for higher efficiency (maximum storage efficiency of around 73.91 %)

and higher final temperature of water (59.7 o C).

Keywords: Parabolic Trough Collector (PTC), Thermal stratification, insulation, Efficiency.

References:

1. Pirasteh.G., Saidur.R., Rahman.S.M.A., Rahim.N.A., “A review on development of solar drying applications”, Renewable and

Sustainable Energy Review, vol. 31, pp. 133-148, 2014.

2. Ekechukwua.O.V., Norton.B., “Review of solar-energy drying systems III: low temperature air-heating solar collectors for crop

drying applications”, Energy Conversion & Management, vol. 40, pp. 657-667, 1999. 3. S.Anil kumar, K.Sridhar, G. Vinod kumar, “Heat Transfer Analysis of Solar Air Heating System for Different Tilt Angles”,

Applied Solar Energy, vol. 54,no. 1, pp. 17–22, 2018.

4. Abbasov.E.S., Umurzakova.M.A., and Boltoboeva.M.P., “Efficiency of solar air heaters”, Applied Solar Energy, vol. 52, no. 2, pp. 97–99, 2016.

5. Tyagi.R.K., Ravi Ranjan, and Kunal Kishore, “Performance studies on flat plate solar air heater subjected to various flow

patterns”, Applied Solar Energy J., vol. 50, no. 1, pp. 98–102, 2014. 6. Zhenjieren, Zhili Chen, Li-an Hou, Wenbiao Wang ,Kaishengxiong, Xiao xiaowantuzhang, “Design investigation of a solar

energy heating system for anaerobic sewage treatment”, energy procedia, vol.14, pp. 255-259, 2012.

7. Prem kumar.T, Moulieswaran.S, Pradeep.S, "Effect Of glazing and cooling on Solar Waste Water Still", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), vol. 6, no. 1, pp.927-932, 2019.

8. Rai.G.D., “Solar Energy Utilization”, Khanna publishers, 2011.

9. Tian.Y., Zhao.C.Y., “A review of solar collectors and thermal energy storage in solar thermal applications”, Applied Energy, vol. 104, pp. 538-553, 2013.

10. Alok Kumar, “Improvements in efficiency of solar parabolic trough”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), vol. 7, no. 6, pp. 63-75, 2013.

11. Rafael Almanza, Alvaro Lentz, Gustavo JimeŃez, “Receiver Behavior In Direct Steam Generation With Parabolic Troughs”,

Solar Energy, vol. 61, no. 4, pp. 275–278, 1997. 12. Zilong Wang, Hua Zhang Huajie Huang, Binlin Dou, Xiuhui Huang, Maria A.Goula., “The experimental investigation of the

thermal stratification in a solar hot water tank”, Renewable Energy, vol. 134, pp. 862-874, 2019.

13. Karthick, S. "Semi supervised hierarchy forest clustering and knn based metric learning technique for machine learning system", Journal of Advanced Research in Dynamical and Control Systems, vol. 9, no. Special Issue 18, pp. 2679-2690, 2017.

14. Huang, H., Wang, Z., Zhang, H., Dou, B., Huang, X., Liang, H., & Goula, M. A. (2019). An experimental investigation on

thermal stratification characteristics with PCMs in solar water tank. Solar Energy, vol. 177, pp. 8–21. 15. Weian Du., Yusheng Liu., Hongsheng Yuan., Shouxu Qiao., Sichao Tan., “Experimental investigation on natural convection and

thermal stratification of IRWST using PIV measurement”, International journal of Heat and mass transfer, vol. 136, pp. 128-145,

2019. 16. Kang, M., Kim, J., You, H., & Chang, D., “Experimental investigation of thermal stratification in cryogenic tanks”, Experimental

Thermal and Fluid Science, vol. 96, pp. 371–382, 2018.

17. Leonardi, M., Pizzarelli, M., & Nasuti, F., “Analysis of thermal stratification impact on the design of cooling channels for liquid rocket engines”, International Journal of Heat and Mass Transfer, vol. 135,pp. 811–821, 2019.

18. Wang, M., Feng, T., Fang, D., Hou, T., Tian, W., Su, G. H., & Qiu, S., “Numerical study on the thermal stratification

characteristics of AP1000 pressurizer surge line”, Annals of Nuclear Energy, vol. 130, pp. 8–19, 2019. 19. Ward, B., Clark, J., & Bindra, H., “Thermal stratification in liquid metal pools under influence of penetrating colder jets”,

Experimental Thermal and Fluid Science, vol. 103, pp. 118–125, 2019.

20. Liu.Z., Li.Y., & Zhou.G., “Study on thermal stratification in liquid hydrogen tank under different gravity levels”, International Journal of Hydrogen Energy, vol. 43, no. 19, pp. 9369–9378, 2018.

21. Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of

Intelligent Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09 22. T. Sathish, and J. Jayaprakash, “Meta-Heuristic Approach to Solve Multi Period Disassembly-To-Order Problem of End-Of-Life

Products using Adaptive Genetic Algorithm”, International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS,

Vol. 15, No. 3, pp. 59-67, 2015. 23. T. Sathish, “Experimental investigation on degradation of heat transfer properties of a black chromium-coated aluminium surface

solar collector tube”, International Journal of Ambient Energy, Taylor and Francis Publishers, Vol. 39, doi:

https://doi.org/10.1080/01430750.2018.1492456. 24. Sathish, T., Jayaprakash, J. “Multi period disassembly-to-order of end-of-life product based on scheduling to maximise the profit

in reverse logistic operation”, International Journal of Logistics Systems and Management, vol. 26, no. 3, pp. 402-419, 2017.

25. T. Sathish, “Heat Transfer Analysis of Nano-Fluid Flow in a converging Nozzle with different aspect Ratios”, Journal of New Materials for Electrochemical Systems, Vol. 20, pp. 161-167, 2017.

26. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”,

Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

1-5

2.

Authors: D.Muruganandam, J.Jayapriya, M.K.Karthik, S.Suraj

Paper Title: Comparative Study of Different Inter-laminar Strengthening Techniques

Abstract : Composite materials has found itself a huge role in the aircraft industry. Though composite comes

with a lot of advantages it has its own drawbacks. One such drawback in composite laminates is the low

interlaminar strength which affects the performance of composites. In this paper we try to gain knowledge about

the various techniques that have been developed over the years to overcome this drawback, so that the strength

of composites can be improved which makes them to be used at critical places in aircraft also. Efforts have been

made to study the principle behind techniques like z-pinning, 2D and 3D woven composites, polymer additive

manufacturing and addition of carbon nanotubes.

Keywords: z-pinning, polymer additive manufacturing, woven composites, carbon nano-tubes.

References:

1. David John Barrett “The Analysis of a Z-fiber Reinforced plate”, Naval Air Warfare Center Aircraft Division, PA

2. X. Zhang, L. Hounslow, and M. Grassi, “Improvement of low- velocity impact and Compression-after-Impact performanceby Z-fiber pinning” Composite Science and Technology, vol. 66, pp. 2786–2794, 2006.

3. D. P. C. Aiman, M. F. Yahya, and J. Salleh, “Impact properties of 2D and 3D woven composites: A review”, American Institute

of Physics, 1774, 02002, 2016. 4. Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of

Intelligent Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09

5. M.S. Islama, P. Prabhakar, “Interlaminar strengthening of multidirectional laminates using polymeradditive manufacturing”, Materials and Design 133 (2017), pp.332–339

6. Sathish, T. “Performance measurement on extracted bio-diesel from waste plastic”, Journal of Applied Fluid Mechanics, vol. 10, pp. 41-50, 2017.

7. T. Sathish, “Experimental investigation on degradation of heat transfer properties of a black chromium-coated aluminium surface

solar collector tube”, International Journal of Ambient Energy, Taylor and Francis Publishers, Vol. 39, doi: https://doi.org/10.1080/01430750.2018.1492456.

8. Sudhir Tiwari1, J. Bijwe1 and S. Panier, “Strengthening of a Fibre-Matrix Interface: A Novel Method Using Nanoparticles”,

Bijwe and S. Panier: Strengthening of a Fibre-Matrix, pp.1-8 9. Karthick, S. "Semi supervised hierarchy forest clustering and knn based metric learning technique for machine learning system",

Journal of Advanced Research in Dynamical and Control Systems, vol. 9, no. Special Issue 18, pp. 2679-2690, 2017.

10. Larissa Gorbatikh, Stepan V. Lomov, Ignaas Verpoest, “Nano-engineered composites: a multiscale approach for adding toughness to fibre reinforced composites”, Larissa Gorbatikh et al. / Procedia Engineering 10 (2011) 3252–3258.

11. Sathish, T., Jayaprakash, J. “Multi period disassembly-to-order of end-of-life product based on scheduling to maximise the profit

in reverse logistic operation”, International Journal of Logistics Systems and Management, vol. 26, no. 3, pp. 402-419, 2017.

12. T. Sathish, “Heat Transfer Analysis of Nano-Fluid Flow in a converging Nozzle with different aspect Ratios”, Journal of New

Materials for Electrochemical Systems, Vol. 20, pp. 161-167, 2017.

13. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

6-9

3.

Authors: S.Krishnamoorthi, D.Dinesh, R.Karthikeyan, G.Manikandan

Paper Title: Heat Treatment Parameters to Optimize Hardness Behavior of Carbon Steel using Taguchi

Technique

Abstract : There are so many engineering materials exists in this Universe. However, here we are discussing

steel. We go through the different parameter, which is used in this experiment, for instance, heat treatment

temperature. The selected material was medium carbon steel .the maximum temperature is around 1370 degrees

so I selected the three levels of temperatures below the melting point i.e. 800, 900, 1000. The 800 degrees the

heating time is 1hr, the 900degrees the heating time is 1.15hr, the 1000 degrees the heating time is 1.30hr. The

hardness value is good at the 1000 degrees i.e. is 80 its coolant is salt water. The method was used in this

process is the Taguchi method with the L9 array with 3 levels and 4 factors. The result and the calculated values

are drawn by using the Minitab app in this app the ANOVA type is the general linear model. The percentage of

variation i.e. Rsqu is between the 0-100percent. The good Rsqu values are 90-100%. I got a value of 95.06%.

Therefore, the chosen process parameters are good. The two types of graphs are plotted i.e. mean of signal noise

ratio and main effect plot for signal noise ratio, the next graph is mean of means and main effect plot for SN

ratio.

Keywords: heat treatment, melting point, temperature, hardness, taguchi methods, minitab, ANOVA,

S/Nratio, Rsqu.

References: 1. Sahira Hassan Ibrahim ,Sahar Hussein Ahmed, Iman Ahmed Hameed Evaluated of Mechanical Properties for Aluminium Alloy Using

Taguchi Method International Journal of Modern Studies in Mechanical Engineering (IJMSME) Volume 2, Issue 1, 2016, PP 29-37

ISSN 2454-9711 (Online). 2. Ajay Kumar1, Prof. A.R.Ansari2, Dr. B.N.Roy3, Prof. subodh kumar4 Heat Treatment Parameter Optimization Using Taguchi

Technique Ajay Kumar et al IJSRE Volume 4 Issue 10 October 2016

3. V.C.Uvaraja Heat Treatment Parameters to Optimize Friction and Wear behavior of Novel Hybrid Aluminium Composites Using Taguchi Technique.

4. Dr.S.S.Deshmukha, S.R.Thakareb Implementation of Taguchi Technique on Heat Treatment Process of Pinion Gear. Dr.S.S.Deshmukh,

S.R.Thakare International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.592-59.

5. Vimal B PatelP1P, Smit D ParikhP2P, Parin R PatelP3P, Mohit S MishraP4 Investigating the Heat Treatment Parameters of En-31

10-14

Using Taguchi Metho - International Journal of Innovative Science, Engineering & Technology, Vol. 5 Issue 5, May 2018 ISSN (Online) 2348 – 7968 .

6. Palguna Kumar1, Prateek K B2, M Shilpa3, C S Chethan Kumar4 Optimization of Heat Treatment Parameters for the A2024 aluminum

Alloy Using Taguchi’s Orthogonal Array Approach International Journal of Advanced Engineering & Innovative Technology (IJAEIT) Volume 1, Issue 3, August-2014.

7. Rajesh Mahto1, Sunny Kumar2, Shashi Kumar3, Prof. Prakash kumar4 Optimization of Heat Treatment Process Parameter using

Taguchi and Fuggy Logic Approach in Bearing Manufacturing Industry International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -.

8. V.K. Murugan, 2Dr.P. Koshy Mathews Optimization of Heat Treatment Processes Using Taguchi’s Parameter Design Approach

International Journal of Research in Mechanical Engineering Volume 1, Issue 1, July-September, 2013, pp. 16-21, IASTER 2013 www.iaster.com, ISSN Online: 2347-5188 Print: 2347-8772.

9. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc based

artificial neural network (ANN)”, Current Medical Imaging Reviews, vol. 13, no. 3, pp. 223-230, 2017. DOI: 10.2174/1573405612666160617082639

10. Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of Intelligent

Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09 11. Sathish, T. “Performance measurement on extracted bio-diesel from waste plastic”, Journal of Applied Fluid Mechanics, vol. 10, pp. 41-

50, 2017.

12. T. Sathish, “Experimental investigation on degradation of heat transfer properties of a black chromium-coated aluminium surface solar collector tube”, International Journal of Ambient Energy, Taylor and Francis Publishers, Vol. 39, doi:

https://doi.org/10.1080/01430750.2018.1492456.

13. Sathish, T., Jayaprakash, J. “Multi period disassembly-to-order of end-of-life product based on scheduling to maximise the profit in reverse logistic operation”, International Journal of Logistics Systems and Management, vol. 26, no. 3, pp. 402-419, 2017.

14. T. Sathish, “Heat Transfer Analysis of Nano-Fluid Flow in a converging Nozzle with different aspect Ratios”, Journal of New Materials

for Electrochemical Systems, Vol. 20, pp. 161-167, 2017.

15. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in

Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

4.

Authors: D. Stalin Alex, P. Subramanian, S. Subashini, T. Kumaresan, B. Stalin

Paper Title: Counterfeit Currency Detection Based on Fluorescence in HSV Color Space

Abstract : Fake currency notes are so perfect nowadays that it is very tough to differentiate them from original currency

notes. Due to technological advancements, it has become very easy for counterfeiter’s to imitate all the characteristics of the

original currency except the illumination or glow that occurs when the currency notes are induced to UV radiation. The

proposed approach is based on this selective feature of color illumination which can be identified when the captured image is

converted into HSV color space. Histogram equalization is done in HSV color space which collectively performs noise

reduction, filtering and sharpening to enhance image quality for effective use in applications. The original currency will have

high intensity color values compared to the fake currency that can be verified by using the thresholding technique. Thus, the

proposed method is very simple, efficient and time saving.

Keywords: HSV (Hue, Saturation and value), color space, histogram equalization, threshold, fake Currency.

References:

1. R. W. Hardin, “Optical Tricks Designed to Foil Counterfeiters”. OE Reports Number 191, International Society for Optical

Engineering, November 1999.

2. S. J. Murdoch, “Software Detection of Currency”. University of Cambridge Computer Lab, 2004. 3. Robert Fiete, “Identifying falsified images can be straightforward if you know a few trick”, from oemagazine, SPIE Newsroom, 31

January 2005.

4. L. Burke Files, “A manual for the Identification of Counterfeit Currency, Credit Cards, Traveler's Cheques and Bank Checks”. Kumar Parasuraman, Member, IEEE and P.Vasantha Kumar, 2017.

5. A.Ms.Trupti Pathrabe and B.Dr. N.G.Bawane (2010), Paper Currency Recognition System Using Characteristics Extraction and

Negativity Correlated NN Ensemble. Int. Journal of Latest Trends in Computing 1(2), pp. 121-124. 6. A. Frosini, M. Gori, P. Priami (1996), A Neural Network-Based Model for Paper Currency Recognition and Verification. IEEE

Transactions on Neural Network 7(6), pp. 1-20.

7. Dr.Kenji Yoshida, Mohammed Kamruzzaman, Faruq Ahmed Jewel, Raihan Ferdous Sajal, “Design and Implementation of a Machine Vision Based but Low Cost Stand Alone System for Real Time Counterfeit Bangladeshi Bank Notes Detection” IEEE 1-4244-1551-

9/07/2007.

8. Euisun Choi, Jongseok Lee and Joonhyun Yoon, “Feature Extraction for Bank Note Classification Using Wavelet Transform” @ISBN

ISSN: 1051-4651, 0-7695-2521-0, IEEE, 2006.

9. Ahmadi and S.Omatu,” A Methodology to Evaluate and Improve Reliability in Paper Currency Neuro-Classifiers” Proceedings 2003

IEEE International Symposium on Computational intelligence in Robotics and Automation , Kobe, Japan, July 16-20,2003. 10. S.Sural, G.Qian and S.Pramanik, “A Histogram with Perceptually Smooth Color Transition for Image Retrieval”, Proc. Fourth Int.

Conf. on CVPRIP, Durham, 2002.

11. M.Swain and D.Ballard (1991), Color Indexing. Int. Journal of Computer Vision 79(1), pp. 11-32. 12. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc based


13. Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of

Intelligent Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09 14. Sathish, T. “Performance measurement on extracted bio-diesel from waste plastic”, Journal of Applied Fluid Mechanics, vol. 10, pp.

41-50, 2017.

15. T. Sathish, “Experimental investigation on degradation of heat transfer properties of a black chromium-coated aluminium surface solar collector tube”, International Journal of Ambient Energy, Taylor and Francis Publishers, Vol. 39, doi:

https://doi.org/10.1080/01430750.2018.1492456.

16. Sathish, T., Jayaprakash, J. “Multi period disassembly-to-order of end-of-life product based on scheduling to maximise the profit in reverse logistic operation”, International Journal of Logistics Systems and Management, vol. 26, no. 3, pp. 402-419, 2017.

17. T. Sathish, “Heat Transfer Analysis of Nano-Fluid Flow in a converging Nozzle with different aspect Ratios”, Journal of New

Materials for Electrochemical Systems, Vol. 20, pp. 161-167, 2017. 18. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress

in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018.

15-18

5.

Authors: T. Kumaresan, P. Subramanian, D. Stalin Alex, M.I. Thariq Hussan, B. Stalin

Paper Title: Email Image Spam Detection Using Fast Support Vector Machine and Fast Convergence Particle

Swarm Optimization

Abstract : Today’s internet scenario, spam email is a major problem of internet users. The spam field has two

different types namely email text spam and image spam. Now a day’s email spam filters are available in market,

but the filters are capable to detect the text based spam only. Spammers are using intelligent ways to bypass the

spam filters like embedding the spam text in an image so that spam filters are not able to detect the image spam.

This paper analyses the various attributes of image spam with the careful attention given with the existing

system. The proposed method uses the fast convergence particle swarm optimization technique which uses the

diversity location of each particle by presenting a new classifier. Experimental results show that proposed

method has achieved better accuracy than the other existing methods.

Keywords: Image spam detection, Email spam, Support Vector Machine (SVM), Standard Particle Swarm

Optimization (PSO), Fast Convergence Particle Swarm Optimization (FCPSO).

References:

1. T. Kumaresan and C. Palanisamy (2017), E-mail spam classification using S-Cuckoo search and support vector machine. International

Journal of Bio-Inspired Computation 9(3), pp. 142-156.

2. T. Kumaresan, S. Sanjushree and C. Palanisamy (2014), Image spam detection using color features and K-Nearest neighbor classification, International Journal of Computer, Information, Systems and Control Engineering 8(10), pp. 1746-1749.

3. T. Kumaresan, S. Saravanakumar and R. Balamurugan (2017), Visual and Textual Features Based Email Spam Classification Using

S-Cuckoo Search and Hybrid Kernel Support Vector Machine. Cluster Computing, Springer, DOI : https://doi.org/10.1007/s10586-017-1615-8

4. Hayati, Pedram, and Vidyasagar Potdar, “Evaluation of spam detection and prevention frameworks for email and image spam’: a state

of art”, In Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services, 2008, pp. 520-527.

5. Mehta, Bhaskar, Saurabh Nangia, Manish Gupta, and Wolfgang Nejdl, “Detecting image spam using visual features and near

duplicate detection”, In Proceedings of the 17th international conference on World Wide Web, 2008, pp. 497-506. 6. Vapnik, Vladimir Naumovich, and Vlamimir Vapnik, Statistical learning theory, New York: Wiley, Vol. 2, 1998.

7. Hsia, Jen-Hao, and Ming-Syan Chen, “Language-model-based detection cascade for efficient classification of image-based spam e-

mail”, In Multimedia and Expo, 2009. ICME 2009. IEEE International Conference, 2009, pp. 1182-1185. 8. Attar, Abdolrahman, Reza Moradi Rad, and Reza Ebrahimi Atani (2013), A survey of image spamming and filtering techniques.

Artificial Intelligence Review 40(1), pp. 71-105.

9. Zhong, Jian, YiLu Zhou, and Wei Deng (2013), Filtering image-based spam using multifractal analysis and active learning feedback-driven semi-supervised support vector machine. In Conference Anthology, IEEE, pp. 1-5.

10. Caruana, Godwin, Maozhen Li, and Yang Liu (2013), An ontology enhanced parallel SVM for scalable spam filter

training. Neurocomputing 108, pp. 45-57. 11. Gleeson, Matt, David Hoogstrate, Sandy Jensen, Eli Mantel, Art Medlar, and Ken Schneider (2004). ‘Method and apparatus for

filtering email spam based on similarity measures’, U.S. Patent Application 10/846,723.

12. Zhou, Bing, Yiyu Yao, and Jigang Luo 2010. ‘A three-way decision approach to email spam filtering’, In Advances in Artificial Intelligence, pp. 28-39. Springer Berlin Heidelberg.

13. Sahu, Amaresh, Sushanta Kumar Panigrahi, and Sabyasachi Pattnaik (2012), Fast Convergence Particle Swarm Optimization for

Functions Optimization. Procedia Technology 4, pp. 319-324. 14. G. Fumera, I. Pillai, and F. Roli (2006), Spam Filtering based on the Analysis of Text Information Embedded into Images. Journal of

Machine Learning Research (special issue on Machine Learning in Computer Security) 7, pp. 2699-270.

15. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc based artificial neural network (ANN)”, Current Medical Imaging Reviews, vol. 13, no. 3, pp. 223-230, 2017. DOI:

10.2174/1573405612666160617082639 16. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free Space

Optical Communication”, Optical Memory and Neural Networks (Information Optics), vol. 27, no. 2, pp. 81-88, 2018. DOI:

10.3103/S1060992X18020029 17. Sathish, T. “Performance measurement on extracted bio-diesel from waste plastic”, Journal of Applied Fluid Mechanics, vol. 10, pp.

41-50, 2017.

18. Sathish, T., Jayaprakash, J. “Optimizing Supply Chain in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 07, pp. 551-560, 2017.

19. Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse

Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019. 20. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile

steel”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 08,Issue 1, pp. 1119-1126,

2018. 21. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress

in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

19-22

6.

Authors: Benit Benny C, Soptavik Samanta, Pal Pandian P, Anil Raj

Paper Title: Parametric Investigation on the Tool Wear While Performing Face Milling on Inconel 718 Using

Round Insert

Abstract : Machining of Inconel 718 has become a material of great purpose in the machining industry. Since

Inconel is considered to be a super alloy which possess high material property such as high thermal fatigue, high

strength and high resistance to corrosion it is thus considered to be a material which is hard to machine. This

paper gives an overview on the tool wear that takes place while machining. Machining was carried out in a CNC

milling machine with the help of a tungsten carbide circular insert. The cutting parameters taken into

consideration were cutting speed, feed rate and depth of cut. Tool wear was analysed with the help of tool

23-27

https://doi.org/10.1007/s10586-017-1615-8

https://doi.org/10.1007/s10586-017-1615-8

maker’s microscope. Statistical analysis was done on the MINITAB 18 software by using regression analysis.

The regression analysis was carried out by using Response Surface Methodology (RSM) the mathematical

model for each individual response has been developed from regression equations considering analysis of cutting

parameters as independent variables which was found to be significantly accurate.

Keywords: Face milling, Inconel 718, RSM, Round insert

References:

1. Anil A. Chavan, P.V. Deshmukh, “Prediction of tool life of different coated cutting tools during machining of Inconel 718”,

International Research Journal of Engineering and Technology (IRJET), vol.04, pp. 941-949, 2017. 2. Dr P. Pal Pandian, Ivan Sunit Rout, “Parametric investigation of machining parameters in determining the machinability of

Inconel 718 using taguchi technique and grey relation analysis”, International Conference on Robotics and Smart Manufacturing

(RoSMa2018), Published by Elsevier, vol. 133, pp. 786-792, 2018.

3. T. Tamizharasan, N. Senthilkumar, “Numerical simulation of effects of machining parameters and tool geometry using

DEFORM-3D: Optimization and experimental validation”, World Journal of Modelling and Simulation, vol. 10, pp. 49-59, 2014.

4. A.Kiran Kumar and Dr. P. Venkataramaiah, “Optimization of Process parameters in hot machining of Inconel 718 alloy using FEM”,International Journal of Applied Engineering Research ISSN, vol. 13, pp. 2158-2162, 2018.

5. Nandkumar N. Bhopale, Raju S. Pawade, “Modeling and Analysis of Ball End Milling Parameters of Inconel 718 Cantilevers

Using RSM’’, International Journal of Innovative Research in Science, Engineering and Technology, vol. 03, pp. 2319-8753, 2014.

6. M R Soleymani Yazdi, A Khorram, “Modelling and Optimization of Milling Process by using RSM and ANN Methods”, IACSIT

International Journal of Engineering and Technology, vol. 2, pp. 1793-8236, 2010.

7. M. Anthony Xaviora, M.Manohar, “Tool Wear Assessment During Machining of Inconel 718”, 13th Global Congress on

Manufacturing and Management, vol. 174, pp. 1000-1007, 2017.

8. Duong Xuan-Truong,”Effect of cutting conditions on tool wear and surface roughness during machining of Inconel 718”,

International Journal of Advanced Engineering Technology, vol. 04, pp. 108-112, 2013. 9. Mayur N. Trimbakwade, “Face Milling Process Parameters Optimization for Inconel 718 by Taguchi Method”, International

Journal of Research in Advent Technology, vol. 5, pp. 2321-9637, 2017.

10. Ivan Sunit Rout* and Dr P. Pal Pandian, “Relection of cutting parameters for the machinability of Inconel 718 usin-g Grey Relational Analysis”, 3rd International Conference on Design, Analysis, Manufacturing and Simulation (ICDAMS 2018), vol.

172, pp. 1-5, 2018.

11. S.A. Khan, S.L. Soo, D.K. Aspinwall, “Tool wear/life evaluation when finish turning Inconel 718 using PCBN tooling”,5th CIRP Conference on High Performance Cutting 2012, vol. 01, pp. 283–288, 2012.

12. Chen Zhang, Laishui Zhou & Xihui Liu, “Investigations on model-based simulation of tool wear with carbide tools in milling

operation”, The International Journal of Advanced Manufacturing Technology, vol. 06, pp. 1373–1385, 2012. 13. A. Attanasio, E Ceretti, “3D finite element analysis of tool wear in machining”, International journal of Elsevier, Vol. 57, pp. 61-

64, 2008

14. Ramesh Kannan C, Prathap J, “Experiment investigation of tool wear in turning of Inconel 718 material – Review”, International Journal of Advance Engineering and Research Development, vol. 02, pp. 2348-4460, 2015.

15. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc

based artificial neural network (ANN)”, Current Medical Imaging Reviews, vol. 13, no. 3, pp. 223-230, 2017. DOI: 10.2174/1573405612666160617082639

16. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free

Space Optical Communication”, Optical Memory and Neural Networks (Information Optics), vol. 27, no. 2, pp. 81-88, 2018. DOI: 10.3103/S1060992X18020029

17. Sathish, T. “Performance measurement on extracted bio-diesel from waste plastic”, Journal of Applied Fluid Mechanics, vol. 10,

pp. 41-50, 2017. 18. Sathish, T., Jayaprakash, J. “Optimizing Supply Chain in Reverse Logistics”, International Journal of Mechanical and Production

Engineering Research and Development, Vol. 07, pp. 551-560, 2017.

19. Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019.

20. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high

Tensile steel”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 08,Issue 1, pp. 1119-1126, 2018.

21. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018.

7.

Authors: P.S. Senthil Kumar, S. Marichamy, B. Stalin, M. Ravichandran, K. Vinothbabu

Paper Title: Corrosion and Wear Properties on Synthesized Silicon Carbon Nanotubes

Abstract : The purpose of adding Carbon nanotubes (CNT) to metal is to obtain excellent atomic structure and

material properties. The strength, wear and corrosion resistance was improved when the addition of carbon

nanotubes to the silicon metal. A uniform mixing of carbon nanotubes were achieved by molecular level mixing

through the Spark plasma sintering (SPS) process. The interfacial bonding strength is also improved by this

process. The material properties and wear properties were also studied. The most influential factor was found by

analysis of variance during wear behavior experiment. The deflection test was also conducted under various load

conditions.

Keywords: Carbon nanotubes, Spark plasma sintering, Wear behavior, Corrosion resistance, Deflection test.

References:

1. H. Kwon, M. Estili, K. Takagi, T. Miyazaki and A. Kawasaki (2009). Combination of hot extrusion and spark plasma sintering

for producing carbon nanotube reinforced aluminum matrix composites. Carbon 47, pp. 570-577.

2. Y. Zhang, T. Ichihashi, E. Landree, F. Nihey, and S. Iijima (1999). Heterostructures of single-walled carbon nanotubes and

28-32

https://www.sciencedirect.com/science/journal/18770509/133/supp/C


https://www.sciencedirect.com/science/article/pii/S1877705817302527#!


carbide nanorods. Science 285, pp. 1719-1722. 3. J.-z. Liao, M.-J. Tan, and I. Sridhar (2010). Spark plasma sintered multi-wall carbon nanotube reinforced aluminum matrix

composites. Materials & Design 31, pp. S96-S100.

4. K. Chu, C.-c. Jia, L.-k. Jiang, and W.-s. Li (2013). Improvement of interface and mechanical properties in carbon nanotube reinforced Cu-Cr matrix composites. Materials & Design 45, pp. 407-411.

5. C. Guiderdoni, C. Estournès, A. Peigney, A. Weibel, V. Turq, and C. Laurent (2011). The preparation of double-walled carbon

nanotube/Cu composites by spark plasma sintering, and their hardness and friction properties. Carbon 49, pp. 4535-4543. 6. S. C. Tjong (2013). Recent progress in the development and properties of novel metal matrix nanocomposites reinforced with

carbon nanotubes and graphene nanosheets. Materials Science and Engineering: R: Reports 74, pp. 281-350.

7. K. Chu, C.-c. Jia, L.-k. Jiang, and W.-s. Li (2013). Improvement of interface and mechanical properties in carbon nanotube reinforced Cu–Cr matrix composites. Materials & Design 45, pp. 407-411.

8. B. Stalin, M. Meignanamoorthy, M. Ravichandran (2018). Synthesis of metal matrix composites and alloys by mechanical

alloying: A Review. IOP Conf. Series: Materials Science and Engineering 402, pp.1-6, 012097. 9. M. Sangeetha and S. Prakash (2017). Experimental investigation of process parameters in drilling LM25 composites coated with

multi wall carbon nano tubes using sonication process. Arch. Metall. Mater. 62( 3), pp. 1761-1770.

10. K.L. Meena, A. Manna S.S. Banwait and Jaswanti (2013). Parametric Effects during Nonconventional Machining of PR-AL-SiC-MMC, s by CNC Wire cut EDM. International Journal of Engineering and Innovative Technology 3(1), pp. 341-345.

11. F. Wang, G. Jin, and X. Guo (2006). Formation mechanism of Si nanowires via carbothermal reduction of carbonaceous silica

xerogels. Journal of Physical Chemistry B 110(30), pp. 14546–14549. 12. D. Pan, Z. Shuyuan, Y. Chen, and J.G. Hou (2002). Hydrothermal preparation of long nano wires of vanadium oxide. Journal of

Materials Research 17(8), pp.1981–1984.

13. C.N.R. Rao, G. Gundiah, F.L.Deepak, A. Govindaraj and A.K. Cheetham (2004). Carbon-assisted synthesis of inorganic nanowires. Journal of Materials Chemistry 14(4), pp. 440–450.

14. X.C. Wu, W.H. Song and B. Zhao et al (2000). Synthesis of coaxial nanowires of silicon nitride sheathed with silicon and silicon

oxide. Solid State Communications 115(12), pp.683–686.

15. W. Han, S. Fan, Q. Li, B. Gu, X. Zhang, and D. Yu (1997). Synthesis of silicon nitride nanorods using carbon nanotube as a

template. Applied Physics Letters 71(16), pp. 2271–2273.

16. S. D. Kumar, M. Ravichandran (2018). Synthesis, Characterization and Wire Electric Erosion Behaviour of AA7178-10 wt.% ZrB2 Composite. Silicon 10(6), pp. 2653-2662.

17. B. Stalin, M. Ravichandran, S. Arivukkarasan, V. Mohanavel (2018). Weight Loss Corrosion Studies of Aluminium-LM4

Reinforced With Alumina Silicate (Al2O3SiO2) Particulates Composites in Sodium Chloride (NaCl) Solution. International Journal of Mechanical and Production Engineering Research and Development, Special Issue, pp. 329-336.

18. J. Hwang, T. Yoon, S. H. Jin, J. Lee, T.-S. Kim, S. H. Hong et al. (2013). Enhanced Mechanical Properties of Graphene/Copper

Nanocomposites Using a Molecular-Level Mixing Process. Advanced Materials 25, pp. 6724-6729. 19. D. H. Nam, S. I. Cha, B. K. Lim, H. M. Park, D. S. Han, and S. H. Hong (2012). Synergistic strengthening by load transfer

mechanism and grain refinement of CNT/Al–Cu composites. Carbon 50, pp. 2417-2423.

20. K. Kondoh, T. Threrujirapapong, H. Imai, J. Umeda, and B. Fugetsu (2009), Characteristics of powder metallurgy pure titanium matrix composite reinforced with multi-wall carbon nanotubes. Composites Science and Technology 69, pp. 1077-1081.

21. D. Prasai, J.C. Tuberquia, R.R. Harl, G.K. Jennings, and K I. Bolotin (2012). Graphene: corrosion-inhibiting coating. ACS Nano. 6, pp. 1102-1108.

22. H. J. Choi, S.M. Lee and D.H. Bae (2010). Wear characteristic of aluminum-based composites containing multi-walled carbon

nanotubes. Wear 270, pp. 12-18. 23. S.-m. Zhou, X.-b. Zhang, Z.-p. Ding, C.-y. Min, G.-l. Xu, and W.-m. Zhu (2007). Fabrication and tribological properties of

carbon nanotubes reinforced Al composites prepared by pressure less infiltration technique. Composites Part A: Applied Science

and Manufacturing 38, pp. 301-306. 24. B. Stalin, G.T. Sudha, M. Ravichandran (2018). Investigations on Characterization and Properties of Al-MoO3 Composites

Synthesized Using Powder Metallurgy Technique. Silicon 10(6), pp. 2663- 2670.

25. S. R. Dong, J. P. Tu, and X. B. Zhang (2001). An investigation of the sliding wear behavior of Cu-matrix composite reinforced by carbon nanotubes. Materials Science and Engineering: A 313, pp. 83-87.

26. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion

Fourier transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research, ICETIETR 2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014

27. Karthick, S., Perumal Sankar, S., and Arul Teen, Y.P. “Trust-distrust protocol for secure routing in self-organizing networks”, In

Proceedings of 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research, ICETIETR 2018, art. no. 8529016, 2018. DOI: 10.1109/ICETIETR.2018.8529016

28. Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”,

International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 11, pp. 2263-2271, 2018. 29. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”,

International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018.

30. Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of Mechanical and Production Engineering Research and Development, Special Issue, pp. 582-590, 2018.

31. Sathish, T., Vijayakumar, M.D., Krishnan Ayyangar, A. “Design and Fabrication of Industrial Components Using 3D Printing”,

Materials Today Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14489-14498, 2018.



8.

Authors: A.Ravinthiran, D.K.Jayanth Naidu, S.Hareesh, N.Gurusubramani, K.S.Athvaith Muthukumar

Paper Title: Investigation of Heat Dissipation between Dimple and Normal Silencer made of Chrome Steel

Abstract : The purpose of the exhaust system in all automobile vehicle is simply to channel the fiercely hot

products of fuel combustion away from the engine or generator and the car's occupants and out into the

atmosphere. The main purpose of the exhaust system is to reduce the noise. The gases that are getting exhausted

from the engine will be at high speeds. Because of the opening and shutting of the exhaust valves in each cycle

of combustion, the gas pressure changes from high to low, this cause vibration and hence produces heavy sound.

Due to the continuous operation of the engine the silencer becomes too hot. It will injure seriously if anyone

touches it with naked body.

The dimple silencer will have increase in the rate of heat dissipation, because of the dimples provided on the

surface of the silencer. The dimples on the silencer will perform a function similar to that of the cooling fins of

an IC engine. The dimples act as the extended surface over the surface of the silencer & provides better heat

transfer rate. In this work clear Analysis is made using Ansys software for Normal & Dimple Silencer and

Results are compared with the rate of Heat dissipation.

33-40

Keywords: Exhaust system, silencer, cooling fins, dimples

References:

1. M. H. ShojaefardAutomotive Engineering Department , Iran University of Science and Technology , Tehran, Iran,A. R. Noorpoor,D. A. Bozchaloe &M. Ghaffarpour , 24 Feb 2007, Pages 627-644, Transient Thermal Analysis of Engine exhaust valve.

2. Sweta Baruah, Sushovan Chatterjee, Structural analysis for exhaust gas flow through an elliptical chamber muffler under static and

dynamic loading condition, Advances in Modelling and Analysis B Vol. 61, No. 2, June, 2018, pp. 92-98 3. Avinash Kumar Agrawal, Shrawan Kumar Singh, Shailendra Sinha1 and Mritunjay Kumar Shukla June 2004,effect of EGR on the

exhaust gas temperature and exhaust opacity in compression ignitionAccepted for Publication, August 2010. (ISSN # 0306-2619).

4. Kavita H. Dhanawade, Vivek K. Sunnapwar and Hanamant S. Dhanawade, 07 January 2014.Thermal Analysis of Square and Circular Perforated Fin Arrays by Forced Convection Heat dissipation is a drastic issue to tackle due to continued integration, miniaturization,

compacting and lightening of equipment.Accepted 07 January 2014, Available online 01 February 2014, Special Issue-2, (February 2014)

5. Juhi Sharaf, Jul-Aug 2013, Exhaust Emissions and Its Control Technology for an Internal Combustion Engine, ISSN: 2248-9622, Vol.

3, Issue 4, Jul-Aug 2013, pp.947-960 6. Dhirajkumar Dr. Prashant, D. Deshmukh, June 2016, Thermal Analysis of Two Wheeler Exhaust Silencer using Computer Aided

Engineering,June 2016, Volume 4, Issue 6, ISSN 2349-4476. 87

7. Ong kok seng, C.F. Tan, Koon Chun Lai, Kia Hock Tan, September 2016 , Heat spreading and heat transfer coefficient with fin heat sink.

8. Mahesh S. Vasava, P. V. Jotaniya, Heat transfer analysis in automotive exhaust system, Vol. 4, Issue 6, June 2015.

9. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings

of 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET

2017, 2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688

10. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free Space Optical Communication”, Optical Memory and Neural Networks (Information Optics), vol. 27, no. 2, pp. 81-88, 2018. DOI:

10.3103/S1060992X18020029

11. Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”, International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 11, pp. 2263-2271, 2018.

12. Sathish, T., Jayaprakash, J. “Optimizing Supply Chain in Reverse Logistics”, International Journal of Mechanical and Production

Engineering Research and Development, Vol. 07, pp. 551-560, 2017. 13. Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse

Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019.

14. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile steel”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 08,Issue 1, pp. 1119-1126,

2018.


9.

Authors: Manyam Chidvilas, Raj Mohan R, S. Raghuraman

Paper Title: Machinability Study on Al-SiC Metal Matrix Composite (MMC) using Electrical Discharge

Machining (EDM)

Abstract: In today’s world of manufacturing, aluminum metal matrix composites are playing a vital role in

enhancing the properties of different automobile and aircraft components. The post-production processing of the

Al-SiC Metal Matrix Composites (MMC) is comparatively tricky because of dispersion of SiC particles in the

aluminum matrix. One such processing method for machining the Al-SiC metal matrix composite to produce

acceptable output properties is Electrical Discharge Machining (EDM). Generally, when abrasive particulates

such as SiC are dispersed in the aluminum matrix, it is challenging to follow conventional machining operations

due to high tool wear rate, frequent mechanical shocks on the tool and effects like delamination on the

workpiece where the unconventional machining process like EDM is used since there is no direct contact

between tool and the workpiece. In this paper, a unique powder metallurgy approach is attempted for fabricating

four different compositions (2%, 4%, 6%, 8% of SiC) of Al-SiC composite and machinability study is carried

out through EDM to reveal the effect of SiC particles on machinability of Al-SiC composite. The experimental

investigation is made on EDM input parameters like a pulse on time, pulse off time and current, and their

influence on machinability of Al-SiC composite is analyzed. The output results like Material Removal Rate

(MRR) and Surface Roughness are obtained for different compositions of Al-SiC composite, and its influencing

input parameters are observed..

Keywords: EDM, MMC, MRR, Surface Roughness.

References:

1. L. W. Moon, Sintering of advanced materials Part III, vol. 12, no. 2. 2008, pp. 228-236.

2. M. Singla, L. Singh, and V. Chawla, “Study of Wear Properties of Al-SiC Composites,” Journal of Minerals and Materials Characterization and Engineering, vol. 08, no. 10, pp. 813–821, 2015.

3. S. Soltani, R. Azari Khosroshahi, R. Taherzadeh Mousavian, Z. Y. Jiang, A. Fadavi Boostani, and D. Brabazon, “Stir casting

process for the manufacture of Al–SiC composites,” Rare Metals, vol. 36, no. 7, pp. 581–590, 2017. 4. S. G. Shelvaraj and S. A. Naveen, “Optimization of EDM parameters for Al - tic composites prepared through powder metallurgy

route,” Mechanika, vol. 24, no. 1, pp. 135–142, 2017.

5. S. Harpreet and S. Amandeep, “Effect of Pulse On / Pulse Off Time On Machining Of AISI D3 Die Steel Using Copper And Brass Electrode In EDM,” RESEARCH INVENTY: International Journal of Engineering and Science, vol. 1, no. 9, pp. 19–22,

2012.

6. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging

Technologies, ICAMMAET 2017, 2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688 7. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free

41-50

https://www.tandfonline.com/author/Shojaefard%2C+M+H

https://www.tandfonline.com/author/Noorpoor%2C+A+R

https://www.tandfonline.com/author/Bozchaloe%2C+D+A

https://www.tandfonline.com/author/Bozchaloe%2C+D+A

https://www.researchgate.net/profile/Ong_Kok_seng

https://www.researchgate.net/scientific-contributions/2116163487_CF_Tan

https://www.researchgate.net/profile/Koon_Chun_Lai

https://www.researchgate.net/profile/Kia_Hock_Tan

Space Optical Communication”, Optical Memory and Neural Networks (Information Optics), vol. 27, no. 2, pp. 81-88, 2018. DOI: 10.3103/S1060992X18020029

8. Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”,

International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 11, pp. 2263-2271, 2018. 9. Sathish, T., Jayaprakash, J. “Optimizing Supply Chain in Reverse Logistics”, International Journal of Mechanical and Production


10. Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019.

11. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high

Tensile steel”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 08,Issue 1, pp. 1119-1126, 2018.



10.

Authors: T. Babu, R.V. Aswin, P. Aravind, V. Prasanna

Paper Title: Design and Transient Thermo-Structural Analysis of Wind Turbine Disc Brake

Abstract: Disk brake is a major component in wind turbine to reach rest position. While applying braking effect

disc brake receives Kinetic or static force due to powerful pressure applied on it. While driving the wind turbine,

the driving shaft receives increase in sliding velocity and specific pressure. Due to that it receives high wear and

increase in temperature. In order to solve this problem, computational methods are used to simulate to identify the

problems and do necessary improvements during braking. The numerical simulation is carried out to predict the

thermo-structural behavior of the brake disc. This analysis is carried for the two different materials such as steel

and aluminum alloy. This analysis is simulated using finite element code called ANSYS. This study shows how

the heat generated by the brake disc influence the structural properties of disc and to predict the fatigue life of the

brake disc. Comparison of existing material with aluminum alloy brake disc has been discussed.

Keywords: EDM, MMC, MRR, Surface Roughness.

References: 1. G. Cueva, A. Sinatora, W.L. Guesser, A.P. Tschiptschin, (2003) ‘Wear resistance of cast irons used in brake Disc rotors’, WEAR,

255, 1256-1260. 2. Lee, K. and Barber, J.R. (2006) ‘Frictionally-Excited Thermo elastic Instability in Automotive Disc Brakes’, ASME J. Tribology,

vol. 128, pp. 718.

3. Yun-Bo Yi (1993) ‘Finite Element Analysis of Thermo elastodynamic Instability Involving Frictional Heating’ ASME J. Tribology, vol. 115, pp.607-614.

4. M. Eltoukhy, S. Asfour, M. Almakky, C. Huang Thermoelastic Instability in Disc Brakes: Simulation of the Heat Generation Problem

5. T Nakatsuji, K Okubo, T Fujii, M Sasada, Y Noguchi (2002) ‘Study on Crack Initiation at Small Holes of One-piece Brake Discs’.

Society of Automotive Engineers, Inc 2002-01-0926

6. S. P. Jung, T. W. Park, J. H. Lee, W. H. Kim, and W. S Chung (2010) ‘Finite Element Analysis of Thermal elastic Instability of Disc

Brakes’, Vol II WCE 7. H. Mazidi, S Jalalifar, et al, (2011) ‘Mathematical Modeling of Heat Conduction in a Disc Brake System During Braking’, Asisn

journal of Applied Science 4(2): pp.119-136

8. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET

2017, 2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688

9. Karthick, S., Perumal Sankar, S., and Arul Teen, Y.P. “Trust-distrust protocol for secure routing in self-organizing networks”, In Proceedings of 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research,

ICETIETR 2018, art. no. 8529016, 2018. DOI: 10.1109/ICETIETR.2018.8529016


11. Sathish, T., Jayaprakash, J. “Optimizing Supply Chain in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 07, pp. 551-560, 2017.


Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019. 13. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile


2018. 14. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress


51-56

11.

Authors: M.D.Vijayakumar, G.Gopalaramasubramaniyan, V. Dhinakaran

Paper Title: Microstructural Investigation of Is513cr3 by Comparing with Plain Coolant, Ice, Ln2 Gas in Single

Point Incremental Forming

Abstract: Sheet metal incremental forming is an emerging manufacturing technology that allows formation of

complex profiles by CNC contoured paths using a semi hemispherical tool. Single point incremental forming

(SPIF) is a novel methodology of sheet metal forming operation which provides higher formability limits. This

paper deals with the microstructural changes in the sheet metal which is subjected to die less forming operation.

For the study of microstructure and the nature of deformation in detail, IS513Cr3 sheet is chosen as an

experimental sheet metal sample. The evolution of microstructure after incremental sheet forming operation has

been investigated for different samples of ice, ambient and LN2 conditions. The sheets are successfully

deformed to required shape by sheet metal incremental forming operation and microstructural investigation is

done by trinocular metallurgical microscope. This paper analyses the influence of material grain size that has an

adverse effect on the material properties with respect to its application. The primary research scope is to fully

analyze the microstructure of sheet metal influenced by various parameters and the results are compared. Further

57-62

hardness is calculated in the sample with varying thickness at various locations and the results are discussed.

Keywords: Incremental sheet forming, CNC paths, formability limits, microstructure, trinocular metallurgical

microscope, hardness.

References: [1] K.K.Tang, Fatigue crack growth in the micro to large scale of 7075-T6 Al Sheets at different R ratios, International Journal of

Theoretical and applied fracture mechanics, 83, (2016), 93-104.

[2] Kawai Kwok, Homogenization of steady state creep of porous metals using three dimensional microstructural reconstructions,

International journal of solids and structures, 78-79, (2016), 38-46. [3] Nagarajan Devarajan, Complex incremental sheet forming using back die support on aluminium 2024, 5083 and 7075 alloys,

Proceedings on 11th International Conference on Technology of Plasticity,81,(2014), 2298-2304.

[4] K.Isik, Formability limits by fracture in Sheet metal forming, International Journal of material processing technology, 214, (2014), 1557-1565.

[5] Wenke Bao, Experimental Investigation on Formability and microstructure of AZ31B alloy in electropulse assisted incremental forming, International Journal of Materials and design, 87, (2015), 632-639.

[6] J.Jeswiet, F.Micari, G.Hirt, A.Bramley, J.Duflou, J.Allwood, Asymmetric Single Point Incremental Forming of Sheet Metal, CIRP

Annals, 54(2), (2005),88-114. [7] Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of

2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET 2017,

2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688 [8] Karthick, S., Perumal Sankar, S., and Arul Teen, Y.P. “Trust-distrust protocol for secure routing in self-organizing networks”, In

Proceedings of 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research,


[9] Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”, International

Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 11, pp. 2263-2271, 2018.

[10] Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”, International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018.

[11] Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse

Logistics”, Applied Mathematics and Information science, Vol. 13, No. 01, pp. 1-6, 2019. [12] Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile


2018. [13] Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in


12.

Authors: K. Vijayakumar, C. Dhanasekaran, R. Pugazhenthi, S. Sivaganesan

Paper Title: Digital Twin for Factory System Simulation

Abstract: : In the early days, building a manufacturing facility requires a lot of people to work at that

facility. With the advent of CAD/CAE/CAM Softwares and high power computers, facility layout can be

planned and engineered from a remote location. Manufacturing facilities undergo a lot of changes due to change

in customer requirements, government regulations, safety standards, the complexity of products and process

improvisation. It becomes indispensable to keep manufacturing facility digitally updated for upfront simulations

and to capture facility change requirements caused by product variations. In the current methodology, it takes a

significant amount of time and cost to keep manufacturing facility digitally updated. Digital Twin is an emerging

technology that nowadays almost all consumer goods, airplanes, manufacturing plants, oil refineries,

transportation networks, and even entire cities are having the potential to have Digital Twin of its own. This

paper suggesting a methodology to apply Digital Twin to a manufacturing facility for keeping the model updated

and simulated in real time. Real Time Location Sensing (RTLS) technology with transmitter tags, receiver

nodes, and gateways (IOT) deployed at the facility. Manage transformation matrix for each asset in the Digital

manufacturing facility at PLM/CAD software. The Discrete Event Simulation tool is integrated into PLM to run

model directly from PLM in real time. Tags attached to each physical asset sends a signal to the receiver when

asset movement or orientation occurs at the physical facility, Tags attached to each asset sends a signal to the

receiver. The receiver sends pre-processed information to a gateway which acts as an edge device to process and

converts those signals as transformation matric which gets synced to PLM system. Now digital manufacturing

facility is up-to-date. Any facility change requirement can do in this digital model and simulated in real time for

the quickest decision. The time factor has been compared with this new digital twin methodology and

conventional methodology and discussed the results.

Keywords: Digital twin, Internet of things, Object linking and embedding, Real-time location sensing,

Simulation

References: 1. Software-Defined Industrial Internet of Things in the Context of Industry 4.0

2. Enabling Digital Twins for Smart Connected Assets: Moving from Hype to (Mixed) Reality by LNS Research. 3. Enabling Digital Twins for Smart Connected Assets: Moving from Hype to (Mixed) Reality by Infosis report

4. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison – IEEE

5. Digital Twin in Industry: State-of-the-Art - Fei Tao, Senior Member, IEEE, He Zhang, Ang Liu, and A.Y.C. Nee 6. Digital Twin – Proof of concept – SebastianHaagReinerAnderl

7. A Digital Twin architecture reference model for the cloud-based cyber physical systems - Kazi Masudul Alam ; Abdulmotaleb El

Saddik 8. Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing - Fei Tao ; Meng Zhang

9. Shaping the digital twin for design and production engineering - BenjaminSchleichaNabilAnwer(2)bLucMathieu(1)bSandroWartzacka

63-68

10. A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration – KlausDrödera PaulBobkaa Tomas Germanna Felix Gabriela Franz Dietricha

11. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison - Qinglin Qi ; Fei Tao

12. On the Effects of Modeling As-Manufactured Geometry: Toward Digital Twin - Cerrone, Albert; Hochhalter, Jacob; Heber, Gerd; Ingraffea, Anthony

13. Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process –

Martin Kunatha, Herwig Winklera 14. The role of the Industry 4.0 asset administration shell and the digital twin during the life cycle of a plant - Constantin Wagner ; Julian

Grothoff ; Ulrich Epple ; Rainer Drath ; Somayeh Malakuti ; Sten Grüner

15. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET 2017,

2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688




18. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”,

International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018. 19. Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of Mechanical

and Production Engineering Research and Development, Special Issue, pp. 582-590, 2018.

20. Sathish, T., Vijayakumar, M.D., Krishnan Ayyangar, A. “Design and Fabrication of Industrial Components Using 3D Printing”, Materials Today Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14489-14498, 2018.



13.

Authors: G. Jehovah Jerone, R. Pugazhenthi, C. Dhanasekaran, M. Chandrasekaran

Paper Title: Enhancement of Throughput Simulation Accuracy Using AI

Abstract: : This research article study and analyze the feasibility of deploying the time study analysis, which

has been created by using Artificial Intelligence (AI). Artificial Intelligence is used to reach accurate results by a

throughput simulation study and which is also used to reduce the percentage of variability in the pre-production

study versus the physical implementation. The modern manufacturing facility is very keen in implementing the

optimized production system to avoid the unwanted cost investment and smooth running without stoppage like

starving and blocking prior to the physical implementation. But the level of output accuracy differs in

Throughput study when we use the designed cycle time instead of the real physical time studies. Deriving the

physical time study is possible only when the facility is implemented in the manufacturing area. In this study, the

correlation between the AI and physical time would be validated and Throughput simulation result will be

compared to improve the accuracy and difference. Usual data usage for the Throughput study are designed to

cycle time, Mean Time To Repair (MTTR), Mean Time Between Failures (MTBF). This feasibility study will

replace the designed cycle time by Artificial Intelligence (AI) time. Expected results from this study are to find

the benefits by using the AI time studies from the Throughput simulation when compared to the designed cycle

time.

Keywords: Artificial Intelligence, Automod, MTBF, Simulation,

References: 1. Brewka, Gerd. "Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial

Intelligence, Englewood Cliffs, NJ." The Knowledge Engineering Review 11, no. 1 (1996): 78-79.

2. Minsky, Marvin. The emotion machine: Commonsense thinking, artificial intelligence, and the future of the human mind. Simon and

Schuster, 2007. 3. Jackson, Philip C. Introduction to artificial intelligence. Courier Corporation, 1985.

4. Jordan, Michael I., and Tom M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349, no. 6245 (2015): 255-

260. 5. Kurzweil, Ray. How to create a mind: The secret of human thought revealed. Penguin, 2013.

6. Kurzweil, Ray. "The singularity is near." In Ethics and emerging technologies, pp. 393-406. Palgrave Macmillan, London, 2014.

7. Müller, Vincent C., and Nick Bostrom. "Future progress in artificial intelligence: A survey of expert opinion." In Fundamental issues of artificial intelligence, pp. 555-572. Springer, Cham, 2016.

8. Kaplan, Jerry. Artificial Intelligence: What everyone needs to know. Oxford University Press, 2016.

9. Taylor, Simon JE, Stephen E. Chick, Charles M. Macal, Sally Brailsford, Pierre L'Ecuyer, and Barry L. Nelson. "Modeling and simulation grand challenges: An OR/MS perspective." In 2013 Winter Simulations Conference (WSC), pp. 1269-1282. IEEE, 2013.

10. Sage, Andrew P., and William B. Rouse, eds. Handbook of systems engineering and management. John Wiley & Sons, 2009.

11. Ottino, Julio M. "Engineering complex systems." Nature 427, no. 6973 (2004): 399. 12. Hirsch, Penny L., Barbara L. Shwom, Charles Yarnoff, John C. Anderson, David M. Kelso, Gregory B. Olson, and J. Edward Colgate.

"Engineering design and communication: The case for interdisciplinary collaboration." International Journal of Engineering Education

17, no. 4/5 (2001): 343-348. 13. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of


2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688 14. Karthick, S., Perumal Sankar, S., and Arul Teen, Y.P. “Trust-distrust protocol for secure routing in self-organizing networks”, In

Proceedings of 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research,

ICETIETR 2018, art. no. 8529016, 2018. DOI: 10.1109/ICETIETR.2018.8529016 15. Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”,


16. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”,


17. Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of Mechanical


69-74




14.

Authors: Sachin Kattookaren, Sanath Vimal M, Pal Pandian P, Ivan Sunit Rout

Paper Title: Analysis of Machining Parameters for Face Milling of Inconel 718 using Response Surface

Methodology

Abstract: The machining of Inconel 718 which is a nickel based super alloy has become a material of great importance

mainly in the aerospace industry. Reason being the materials possesses properties of increase in strength at elevated

temperature, high resilience to chemical reaction and high wear resistance. Gaining optimum machining parameters have

become a great concern in the manufacturing industry, where economy of machining plays a very important key role in the

market. This paper gives an overview of the experimentation conducted on the basis of Response Surface Methodology

(RSM). Regression equations have been developed for surface roughness, by taking into consideration the machining

parameters like cutting speed, feed rate and depth of cut for face milling operation performed in CNC machine. RSM

analysis was carried out with the help of Mini Tab 18 software. The Mathematical equation developed after regression

analysis shows to be very efficient.

Keywords: Face milling, Inconel 718 and RSM

References: 1. .M. D'Addona, “High speed machining of Inconel 718: tool wear and surface roughness analysis”, 10th CIRP Conference on Intelligent

Computation in Manufacturing Engineering - CIRP ICME '16, vol. 62, pp. 269-274, 2017.

2. S Thamizhmanii, R Mohideen, “Surface Roughness and Tool Wear on Cryogenic Treated CBN Insert on Titanium and Inconel 718

Alloy Steel”, 3rd International Conference of Mechanical Engineering Research (ICMER 2015), vol. 100, pp. 1-9, 2015.

3. Dr P. Pal Pandian, Ivan Sunit Rout, “Parametric investigation of machining parameters in determining the machinability of Inconel 718

using taguchi technique and grey relation analysis”, International Conference on Robotics and Smart Manufacturing (RoSMa2018),

Published by Elsevier, vol. 133, pp. 786-792, 2018.

4. Lin W.S., “The reliability analysis of cutting tools in the HSM processes”, International Scientific Journal published by the World

Academy of Materials and Manufacturing Engineering, vol. 30, pp. 97-100, 2008.

5. Amit Kumar, Tarun Soot, “Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology”, Journal of

Industrial Engineering International, vol. 14, pp. 821-829, 2018.

6. Dinesh Thakur, B Ramamoorthy & L Vijayaraghavan, “Optimization of high speed turning parameters of super alloy Inconel 718

material using Taguchi technique”, Indian Journal of Engineering & Materials Sciences, vol. 16, pp. 44-50, 2009.

7. Samir Khamel, “Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool”, Journal of

Mechanical Science and Technology, vol. 11, pp. 3605-3616, 2012.

8. Sasikumar.T and V.P. Srinivasan, “Machining of Inconel 718 with liquid nitrogen as coolant using Response Surface Methodology”,

Journal of Applied Sciences Research, vol. 23, pp. 70-78, 2015.

9. Ivan Sunit Rout and Dr P. Pal Pandian, “Relection of cutting parameters for the machinability of Inconel 718 using Grey Relational

Analysis”, 3rd International Conference on Design, Analysis, Manufacturing and Simulation (ICDAMS 2018), vol. 172, pp. 1-5, 2018.

10. R. Ramanujam, K. Venkatesan, Vimal Saxena, Philip Joseph, “Modeling and optimization of cutting parameters in dry turning of

Inconel 718 using Coated Carbide Inserts”, International Conference on Advance Manufacturing and Materials Engineering. ICAMME

2014, vol. 05, pp. 2550-2559, 2014.

11. M R Soleymani Yazdi, A Khorram, “Modelling and Optimization of Milling Process by using RSM and ANN Methods”, IACSIT

International Journal of Engineering and Technology, vol. 2, pp. 1793-8236, 2010.

12. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of



13. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free Space


10.3103/S1060992X18020029

14. Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S., Venkatesh, R. “Performance Analysis in End Milling operation”, International

Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 11, pp. 2263-2271, 2018.





17. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile


2018.



75-78

15.

Authors: V. Dhinakaran, Rakesh Patil, Gokulakrishnan Sriram, N.Siva Shanmugam

Paper Title: Studies on Crack Propogation in Plasma Arc Welded Ti-6Al-4V Joint during Erichsen Cupping

Test

Abstract: Formability is one of the important parameter which describes the quality of the sheet metal. It is the

ability of sheet metal to undergo plastic deformation without being damaged. There are two types of formability

tests. One is a simple test which includes tensile, bulge and hardness test, another is simulative test like Erichsen

cupping test, limit done height test, swift cup test. In this paper, numerical and experiemntatal investigations of

formability of Ti-6Al-4V sheet are presented. Plasm arc welding experiment is conducted on thin Ti-6Al-4V

sheet and Erichsen cupping test is uysed as the test method to determine the Erichsen cupping parameters. Tests

have been conducted on plasma arc welded thin Ti-6Al-4V joint sheet as well as on the parent metal. Erichsen

cupping index (IE) and acture load are determined during experimentation. Finite element analysis is done using

commerical ABAQUS code. The simulation results are compared with experimental results. Based on the results

79-83


, it is observed that the formability of welded Ti-6Al-4V is more than the base metal and the predicted Erichsen

cupping index (IE), fracture load are good in agreeembnt with the experimental results.

Keywords: PAW, Dhinakran’s model Erichsen Cupping Index, ABAQUS\CAE

References: 1. L.R Hawtin and G.M Parkes, ''Erichsen Test for Formability of Metal Sheet'', Sheet Metal Industries, 495-499, 1963

2. Hawtin, L.R., Mear, D.R. & Johnson, R.H.C., ''Appraisal Current Information on the Erichsen Test'', Sheet Metal Industries, 495–

499, 1963 3. I. Gurrappa, “Characterization of titanium alloy Ti-6Al-4V for chemical, marine and industrial applications”, Materials

Characterization, October 2003

4. Ikuhiro INAGAKI, et al “Application and Features of Titanium for the Aerospace Industry”, Nippon steel & sumitomo metal technical reportno.106, July 2014

5. C. N. Elias et al “Biomedical Applications of Titanium and its Alloys”, Biomaterials Science

6. Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Some studies on temperature field during plasma arc welding of thin titanium alloy sheets using parabolic Gaussian heat source model." Proceedings of the Institution of Mechanical

Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 4 (2017): 695-711.

7. Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Experimental investigation and numerical simulation of weld bead geometry and temperature distribution during plasma arc welding of thin Ti-6Al-4V sheets." The Journal of Strain

Analysis for Engineering Design 52, no. 1 (2017): 30-44.

8. Yanli Song,Lin Hua “Influences of thickness ratio of base sheets on formability of tailor welded blanks”, 11th International Conference on Technology of Plasticity, ICTP 2014, October 2014

9. M. Rama Narasimha Reddy, et al. ''Modified Erichsen Cupping Test for Copper, Brass, Aluminium and Stainless Steel'', The SIJ

Transactions, Industrial and Finamce management, Vol.1, No.2, May-June 2013 10. K. Narooei, et al. “A study on sheet formability by a stretch forming process using assumed strain FEM”, Springer Science

Business Media B.V., July 2009

11. Dhinakaran, V., Suraj Khope, N. Siva Shanmugam, and K. Sankaranarayanasamy. "Numerical prediction of weld bead geometry in plasma arc welding of titanium sheets using COMSOL." In Proceedings of the 2014 COMSOL Conference in Bangalore,

Bangalore, India, pp. 13-14. 2014.

12. [Dhinakaran, V., N. Siva Shanmugam, K. Sankaranarayanasamy, and R. Rahul. "Analytical and numerical investigations of weld bead shape in plasma arc welding of thin Ti-6al-4v sheets." Simulation 93, no. 12 (2017): 1123-1138.

13. Kaushik Bandyopadhyay, et al. “Prediction of formability of laser-welded dual-phase steel by finite element analysis”, Journal of

Engineering Manufacture, Vol.228(9), 2014 14. Abraham Marthinius Erichsen Patent copy from United States of Patent Office, April 1914

15. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier

transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research, ICETIETR 2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014



17. T. Sathish, “BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM”, Materials Today

Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018.


Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018 19. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”,





16.

Authors: R Siva Kumar, K Tirupathi Reddy, K Hema Chandra Reddy

Paper Title: Investigation on the Performance of Diesel Engine with Modified Piston Geometry and Using Waste

Plastic Pyrolysis Oil Blend

Abstract: The performance evaluation of geometrically modified IC engine against pyrolysis blended diesel

fuel was studied. The pyrolysis oil was derivative of waste plastic oil and blended with diesel at different weight

ratios. The engine piston considered to be as the most predominant component towards contribution of engine

performance and emissions. An attempt has been made to change the piston geometry by offering additional

holes in the piston crown and making groves in the piston bowl. The modification in the piston geometry helped

to attain the strengthening and boosting the inlet air swirl movement which further assisted improvement in the

performance and engine emission characteristics. The experiments were successfully carried out with modified

piston geometry based diesel engines with pyrolysis blended diesel oils. The outcome of the present work further

proves that the pyrolysis blended diesel oil showing significantly improved performance in brake thermal

efficiency and specific fuel consumption. The emission parameter confirms that the Unburnt hydrocarbon, Co

and NOx levels also measurably reduced. The results further proves that it could be considered as rapidly

emerging blended fuel in replacement of diesel fuels in near future.

Keywords: Grooved Piston, Swirling of Air, Tyre Pyrolosis Oil, Volumetric Efficiency (VE), NOX Emission.

References:

1. Mani, G. Nagarajan, and S.Sampth. (2011). Characterisation and effect of using waste plastic oil and diesel fuel blends in CI engines. Energy. [Online]. 36 (1). pp. 212-219. Available:

https://www.sciencedirect.com/science/article/abs/pii/S0360544210006122

2. A. K. Panda, S. Murugan, and R. K. Singh. (2016). Performance and emission characteristics of diesel fuel produced from waste plastic oil obtained by catalytic pyrolysis of waste poly propylene. Energy Sources, part A; Recovery,Utilization, and

84-89

Environmental Effects. [Online]. 38 (4). Available: https://www.tandfonline.com/doi/abs/10.1080/15567036.2013.800924

3. A. Balaji, C. Sundar Raj, B. Karthikeyan and R. Premkumar. (2013). Performance and Emission Analysis of Plastic Blended

Diesel On C.I Engine. Int. J of Mech. Engg. Res. & App. [Online]. 1 (4). pp. 78 – 83. Available:

https://www.researchgate.net/publication/272952385_Performance_And_Emission_Analysis_of_Plastic_Blended_Diesel_On_CI

_Engine 4. S. Tamilkolundu, C. Murugesan. (2012). The Evaluation of blend of easte plastic oil Diesel fuel for use as alternate fuel for

transportation. 2nd International Conference on Chemical, Ecology and Environmental Sciences (ICCEES’2012) Singapore. 28-

29. [Online]. pp. 66-70. Available: http://psrcentre.org/images/extraimages/45.%20412655.pdf 5. R. Baskaran, P. Sathish Kumar. (2015). Evaluation on performance of CI engine with waste plastic oil –diesel blends as

Alternative fuel. Int. J Res. App. Sci. & Engg. Tech. [Online]. 3 (IV). pp. 642-646. Available:

https://www.ijraset.com/fileserve.php?FID=2268 6. R. Guntur, M. L. S. D. Kumar, K.V. K. Reddy. (2011). Experimental evaluation of a diesel engine with blends of diesel – plastic

pyrolysis oil. Int. J. Engg. Sci. Tech. [Online]. 3 (6). pp. 5033-5040. Available:

https://www.researchgate.net/publication/291049681_Experimental_evaluation_of_a_diesel_engine_with_blends_of_diesel-plastic_pyrolysis_oil

7. K. Arumugan, M. Punnaivanam and S. Koodalingam. (2017). Certain Investigation in a Compression Ignition Engine Using Rice

Bran Methyl Ester Fuel Blends with Ethanol Additive. Thermal Science. [Online]. 21 (1B). pp. 535-542. Available: http://www.doiserbia.nb.rs/img/doi/0354-9836/2017/0354-98361600152K.pdf

8. R. Senthil, P. Ganesan and S. Rajendran. (2016). Use of Antioxidant Additives for NOx Mitigation in Compression Ignition

Engine Operated with Biodiesel from Annona Oil. Thermal Science. [Online]. 20 (4). pp. 967-972. Available: https://www.researchgate.net/publication/309322767_Use_of_antioxidant_additives_for_NOx_mitigation_in_compression_igniti

on_engine_operated_with_biodiesel_from_annona_oil

9. M. R. Ellis, “Effect of Piston Bowl Geometry on Combustion and Emissions of a Direct Injected Diesel Engine”, Brunel

University School of Engineering and Design, London, 1999. pp. 1-299

10. V Gnanamoorthi, N. Marudhan and D. Gobalakichenin. (2016). Effect of Combustion Chamber Geometry on Performance,

Combustion, and Emission of Direct Injection Diesel Engine with Ethanol-Diesel Blend. Thermal Science. [Online]. 20 (4). pp. 937-946 Available:

https://www.researchgate.net/publication/309360482_Effect_of_combustion_chamber_geometry_on_performance_combustion_a

nd_emission_of_direct_injection_diesel_engine_with_ethanol-diesel_blend 11. K. Rajan and K. R. S. Kumar. (2010). Performance and Emission Characteristics of Dies el Engine with Internal Jet Piston Using

Biodiesel. Int. J. Env. Stu. [Online]. 67 (4). pp. 557-566 Available:

https://www.tandfonline.com/doi/abs/10.1080/00207233.2010.508252 12. J. Li, W. M. Yang, H. An, A. Maghbouli and S.K. Chou. (2014). Effects of Piston Bowl Geometry on Combustion and Emission

Characteristics of Biodiesel Fueled Diesel Engines. Fuel. [Online]. 120. pp. 66-73. Available:

https://www.sciencedirect.com/science/article/pii/S0016236113011423 13. S. Jaichandar and K. Annamalai. (2013). Combined Impact of Injection Pressure and Combustion Chamber Geometry on the

Performance of a Biodiesel Fueled Diesel Engine. Energy. [Online]. 55. pp. 330-339. Available: https://www.sciencedirect.com/science/article/abs/pii/S0360544213003216

14. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion

Fourier transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research, ICETIETR 2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014




Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018. 17. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”,




Materials Today Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14489-14498, 2018. 20. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”,


17.

Authors: C.Vengatesha Rajaperumal, Servai Rakesh Ramanathan

Paper Title: Power Generation using Magnets in Windmill System

Abstract: The depletion of non-renewable resources and higher energy requirement has led to a severe power crisis

though there are many renewable sources and they are being harnessed to generate power wind energy is considered one

among the least expensive renewable sources. Generation of energy from non renewable sources creates various

complications such as a noise pollution, water pollution and air pollution. This serves as a major region to choose wind

energy to generate electricity. Wind energy is considered as green energy and it can be used to harness energy in

environmentally benign manner. The efficiency of the windmill is much lower than other sources of energy. This project is

aimed at improving the efficiency and to simplify the mechanism of normal windmill to produce large amount of current

with the help of magnets. The magnetic lines of force generated by the nature of magnets by North and South Pole of a bar

magnet creates a magnetic field. To generate electricity double rotation of wind mill blades in alternate direction is used to

produce more displacement for coil windings and magnets. The power generated by this process is alternative current (AC)

can be directly connected to the house main supply for the further use and it needed to be stored in batteries for emergency

cases. The energy generated can be used to supply power for the seashore villages or individual houses.

Keywords: Renewable energy, Slip ring

References:

1. [1]Ajinkya Kulkarni, Sumedh Kulkarni and Ranjit Bhosale. Power Generation using maglev Windmills (2017).

2. [2]Changliang Xia, Zhiqiang Wang, Tingna Shi, Zhanfeng Song. A Novel Cascaded Boost Chopper for the Wind Energy Conversion System Based on the Permanent Magnet Synchronous Generator (2013).

3. [3]Dante Fernando Recalde Melo, Le-Ren Chang-Chien. Synergistic Control Between Hydrogen Storage System and Offshore

90-93






Wind Farm for Grid Operation (2014). 4. [4]Eduardo Giraldo, Alejandro Garces. An Adaptive Control Strategy for a Wind Energy Conversion System Based on PWM-CSC

and PMSG (2014).

5. [5]Feifei Bu, Yuwen Hu, Wenxin Huang, Shenglun Zhuang, Kai Shi. Wide-Speed-Range-Operation Dual Stator-Winding Induction Generator DC Generating System for Wind Power Applications. (2015).

6. [6]Kyung-Hyun Kim, Tan Luong Van, Dong-Choon Lee, Seung-Ho Song, Eel-Hwan Kim. Maximum Output Power Tracking

Control in Variable-Speed Wind Turbine Systems Considering Rotor Inertial Power (2013). 7. [7]Marius Fatu, Frede Blaabjerg, Ion Boldea. Grid to Standalone Transition Motion Sensorless Dual-Inverter Control of PMSG

With Asymmetrical Grid Voltage Sags and Harmonics Filtering (2014).

8. [8]Nuno M. A. Freire, Jorge O. Estima, António J. Marques Cardoso. A New Approach for Current Sensor Fault Diagnosis in PMSG Drives for Wind Energy Conversion Systems (2014).

9. [9]Natalia Angela Orlando, Marco Liserre, Rosa Anna Mastromauro, Antonio Dell’Aquila. A Survey of Control Issues in PMSG-

Based Small Wind-Turbine Systems (2013).

10. [10]Salvador Alepuz, Alejandro Calle, Sergio Busquets-Monge, Samir Kouro, Bin Wu. Use of Stored Energy in PMSG Rotor Inertia for Low-Voltage Ride-Through in Back-to-Back NPC Converter-Based Wind Power Systems (2013).

11. [11]Venkata Yaramasu, Bin Wu, Salvador Alepuz, Samir Kouro. Predictive Control for Low Voltage Ride Through Enhancement

of Three-Level Boost and NPC Converter Based PMSG Wind Turbine (2014). 12. [12]Ye Wang, Herman Bayem, Maria Giralt-Devant, Vera Silva, Xavier Guillaud, Bruno Francois. Methods for Assessing

Available Wind Primary Power Reservew(2015).

13. [13]Youssef Errami, Abdellatif Obbadi, Smail Sahnoun, Mohammed Ouassaid,and Mohamed Maaroufi. Power Extraction Control of Variable Speed Wind Turbine Systems Based on Direct Drive Synchronous Generator in All Operating Regimes (2018).

14. [14]Y. Errami, M. Ouassaid, M. Maaroufi. Control scheme and maximum power point tracking of variable speed wind farm based

on the PMSG for utility network connection (2012). 15. [15]Y. Errami, M. Hilal M. Benchagra, M. Ouassaid, M. Maaroufi. Nonlinear Control of MPPT and Grid Connected for Variable

Speed Wind Energy Conversion System Based on the PMSG (2012).

16. [16] Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological

Research, ICETIETR 2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014

17. [16] Karthick, S., Perumal Sankar, S., and Arul Teen, Y.P. “Trust-distrust protocol for secure routing in self-organizing networks”, In Proceedings of 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research,


18. [17] T. Sathish, “BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM”, Materials Today Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018.

19. [18] Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”,

Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018 20. [19] Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket

Milling”, International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018.

21. [20] Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of Mechanical and Production Engineering Research and Development, Special Issue, pp. 582-590, 2018.

22. [21] Sathish, T., Vijayakumar, M.D., Krishnan Ayyangar, A. “Design and Fabrication of Industrial Components Using 3D Printing”, Materials Today Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14489-14498, 2018.

18.

Authors: E Vijayaragavan, Aditya Vatsa, Adil Hossain, Agam Dubey

Paper Title: Design of Torque Enhancer for Hybrid Vehicles using Planetary Gear

Abstract: This work presents the development of an alternate powertrain for hybrid vehicle called the ‘Torque

Enhancer’. It is a planetary gear system with a sun gear, an internal ring gear, three planet gears, a planet carrier

and an outer ring casing. The primary inputs from the electric motor (EM) and an internal combustion (IC)

engine were connected to the sun gear and the planet carrier respectively. The ring casing provides the necessary

output. The sun gear and the planet carrier were coupled to generate higher torque and higher rpm at the output.

The 3D modelling and simulation for dynamic motion were performed in SOLIDWORKS 2018. The results

showed that a higher rpm and a higher torque were achieved on the output ring casing. For design optimization,

a Nonlinear- Dynamic Study was performed to check for different analysis like the Stress, Displacement, Strain

and velocity for different designs of carrier plates. The Stress, Strain and velocity decreases with the thickness,

the displacement is found to be almost constant around 25mm and the velocity decreases with increase in

thickness. A ribbed carrier plate of thickness 4mm is chosen to be the most optimized carrier plate.

Keywords: Parallel Hybrid Vehicle; Planetary Gear Drive; Torque Enhancer; Planet Carrier.

References:

1. Chris Mi, M. Abul Masrur and David Wenzhong Gao, Hybrid Electric Vehicles: Principles and Applications with Practical

Perspectives, Second Ed. John Wiley & Sons Ltd, (2018), 15.

2. https://www.greencarcongress.com/2016/06/20160615-accord.html 3. https://hondanews.com/honda-automobiles/videos/2014-honda-accord-hybrid-intelligent-multi-mode-drive-i-mmd?page=4

4. http://eahart.com/prius/psd/

5. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological

Research, ICETIETR 2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014

6. Karthick, S. “Semi supervised hierarchy forest clustering and knn based metric learning technique for machine learning system”, Journal of Advanced Research in Dynamical and Control Systems, vol. 9, no. Special Issue 18, pp. 2679-2690, 2017.


Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018. 8. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”,


9. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”, International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018.

10. Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of

Mechanical and Production Engineering Research and Development, Special Issue, pp. 582-590, 2018. 11. Sathish, T., Vijayakumar, M.D., Krishnan Ayyangar, A. “Design and Fabrication of Industrial Components Using 3D Printing”,


94-97

https://www.hindawi.com/74614197/





https://www.greencarcongress.com/2016/06/20160615-accord.html

https://hondanews.com/honda-automobiles/videos/2014-honda-accord-hybrid-intelligent-multi-mode-drive-i-mmd?page=4

http://eahart.com/prius/psd/

19.

Authors: S.Krishnamoorthi, S.Nagendharan G.Manikandan, R.Karthikeyan

Paper Title: Studies on Mechanical and Thermal Bheviours of Nano Clay Filled Linear Low Density

Polyethylene Composites

Abstract: Nowadays, most of engineering applications the polymer composites are utilize. In this paper we are

investigated the only polymer matrix composites. The polymer matrix composite materials are has good strength

and better lightweight materials. Composites were fabricated by a compression molding method using the Nano

clay with varying weight percentages. Studies on mechanical properties of tensile strength, flexural strength and

impact strength are investigated. The thermal behaviors have been investigated by using simultaneously thermal

analysis equipment. The maximum tensile strength of 29.6 Mpa at 4 wt. % of Nano clay and 96 Wt. % of

LLDPE compositions have been achieved. The maximum flexural strength has been observed as 8.9 Mpa and

the impact strength as 236.4 KJ/M.SQ. Thermogravimetry analysis showed that mass loss decreases very

slightly in the LLDPE/Nano clay Nano composite compared with those in neat LLDPE, and increased with the

increase of Nano clay concentration. The analysis of thermal degradation in airflow showed a clear improvement

of thermal stability for LLDPE/Nano clay Nano composites, proportionally to Nano clay content.

Keywords: LLDPE, polymer matrix composites, compression molding, tensile strength, flexural strength and

impact strength, Thermogravimetry, Nano composites, Nano clay.

References:

1. Nawang, I.D. Danjaji, U.S. Ishiaku, H. Ismail, Z.A. Mohd Ishak 2000, Mechanical properties of sago starch-filled linear low density

polyethylene (LLDPE) composites, Polymer Testing 20 (167–172). 2. M.A. Mokoena, V. Djokovi, A. S. Luyt, 2004, Composites of linear low density polyethylene and short sisal fibres, Materials Science

39 (3403 – 3412). 3. Wenying Zhou, Demei Yu, Chao Min, Yinping Fu, Xiusheng Guo, 2008, thermal, dielectric, and mechanical properties of sic particles

filled linear low-density polyethylene composites, polymer testing.

4. Wenying Zhoua, 2011, thermal and dielectric properties of the aln particles reinforced linear low-density polyethylene composites, 5. Dr. M. S. R Niranjan Kumar, 2014, Effect of Nano clay on the Tensile properties of Polyester and S-glass Fibre(Al)” M. Somaiah

Chowdary, International Journal of Engineering Research & Technology, (IJERT).

6. Girish Chandran V., Sachin D. Waigaonkar, 2015, Mechanical Properties and Creep Behaviour of Rotationally Mouldable Linear Low Density Polyethylene-Fumed Silica Nano composites, polymer composites.

7. I.N. Hidayah, M. Mariatti, H. Ismail and M. Kamaro, 2016, Effect of selective localization on dielectric properties of boron nitride

Nano filler filled linear low density polyethylene (LLDPE)/silicone rubber (SR) blends, Polymer Testing. 8. Kamil Şirin, Mehmet Balcan and Fatih Doğan, 2012, the influence of filler component on mechanical properties and thermal analysis

of pp-ldpe and pp-ldpe/dap ternary composites

9. Anshu Anjali Singh and Sanjay Palsule, 2014, Thermal Properties of Jute Fiber Reinforced Chemically Functionalized High Density Polyethylene (JF/CFHDPE) Composites Developed by Palsule Process, Applied Polymer Composites, Vol. 2, No. 2, 2014.

10. Tai Jin-huaa, Liu Guo-qinb, Caiyi Huangc, Shangguan Lin-jiana, 2012, Mechanical Properties and Thermal Behaviour of

LLDPE/MWNTs Nano composites, Materials Research. 2012; 15(6): 1050-1056. 11. G. Dikobe, A. S. Luyt, 20110, Comparative study of the morphology and properties of PP/LLDPE/wood powder and

MAPP/LLDPE/wood powder polymer blend composites, eXPRESS Polymer Letters Vol.4, No.11 (2010) 729–741.

12. P. Noorunnisa Khanam, MA AlMaadeed1, M. Ouederni3, Eileen Harkin-Jones, Beatriz Mayoral, Andrew Hamilton, Dan Sun, 2016, Melt Processing and Properties of Linear Low Density Polyethylene-Graphene Nanoplatelet Composites, Vacuum, 130, 63-71.

13. Shaharuddin Kormin, Faridah Binti Kormin, Mohammad Dalour Hossen Beg, Mohd Bijarimi Mat Piah, 2015, Study the Physical,

Mechanical, Thermal And Morphological Properties Of Ldpe/Sago Starch Blend, ARPN Journal of Engineering and Applied Sciences. 14. N. Shebani And A. J. Van Reenen, 2009, The Effect Of Wood Species On The Mechanical And Thermal Properties Of Wood–Lldpe

Composites, Journal Of Composite Materials, Vol. 43, No. 11/2009.

15. Regina Jeziórska,1 Maria Zielecka,1 Beata Gutarowska,2 and Zofia gakowska2, 2013, High-Density Polyethylene Composites Filled with Nanosilica Containing Immobilized Nanosilver or Nanocopper: Thermal, Mechanical, and Bactericidal Properties and

Morphology and Interphase Characterization, International Journal of Polymer Science Volume 2014.

16. Junwei Gu, Qiuyu Zhang, Jing Dang, Junping Zhang, Zhaoying Yang, 2009, Thermal Conductivity And Mechanical Properties Of Aluminum Nitride Filled Linear Low-Density Polyethylene Composites, Polymer Engineering And Science.

17. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier


18. Karthick, S. “Semi supervised hierarchy forest clustering and knn based metric learning technique for machine learning system”,




20. Venkatesh, R., Vijayan, V., Parthiban, A., Sathish, T., Siva Chandran, S. “Comparison of Different Tool path in Pocket Milling”, International Journal of Mechanical Engineering and Technology, Vol. 09, Issue. 12, pp. 922-927, 2018.


Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018 22. Madan, D., Sivakandhan, C., Sagadevan, S., Sathish, T. “Ocean Wave Energy Scenario in India”, International Journal of Mechanical



98-102

20.

Authors: S. Dineshkumar, Shrinidhy Sriram, R Surendran, V.Dhinakaran

Paper Title: Experimental Investigation of Tensile Properties of Ti-6Al-4V alloy at Elevated Temperature

Abstract: In this research work, Tungsten Inert Gas welded titanium sheet of 1.5 mm thickness is considered

as candidate material for investigation. Weld joint is prepared using 80 A welding current and 200 mm/min

travel speed. Tensile properties are evaluated at room temperature, 250 °C, 450 °C and 650 °C. It is evident that

the ultimate strength and yield strength decreases with increase in temperature and also the percentage

elongation increases with increase in temperature. At temperatures above 650°C, the ultimate tensile strength

(UTS) had decreased to about 66% than the ultimate tensile strength (UTS) at room temperature.

103-107

Keywords:

References: 1. Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Experimental investigation and numerical simulation of weld

bead geometry and temperature distribution during plasma arc welding of thin Ti-6Al-4V sheets." The Journal of Strain Analysis for Engineering Design 52, no. 1 (2017): 30-44.

2. Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Some studies on temperature field during plasma arc welding of

thin titanium alloy sheets using parabolic Gaussian heat source model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 4 (2017): 695-711.

3. Lee, Chin Hyung, H. C. Park, Gab Chul Jang, J. H. Lee, and Kyong Ho Chang. "Characteristics of high temperature tensile properties

and residual stresses in welded joints of the SM570-TMCP steel." In Key Engineering Materials, vol. 353, pp. 527-532. Trans Tech Publications, 2007.

4. Kim, Woo-Gon, Jae-Young Park, I. M. W. Ekaputra, Sung-Deok Hong, Seon-Jin Kim, and Yong-Wan Kim. "Comparative study on the high-temperature tensile and creep properties of Alloy 617 base and weld metals." Journal of Mechanical Science and Technology

27, no. 8 (2013): 2331-2340.

5. Dhinakaran, V., N. Siva Shanmugam, K. Sankaranarayanasamy, and R. Rahul. "Analytical and numerical investigations of weld bead shape in plasma arc welding of thin Ti-6al-4v sheets." Simulation 93, no. 12 (2017): 1123-1138.

6. Wang, Kehuan, Gang Liu, and Shijian Yuan. "Deformation behaviour of laser-welded tube blank of TA15 Ti-alloy for gas forming at

elevated temperature." In MATEC Web of Conferences, vol. 21, p. 06005. EDP Sciences, 2015. 7. Qu, Feng-sheng, Xu-guang Liu, X. I. N. G. Fei, and Kai-feng Zhang. "High temperature tensile properties of laser butt-welded plate of

Inconel 718 superalloy with ultra-fine grains." Transactions of Nonferrous Metals Society of China 22, no. 10 (2012): 2379-2388.

8. Isozaki, Toshikuni, & Oba, Toshihiro . (1980). High velocity tensile test of austenitic stainless steel at elevated temperature, (2).

Nippon Kikai Gakkai Ronbunshu, A Hen, 46(403), 292-301.

9. Ufuah, E. "Characterization of elevated temperature mechanical properties of butt-welded connections made with HS steel grade

S420M." In Proceedings of the World Congress on Engineering, vol. 3. 2012. Elevated Temperature Mechanical Properties of Al-Li-Cu-Mg Alloy, T. ZACHARIA AND D. K. AIDUN

10. Klueh, R. L., and J. F. King. "Elevated-Temperature Tensile Behavior of ERNiCr-3 Weld Metal." Weld. J 59 (1980): l14s-120s.

11. Y. M., G. H. Chen, J. Q. Wang, J. J. Liu, X. H. Yu, J. Hua, X. L. Bai, T. Zhang, J. H. Zhang, and W. M. Tang. "Short-term high-temperature tensile tests and prediction of long-term strength of welded joints of dissimilar steels T92/HR3C." Metal Science and

Heat Treatment 55, no. 11-12 (2014): 614-621.

12. Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research,


13. Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of Intelligent Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09


Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018. 15. Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic

Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec

2018, pp. 1515-1535, 2018. 16. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress





21.

Authors: M.Bhavani, S.Kalaiselvan, S.Jagan, S.Gopinath

Paper Title: Semi Automated Wireless Beach Cleaning Robot Vehicle

Abstract This research work proposed is design and fabrication of Semi Automated Wireless Beach Cleaning

Robot Vehicle. The work has done looking at the current situation of our beaches which are dump with core

litters of dirt and encumbered with pollutants, toxic materials, debris etc. By taking this into consideration, this

machine has designed to clean beach surface. Almost all the manufacturing process is being automated for

delivering the products at a faster rate. Automation plays an important role in mass production. In this research

work we have fabricated the river cleaning machine which is remote operated. The major focus of this research

work is to decrease the man power, time consumption for cleaning the river. In this research work we have done

the automation of the river cleaning with the use of motor and chain drive arrangement. Here we are using

transmitter and receiver of RF type to control the cleaning machine. Computers, pneumatics, robotics,

hydraulics, etc., are used for Automation. Among these sources, pneumatics used for low cost automation

Keywords: Automation, Cleaning, Waste, Robot

References: [1] M. Mohamed Idhris, M.Elamparthi,C. Manoj Kumar, Dr.N.Nithyavathy, Mr. K. Suganeswaran, Mr. S. Arunkumar, “Design and fabrication of remote controlled sewage cleaning Machine”, IJETT – Volume-45 Number2 -March 2017

[2] Mr.Abhijeet.M. Ballade, Mr. Vishal.S. Garde, Mr.Akash.S. Lahane and Mr.Pranav.V.Boob, “Design & fabrication of river cleaning

system”, IJMTER Volume 04, Issue 2, [February– 2017] ISSN (Online):2349–9745. [3] Mr. P. M. Sirsat, Dr. I. A. Khan, Mr. P. V. Jadhav, Mr. P. T. Date, “Design and fabrication of River Waste Cleaning Machine”, IJCMES

2017 Special Issue-1 ISSN: 2455-5304

[4] Pankaj Singh Sirohi, Rahul Dev, Shubham Gautam, Vinay Kumar Singh, Saroj Kumar,“Review on Advance River Cleaner”, IJIR Vol-3, Issue-4, 2017 ISSN: 2454-1362.

[5] Ndubuisi c. Daniels, “Drainage System Cleaner A Solution to Environmental Hazards”, IRJES) ISSN (Online) 2319-183X, Volume3,

Issue 3(March 2014) [6] Osiany Nurlansa, Dewi Anisa Istiqomah, and Mahendra Astu Sanggha Pawitra, “AGATOR (Automatic Garbage Collector) as Automatic

Garbage Collector Robot Model” International Journal of Future Computer and Communication, Vol. 3, No. 5, October 2014.

[7] Basant Rai, “Polltution and Conservation of ganga river in modern India”, International Journal of Scientific and Research Publications,

108-110

Volume 3, Issue 4, April 2013 1 ISSN 2250-315 [8] Huang Cheng, Zhang Zhi*,“Identification of the Most Efficient Methods For Improving Water Quality in Rapid Urbanized Area Using

the MIKE 11 Modelling System”, 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation.

[9] Emaad Mohamed H. Zahugi, Mohamed M. Shanta and T. V. Prasad,“Design Of Multi-Robot System For Cleaning Up Marine Oil Spill”, IJAIT Vol. 2, No.4, August 2012.

[10] Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier


[11] Karthick, S. “TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks”, International Journal of

Intelligent Engineering and Systems, vol. 11, no. 2, pp. 76-84, 2018. DOI: 10.22266/ijies2018.0430.09 [12] T. Sathish, “BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM”, Materials Today


[13] Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec 2018, pp. 1515-

1535, 2018.

[14] Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

[15] Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel

Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018, [16] Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L

Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development, Special

Issue, pp. 711-716, 2018.

22.

Authors: V. Jaiganesh, R. Ravi Raja Malarvannan, G. Manikandan, S.Krishnamoorthi

Paper Title: Investigation of the Mechanical Properties of Natural Fiber Reinforced Composites of Morinda

Citrifolia with Epoxy and Bisphenol Resin

Abstract: The application of natural fibre is increasing day by day since they are eco-friendly. The natural fibre-

reinforced composites have in many advantages like lightweight, renewable, biodegradable, and cheap and eco-

friendly. There is a need to investigate the potential of natural fibre composites, where can be used in highly

demanding situations. An attempt has been made in explore the possible use of a variety of wild grown fibres in

nature in the development of new composites for load carrying structures. The natural fibers have been

abundantly available in the world. It has unique properties compared to synthetic fibre and reduces the plastic

usage. This article reports the extraction process of natural fibers, characterization of natural fibre-reinforced

composites. Reinforcement of natural fibers like Moninda citrifolia made with epoxy and bisphenol resin. The

investigation of the natural fiber composites was carried out by means of FTIR (Fourier Transform Infra-Red

Spectroscopy), SEM (Scanning Electron Microscope) and the mechanical properties like tensile, flexural,

compression and hardness properties of the composites without chemically treated fibers were reported.

Keywords: Natural Fibers, Epoxy and Bisphenol Resin, Mechanical properties, FTIR, SEM

References:

1. Ramakrishna Malkapuram., Vivek Kumar. And Yuvraj Singh Negi. (2008). Recent development in natural fibre reinforced

polypropylene composites. J Reinf Plast Compos (2008). DOI: 10.1177/0731684407087759.

2. Wambua P., Ivens J. And Verpoest I. (2003). “Natural fibres: can they replace glass in fibre reinforced plastics?”. Composites Science and Technology (2003). DOI: 10.1016/S0266-3538(03)00096-4.

3. Iman Entezari., Benoit Rivard., Mirjavad Geramian. And Michael G. Lipsett. (2017). Predicting the abundance of clays and quartz in

oil sands using hyperspectral measurements. International Journal of Applied Earth Observation and Geo information (2017). DOI: 10.1016/J.JAG.2017.02.018.

4. Nabi Saheb D., Jog JP. (1999). Natural fiber polymer composites: a review. Advances in Polymer Technology (1999). DOI: 10.1002/

(SICI) 1098-2329(199924)18:4<351 5. Mark C. Symington., W.M. Banks., Opukuro David West. And R.A. Pethrick. (2009). Testing of Cellulose Based Natural Fibers for

Structural Composite Applications. Journal of Composite Materials (2009). DOI: 10.1177/0021998308097740.

6. Xue Li., Lope G. Tabil. And Satyanarayan Panigrahi. (2007). Chemical Treatments of Natural Fiber for Use in Natural Fiber-Reinforced Composites: A Review. Journal of Polymers and the Environment (2007) 15:25–33. DOI: 10.1007/s10924-006-0042-3.

7. Ming Qiu Zhang., Min Zhi Rong. And Xun Lu. (2005). Fully biodegradable natural fiber composites from renewable resources: All-

plant fiber composites. Composites Science and Technology (2005). DOI: 10.1016/j.compscitech.2005.06.018. 8. A.Valadez-Gonzalez., J.M. Cervantes-Uc., R. Olayo. And P.J. Herrera-Franco. (1999). Effect of Fiber Surface Treatment on the Fiber–

Matrix Bond Strength of Natural Fiber Reinforced Composites. Composites Part B: Engineering (1999). DOI: 10.1016/S1359-

8368(98)00054-7. 9. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc based

artificial neural network (ANN)”, Current Medical Imaging Reviews, vol. 13, no. 3, pp. 223-230, 2017. DOI:

10.2174/1573405612666160617082639 10. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free Space


10.3103/S1060992X18020029 11. T. Sathish, “BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM”, Materials Today


12. Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec

2018, pp. 1515-1535, 2018.


14. Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel

Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018, 15. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L

Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development,

111-116

Special Issue, pp. 711-716, 2018.

23.

Authors: R.Kishore, G.Karthick, M.D.Vijayakumar, V. Dhinakaran

Paper Title: Analysis of Mechanical Behaviour of Natural Filler and Fiber Based Composite Materials

Abstract: This Research work is mainly concentrated on the development of the new trends of the particulate

based polymer composites to explore the in-depth utilization of the natural resource’s residue. For the past

decade the natural fibers are been used as reinforcement for the polymer composite in research. Researchers are

showing immense interest to adapt natural fibers in the place of glass fibers, mainly attracted by its weight to

strength ratio; these fibers are available at very low price and having more natural advantages such as using

green resources, renewable and biodegradable. In this Research, the residue such as egg shell, Red mud and coir

fiber are selected as reinforcement for the fabrication, with the selected constituents’ composites is fabricated

using compression moulding and the mechanical behavior is analyzed for the same. This work mainly

concentrates on the comparison of different lengths of coir fiber and different compositions of the filler materials

with the matrix as polyester. The mechanical behavior such as Tensile strength, Flexural strength and Impact

strength are compared between different runs of samples with varying fillers and fiber length.

Keywords: Coir, composite, compression moulding, red mud, egg shell fillers

References:

1. M. Z. M., et al., Mechanical Properties of Short Random Oil Palm Fibre Reinforced EpoxyComposites. SainsMalaysiana 2010, 39, 87-92.

2. Sapuan, S. M., M. Harimi, and A. M. Maleque, Mechanical Properties of Epoxy/Coconut Shell FillerParticle Composites. The Arabian

Journal for Science and Engineering 2003, 28, 171-181. 3. Affandi, N. B., et al., A Preliminary Study on Translational Kinetic Energy Absorption Using Coconutfiber(Coir) Sheets as a Potential

Impact-worthy Constituent in Advanced Aerospace Material. KeyEngineering Materials 2011, 471-472, 1028-1033.

4. S.J. Eichhom et al., “Review: Current International Research into Cellulosic Fibers 5. and Composites”, J. of Mat. Sci., 36 (2001), pp. 2107–2131.CrossRef

6. Ray, Bankim Chandra, and Dinesh Rathore. " Durability and integrity studies of

7. environmentally conditioned interfaces in fibrous polymeric composites: Critical

8. concepts and comments. &quote; Advances in colloid and interface science 209 (2014): 68-83.

9. A. May-Pat, A. Valadez-González, and P. J. Herrera-Franco, “Effect of fiber surface treatments on the essential work of fracture of

HDPE-continuous henequen 10. fiber-reinforced composites,” Polymer Testing, vol. 32, no. 6, pp.1114–1122, 2013

11. Amar,K.M.,Manjusri.Mand Lawrence, T.D., 2005. “natural fibers, biopolymers, and Bio-composite". CrC press, tailor & Francis Vol.

34, No7,pp. 568-624, 2001. 12. Alhuthali, H.,Low, H.I, “Mechanical properties and water absorption behaviour of recycled cellulose fiber reinforced epoxy”,

composites polymer testing,Vol,63,pp.23-27,2009.

13. Angelo.G.Facca, et al. Effect of Compounding and Injection Molding on the Mechanical Properties of Flax Fiber Polypropylene Composites”, Journal of reinforced plastics and composites, vol. 29, No. 9, 2010.

14. Ana Espert.,FranciscoVilaplana., SigbrittKarlsson., “Comparison of water absorption in natural cellulosic fibers from wood and one-

year crops in polypropylene composites and its influence on their mechanical properties” Composites: Part A 35, Vol. 9, 2004. 15. Aziz, S.H and Ansell M. P.: “the effect of alkalization and fiber alignment on the mechanical” and thermal properties of kenaf and

hemp bastfibre composites: part 1 – polyester resin Matrix. Composites science and technology. Vol. 64, pp.1219–1230, 2009.

16. Bledzki, A.K. and Gassan J. ― “composites reinforced with cellulose based fibers”. ProgPolym sci. Vol. 24(2), pp. 221-74, 1999. 17. Bledzki, A. K., Reinhmane.S and Gassan, J..thermoplastics reinforced with wood fillers. Polymplast. Technol. Eng Vol. 37, pp.451-

468.

18. Buzarovska, A., Bogoeva. G., G. Grozdanov. A., Avella, M.Gentile, and G.Errico, M.”Potential use of rice straw as filler in eco-composite materials”.Vol. 1 No. 2 pp. 37-42(2008).

19. Geoff Cresswell,.”coir dust a proven alternative to peat”, cresswell horticultural services, Grose vale nsw. Vol. 7, pp.2753, 1999.

20. Geethamma, V.G., G. Kalaprasad, GabriëlGroeninck and Sabu Thomas, “Dynamic mechanical behavior of short coir fiber reinforced natural rubber composites”, Composites Part A: Vol.36 , pp.1499–1506, 2005.

21. Harish, .S.Peter, Michael, .D,Bensley, .A.MohanLal, .DandRajadurai, .A “mechanical property evaluation of natural fiber coir

composite”materialcharacterstics vol.60.pp.44-49,2009. 22. Hull, D and Clyne .T.W.. “an Introduction To Composite Materials” 2nd ed., Cambridge university, press, cambridge, Vol. 8,pp.1-3,

2001.

23. Jiang and Hinrichsen. G.. “flax and cotton fiber reinforced biodegradable polyester”Amide.dieangew. Makromol.chem. Vol. 268, pp.13-17, 2007.

24. JutaratPrachayawarakom, C.S.K. Pillai, V.S. Prasad, K.G. Satyanarayana, “Effect of weathering on the mechanical properties of

midribs of coconut leaves”, J. Mater. Vol. 22,pp. 3167, 1989. 25. Li,S. M. Sapuan, M.Ahmad, and N. Yahya,“Mechanical and Electrical Properties of Coconut Coir Fiber-Reinforced Polypropylene

Composites”, Polymer-Plastics Technology and Engineering, Vol 44, pp.619–632, 2005. 26. Keener, T. J., Stuart, R. K. and Brown, T. K. (2004). Maleated Coupling Agents for Natural Fiber Composites, Composites, Vol. 35,

pp. 357–362, 2001.

27. Maldas, D., and koota, B.V.” current Trends In The Utilization Of Cellulosic Materials In The Polymer industry” trends in the utilization of cellulosic materials in the polymer industry .Trends polymer sci Vol. 12,pp.174-178, 2003.

28. Marion, P., Andréas. R. and Marie, H.M, “study of wheat gluten plasticization with fatty Acids”. Polym. Vol. 44, pp.115-122, 2001.

29. Mcmullen, P., “fiber/resin composites for aircraft primary structures”: a short history, (1936-1984), composites, Vol.15(3), pp.22-30, 2011.

30. Sankar, S.P., Vishwanath, N., Lang, H.J., and Karthick, S. “An effective content based medical image retrieval by using abc based


31. Arul Teen, Y.P., Nathiyaa, T., Rajesh, K.B., and Karthick, S. “Bessel Gaussian Beam Propagation through Turbulence in Free Space

Optical Communication”, Optical Memory and Neural Networks (Information Optics), vol. 27, no. 2, pp. 81-88, 2018. DOI: 10.3103/S1060992X18020029


Proceedings, Elsevier Publisher, Vol. 05, Issue 6, pp. 14416-14422, 2018. 33. Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic

117-121

http://www.cabdirect.org/search.html?q=au%3A%22Buzarovska%2C+A.%22

http://www.cabdirect.org/search.html?q=au%3A%22Bogoeva%2C+G.+G.%22

http://www.cabdirect.org/search.html?q=au%3A%22Grozdanov%2C+A.%22

http://www.cabdirect.org/search.html?q=au%3A%22Avella%2C+M.%22

http://www.cabdirect.org/search.html?q=au%3A%22Gentile%2C+G.%22

http://www.cabdirect.org/search.html?q=au%3A%22Errico%2C+M.%22

Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec 2018, pp. 1515-1535, 2018.

34. Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress

in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018 35. Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel

Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018,

36. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development,


24.

Authors: Jacob, D. Narayana, Soumya S, Subhash N, Jagadeesha T

Paper Title: Estimation of Fatigue Strength of Crank Shaft with and Without Flywheel

Abstract: Evaluation of life and performance of complex engineering structures subjected to inherent

randomness in loading, material properties and geometric parameters is becoming increasingly important in the

design of structures. Fatigue analysis provides a means to quantify the reliability of complex systems and

estimate the life by quantifying the cycles to failure. Crankshaft design is a complex task as engine runs at wide

range of operating conditions. Loads are produced both by pressure (released by internal combustion) and

inertia. It is designed to withstand high cyclic loads for 10 to 10 cycles. Crankshaft operates under high loads

requiring high strength in tension and compression as well as fatigue strength. In this paper we describe the

Computer aided fatigue analysis of crankshaft, a critical component in the functioning of an automotive. 3-D

solid part of the crankshaft is modeled using CATIA V5 R15 and meshed using HYPERMESH 7.0. Boundary

conditions and loading conditions are given in ABAQUS 6.5 and fatigue analysis is carried out using FE-SAFE

software. Crack initiation location is also determined using FE-SAFE software.

Keywords: Carbon nanotubes, Spark plasma sintering, Wear behavior, Corrosion resistance crankshaft, Fatigue

life, White curve, Gas pressure.

References:

1. Mills, W. J. and Hertzberg, R.W (1975). “The effect of sheet thickness on fatigue crack retardation in 2024-T3 Aluminum alloy”

Engineering Fracture Mechanics 7, 705-708. 2. Paris, P.C and Gomez, M.P and Anderson, W P (1961). A rational analytic theory of Fatigue. The trend in Engineering 13, 9-14.

3. Paris, P.C and Erdogan F (1963). “A critical analysis of crack propagation laws” Basic Engineering 85,528-534.

4. Richie R.O (1988). “Mechanism of Fatigue crack propagation in metals, ceramics and composites: Role of crack tip shielding. Materials science and Engineering A103, 15-28.

5. Richie R.O and Suresh S (1982). “Some considerations on fatigue crack closure at near threshold stress intensities die to fracture

morphology. Metallurgical Transactions. 13 A, 937 -940. 6. Suresh S (1998). “Fatigue of Materials”, Cambridge University press. United Kingdom.

7. Richie R.O and Suresh S (1982A). “A Geometrical model for fatigue crack closure induced by fracture surface morphology.

Metallurgical Transactions. 13 A , 1627-31 8. Richie R.O and Suresh S (1982A). “A Geometrical model for fatigue crack closure induced by fracture surface morphology.

Metallurgical Transactions. 13 A , 1627-31

9. Richie R.O and Suresh S (1982B). “Mechanistic dissimilarities between environmentally influenced fatigue crack propagation at near threshold and higher growth rates in lower strength steels. Metal Science 16, 529-538.

10. Klesnil M and Lukas P (1972 “Influence of strength and stress history on growth and stabilization of fatigue cracks” Engineering

Fracture Mechanics 4, 77-92. 11. Suresh S and Vasudevan A K and Bretz P E (1984). “Mechanisms of slow fatigue crack growth in High strength aluminum alloys:

Role of Microstructure and environment. Metallurgical Transactions. 15 A, 369-379.

12. Broek D (1986). Elementary Engineering fracture Mechanics (4th Ed.), Martinus Nijhoff Publishers, Boston. 13. Kumar P (1999). Elements of Fracture Mechanics. Wheeler Publishing, New Delhi.

14. Anderson, T.L. (1995). Fracture Mechanics. Fundamentals and Applications. Boca Raton: CRC Press LLC.

15. Elber, W. (1970). Fatigue crack closure under cyclic tension. Engineering Fracture Mechanics, 2, 37-45. 16. Foreman, R.G., Keary, V, E., and Engle, R.M. (1967). Numerical Analysis of crack propagation in cyclic loaded structures. Journal of

Basic Engineering 89,459-464. 17. Forsyth, P.J.E (1953) “Exudation of material from slip bands at the surface of fatigued crystals of an aluminum copper alloy”. Nature

171,172-173.

18. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of





20. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling K-Nearest Neighbour technique for the parameter prediction of cryogenic treated tool in surface rough minimization”, International Journal of Mechanical and Production Engineering

Research and Development, Special Issue, pp. 705-710, 2018.

21. Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec

2018, pp. 1515-1535, 2018.


23. Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel

Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018, 24. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L

Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development,


122-127

Authors: Thammaiah Gowda, Jagadeesha T, V.Dhinakaran

25.

Paper Title: Optimization of Design Parameters of Aircraft Wing Structure with Large Cut Outs using Damage

Tolerant Design and Finite element analysis Approach

Abstract: Wings are the lift generating components in the airframe structure. Wings are also used as fuel tanks

in the transport aircraft. Cutouts are provided in the bottom skin of the wing to permit entry into the airplane fuel

tanks for inspection or component repair. Finite element method is adopted for stress analysis of the structural

components. MSC NASTRAN and MSC PATRAN FEM packages are used to carry out the analysis. The

damage tolerance capabilities of a wing box with a fuel access cutout to ensure the structural integrity while

achieving the maximum possible safety margin and a reasonable lifetime of the aircraft structure is investigated.

First finite Element Modeling of the Wing box in standard FE package (Global model) is carried out followed by

structural analysis of the wing box to identify the critical location for fatigue crack initiation. Once critical

location is found out, then Finite Element Modeling of the Panel with critical location for more detailed analysis

(Local model) is carried out. Simulation of cracks of various lengths in the Local FE model and Stress intensity

factor (SIF) calculations for each crack length using Modified Virtual Crack Closure Integral (MVCCI) method.

Qualitative comparison of SIF with Fracture toughness for every crack length is carried.

Keywords: Stress concentration, Stress intensity factor, MVCCI, Finite element, Damage Tolerance.

References:

1. Micheal Chun., Yung Niu.: Airframe structural design, Practical design information and data on aircraft structures, Conmilit press,

Hong Kong (2001).

2. Ralph I. Stephens., Ali Fatemi., Robert R., Stephens, Henry O. Fuchs: Metal Fatigue in Engineering, 2nd Edition, John Wiley (2000).

3. Pir M.Toor.: A review of some damage tolerance design approaches for aircraft structures, Engineering fracture mechanics, 5, 837-880

(1973). 4. Chiheb Chaker.: Stress trajectories in the presence of friction, International Journal of Solids and Structures, 40, 4033-4041 (2003).

5. Griffith, A.: The phenomena of rupture and flow in solids, Philosophical Transactions of the Royal Society, 163-198 (1921).

6. RamaChandra Murthy, A., Palani, G.S., Nagesh Iyer.: Damage tolerant evaluation of cracked stiffened panels under fatigue loading, Sadhana, 37, 171-186 (2012).

7. Sethuraman, R., Maiti,S.K.: Finite element based computation of strain energy release rate by modified crack closure integral,

Engineering Fracture Mechanics,30,227-231 (1988). 8. Carlson,R.L.,Steadman, D.L.,Dancila.,D.S.: Fatigue growth of small corner cracks in aluminum 6061-T651. In:Proceedings of the

FAA-NASA symposium on continued airworhiness of aircraft structures, pp.279-285,New York (1997).

9. Og Uzhan Demir., Ali O. Ayhan.: Evaluation of mixed mode criteria for fatigue crack propagation using experiments and modelling, Chinese Journalof Aeronautics, 31(7), 1525-1533 (2018).

10. Lin Lin., Luo Lin., Zhong.: Development and application of maintenance decision-making support system for aircraft fleet, Advances

in Engineering Software, 114, 192-207 (2017). 11. Karthick, S., Devi, E.S., Nagarajan, R.V. “Trust-distrust protocol for the secure routing in wireless sensor networks”, In Proceedings of

2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, ICAMMAET 2017, 2017-January, pp. 1-5, 2017. DOI: 10.1109/ICAMMAET.2017.8186688



13. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling K-Nearest Neighbour technique for the parameter

prediction of cryogenic treated tool in surface rough minimization”, International Journal of Mechanical and Production Engineering Research and Development, Special Issue, pp. 705-710, 2018.

14. Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic

Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec 2018, pp. 1515-1535, 2018.


Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018 16. Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel

Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018,

17. Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development,


128-132

26.

Authors: Gokulakrishnan Sriram, V. Dhinakaran, Jagadeesha T, Rishiekesh Ramgopal

Paper Title: Finite Element Simulation of Temperature Distribution and Residual Stress in Single Bead on Plate

Weld Trial using Double Ellipsoidal Heat Source Model

Abstract: Tungsten Inert Gas Welding of Ti-6Al-4V plate was simulated using commercial finite element code

to predict temperature distribution and residual stress distribution. Because of the geometrical symmetry, only

one plate was modelled to reduce the simulation computation time. The temperature dependent material

properties of thermal conductivity, Specific heat, density were used for thermal transient analysis. Double

ellipsoidal volumetric heat source was used as the heat source model and the heat source parameters were found

out by comparing the simulation macrograph and experiment result. Heat source fitting was done using

SYSWELD software and the function database was saved after achieving the macrograph similar to

experimental macrograph by iteration. The transient thermal analysis was done with Welding Advisor tool

which is inbuilt in the VISUAL WELD software using the database obtained from the heat source fitting. The

successful completion of TIG welding of Ti-6Al-4V will prove useful in, determining the weld bead geometry,

prediction of temperature distribution and residual stress distribution.

Keywords: TIG welding, Heat Source Model, Ti-6Al-4V.

References:

[1] Qi Yunlian, Deng Ju, Hong Quan, Zeng Liying “Electron beam welding, laser beam welding and gas tungsten arc welding of titanium sheet.,” Material Science and Engineering A280(2000) 177-181

133-138

[2] Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Some studies on temperature field during plasma arc welding of thin titanium alloy sheets using parabolic Gaussian heat source model." Proceedings of the Institution of Mechanical Engineers, Part C:

Journal of Mechanical Engineering Science 231, no. 4 (2017): 695-711

[3] Dhinakaran, V., N. Siva Shanmugam, and K. Sankaranarayanasamy. "Experimental investigation and numerical simulation of weld bead geometry and temperature distribution during plasma arc welding of thin Ti-6Al-4V sheets." The Journal of Strain Analysis for Engineering

Design 52, no. 1 (2017): 30-44.

[4] K. H. Tseng, S. T. Hsieh and C. C. Tseng. “The effects of the process parameters of micro-plasma arc welding on morphology and quality in stainless steel edge joints” Science and Technology of welding and joining 2003 Vol.8 No.6

[5] Douglas Bezerra de Araújo,Paulo Roberto de Freitas Teixeira,Luiz Antônio Bragança da “Applicability of the Gaussian Distribution Heat

Source Model to The Thermal Simulation of Welding Processes” 22nd International Congress of Mechanical Engineering (COBEM 2013),November 3-7, 2013, Ribeirão Preto, SP, Brazil

[6] RN Lidama, HPM Yupiter, MR Redzaa, Simulation Study on Multipassed Welding Distortion of Combined Joint Types using hermo-

Elastic-Plastic FEM” The Journal of Engineering Research Vol. 9, No. 2, 1-16 [7] J. A. Goldak and M. Akhalaghi:. “Computational Welding Mechanics,” Springer, New York, (2005), 88

[8] Nirsanametla YADAIAH ,Swarup BAG” Effect of Heat Source Parameters in Thermal and Mechanical Analysis of Linear GTA

Welding Process” ISIJ International, Vol. 52 (2012), No. 11, pp. 2069–2075 [9] M. I. ONSØIEN, M. MʼHAMDI, AND O. M. AKSELSEN “Residual Stresses in Weld Thermal Cycle Simulated Specimens of X70

Pipeline Steel,” Welding Journal JUNE 2010, VOL. 89

[10] Dean Denga, Shoichi Kiyoshimab“Numerical simulation of residual stresses induced by laser beam welding in a SUS316 stainless steel pipe with considering initial residual stress influences,” Nuclear Engineering and Design 240 (2010) 688–696

[11] Dhinakaran, V., Suraj Khope, N. Siva Shanmugam, and K. Sankaranarayanasamy. "Numerical prediction of weld bead geometry in

plasma arc welding of titanium sheets using COMSOL." In Proceedings of the 2014 COMSOL Conference in Bangalore, Bangalore, India, pp. 13-14. 2014.

[12] Dhinakaran, V., N. Siva Shanmugam, K. Sankaranarayanasamy, and R. Rahul. "Analytical and numerical investigations of weld bead

shape in plasma arc welding of thin Ti-6al-4v sheets." Simulation 93, no. 12 (2017): 1123-1138.

[13] Karthick, S., Perumal Sankar, S., and Raja Prathab, T. “An approach for image encryption/decryption based on quaternion Fourier

transform”, 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research, ICETIETR

2018, art. no. 8529014, 2018. DOI: 10.1109/ICETIETR.2018.8529014 [14] Karthick, S. “Semi supervised hierarchy forest clustering and knn based metric learning technique for machine learning system”,


[15] Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling K-Nearest Neighbour technique for the parameter prediction of cryogenic treated tool in surface rough minimization”, International Journal of Mechanical and Production Engineering

Research and Development, Special Issue, pp. 705-710, 2018.

[16] Sathish, T., Muthukumar, K., Palani Kumar, B.“A Study on Making of Compact Manual Paper Recycling Plant for Domestic Purpose”, International Journal of Mechanical and Production Engineering Research and Development, Vol. 8, Special Issue 7, Dec 2018, pp. 1515-

1535, 2018.

[17] Sathish, T., and Karthick, S. “HAIWF-based fault detection and classification for industrial machine condition monitoring”, Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 46-58, 2018

[18] Sathish, T., Muthulakshmanan, A. “Modelling of Manhattan K-Nearest Neighbor for Exhaust Emission Analysis of CNG-Diesel Engine”, Journal of Applied Fluid Mechanics, Vol. 11, pp. 39-44, 2018,

[19] Sathish, T., Periyasamy, P., Chandramohan, D., Nagabhooshanam, N. “Modelling of cost based optimization system E-O-L

Disassembly in Reverse Logistics”, International Journal of Mechanical and Production Engineering Research and Development, Special Issue, pp. 711-716, 2018.

27.

Authors: H M Gurudatt, B Sadashivegowda

Paper Title: Trans-esterification of Non Edible Pongamia oil for Synthesis of Bio Lubricant

Abstract: In this research work it is planned to produce pongamia oil bio lubricant, Initial The commercial

pongamia oil is tested for free fatty acid(FFA) content which was found to be in the range of 25-28% then the

same oil is subjected to esterification in which the percentage free fatty acid(FFA) was found to be reduced to

10% later the esterified pongamia oil is subjected to transesterification and finally the percentage free fatty acid

was found to be in the range of 0.4 – 0.6 which is less than 1%.

Keywords: Pongamia oil, Bio lubricant, Free fatty acid(FFA), Esterification, transesterification

References:

19. Manufacturing of environment friendly biolubricants from vegetable oils, Ebtisam K. Heikal, M.S. Elmelawy , Salah A. Khalil, N.M.

Elbasuny, Egyptian Journal of Petroleum (Elsevier) pp 53-59,Issue 26, year 2017.

20. Comparative Study of Free Fatty Acid Composition and Physico Chemical Properties of Biodiesel Produced from Various Non Edible

Oil Seeds, Prof. S. K. Pawar, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 21. ‘Preparation of Methyl Ester (Biodiesel) from Karanja (Pogamia Pinnata) Oil’. Bobade S. N. and Khyade V. B, Research Journal of

Chemical Sciences 2331-606X, pp 43-50,Aug. 2012

22. ‘Biodiesel production from Karanja oil’, Vivek and A. K. Gupta, Journal of Scientific & Industrial Research, Vol 63, pp 39-47,2004. 23. Hariram. V & Vagesh Shangar R, ‘Characterization and Optimization of Biodiesel Production from Crude Mahua Oil by Two Stage

Transesterification’,American Journal of Engineering Research. 2320-0847, pp 233-239

24. Salih N, Salimon J, Yousif E. Synthetic biolubricant basestocks based on environmentally friendly raw materials. Journal of King Saud University –Science 2011.

25. Mofijur M, Masjuki HH, Kalam MA et al. Palm Oil Methyl Ester and Its Emulsions Effect on Lubricant Performance and Engine

Components Wear. Energy Procedia 2012; 14:1748-1753 26. Ponnekanti N, Kaul S. Development of ecofriendly/biodegradable lubricants: An overview. 2012.

139-140

28.

Authors: Shrinivas Hebbar A, Shrinidhi D Kulal, Tajmul Pasha, Prasanta Kumar Samal, K Gourav

Paper Title: Numerical and Experimental Investigation of Vibration Isolation of Three-storied Building

Structure using Tuned Mass Damper

Abstract: A tuned mass damper (TMD) is a passive energy dissipating device which is comprised of a mass,

spring and a damper. The idea behind this type of dampers is that if a smaller mass is attached to the multiple

degrees of freedom system and its parameters are tuned precisely, then the oscillation of the whole system can

be reduced by this smaller mass. In this work, different dampers of different frequencies were designed and

141-149

integrated with the three-storied building frame model to minimize its first mode of vibration along x-axis at a

frequency of 3Hz. The best suitable damper was determined through the numerical analysis was then fabricated

and tested for the validation of the result. It was found that for the TMD of 3Hz the reduction in the response of

the structure was found to be around 83.72%.

Keywords: Tuned Mass Damper (TMD), Modal Analysis, Harmonic Analysis, Fast Fourier Transform (FFT).

References: 1. Design, Construction and Testing of an Adaptive Pendulum Tuned Mass Damper By Richard Lourenco A thesis presented to the

University of Waterloo in fulfilment of the thesis requirement for the degree of Master of Applied Science in Mechanical

Engineering Waterloo, Ontario, Canada, 2011 ©Richard Lourenco 2011

2. Baker, W.F., Korista, S., and Novak, L.C., 2007. Burj Dubai: Engineering the World’s Tallest Building, The Structural Design of Tall and Special Buildings, 16(4), pp.361-375

3. Sain, T., and Chandra Kishen, J.M., 2007. Prediction of Fatigue Strength in Plain and Reinforced Concrete Beams, ACI Structural Journal, 104(5), pp.621-628

4. Kareem, A., Kijewski, T., and Tamura, Y., 2007. Mitigation of Motion of Tall Buildings with Specific Examples of Recent

Applications, Wind and Structures, 2(3), pp.201-251 5. Mendis, P., Ngo, T., Haritos, N., Hira, A., Samali, B., and Cheung, J., 2007. Wind Loading on Tall Buildings, Electronic Journal

of Structural Engineering, 7(Special Issue), pp.41-54

6. Gerges, R.R., and Vickery, B.J., 2005. Optimum Design of Pendulum-Type Tuned Mass Dampers, The Structural Design of Tall and Special Buildings, 14(4), pp.353-368

7. Tamboli, A., Joseph, L., Vadnere, U., and Xu, X., 2008. Tall Buildings: Sustainable Design Opportunities, In: Council on Tall

Buildings and Urban Habitat, CTBUH 8th World Conference, Dubai, March 3-5 8. Chang, M.L., Lin, C.C., Ueng, J.M., and Hsieh, K.H., 2010. Experimental Study on Adjustable Tuned Mass Damper to Reduce

Floor Vibration Due to Machinery, Structural Control and Health Monitoring, 17(5), pp.532-548

9. Clark, A.J., 1988. Multiple Passive Tuned Mass Dampers for Reducing Earthquake Induced Building Motion, Proceedings of the 9th World Conference of Earthquake Engineering, 5, pp.779-784

10. Setareh, M., 2002. Floor Vibration Control Using Semi-Active Tuned Mass Dampers, Canadian Journal of Civil Engineering,

29(1), pp.76-84 11. Conner, J.J., 2003. Introduction to Structural Motion Control. Pearson Education Inc.

12. Nishimura, T., Kobori, T., Sakamoto, M., Koshika, N., Sasaki, and K., Ohrui, S., 1992. Active Tuned Mass Damper, Smart

Materials and Structures, 1(4), pp.306-311 13. Introduction to Modal Analysis (https://www.sharcnet.ca/Software/Ansys/17.2/en-us/help/wb_sim/ds_modal_analysis_type.html)

14. Tuned mass dampers in Skyscrapers (http://practical.engineering/blog/2016/2/14/tuned-mass-dampers-in-skyscrapers)

29.

Authors: Naveen A, L Krishnamurthy, T N Shridhar

Paper Title: Investigation and Simulation of Mechanical Properties of W &Al2O3 Thin Films Co-Sputtered on

SS304 Substrates

Abstract: Tungsten (W) and Alumina (Al2O3) thin films developed on SS304 substrates have been made

under various deposition conditions of magnetron co-sputtering. Deposition Conditions have been defined using

the DOE approach. Measured thicknesses of films have been ranges from 130.5 nm to 445nm. Thin films have

assessed by means of mechanical properties viz., Young's modulus (E) and hardness (H). Assessment has been

done by using the nanoindentation experiment and the numerical simulations. Nanoindentation experiment has

been conducted for five different thickness’s values. These results were processed to simulate for remaining

deposition conditions in order to achieve more information. Experimental results and simulated values have been

summarized as a final opinion. Based on these results, best E and H mechanical properties have been selected to

present optimum condition. Optimum condition has been found for thin film thickness 419 nm.

Keywords: Co-sputtering, Nanoindentation, Tungsten and Alumina thin film, Young’s modulus and Hardness

References:

1. Hardwick “The Mechanical Properties of Thin Films: A Review” Thin Solid Films, 154 (1987) 109-124

2. J.M.AntunesabJ.V.FernandesaN.A.SakharovaaM.C.OliveiraaL.F.Menezesa “On the determination of the Young’s modulus of thin films using indentation tests” International Journal of Solids and Structures ,Volume 44, Issues 25–26, 15 December 2007, Pages 8313-

8334

3. G.M. Pharr and W.C. Oliver “Measurement of Thin Film Mechanical Properties Using Nanoindentation” MRS BULLETIN, Volume

17, Issue 7 July 1992, pp. 28-33

4. J. Menčík), D. Munz , E. Quandt , E. R. Weppelmann and M. V. Swain “Determination of elastic modulus of thin layers using

nanoindentation” Journal Material Research, Volume 12, Issue 9,September 1997 , pp. 2475-2484 5. Kisan Sheshan. “Handbook of Thin-Film Deposition Processes and Techniques”. Second Edition, N oyes Publications /William

Andrew Publishing, Norwich, New York, U.S.A. © 2002

6. Kauko Leiviskä “Introduction to Experiment Design” University of Oulu ,Control Engineering Laboratory, 2013 7. I N Martev, D A Dechev, N P Ivanov, T D Uzunov and E P Kashchieva, “Characterization and properties of highly adhesive titanium

nitride and tungsten nitride thin films” Journal of Physics: Conference Series, Volume 113.

8. T. Hirasawa, H. Kotera, S. Tawa,S. Shima “A study of mechanical properties of multi-layered thin films” Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM’99 (Cat. No.99EX296). doi:10.1109/ipmm.1999.792477

9. S. Chen , Lei Liu, Tzuchiang Wang , “Investigation of the mechanical properties of thin films by nanoindentation, considering the

effects of thickness and different coating–substrate combinations” Surface & Coatings Technology 191 (2005) 25–32 (the Poisson’s ratio has a minoreffect on the indentation results)

10. Jeffrey Schirer and Julia Nowak, “ Full Nano mechanical Characterization of Ultra-Thin Films” Hysitron, Inc., APPLICATION

NOTE , WWW.HYSITRON.COM 11. S.U. Jen *, T.C. Wu, “Young’s modulus and hardness of Pd thin films” Thin Solid Films, 492 (2005) 166 – 172

12. Jennifer Hay Young’s Modulus and Hardness of Thin Low-κ Films Using Nanoindentation, Nanomechanics, Inc.

150-153

30.

Authors: Bhavana Umanath, A.N.Santosh Kumar

Paper Title: Enterprise Risk Management Framework for Small and Medium Enterprises

https://www.sharcnet.ca/Software/Ansys/17.2/en-us/help/wb_sim/ds_modal_analysis_type.html

http://practical.engineering/blog/2016/2/14/tuned-mass-dampers-in-skyscrapers

Abstract: In today’s profoundly unpredictable business environment, managing the risks effectively is a

stumbling block for almost all the organizations. A diverse range of risks are faced by every organization, both

from internal and external sources. The small and medium enterprises in India are exposed to a plethora of risks.

Adapting to an Enterprise Risk Management framework will proactively help in reducing the exposure to risks

and mitigate them. In this regard, this paper discusses about the role of SMEs in Indian economy, the common

risks faced by them, the components of COSO ERM framework and its benefits to SMEs by implementing them.

Keywords: COSO framework, Enterprise risk management, ERM frameworks, Small and Medium Enterprise.

References:

1. Annual Report, 2017-18. Ministry of Micro, Small and Medium Enterprises, www.msme.gov.in.

2. Press Information Bureau, Government of India. Ministry of Micro, Small & Medium Enterprises, February 7th 2018, http://pib.nic.in/newsite/PrintRelease.aspx?relid=176353.

3. Report of the Working Group on Micro, Small and Medium Enterprises (MSMEs) growth for 12th Five Year Plan (2012–17), Ministry

of Micro, Small and Medium Enterprises, New Delhi. http://www.dcmsme.gov.in/working_group2012.pdf. 4. Sonia Mukherjee. (October 2, 2018). Challenges to Indian micro small scale and medium enterprises in the era of globalization. Journal

of Global Entrepreneurship Research (2018) 8:28. https://doi.org/10.1186/s40497-018-0115-5.

5. Duong, L. (2005). Effective risk management strategies for small-medium enterprises and micro companies - A case study for Viope Solutions Ltd. Degree Thesis: International Business.

6. James W. De Loach. (2000). “Enterprise-wide Risk Management: Strategies for Linking Risk and Opportunity”. London: Financial

Times Prentice Hall, p-5.

7. Gatzert, N., & Martin, M. (2015). Determinants and value of enterprise risk management: Empirical evidence from the literature. Risk

Management and Insurance Review, 18(1), 29-53

8. COSO’s Enterprise Risk Management – Integrated Framework, Committee of Sponsoring Organizations of the Treadway Commission (COSO), New York, NY, September 2004 (www.coso.org).

9. Ballou, Brian; Heitger, Dan L. (2005). “A Building- Block Approach for Implementing COSO’s Enterprise Risk Management –

Integrated Framework”. Management Accounting Quarterly, Winter 2005, Vol.6, No.2. 10. Shad, M. K., & Lai, F. (2015). A conceptual framework for enterprise risk management performance measure through economic value

added. Global Business and Management Research, 7(2), 1-11.

154-156

31.

Authors: Ganesha B B, G V Naveen Prakash

Paper Title: Forced Convection Heat Transfer through the Rectangular Fins of Different Geometry of

perforations

Abstract: All engineering systems under operations generates heat. If this heat is not removed periodically the

system will fail due to overheating of components. Hence, extended surface or fin are used to remove the heat

from the system. In this paper experimental study on aluminium rectangular fins with triangular, rectangular and

circular perforated fins are made under forced convection mode with different voltages, air velocities and fin

spacing of 8 mm. Results are compared with solid fins with perforated fins of same spacings. Results shows

significant increase in heat transfer rate for perforated fins compard with solid fins under some conditions also

there is a weight reduction up to 23.6% compared to solid fins.

Keywords: Fins, forced convection, Heat transfer co-efficient, perforated fns.

References:

1. J. Kalil Basha, P. Rajakrishnamoorthy, S. Suthagar and T. Gopinath, “Experimental Study of the Characteristics of Various types of Fins

using Forced Convection Heat Transfer”, International Journal of Innovative Science, Engineering & Technology, ISSN: 2348 -

7968, Vol.2, Issue. 6, June. 2015.

2. K.H. Dhanawade and S. Dhanawade, “Enhancement of Forced Convection Heat Transfer from Fin Arrays with Circular Perforation”, International Journal of Frontiers in Automobile and Mechanical Engineering, 2010, pp. 192-196

3. Shinde Sandip Chandrakant, Shinde Sunilkumar and Nitin Gokhale, “Numerical and Experimental Analysis of Heat Transfer through

Various types of Fin Profiles by Forced Convection”, International Journal of Engineering Research & Technology, ISSN: 2278 -0181, Vol. 2, Issue. 7, July. 2013, pp. 2493-2501.

4. M R Shaeri, M. Yaghoubi, “Numerical analysis of turbulent convection heat transfer from an array of perforated fins”, International

Journal of Heat and Fluid Flow, Vol. 30, 2009, pp. 218-228 5. Md. Farhad Ismail, Muhammad Noman Hasan and Mohammad Ali, “Numerical Simulation of Turbulent Heat Transfer from Perforated

Plate-fin Heat Sinks”, Heat Mass Transfer, 2014, Vol. 50, pp. 509-519.

6. Kavita H. Dhanawade, Vivek K. Sunnapwar and Hanamant S. Dhanawade, “Thermal Analysis of Square and Circular Perforated Fin Arrays by Forced Convection”, International Journal of Current Engineering and Technology, ISSN: 2277- 4106. Pp. 109-114.

7. A.B. Ganorkar and V.M. Kriplani, “Experimental Study of Heat Transfer Rate by using Lateral Perforated Fins in a Rectangular

Channel”, MIT International Journal of Mechanical Engineering, Vol. 2, No. 2, Aug. 2012, pp. 91-96. 8. Abdullah H. Aiessa, Ayman M. Maqableh and Shatha Ammourah, “Enhancement of Natural Convection Heat Transfer from a Fin by

Rectangular Perforations with Aspect Ratio of Two”, International Journal of Physical Sciences, Vol. 4, Oct. 2009, pp. 540-547.

9. Aiessa, Mohamad I and Al-Odat, “Enhancement of Natural Convection Heat Transfer from a Fin by Triangular Perforations of Bases Parallel and Towards its Base”, The Arabian Journal for Science and Engineering, Vol. 34, Nov. 2009, pp. 531-544.

10. A.B. Ganorkar and V.M. Kriplani, “Experimental Study of Heat Transfer Rate by Using Lateral Perforated Fins in a Rectangular

Channel”, MIT International Journal of Mechanical Engineering, 2 (2012), 91-96, ISSN: 2230-7680. 11. Alazab Tari1, Abdullah H. Al-Essa, “Effect of Rectangular Perforation Aspect Ratio on Fin Performance”, International Journal of Heat

and Technology, 28 (2010), 53-60.

12. Alhassan Salami Tijani and Nursyameera Binti jaffri, “Thermal Analysis of Perforated Pin-Fins Heat Sink under Forced Convection condition”, Procedia Manufacturing, 24 (2018), 290-298.

13. Nabil J. Yasin and Mahmood H. Oudah, “The Effect of Solid and Perforated Pin Fin on the Heat Transfer Performance of Finned Tube

Heat Exchanger”, International Journal of Energy Engineering, 8 (2018), 1-11.

14. Rahul Sonawane, D.D.Palande, “Heat Transfer Enhancement by using perforation: A Review”, International Research Journal of

Engineering and Technology (IRJET), 3 (2016), e-ISSN: 2395 -0056.

15. Mehedi Ehteshum, Mohammad Ali, Md. Quamrul Islam and Muhsia Tabassum, “Thermal and Hydraulic performance analysis of Rectangular Fin Arrays with Perforation size and Number”, Procedia Engineering, 105 ( 2015 ), 184 – 191.

157-163

32.

Authors: Karthik Hebbar A, Prithvi C, Dr. Srinidhi Ramachandracharya

Paper Title: Analytical Modeling of Railway Suspension System using MATLAB Simulink

Abstract: In this paper the performance of quarter railway suspension system is investigated using MATLAB

Simulink under step input condition of the track. Integral Coach Factory (ICF) Bogie is considered for the

Analytical Modeling. A linear dynamic system model of quarter ICF Bogie is made and according to the model

the mathematical equations are written. The equivalent Simulink model corresponding to the equations are made

in MATLAB Simulink. The system is simulated under step input condition of the track to get the performance

characteristics such as displacement, velocity and acceleration. The result shows that for the given step input, the

major vibrations occurring to the bogie frame and the coach. These vibrations of the ICF Bogie affect the ride

comfort of the passenger. The addition of proper controlling element like hydraulic actuator controlled by PID

controller to the suspension system of ICF Bogie is suggested in order minimize the vibrations and to achieve

the ride comfort for the passenger

Keywords: Bogie, modeling, simulation, suspension

References:

1. Ash Soyic Leblebici and Semiha Turkey, “Track Modelling and Control of a Railway Vehicle,” IFAC-PapersOnLine 49-21 (2016)

274-281. 2. ICF Coaches, “Carriage and Wagon Engineers Hand Book,” pp.6–83.

3. Oytun Eris, Ali Fuat Ergenc, “A Modified Resonator for Active Suspension System of Railway Vehicles,” IFAC-PapersOnLine 48-12

(2015) 281-285. 4. K. Vishwanath Allamraju, “Nonlinear Behavior of Quarter Locomotive System” in Materials Today: Proceedings 5 (2018) 4887-4892.

5. M.M Moheyeldein, Ali M, Abd-El-Tawwab, M.M.M. Salem, “An analytical study of the performance indices of air spring suspensions

over the passive suspension” in Beni-Suef University Journal of Basic and Applied Sciences 7 (2018) 525–534. 6. Mahmoud Omar, M.M. El-kassaby, Walid Abdelghaffar, “Parametric numerical study of electrohydraulic active suspension

performance against passive suspension” in Alexandria Engineering Journal (2018) 57, 3609–3614.

7. Georges Kouroussis, David P. Connolly, Georgios Alexandrou, Konstatinos Vogiatzis, “The effect of railway local irregularities on ground vibartion” in Transportation Research Part D 39 (2015) 17–30.

164-168

33.

Authors: Rudresh B M, Ravikumar B N, Madhu D

Paper Title: Synergistic Effect of Hybrid Micro Fillers on Tensile and Flexural Properties of PA66/PTFE Blend

Micro Composites: Effect of Strain Rate

Abstract: Influence of strain rate on tensile and flexural properties of hybrid micro fillers filled PA66/PTFE

based micro composites was studied. The materials systems used for the study were Blend (PA66/PTFE)/

Molybdenum Disulphide(MoS2) (F1), Blend (PA66/PTFE)/Molybdenum Disulphide/Silicon Carbide(SiC)(F2)

and Blend (PA66/PTFE)/ Molybdenum Disulphide/Silicon Carbide /Alumina (Al2O3) (F3). The micro fillers

such as MoS2, SiC and Alumina were micro fillers for the development of micro composites. These hybrid

composites were developed using melt mix followed by extrusion. The effect of high strain rate (5, 25 and 50

mm/min) on tensile strength and (1.33, 2 and 3 mm/min) on flexural strength are studied as per ASTM method.

It was observed from the experimentation results that the increase in strain rate increases the strength of

composites both in tension and bending. The stress strain behavior is linear I the beginning later it was nonlinear.

The hybridization effect of hybrid fillers helps in shearing the load across the matrix. It was noticed that the

yield point of composites is a purely dependent on strain rate. The morphology of failure surfaces was studied

through scanning electron microscope photographs (SEM). They revealed that the fillers disintegration and

fillers dislocations are the failure mechanisms associated with the micro composites.

Keywords: Strain rate, PA66/PTFE, Hybrid fillers, Hybridization, tensile, flexural

References:

1. Tang, S.H.;Kong, Y.M.;Sapun,S.M. Design and thermal analysis of plastic injection mould. J.Mater.Process. Technol., vol.171 (2),

2006, pp. 259- 267

2. Chen W;Lu F; Cheng M. Tension and compressive tests of two polymers under quasi-static and dynamic loading. Polymer Test, vol.21, 2002,pp.113–121.

3. Kan C,Yang W,Yu W.. Experimental investigation and modeling of the tension behavior of polycarbonate with temperature effects

from low to high strain rates. Solids and structure, vol .51, 2014, pp.2539–2548. 4. Zhang A.;Zhao G.;Gao J.;Guan Y.Effect of Acrylonitrile-Butadiene- Styrene High- Rubber Powder and Strain Rate on the Morphology

and Mechanical Properties of Acrylonitrile-Butadiene-Styrene/Poly (Methyl Methacrylate) Blends. Polym-Plastics Tech and Engg., vol. 49:3,2010,pp. 296- 304.

5. Poomalai P, Siddaramaiah, Thermal and Morphological Studies on Poly(Methyl Methacrylate)/Thermoplastic Polyurethane Blends. J.

of Macromolecular Sci., Part A: Pure and App. Chem., vol. 43:4-5,2007, pp.695- 702. 6. Qifang, L.; Ming, T.; Kim, D.; Liqun, Z.; Riguang, J. Compatibility and thermal properties of poly(acrylonitrile–butadiene–styrene)

copolymer blends with poly(methyl methacrylate) and poly(styrene-acrylonitrile).J. Appl. Polym. Sci., vol. 85 (13),2002, pp.2652–

2660. 7. Hadriche I.;Ghorbel E.;Masmoudi N.;Halouani F.E. Influence of strain rate on the yielding behavior and on the self heating of

thermoplastic polymers loaded under tension. Key Engg.Mat. , vol. 446, 2010, pp.63-72.

8. Senol Sahin and Pasa Yayla, effect of testing parameters on the mechanical properties of polypropylene random copolymer, Polymer testing , vol. 24 , 2005, pp.613-619

9. Yuanxin Zhou and P K Mallick, Effects of temperature and strain rate on the tensile behavior of unfilled and talc filled polypropylene.

PART 1: Experiments, Polymer Engineering and Science, vol. 42 (12) , 2002, pp.2449- 2460 10. Shashidhara G.M.; Kameshwari Devi S.H.; Shreyas D. K. Effect of Thermoplastic Polyurethane content on properties of PC/TPU

169-176

blend filled with Montmorillonite. Carbon – Sci. & Tech., 5/1, 2013, pp.218 – 224 11. Tang, S.H, Kong, Y.M, and Sapun,S.M, Design and thermal analysis of plastic injection mould (2006), J. Mater. Process. Technol.,

vol. 171(2), 2006, pp. 259- 267

12. Rudresh B M, B N Ravikumar and Madhu D,(2016), Hybrid effect of micro fillers on the mechanical behavior of PA66/PTFE blend , Ind.J.adv.Chem.Sci., 4(1) :77-84

13. He D and Jiang B, The elastic modulus of filled polymer composites, (1993) J. of Appl. Polym. Sci., 49: 617-621

14. S. Bose and P.A. Mahanwar, Effect of particle size of filler on properties of nylon-6, J.Min.Mater. Char. Minerals and Materials Characterization, 3(1), 2004, pp. 23-31.

15. G. Zhang, M. Schehl and T. Burkhart, Effect of low-loading Nanoparticles on the tribological property of short carbon

fiber/PTFE/Graphite reinforced PEEK, Proceedings- ICCM-17, 2009 16. R. D. K. Misra, R. S. Hadal and S. J. Duncan, Surface damage behavior during scratch deformation of mineral reinforced polymer

composites, Acta Mater., vol. 52, 2004, pp. 4363-4373

17. L. H. Sun, Z. G. Yang and X. H. Li, Mechanical and tribological properties of polyoxymethylene modified with nano particles and solid lubricants, Polym. Eng.Sci. vol. 48, 2008, pp. 1824-1832.

34.

Authors: DeepthiAmith,Vinay K B, Y P Gowramma

Paper Title: Effective Strategies for Stress Management in Work Life Balance among Women Teaching

Profession

Abstract: This research paper proposes the effective strategies to manage the stress generated by the multirole

of the working women. Personal and professional balance itself is the multirole. Further multirole in personal

and professional balance needs effective strategies for the stress management are presented. The research

proposes at most stress management strategies such as effective utilization of professional time, at most

commitment towards work and balance of stress..etc are presented. Personal balance maintenance such as health

balance of herself, her family health, child care, elder health, economic balance, supportive spouse solves many

stress related issues. Mental health and mental food (spiritual solutions) are presented. Sample frame considered

as Kalpataru institute of Technology and Management women faculties are considered.

Keywords: Work life balance, Multirole, Stress management, Working women teaching profession

References:

1. Adams GA, KingLA,King DW(1996) “Relationships of job and family involvement, family social support, and work-family conflict

with job and life satisfaction ”,Journal of Applied Psychology.81:411-420. 2. NiharikaDoble and M.V. Supriya(2010),”Gender Differences in the Perception of Work-life Balance ”, Management 5(4),331-342.

3. ReimaraValk, VasanthiSrinivasan(2011) “Work-family balance of indian women software professionals: A qualitative study”,

Journalhomepage:www.elsvier.com/locate/iimb,23,39-50. 4. G.Delina and R.PrabhakaraRaya(2013),”A Study on Work-Life Balance in Working Women”, International Journal of commerce,

Bussiness and Management(IJCBM),2(5),274-282.

5. K.Santhana Lakshmi and S. SujathaGophinath(2013),” Work Life of Women Employees with Reference to Teaching Faculties”,

International Refereed Journal of Research in Management & Technology, Volume II,53-62.

6. K.ThriveniKumari and V.RamaDevi(2013), “Work-life Balance of Women Employees-A Challenge for The Employee and The

Employer in 21stcentury”,Pacific Business Review International, 6(6),33-40. 7. SatinderSingh,”Work-Life Balance:A Literature Review(2013)”, Global Journal of Commererce& Management perspective, 2(3),84-

91.

8. J. Sudha and P.Karthikeyan(2014), “ Work Life Balance of Women Employee:A Literature Review”, International Journal of Management Research& Review, 4(8), 797-804.

9. K.Maran and S. Usha(2014), “Work Life Balance of Women Employees Satisfaction –A study with Reference to it Sector in India”,

Asia Pacific Journal of Research, 1(15), 127-133. 10. ShobhaSudaresan(2014), “Work- Life Balance- Implications For Working Women”, http://www.ssrn.com/link/OIDA-Intl-Journal-

Sustainable-Dev.html,7(7),93-101.

11. Rahul Signal, Parvash(2015), “Work bLife Balance of Women Employees with Reference to Teaching Faculties”, International Journal of Research in Management, Science & Technology, 3(3), 53-56.

12. Michael Galanakis, Anastasia Palaiologou,GeorgiaPatsi, Ioanna-Maria Velegraki,ChristinaDarviri(2016), “A Literature Review on the

Connection between Stress and Self-Esteem”,ScientificResearch Publishing,Psychology,7,687694.http://dx.doi.org/10.4236/psych.2016.75071.

13. TapasyaJulka, UrvikaMathur(2017),”A Conceptual Study of Work-Life Balance among Women Employees”, International Journal of

Emerging Research in Management & Technology, 6(2),74-78. 14. K.Agha, F.T.Azmi, and A.Irfan(2017), “work-life Balance and Job Satisfaction:An Empirical study Focusing on Higher Educating

Teachers in Oman”, International Journal of Social Science and Humannity,7(3),164-171.

15. MayeshaTasnim, MuhammedZakirHossain, FabinaEnam(2017), “Work-Life Balance: Reality Check for the Working Women of Bangladesh”, Journal of Human Resource and Sustainability Studies, Volume 5, 75-86.

16. Mark Wickham, Melissa Parker(2007-2008), “Effectively Managing the Work-Family and Work-Life Balance: An Organizational

Role Theory Perspective” University of Tasmania school of Management ,Locked Bag 16 Hobart Campus Tasmania AUSTRALIA, 7001.

17. Santhosh Kumar Das, Abhishek Kumar, Bappaditya Das and A.P. Burnwal(2013),”On Soft Computing Techniques in Various Areas”,

CS&IT-CSCP, 59-68, 18. Lotfi A. Zadesh(1994), “Fuzzy Logic, Neural Network and Soft Computing”, Communication of the ACM, 37(2), 77-84.

19. Victor Davadoss and J. BefijaMinnie(2014), “Analyzing the personal Factors Causing Work Life Imbalance using Induced Fuzzy

Cognitive Mapping(IFCM)”, International Journal of Science & Engineering Research, 5(6),502-206. 20. A.VictorDevadoss and J. Befija Minnie(2013),“A Study of Personality influence in building Work life balance using Induced Bi-

Directional Associative Memories(IBAM)”, international Journal of Engineering Research & Technology(IJERT), 2(11), 4137-4142.

21. Tammy D.Allen, Kaitlin M. Kiburz, “Trait mindfulnass and work-family balance among working parents: The mediating effects of vitality and sleep quality”, Journal of Vocational Behavior,Elsevier Science, 80(2012),372-379.

22. Tait D Shanafelt, Sonja Boone, Litjen Tan, Lotte N. Dyrdye (2012), Burnout and Satisfaction with Work-Life Balance Among US Physicians Relative to the General US Population”, American Medical Assiciation. 172(18), 1377-1385.

23. Affandi H, Raza N(2013) Leaders’ Emotional intelligence and its outcomes, A Study of medical professionals in Pakistan,

Interdisciplinary Journal of Contemporary Research in Bussiness. 5(7), 279-297.

24. Kristine Scotto ,”The Importance of Work-Life Balance and Health”, www.twitter.com/kristinescotto.

25. Andre Costa and Adriano Veloso, “Employee Analysis through Sentiment Analysis”,http://www.gallup.com/services/176708/state-

american-workplace.aspx.

177-182

26. P. Julia Grace and N.NasreenaBanu, “Machine on Emotional Intelligence and Work Life Balance”, International Journal of Computer Applications,116(10),36-39.

27. F. Mokhayeri and M-R. Akbarzadesh,”Mental Stress Detection Based on Soft Computing Techniques”, IEEE International Conference

on Bioinformatics and Biomedicine,430-433. 28. Lore Arthur, “Work-Life Balance: Towards an Agenda for Policy Learning Between Britain and Germany ”, Centre for Education

policy and management, Faculty of Education and Languages, Yhe Open University, UK.

29. Jeffrey H.Greenhaus, Karen M.Collins, and Jasonada- D. Shaw,“The relation between work-family balance and quality of life”, Journal of Vocational Behavior, 63,510-531.

30. Cara Willians, “Work-life balance of shift Workers”, Statistics Canada-catalogue no. 75-001-X.5-16.

31. Jennifer Smith, Dianne Garder,“Factors Affecting Employees Use of Work-Life Balance Initiatives” ,School of Psycology Massey University, Auckland, New Zealand.

32. Terence Hogarth, Chris Hasluck and GaellePierre , “Work-Life Balance 2000:Results from the Baseline Study “, Institute for

Employment Research with Mark Winterbotham and David Vivian IF Research,Research Report RR249. 33. T. Alexander Beauregard and Lesley C. Henry, “Marking the link between Work-life balance practices and organizational

performance”, The LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE,

PP49,DOI:http://dx.doi.org/10.1016/i.hrmr.2008.09.001. 34. J Kodz, HH Harper, S Dench,” Work-Life Balance : Beyond the Rhetoric” ,The Institute for employment Studies, MantellBulding,

Falmer, Brighton BNI 9RF UK.

35.

Authors: R. H Desai, L. Krishnamurthy T.N. Shridhar

Paper Title: Fabrication and Performance of Areca Short Fiber Polypropylene Composite at Varying Fiber

Loading

Abstract: The primary focus of this research is to study the density, impact strength, hardness and dielectric

properties polypropylene areca fiber composite reinforced with randomly distributed and varying areca fiber

loading. The test samples of plain polypropylene, polypropylene areca fibers composites have been prepared as

per ASTM standards using injection moulding technique. Different fiber weight loading fractions (10%, 30%,

50%, & 70%) have been used to prepare test samples. The developed polypropylene areca fiber composites have

been characterized for density, izod impact strength, hardness and dielectric strength test. Result showed that

improvement in the properties of polypropylene areca fibers composites increase with fiber loading compare to

plain polypropylene.

Keywords: Areca Fiber, Density, Izod impact, Polypropylene.

References:

1. Srinivasa Chikkol Venkateshappa, Suresh Yalaburgi Jayadevappa, Prema Kumar Wooday Puttiah, “Mechanical Behavior of Areca

Fiber Reinforced Epoxy Composites,” Advances in Polymer Technology, 2012, 31: No. 4, pp. 319–330.

2. C.V. Srinivasa and K.N. Bharath, “Impact and Hardness Properties of Areca Fiber-Epoxy Reinforced Composites,” Journal of

Materials and Environmental Science, 2011, 2 (4), pp. 351-356.

3. Salma Siddika and Ahmed Sharif, “Processing and Characterization of Areca and Waste Nylon Fiber Reinforced Hybrid

Polypropylene Composites,” International Journal of Innovation and Scientific Research, 2015, 19, pp. 319-330.

4. Dr. G. Ramachandra Reddy, Dr. M. Ashok Kumar, K.V.P. Chakradhar, “Fabrication and performance of hybrid Betel nut (Areca catechu) short fiber/ Sansevieriacylindrica (Agavaceae) epoxy composites,” International Journal of Materials and Biomaterials

Applications, 2011, 1 (1), pp. 6-13.

5. G. C. Mohan Kumar, “A Study of Short Areca Fiber Reinforced PF Composites,” Proceedings of the World Congress on Engineering, 2008.

6. M. Masudul Hassan, Manfred H. Wagner, Hayder U. Zaman and Mubarak A. Khan, “Study on the Performance of Hybrid Jute/Betel

Nut Fiber Reinforced Polypropylene Composites,” Journal of Adhesion Science and Technology, 2011, (25), pp. 615–626. 7. W. L. Lai, M. Mariatti, and Mohamad Jani S, “The Properties of Woven Kenaf and Betel Palm (Areca catechu) Reinforced

Unsaturated Polyester Composites,” Polymer-Plastics Technology and Engineering, 2008, (47), pp. 1193–1199.

8. Elammaran Jayamani, SininHamdan, Md Rezaur Rahman and Muhammad Khusairy Bin Bakri, “Investigation of Fiber Surface Treatment on Mechanical, Acoustical and Thermal Properties of Betelnut Fiber Polyester Composites,” ProcediaEngineering, 2014, 97,

pp. 545 – 554.

9. Kestur G. Satyanarayana, Gregorio G.C. Arizaga, Fernando Wypych, “Biodegradable Composites Based On Lignocellulosic Fibers—An Overview,” Progress in Polymer Science, 2009, 34 (9), pp. 982–1021.

10. Umar Nirmal, Jamil Hashim, Saijod TW Lau, Yuhazri MY and BF Yousif, “Betelnut fibres as an alternative to glass fibres to reinforce

thermoset composites: A comparative study,” Textile Research Journal, 2012. 82(11), pp. 1107–1120. 11. B Fyousif and U Nirmal, “On the mechanical properties of a treated betelnut fibre-reinforced polyester composite,” Journal of

Adhesion Science and Technology, 2011, (25), pp. 615–626.

183-186

36.

Authors: T. Babu, R. Sudharshan, R. Akil, S. Chiranjeev

Paper Title: Analysis and Shape Optimization of disc Brake with Alternate Material

Abstract: Every decade the automotive industry has seen significant inventions and improvements, which

diversified the industry's spectrum in whatever ways possible. One such significant invention cum improvement

in the brake systems is the disc brake system. Disc brakes gave way for the commercialization of high-powered

racing bikes. The disc brakes had its own way of revolution in their shape, size and application from their

inception. This paper deals with the shape and size optimization of the disc brakes and analyzing the same with

different probable materials that can be employed for the application considered. Designing three different

shapes of discs and analyzing them with two different materials produces a wide range of conclusions with some

favoring results.

Keywords: Analysis, Disc Brakes, Design, Shape Optimization

References:

1. Vishal Asokan, Arshad Mohammed Gani, Vimal Mohammed Nooh Muballigh, Muthu Mohammed Inzamam Bari. Design and

187-192

Analysis of Reinforced Composite Matrix Disc Brake”. International Journal of Engineering Research and General Science Volume 3, Issue 5, September-October, 2015, ISSN 2091-2730.

2. D. Bhadgaonkar, A. Singh, S. S. Jadhav, S. S. Jadhav. “Vibrational Analysis of Disc Brake Rotor of a Two-Wheeler to Find the Defect

Using Fea”. Iosr Journal of Mechanical and Civil Engineering (Iosr-Jmce) E-Issn: 2278-1684, P-Issn: 2320-334x 3. Shah E Alam, Yuvraj Vidhyadhar, Prashant Sharma, Abhishek Jain. “Thermal Analysis of Disc Brakes Rotor: A Comparative Report”.

SciTech Volume 3, Issue 2 Research Organization April 20, 2015.

4. Hui Lua, Dejie Yu B. “Optimization Design of a Disc Brake System with Hybrid Uncertainties”. Advances in Engineering Software 98 (2016) 112–122.

5. Harshal Suresh Shinde. “Structural Analysis of Disc Brake Rotor for Different Materials”. International Research Journal of

Engineering and Technology (Irjet). Volume: 04 Issue: 07, July -2017. 6. Swapnil D. Kulkarni, J.J.Salunke. “Thermal Analysis of Brake Disc”. International Journal of Research in Engineering and Technology

Eissn: 2319-1163, Pissn: 2321-7308.

7. Prasanth P Suryawanshi, Amol R Tanpore, Sachin R Dhatrack, Bhavesh Lokhande, Rohan D Hucche. “Thermal and Structural Analysis of Two-Wheeler Disc Brake”. International Journal of Science Technology & Engineering, Volume 3, Issue 07, January

2017.

8. Lemi Abebe, Ramesh Babu Nallamothu, K.H.S Subrahmanyam, Seshu Kishan Nallamothu, Anantha Kamal Nallamothu. “Thermal Analysis of Disc Brake Made of Different Materials”. Ssrg International Journal of Mechanical Engineering (Ssrg-Ijme) – Volume 3

Issue 6 – June 2016.

9. Sumeet Satope, Akshaykumar Bote, Swapneel D. Rawool. “Thermal Analysis of Disc Brake”. Ijirst –International Journal for Innovative Research in Science & Technology, Volume 3, Issue 12, May 2017.


Engineering Research and Development, Vol. 07, pp. 551-560, 2017. 11. Sathish, T., Periyasamy, P. “Modelling of HCHS system for optimal E-O-L Combination section and Disassembly in Reverse


12. Sathish, T., Muthulakshmanan, A. “Design and simulation of connecting rods with several test cases using AL alloys and high Tensile


2018.

13. Sathish, T., Muthukumar, K., Palani Kumar, B., "A study on making of compact manual paper recycling plant for domestic purpose", International Journal of Mechanical and Production Engineering Research and Development, vol. 8, no. Special Issue 7, pp. 1515-

1535, 2018.

14. Sathish, T., "Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal", Progress in Industrial Ecology, vol. 12, no. 1-2, pp. 112-119, 2018.

15. Sethiil, P.V., Aakash Sirusshti V.S., Sathish, T., "Equivalent stress prediction of automobile structural member using FEA-ANN

Technique", International Journal of Mechanical and Production Engineering Research and Development, vol. 9, no. 2, pp. 757-768, 2019.

16. Sathish, T., Chandramohan, D., "Experimental study and model development for on-line drill wear monitoring system using lab view",

International Journal of Recent Technology and Engineering, vol. 7, no. 6, pp. 281-286, 2019.

37.

Authors: A.Sivanesh Kumar, S.Brittoraj, M.Rajesh

Paper Title: Implementation of RFID with Internet of Things

Abstract: : Internet of Things (IOT) can be defined as a thing or device, physical and virtual, connected and

communicating together, and integrated to a network for a specific purpose. The IoT uses technologies and

devices such as sensors, RFID (radio frequency identification) and actuators to collect data. IoT is not only about

collecting data generated from sensors, but also about analyzing it. IoT applications must, of necessity, keep out

all attackers and intruders so as to thwart attacks. IoT must allow for information to be shared, with every

assurance of confidentiality, and is about a connected environment, where people and things interact to enhance

the quality of life. IoT infrastructure must be open source, without ownership, meaning that anyone can develop,

deploy and use it. The objective of this paper is to discuss the various challenges, issues and applications

confronting the Internet of Things. The world has shrunk considerably with the dramatic growth in Internet

usage. Every computer and mobile phone in the world can be connected together through Internet technology.

As a result, intelligent devices are connected and communicate together. The Internet of Things envisions a

future where people and intelligent systems cooperate and work together. In the IoT, machine-to-machine

communication helps devices exchange data, requiring power, efficiency, security and reliability. This paper

advances new ideas for designing a security protocol in the IoT so as to facilitate secure machine-to-machine

communication.

Keywords: Internet of Things; Architecture.

References: 1. Amir VahidDastjerdi and RajkumarBuyya, “Fog Computing: Helping the Internet of Things Realize Its Potential”, IEEE

Communications Society, August 2016.

2. ArefMeddeb, “Internet of Things Standards: Who Stands Out from the Crowd?”, IEEE Communications Magazine - Communications Standards Supplement, July 2016.

3. ConstantinosKolias and AngelosStavrou, Irena Bojanova, and Richard Kuhn, “Learning Internet of Things Security Hands-on”,

Copublished by the IEEE Computer and Reliability Societies, January/February 2016. 4. DusitNiyato, Dinh Thai Hoang, Nguyen Cong Luong, Ping Wang, Dong In Kim, and Zhu Han, “Smart Data Pricing Models for

the Internet of Things: A Bundling Strategy Approach”, IEEE Network, March/April 2016.

5. David Park, “The Quest for the Quality of Things: Can the Internet of Things deliver a promise of the quality of things?”, IEEE Consumer Electronics Magazine, April 2016.

6. Daqiang Zhang, Laurence Tianruo Yang, Min Chen, Shengjie Zhao, MinyiGuo, and Yin Zhang, “Real-Time Locating Systems

Using the Active RFID for the Internet of Things”, IEEE Systems Journal, Vol. 10, No. 3, September 2016. 7. David Metcalf, Sharlin T. J. Milliard, Melinda Gomez, and Michael Schwartz, “Wearables and the Internet of Things for Health”,

IEEE Pulse, September / October 2016.

8. Glenn Parsons, “The Internet of Things”, IEEE Communications Magazine, July 2016. 9. Guiou Kobayashi, Maria Eunice Quilici-Gonzalez, Mariana Claudia Broens, and José ArturQuilici-Gonzalez, “The Ethical

Impact of the Internet of Things in Social Relationships”, IEEE Consumer Electronics Magazine, July 2016.

10. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, “On the Networking of Internet of Things: Explorations and Challenges”, IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016.

193-197

11. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, “On the Networking of Internet of Things: Explorations and Challenges”, IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016.

12. Jonathan Margulies, “Garage Door Openers: An Internet of Things Case Study”, IEEE Computer and Reliability Societies,

July/August 2015. 13. KeshavSood, Shui Yu, and Yong Xiang, “Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-

Things: A Review”, IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016.

14. Michele Nitti, Virginia Pilloni, Giuseppe Colistra, and Luigi Atzori, “The Virtual Object as a Major Element of the Internet of Things,” IEEE Communications Surveys & Tutorials, Vol. 18, No. 2, Second Quarter 2016.

15. Mohammad AbdurRazzaque, MarijaMilojevic-Jevric, Andrei Palade, and Siobhán Clarke, “Middleware for Internet of Things: A

Survey”, IEEE Internet of Things Journal, Vol. 3, No. 1, February 2016. 16. Maria Rita Palattella, MischaDohler, and Alfredo Grieco, “Internet of Things in the 5G Era: Enablers, Architecture, and Business

Models”, IEEE Journal of Selected Areas in Communications, Vol. 34, No. 3, March 2016.

17. Mohamed EssaidKhanouche, YacineAmirat, AbdelghaniChibani, MoussaKerkar, and Ali Yachir, “Energy-Centered and QoS-Aware Services Selection for Internet of Things”, IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 3,

July 2016.

18. Oladayo Bello and SheraliZeadally, “Intelligent Device-to-Device Communication in the Internet of Things”, IEEE Systems Journal, Vol. 10, No. 3, September 2016.

19. Phillip A. Laplante and Nancy Laplante, “The Internet of Things in Healthcare: Potential Applications and Challenges”, IT Pro,

IEEE Computer Society, May/June 2016. 20. PawaniPorambage, Mika Ylianttila, Corinna Schmitt, Pardeep Kumar, Andrei Gurtov, and Athanasios V. Vasilakos, “The Quest

for Privacy in the Internet of Things”, IEEE Cloud Computing, March/April 2016.

21. Phillip A. Laplante, JefreyVoas, and Nancy Laplante, “Standards for the Internet of Things: A Case Study in Disaster Response”, IEEE Computer Society, May 2016.

22. Sara Amendola, RossellaLodato, Sabina Manzari, Cecilia Occhiuzzi, and Gaetano Marrocco, “RFID Technology for IoT-Based

Personal Healthcare in Smart Spaces”, IEEE Internet of Things Journal, Vol. 1, No. 2, April 2014.

23. Yi Xu and AbdelsalamHelal, “Scalable Cloud–Sensor Architecture for the Internet of Things”, IEEE Internet of Things Journal,

Vol. 3, No. 3, June 2016.

24. Yunchuan Sun, Houbing Song, Antonio J. Jara, and RongfangBie, “Internet of Things and Big Data Analytics for Smart and Connected Communities”, Digital Object Identifier 10.1109/Access, March 2016.

25. YuvrajAgarwal and Anind K. Dey, “Toward Building a Safe, Secure, and Easy-to-Use Internet of Things Infrastructure”, IEEE

Computer Society, April 2016. 26. Zhangbing Zhou, Beibei Yao, Riliang Xing, Lei Shu, and Shengrong Bu, “E-CARP: An Energy-Efficient Routing Protocol for

UWSNs in the Internet of Underwater Things”, IEEE Sensors Journal, Vol. 16, No. 11, June 2016.

27. S.P. Raja, T. DhiliphanRajkumar and VivekPandiya Raj, Internet of Things: Challenges, Issues and Applications, Journal of Circuits, Systems and Computers, Vol. 27, No. 12, 2018.

28. S.P. Raja, T. Sampradeepraj, Internet of Things: a Research oriented Introductory, International Journal of Ad Hoc and

Ubiquitous Computing, Vol. 29, No. 1/2, 2018. 29. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289.

30. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7 (2015): 1-4.

31. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

32. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

33. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

34. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and Intelligent Systems 6.8: 24-26.

38.

Authors: Ganesh Babu Rajendran, Amudha Veerappan

Paper Title: Distributed Cooperative Ai Techniques for Cognitive Radio Networks

Abstract: Cognitive radio has emerged as one of the Next Generation(xG) wireless communication systems and dynamic

spectrum access utilization in next generation cellular networks. In this paper, a literature survey of many spectrum sensing

techniques are studied and the comparative results are analyzed. Also the challenges and techniques that are involved in

spectrum sensing is discussed in detail. Cooperative Sensing based Artificial Intelligence(AI) technique provides preferable

stability and scalability because of its low complexity under dynamic Primary Users(PUs) activity. Cooperation between

Secondary users to avoid interference, reduce the average time to sense the primary users and to solve the hidden node

problem.

Keywords: Primary User, Secondary User, Cognitive radio, Spectrum sensing, Dynamic spectrum access.

References: 1. Kaushik MK, Yoganandam Y, Sahoo SK.Sensing and Sharing Schemes for Spectral Efficiency of Cognitive

Radios. International Journal of Electrical and Computer Engineering. 2018; 8(5): 2934–2941.

2. Yu Y, Ji Y, Wang W, Zhang Y.Adaptive Two-Stage Sensing in Cognitive and Dynamic Spectrum Access Networks.

TELKOMNIKA Indonesian Journal of Electrical Engineering.2014; 12(5): 3257–3265.

3. Mahajan R, Bagai D.Improved Learning Scheme for Cognitive Radio using Artificial Neural Networks. International Journal of

Electrical and Computer Engineering. 2016; 6(1): 257–267.

4. ArslanH. Cognitive Radio. Software Defined Radio, and Adaptive Wireless Systems. Netherlands.Springer. 2007.

5. Mitola J. Software Radio Architecture: Object-Oriented Approaches to Wireless System Engineering. John Wiley & Sons Ltd.

2000.

6. Biglieri E, GoldsmithAJ, GreensteinLJ, MandayamNB,PoorHV.Principles of Cognitive Radio.Cambridge University Press. 2013.

7. Chen K-C, PrasadR. Cognitive Radio Networks. John Wiley & Sons Ltd. Chichester. UK. 2009.

198-203

8. KhattabA, PerkinsD, MagdyB. Cognitive Radio Networks-From Theory to Practice. Springer Series.Analog Circuits and Signal

Processing. 2009.

9. JasimAM, Al-AnbagiHN.A comprehensive study of spectrum sensing techniques in cognitive radio networks. Proceedings of

IEEE Int. Conference Current Research in Computer Science and Information Technology (ICCIT). Slemani. Iraq.2017; 107–

114.

10. Nguyen-Thanh N, KooI.A Cluster Based Selective Cooperative Spectrum Sensing Scheme In Cognitive Radio.EURASIPJournal

on Wireless Communications and Networking.2013; 1(176): 1–9.

11. ShindeSC,JadhavAN.Centralized cooperative spectrum sensing with energy detecion in cognitive radio and

optimization.Proceedings ofIEEE Int. Conference Recent Trends in Electronics Information and Communication Technology

(RTEICT). Bangalore. India.2016;

12. MehtaT, KumarN,SainiSS.Comparison of Spectrum Sensing Techniques in Cognitive Radio Networks.International Journal of

Electronics and Communication Technology.2013; 4(3): 33–37.

13. LiZ, YuFR, HuangMA Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios.IEEE

Transactions on Vehicular Technology.2010; 59(1): 383–393.

14. AkyildizIF, LeeW-Y, VuranMC,MohantyS.NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A

survey.Computer Networks Elsevier.2006; 50(13): 2127–2159.

15. TangH, YuFR, HuangM, LiZ.Distributed consensus based security mechanisms in cognitive radio mobile ad hoc networks.IET

communications.2012; 6(8): 974–983.

16. ZhangH, JiangC, MaoX, ChenH-H.Interference Limited Resource Optimization in Cognitive FemtocellsWith Fairness and

Imperfect Spectrum Sensing. IEEE Transactions on Vehicular Technology 2016; 65(3):1761–1771.

17. Owayed AA, MohammedZA,MosaAAR.Probabilities of Detection and False Alarm in Multitaper Based Spectrum Sensing for

Cognitive Radio Systems in AWGN.Proceedings IEEE Int. Conference Communication Systems (ICCS). Singapore. 2010; 579–

584.

18. HillenbrandJ.Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems. IEEE Communications

Letters2005; 9(4): 349–351.

19. AroraK, SngalTL, MehtaT.Simulation of Probability of False Alarm and Probability of Detection Using Energy Detection in

Cognitive Radio.International Journal of Computer Science and Technology.2015; 6(1): 37–41.

20. ShanmugavelS, BhagyaveniMA,KalidossR. Cognitive Radio: An Enabler for Internet of Things. Netherlands .River

Publishers.2010.

21. SulimanIM,LehtomakiJ,UmebayashiK.On the effect of false alarm rate on the performance of cognitive radio

networks.EURASIP Journal on Wireless Communications and Networking.2015;1(244): 1–17.

22. DongX, LiY, WuC,CaiY.A learner based on neural network for cognitive radio.Proceeding IEEE Int. ConferenceCommunication

Technology (ICCT). Nanjing. China. 2010; 893–896.

23. R. S. Heydari, Alirezaee, Makki SV, AhmadiM,ErfaniS.Cognitive radio channel behavior prediction using the hidden Markov

model.Seventh Int. Symposium Telecommunications (IST). Tehran.Iran.2014; 993–998.

24. Le H-ST, LyHD, Opportunistic spectrum access using Fuzzy Logic for cognitive radio networks.Proceeding IEEE Int.

ConferenceCommunications and Electronics (ICCE). Hoi an Vietnam. 2008; 240–245.

25. MorabitYE, MrabtiF,AbarkanEH. Spectrum allocation using genetic algorithm in cognitive radio networks.Third International

Workshop on RFID and Adaptive Wireless Sensor Networks (RAWSN).Agadir Morocco.2015; 90–93.

26. ZhaoZ-J, LaiH-C.A cognitive engine based on case-based reasoning quantum genetic algorithm.Proceeding IEEE Int.

ConferenceCommunication Technology (ICCT). Chengdu. China. 2012; 224–228.

27. ZhangH, ZhangZ,ChauY.Energy efficient spectrum aware clustering for cognitive radio sensor networks. Chinese Science

Bulletin2012; 57(28–29):3731–3739.

28. Syed Ali FathimaK. SumithaT.To Enhance the Lifetime of WSN Network using PSO.International Journal of Innovative

Research in Computer and Communication Engineering 2014; 2(1): 1–6.

29. VyasV,MondaA.PSO Based Clustering Approach for WSN. International Journal of Emerging Technologies in Engineering

Research2016; 4(10):48–52.

30. CaiX, CuibZ, ZengJ, TanaY.Dispersed particle swarm optimization.Information Processing Letters.2008; 105(6): 231–235.

31. AnumandlaKK, KudikalaS, VenkataBA,SabatSL.Spectrum Allocation in Cognitive Radio Networks Using Firefly

Algorithm.Proceeding Int. ConferenceSwarm, Evolutionary, andMemetic Computing (SEMCCO2013).Chennai. India.

2013;366–376.

32. SenthilnathJ, OmkarSN, ManiV.Clustering using firefly algorithm: Performance study, Swarm and Evolutionary

Computation.Elsevier. 2011; 1(3):164–171.

33. ManshahiaMS.A Firefly Based Energy Efficient Routing in Wireless Sensor Networks. African Journal of Computing and ICT

2015; 8(4): 27–32.

34. PalSK, RaiCS,SinghAP.Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non Linear.

International Journal of Intelligent Systems and Applications 2012; 4(10): 50–57.

35. SarmaNVSN,GopiM.Energy Efficient Clustering using Jumper Firefly Algorithm in Wireless Sensor Networks. International

Journal of Engineering Trends and Technology2014;10(11): 525–532.

36. Akyildiz IF, LoBF,BalakrishnanR.Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication

2011; 4(1): 40–62.

37. ZhangX, TaoX,CuiQ.Uncoordinated Cooperative Multihop Forwarding in 2D Highly Dynamic Networks. International Journal

of Distributed Sensor Networks 2015; 11(8): 1–13.

38. Ebrahim A, Alsus E,Baidas MW.An Uncoordinated Frequency Allocation Scheme for Future Femtocell Networks.Proceedings

IEEE Int. Conference Wireless Communications and Mobile Computing Conference (IWCMC). Paphos. Cyprus. 2016; 239–243.

39. Ganesh Babu, R. “Helium's Orbit Internet of Things (IoT) Space.” International Journal of Computer Science amd Wireless

Security 3.2 (2016): 123-124.

40. Ganesh Babu, R. “Mismatch Correction of Analog To Digital Converter In Digital Communication Receiver.” International

Journal of Advanced Research Trends In Engineering and Technology 3.19 (2016): 264-268.

41. Ganesh Babu, R. and Dr.V.Amudha. “Analysis of Distributed Coordinated Spectrum Sensing in Cognitive Radio Networks.”

International Journal of Applied Engineering Research 10.6(2015): 5547-5552.

42. Ganesh Babu, R. “Resource Allocatıon in QoS Schedulıng for IEEE 802.16 Systems.” International Journal of Science and

Innovative Engineering & Technology 1.5 (2016): 50–55.

43. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless


44. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex

Systems 5.7 (2015): 1-4.

45. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International

Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

46. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION

TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

47. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

48. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering

and Intelligent Systems 6.8: 24-26

39.

Authors: Asha Abraham, Sumithra M

Paper Title: Health Care Systems using Machine Learning

Abstract: The study of different age people from different lifestyle, what all symptoms came for the disease,

at what stage, what measures taken to get rid of, later changes, what were the side effects, how it decreased

or increased will help in future prediction of possible chance for illness in others. A System which having

the above details, suggestions from good and expert practitioners, can be used to give warnings to people

about the possibility to get affected after 5 or 10 years and to take pre-cautions . Combinational outcome of

Descriptive, Predictive and Prescriptive Data Analytics methods on past and present structured and

unstructured Big Data can be used to predict future after effects which should be prioritized and treated.

Suggestions can be given to people to take appointment with dieticians, change food habits, perform

exercises, practice remedial measures etc. Right step at correct time save lives, gives happiness to families,

reduces medical expenditures. The relevant information hidden in massive amount of data are made

available by the AI assistants to make better clinical decisions in the functional areas of healthcare. To make

such a system, a detailed study on big data, analytics methods, health care, practicing methods, electronic

health records etc is required. A preventive guidance and less cost expert system which is helpful for

common man and experts, for immediate solution and care can be developed in future.Deep machine

learning algorithms to detect later possibility of occurrence should be developed. For this a study on Big

Data and Health Care Analytics is done.

Keywords: Big Data,Data Analytics, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Volume, Velocity,

Veracity

References:

1. Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing Xiao Ma,1 Zie Wang,1 Sheng Zhou,1

Haoyu Wen,1 and Yin Zhang 1,2 Received 9 January 2018; Accepted 20 May 2018; Published 3 July 2018 Academic Editor: Javier Prieto Wireless Communications and Mobile Computing Volume 2018, Article ID 3928080, 16 pages

https://doi.org/10.1155/2018/3928080

2. Big Data in Healthcare Management: A Review of Literature

3. Senthilkumar SA1, Bharatendara K Rai2, Amruta A Meshram2, Angappa Gunasekaran3, Chandrakumarmangalam

S4American Journal of Theoretical and Applied Business 2018; 4(2): 57-69 http://www.sciencepublishinggroup.com/j/ajtab doi: 0.11648/j.ajtab.20180402.14 ISSN: 2469-7834 (Print); ISSN: 2469-7842

4. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining,Md Saiful Islam,Md

Mahmudul Hasan,Xiaoyi Wang,Hayley D. Germack,and Md Noor-E-

Alam,Available:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023432/#B8-healthcare-06-00054

5. Big Data and Its Challenges V. Maria Antoniate Martin*1, Dr. K. David2, A.Vignesh3 . Volume 3, Issue 3 | March-April-2018 | ISSN : 2456-3307. http:// ijsrcseit.com

6. Artificial intelligence in healthcare: past, present and future. Fei Jiang1, Yong Jiang2, Hui Zhi3, Yi Dong4, Hao Li5, Sufeng

Ma6, Yilong Wang7, Qiang Dong4 Haipeng Shen8, Yongjun Wang9https://svn.bmj.com/content/2/4/230

7. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data MiningMd Saiful Islam 1, Md

Mahmudul Hasan 1, Xiaoyi Wang 1, Hayley D. Germack 1,2,3 and Md Noor-E-Alam 1, ID

https://www.mdpi.com/journal/healthcare

8. AI based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics using Reactive Programming Jagreet

Kaur1, Dr. Kulwinder Singh Mann2 1Data Scientist, XenonStack, IT Park, Chandigarh, India. 2Professor and Head of the IT Department, GNDEC, Ludhiana, India IOP Conf. Series: Journal of Physics: Conf. Series 933 (2017) 012010 doi

:10.1088/1742-6596/933/1/012010

9. Big Data Analytics in Healthcare. Ashwin Belle, 1 , 2 Raghuram Thiagarajan, 3 S. M. Reza Soroushmehr, 1 , 2 , * Fatemeh

Navidi, 4 Daniel A. Beard,andKayvanNajarianhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503556/

10. http://www.dataversity.net/data-analytics-important-healthcare/

11. https://archive.siam.org/meetings/sdm13/sun.pdf

12. Analysis of Research in Healthcare Data Analytics. https://arxiv.org/ftp/arxiv/papers/1606/1606.01354.pdf

13. https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/


15. https://www.investopedia.com/terms/s/statistics.asp

16. https://www.framinghamheartstudy.org

17. Alexander1 and Lidong Wang2 Big Data Analytics in Heart Attack Prediction Cheryl Ann 1Department of Nursing,

University of Phoenix, J Nurs Care 2017, 6:2 DOI: 10.4172/2167-1168.1000393

18. The value of analytics in healthcare: From insights to outcomes, J. W. Cortada, D. Gordon, B. Lenihan, IBM Global Business

Services, Executive Report, 2012.

204-207

https://doi.org/10.1155/2018/3928080

http://www.sciencepublishinggroup.com/j/ajtab

https://www.ncbi.nlm.nih.gov/pubmed/?term=Islam%20MS%5BAuthor%5D&cauthor=true&cauthor_uid=29882866

https://www.ncbi.nlm.nih.gov/pubmed/?term=Hasan%20MM%5BAuthor%5D&cauthor=true&cauthor_uid=29882866


https://www.ncbi.nlm.nih.gov/pubmed/?term=Wang%20X%5BAuthor%5D&cauthor=true&cauthor_uid=29882866

https://www.ncbi.nlm.nih.gov/pubmed/?term=Germack%20HD%5BAuthor%5D&cauthor=true&cauthor_uid=29882866

https://www.ncbi.nlm.nih.gov/pubmed/?term=Noor-E-Alam%20M%5BAuthor%5D&cauthor=true&cauthor_uid=29882866


https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023432/#B8-healthcare-06-00054

https://svn.bmj.com/content/2/4/230#aff-1










https://svn.bmj.com/content/2/4/230


https://www.ncbi.nlm.nih.gov/pubmed/?term=Belle%20A%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Thiagarajan%20R%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Soroushmehr%20SM%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Navidi%20F%5BAuthor%5D&cauthor=true&cauthor_uid=26229957


https://www.ncbi.nlm.nih.gov/pubmed/?term=Beard%20DA%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Najarian%20K%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503556/

http://www.dataversity.net/data-analytics-important-healthcare/

https://archive.siam.org/meetings/sdm13/sun.pdf

https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/


https://www.framinghamheartstudy.org/

19. Promises and Challenges of Big Data Computing in Health Sciences, Big Data T. Huang, L. Lan, X. Fang, P. An, J. Min, F.

Wang, Res. 2 (2015) 2–11. doi:10.1016/j. bdr.2015.02.002.

20. Perspectives on Big Data applications of health information. Current Opinion in Systems Biology. Cano, I., et al., (2017) 3:

36-42. 2.

21. Exploring the path to big data analytics success in healthcare. Journal of Business Research. Wang, Y. and N. Hajli, (2017)

70: 287-299. 3.

22. Electronic Health Record–Enabled Research in Children Using the Electronic Health Record for Clinical Discovery.

Sutherland, S.M., et al., (2016) Pediatric Clinics of North America. 63(2): 251-268.

23. Big Data In Health: A New Era For Research And Patient Care, R. Weil, Alan R. Weil, Health Affair, Vol. 33, N° 7, pp 1110,

2014.

24. Big Data in Healthcare and Medical Applications in Romania, A. Alexandru, D. Coardos, IEEE International Conference on

Automation, Quality and Testing, Robotics, THETA 20th edition, 2016.

25. Promises and Challenges of Big Data Computing in Health Sciences, T., Huang, L., Lan, Big Data Research vol. 2, pp 2-11,

2015.

26. Significance of Big Data Analytics, Priyanka Jain, Sanjay Ojha, International Journal of Software and Web Sciences (IJSWS),

2015 [11]

27. A Conception of a Predictive Analytics Platform in Healthcare Sector by Using Data Mining Techniques and Hadoop, Basma

Boukenze, Hajar Mousannif, Abdelkrim Haqiq, in Proc. Conf. International Journal of Advanced Research in Computer

Science and Software Engineering, Volume 6, Issue 8, August 2016.

28. Developing a Big Data-Enabled Transformation Model in Healthcare : A Practice Based View, Y. Wang, L. Kung, W. Y. C.

Wang, C. G. Cegielski, in: Thirty Fifth Int. Conf. Inf. Syst., Auckland, 2014: pp. 1–12. doi:10.13140/2.1.2843.3601.

40.

Authors: Rajesh. M, Sairam. R

Paper Title: Big Data and Health Care System Using Mlearning

Abstract:

Different age people from different lifestyle, what all symptoms came for the disease, at what stage, what

measures taken to get rid of, later changes, what were the side effects, how it decreased or increased will

help in future prediction of possible chance for illness in others. A System which having the above details,

suggestions from good and expert practitioners, can be used to give warnings to people about the possibility

to get affected after 5 or 10 years and to take pre-cautions . Combinational outcome of Descriptive,

Predictive and Prescriptive Data Analytics methods on past and present structured and unstructured Big Data

can be used to predict future after effects which should be prioritized and treated. Suggestions can be given

to people to take appointment with dieticians, change food habits, perform exercises, practice remedial

measures etc. Right step at correct time save lives, gives happiness to families, reduces medical

expenditures. The relevant information hidden in massive amount of data are made available by the AI

assistants to make better clinical decisions in the functional areas of healthcare. To make such a system, a

detailed study on big data, analytics methods, health care, practicing methods, electronic health records etc is

required. A preventive guidance and less cost expert system which is helpful for common man and experts,

for immediate solution and care can be developed in future.Deep machine learning algorithms to detect later

possibility of occurrence should be developed. For this a study on Big Data and Health Care Analytics is

done.

Keywords:Big Data,Data Analytics, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics,

Volume, Velocity, Veracity

References:

1. Healthcare Systems Assisted by Data Analytics and Mobile Computing Xiao Ma,1 Zie Wang,1 Sheng Zhou,1 Haoyu Wen,1

and Yin Zhang 1,2 Received 9 January 2018; Accepted 20 May 2018; Published 3 July 2018 Academic Editor: Javier Prieto

Wireless Communications and Mobile Computing Volume 2018, Article ID 3928080, 16 pages

https://doi.org/10.1155/2018/3928080

2. Big Data in Healthcare Management: A Review of Literature

3. Senthilkumar SA1, Bharatendara K Rai2, Amruta A Meshram2, Angappa Gunasekaran3, Chandrakumarmangalam

S4American Journal of Theoretical and Applied Business 2018; 4(2): 57-69

http://www.sciencepublishinggroup.com/j/ajtabdoi: 0.11648/j.ajtab.20180402.14 ISSN: 2469-7834 (Print); ISSN: 2469-7842

4. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining,MdSaiful

Islam,MdMahmudulHasan,Xiaoyi Wang,Hayley D. Germack,and Md Noor-E-Alam,Available:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023432/#B8-healthcare-06-00054

5. Big Data and Its Challenges V. Maria Antoniate Martin*1, Dr. K. David2, A.Vignesh3 . Volume 3, Issue 3 | March-April-2018

| ISSN : 2456-3307. http:// ijsrcseit.com

6. FadouaKhennouaa*, YounessIdrissiKhamlichibb, Nour El HoudaChaouiaa Improving the Use of Big Data Analytics within

Electronic Health Records: A Case Study based OpenEHR .Available:https://www.sciencedirect.com

7. Health Analytics Types, Functions and Levels: A Review of Literature Mohamed KHALIFA1. Data, Informatics and

Technology: An Inspiration for Improved Healthcare A. Hasman et al. (Eds.) IOS Press, 2018. doi:10.3233/978-1-61499-880-8-137

8. Artificial intelligence in healthcare: past, present and future. Fei Jiang1, Yong Jiang2, Hui Zhi3, Yi Dong4, Hao Li5, Sufeng

Ma6, Yilong Wang7, Qiang Dong4, Haipeng Shen8, Yongjun Wang9https://svn.bmj.com/content/2/4/230

9. A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data MiningMdSaiful Islam 1,

208-210

https://doi.org/10.1155/2018/3928080

http://www.sciencepublishinggroup.com/j/ajtab




https://www.ncbi.nlm.nih.gov/pubmed/?term=Wang%20X%5BAuthor%5D&cauthor=true&cauthor_uid=29882866

https://www.ncbi.nlm.nih.gov/pubmed/?term=Germack%20HD%5BAuthor%5D&cauthor=true&cauthor_uid=29882866



https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023432/#B8-healthcare-06-00054











https://svn.bmj.com/content/2/4/230

MdMahmudulHasan 1, Xiaoyi Wang 1, Hayley D. Germack 1,2,3 and Md Noor-E-Alam 1, ID https://www.mdpi.com/journal/healthcare

10. AI based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics using Reactive Programming Jagreet

Kaur1, Dr. Kulwinder Singh Mann2 1Data Scientist, XenonStack, IT Park, Chandigarh, India. 2Professor and Head of the IT Department, GNDEC, Ludhiana, India IOP Conf. Series: Journal of Physics: Conf. Series 933 (2017) 012010 doi

:10.1088/1742-6596/933/1/012010

11. Big Data Analytics in Healthcare. Ashwin Belle, 1 , 2 Raghuram Thiagarajan, 3 S. M. Reza Soroushmehr, 1 , 2 , * Fatemeh Navidi, 4

Daniel A. Beard,andKayvanNajarianhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503556/

12. http://www.dataversity.net/data-analytics-important-healthcare/

13. https://archive.siam.org/meetings/sdm13/sun.pdf

14. Analysis of Research in Healthcare Data Analytics. https://arxiv.org/ftp/arxiv/papers/1606/1606.01354.pdf



17. https://www.investopedia.com/terms/s/statistics.asp

18. https://www.framinghamheartstudy.org

19. Alexander1 and Lidong Wang2 Big Data Analytics in Heart Attack Prediction Cheryl Ann 1Department of Nursing,

University of Phoenix, J Nurs Care 2017, 6:2 DOI: 10.4172/2167-1168.1000393

20. The value of analytics in healthcare: From insights to outcomes, J. W. Cortada, D. Gordon, B. Lenihan, IBM Global Business

Services, Executive Report, 2012.

21. Promises and Challenges of Big Data Computing in Health Sciences, Big Data T. Huang, L. Lan, X. Fang, P. An, J. Min, F.

Wang, Res. 2 (2015) 2–11. doi:10.1016/j. bdr.2015.02.002.

29. Perspectives on Big Data applications of health information. Current Opinion in Systems Biology. Cano, I., et al., (2017) 3:

36-42. 2.

30. Exploring the path to big data analytics success in healthcare. Journal of Business Research. Wang, Y. and N. Hajli, (2017)

70: 287-299. 3.

31. Electronic Health Record–Enabled Research in Children Using the Electronic Health Record for Clinical Discovery.

Sutherland, S.M., et al., (2016) Pediatric Clinics of North America. 63(2): 251-268.

32. Big Data In Health: A New Era For Research And Patient Care, R. Weil, Alan R. Weil, Health Affair, Vol. 33, N° 7, pp 1110,

2014.

33. Big Data in Healthcare and Medical Applications in Romania, A. Alexandru, D. Coardos, IEEE International Conference on

Automation, Quality and Testing, Robotics, THETA 20th edition, 2016.

34. Promises and Challenges of Big Data Computing in Health Sciences, T., Huang, L., Lan, Big Data Research vol. 2, pp 2-11,

2015.

35. Significance of Big Data Analytics, Priyanka Jain, Sanjay Ojha, International Journal of Software and Web Sciences (IJSWS),

2015 [11]

36. A Conception of a Predictive Analytics Platform in Healthcare Sector by Using Data Mining Techniques and Hadoop,

BasmaBoukenze, HajarMousannif, AbdelkrimHaqiq, in Proc. Conf. International Journal of Advanced Research in Computer

Science and Software Engineering, Volume 6, Issue 8, August 2016.

37. Developing a Big Data-Enabled Transformation Model in Healthcare : A Practice Based View, Y. Wang, L. Kung, W. Y. C.

Wang, C. G. Cegielski, in: Thirty Fifth Int. Conf. Inf. Syst., Auckland, 2014: pp. 1–12. doi:10.13140/2.1.2843.3601.

22. Big Data in Medical Applications and Health Care, L. Wang, C. A. Alexander, Am. Med. J. 6 (2015) 1–8.

doi:10.3844/amjsp.2015.1.8

41.

Authors: Andrya Sara Roy, Sheeja Janardhanan, E.M.S. Nair

Paper Title: Structural Response of Free – Fall Lifeboats During Emergencies

Abstract: A lifeboat is a small floating structure released from ships during an emergency for the rescue of

people onboard.This task is accomplished by dropping the structure from a predetermined height (drop height)

and inclination (fall angle). The structure is now a freely falling body under the influence of gravity with the

bow pointed down. The beginning of the lifeboat’s trajectory includes an initial flight in air followed by its

diving in water and then emerging out of water under the buoyancy effects. During sudden impact on the surface

of water, large slamming loads act on the structure, especially at the bow. Slamming is of great concern as it

results in severe structural damage of the bow as well as its supporting frames and scantlings. The study of

slamming involves an interaction between the structural components of the lifeboat and the fluid load on the

hull. Bow impact is a salient feature since high accelerations are exerted upon the lifeboat when it first hits the

water surface during its water entry phase. In the present study an approach for the design of a typical life boat is

presented. Fluid pressure on the bow has been estimated using a computational fluid dynamics (CFD) approach

coupled with a six degree of freedom (6DOF) solver. A user defined function (UDF) has been written in C

language and has been complied within the solver for accomplishing the body motions. Geometric modeling and

meshing have been carried out using ANSYS ICEM CFD and FLUENT has been used as the solver. The impact

peak pressure has been applied at the bow and a 3D structural analysis has been performed initially at the bow

region of the bare hull without scantlings and later with scantlings. The results seem to provide guidance for the

design modifications in terms of scantling dimensions.

Keywords: Lifeboat,slamming,bow impact,buoyancy,scantlings, hull.

References:

1. Ahmad FauzanZakki et al., 2015 “The Investigation Of Launching Parameters On The Motion Pattern Of Freefall Lifeboat Using FSI Analysis” Elsevier Journal.

2. ArunKamathet al., 2017 “Study of Water Impact and Entry of a Free Falling Wedge Using Computational Fluid Dynamics

211-216


https://www.ncbi.nlm.nih.gov/pubmed/?term=Belle%20A%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Thiagarajan%20R%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Soroushmehr%20SM%5BAuthor%5D&cauthor=true&cauthor_uid=26229957


https://www.ncbi.nlm.nih.gov/pubmed/?term=Beard%20DA%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pubmed/?term=Najarian%20K%5BAuthor%5D&cauthor=true&cauthor_uid=26229957

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503556/

http://www.dataversity.net/data-analytics-important-healthcare/

https://archive.siam.org/meetings/sdm13/sun.pdf



https://www.framinghamheartstudy.org/

Simulations” Journal of Offshore Mechanics and Arc tic Engineering ASME.

3. Boef, W.J.C. 1992 “Launch And Impact Of Free-Fall Lifeboats. Part I. Impact Theory”, Ocean Engg, Vol. 19.

4. Boef, W.J.C. 1992 “Launch And Impact Of Free-Fall Lifeboats. Part II. Implementation And Applications”, Ocean Engg, Vol. 19.

5. Jonas W. Rings berg et al., 2017 “Structural Response Analysis of Slamming Impact on Free Fall Lifeboats”, Elsevier Journal.

6. Knut.O.Ronoldet al., 2009 “New Standards For The Design Of Freefall Lifeboats” ASME Journal.

7. Magultaet al., 1996 “Dynamic Behavior Analysis of Lifeboat under Simulated Accidents” Mechanical Systems and Signal Processing, (1996) 10(6), 763–774.

8. T. Tveitneset al., 2008 “An experimental investigation into the constant velocity water entry of wedge-shaped sections” Elsevier Journal.

9. . M. Nikfarjamet al., 2017 “Investigation of Wedge Water-Entry Under Symmetric Impact Loads by Experimental Tests” Latin American Journal Of Solids And Structures.

10. M. Tenzer et al., 2016 “Experimental investigation of impact loads during water entry” Ship Technology Research.

11. Nabila Berchiche et al., 2013“Experimental Validation of CFD simulations of Free-Fall Lifeboat Launches in Regular Waves”.

12. Vicki L. Willis et al., 1999 “Anticipated Performance of Free-Fall Lifeboats in a High Wind Environment”, Elsevier journal.

13. VidarTregde. et al., 2011 “ Simulation of Free Fall Lifeboats – Impact Forces, Slamming and Accelerations”, ResearchGate.

14. Ole Gabrielsen. et al., 2011 “Study of Davit Launched Lifeboats During Lowering, Water Entry, Release and Sail – away Phases ”.

42.

Authors: Anjaly Jose, Sheeja Janardhanan, Ajith Kumar Arumugham-Achari, Rajesh P Nair

Paper Title: Structural Behavior of a Wing in Deformable Ground Effect of a Seaplane

Abstract: Seaplanes are powered fixed wing aircraft capable of taking off and landing on water. They are

completely independent of regular land based airfields. They are beneficial for navy and coast guard in

patrolling, surveillance and transportation in marine industry. Seaplanes that can take-off on water and land on

airfields are generally under the subclass of amphibians. A true seaplane, which can only take off, and land on

water forms an important element for national security and economic interest. A Wing-in-Ground (WIG) craft is

a very fast marine transportation vehicle. It flies close to the water surface on a dynamic air cushion of high

pressure created by aerodynamic lift due to the ground effect between the vessel and the water surface. Due to its

close proximity to the surface of water, they have large lift to drag ratio (L/D) which means WIG craft has an

increased aerodynamic efficiency. Here seaplanes are considered as a WIG craft. The aerodynamic and

hydrodynamic characteristics of seaplane can be investigated both experimentally and numerically. The present

paper presents a blend of computational fluid dynamics (CFD) approach and finite element method (FEM) to

investigate the structural strength of a symmetric profile, NACA0012 wing whose altitude to chord (h/c) ratio

corresponds to maximum L/D. Two-dimensional CFD simulations have been carried out to investigate the

optimum h/c. The aero-hydrodynamic interactions have been captured using an overset grid system and volume

of fluid (VOF) method. In the present study, maximum L/D is obtained at h/c=0.3. Geometric modeling and grid

generation have been carried out using ANSYS ICEM CFD while ANSYS FLUENT has been used as the

solver. The structural analysis of the wing has been carried out in ANSYS Workbench under a uniformly

distributed load (UDL) on the wing obtained from CFD computations. From static structural analysis, the

compression and tension in the fibers, total deformation, Von Misses stress, also known as equivalent stress,

shear stress, maximum principal stress, maximum shear stress and stress intensity induced in the wing have

been computed by considering the wing as a cantilever beam. These results have also been verified using the

flexural bending equations. The method provides useful insights into the structural behavior of the wing at the

optimum flying conditions.

Keywords: Seaplane, Wing-in-Ground (WIG) Craft, Aero-Hydrodynamic Interaction, Lift to Drag Ratio,

Overset Grid.

References:

1. S Gudmundsson, “APPENDIX C3: Design of Seaplanes”, General Aviation Aircraft Design: Applied Methods and Procedures, Elsevier, Inc.,2013.

2. M.M. Tofa, A Maimun, Y.M. Ahmed, S Jamei and A Priyanto, Rahimuddin,“Experimental Investigation of a Wing-in-Ground Effect Craft”, The Scientific World Journal, Vol. 2014, Article ID 489308, 2014.

3. A.K. Arumugham-Achari,S. Janardhanan, R.P. Nairand A. Jose, “Numerical Investigations on the Wing-in-Deformable Ground Effect for Seaplanes”, International Conference on Computational and Experimental Marine Hydrodynamics, 2018.

4. B.C. Khoo and H.B. Koe, “The hydrodynamics of the WIG (Wing-In-Ground) Effect Craft”,IEEE 6th International Conference on Underwater System Technology: Theory & Applications,2016.

5. M Tavakoli and M Saeed Seif ,“A Practical Method for Investigation of Aerodynamic and Longitudinal Static Stability of Wing-in-Ground Effect”, International Journal of Maritime Technology, Vol.4/Summer (11-19), 2015.

6. W.M. Chan, R.J. Gomez III, S.E. Rogers, P.G. Buning, “Best Practices In Overset Grid Generation”, 32nd AIAA Fluid Dynamics Conference,2002.

217-222

43.

Authors: L. Rama Parvathi, R. Logeshwari

Paper Title: A Design and Analysis of Geo-Crypto Key Exchange Algorithm for Secure Transmission

Abstract: The study of different age people from different lifestyle, what all symptoms came for the disease, 223-226

at what stage, what measures taken to get rid of, later changes, what were the side effects, how it decreased or

increased will help in future prediction of possible chance for illness in others. A System which having the

above details, suggestions from good and expert practitioners, can be used to give warnings to people about the

possibility to get affected after 5 or 10 years and to take pre-cautions . Combinational outcome of Descriptive,

Predictive and Prescriptive Data Analytics methods on past and present structured and unstructured Big Data can

be used to predict future after effects which should be prioritized and treated. Suggestions can be given to people

to take appointment with dieticians, change food habits, perform exercises, practice remedial measures etc. Right

step at correct time save lives, gives happiness to families, reduces medical expenditures. The relevant

information hidden in massive amount of data are made available by the AI assistants to make better clinical

decisions in the functional areas of healthcare. To make such a system, a detailed study on big data, analytics

methods, health care, practicing methods, electronic health records etc is required. A preventive guidance and

less cost expert system which is helpful for common man and experts, for immediate solution and care can be

developed in future. Deep machine learning algorithms to detect later possibility of occurrence should be

developed. For this a study on Big Data and Health Care Analytics is done.

Keywords: Big Data, Data Analytics, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics,

Volume, Velocity, Veracity.

References:

1. G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J.

Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64. 2. W.-K. Chen, Linear Networks and Systems (Book style).Belmont, CA: Wadsworth, 1993, pp. 123–135.

3. H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.

4. B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished. 5. E. H. Miller, “A note on reflector arrays (Periodical style—Accepted for publication),” IEEE Trans. Antennas Propagat., to be

published.

6. J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.

7. C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.

8. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),” IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf.

Magnetics Japan, 1982, p. 301].

9. M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989. 10. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue).

Available: http://www.(URL)

11. J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com 12. (Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given.

Available: http://www.(URL)

13. R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar

14. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289. 15. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex

Systems 5.7 (2015): 1-4.

16. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

17. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION

TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18. 18. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

19. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and Intelligent Systems 6.8: 24-26.

44.

Authors: Aarthi R, S Harini, Hari Prasad V

Paper Title: Hand Region Extraction by Saliency Based Color Component

Abstract: Hand segmentation becomes a challenging task due to uncontrolled environmental conditions,

lighting, rapid motion of the hand and skin colour detection. This paper’s objective is to propose a saliency-

based colour model algorithm for hand segmentation under constrained and non-constrained environments. We

already have colour models for hand segmentation algorithms, but in this work, we are proposing a new model

for the segmentation process. Researchers are actively engaged in hand segmentation to attain natural

interaction with a machine. A secondary objective of this paper is to excel in the region of skin color detection

for human-like interaction between the end user and the computer. Human-computer interaction is achieved by

hand gestures. To make hand gesture identification accurate, we may need to segment the hand from the

background. The proposed work in this paper leads to solving the first problem in human-computer interaction.

Keywords: color map, salient, feature, intensity, saturation

References:

27. AashniHaria, Archanasri Subramanian, NivedithaAsokkumar, Shristi Poddar, Jyothi S Nayak. Hand Gesture Recognition for Human

Computer Interaction, International Conference on Advances in Computing & Communications, ICACC-2017, 22-24 August 2017,

Cochin, India

28. SonalSinghai, Dr C.S.Satsangi. Hand Segmentation for Hand Gesture Recognition, International Journal Of Innovative Research in

Information Security Volume 1, Issue 2 August 2014

227-230

http://www.(url)/

http://www.atm.com/

http://www.(url)/

29. Meenakshi Panwar ,Pawn Singh Mehran,” Hand gesture recognition for human computer interaction”, 2011 International Conference on Image Information Processing

30. Ram Pratap Sharma, Gyanendra K Verma,”Human Computer Interaction using Hand Gesture”, Eleventh International Conference on

Image and Signal Processing, ICISP 2015, August 21-23, 2015, Bangalore, India 31. S. Kolkur , D. Kalbande , P. Shimpi, C. Bapat , and J. Jatakia,” Human Skin Detection Using RGB, HSV and YCbCrColor Models”,

ICCASP/ICMMD-2016. Advances in Intelligent Systems Research. Vol. 137, Pp. 324332.

32. Chen Junhua, Lei Jing,” Research on Color Image Classification Based on HSV Color Space”, 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control .

33. Archana Ghotkar, Gajanan K. Kharate,” Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Computer

Interaction”, International Journal of Human-Computer Interaction 3(1):15-25 · January 2012 34. Zhang Qiu-yu, Lu Jun-chi, Zhang Mo-yi, Duan Hong-xiang and LvLu,”Hand Gesture Segmentation Method Based on

YCbCrColorSpace and K-Means Clustering”,International Journal of Signal Processing,Image processing and Pattern Recognition,Vol.

8,No. 5(2015), pp. 105-116 35. Hemlata Chavan, Mr.PrateekGupta,”A Review on Hand Gesture Detection Using Combine HSI, YCbCr and Morphological Method

with Recognition”,International Research Journal of Engineering and Technology (IRJET) ,Volume: 03 Issue: 05 |May-2016

36. Kathryn Koehler, Fei Guo, Sheng Zhang, and Miguel P. Eckstein,” What do saliency models predict?”, J Vis. Journal of Vision, 2014; 14(3).

37. S. Krishnan, B.A. Sabarish, Gayathri V., and Dr. Padmavathi S., “Enhanced Defogging System on Foggy Digital Color

Images”,Computational Vision and Bio Inspired Computing, Part of the Lecture Notes in Computational Vision and Biomechanics book series, pp. 488-495, 2018 Page | 10

38. R.M. Arunachalam, M. Ashok Gowtham, and R. Aarthi. Identifying Fingertips for Human Computer Interaction. International Journal

of Research and Reviews in Computer Science (IJRRCS),Vol. 3, No. 1, February 2012, ISSN: 2079-2557 39. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless


40. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7

(2015): 1-4.

41. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of

Pure and Applied Mathematics 118.9 (2018): 407-412. 42. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION

TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

43. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

44. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and

Intelligent Systems 6.8: 24-26.

45.

Authors: M.Lawanya Shri, M.Angulakshmi, M.Deepa, K.Santhi, M.B.Benjula Anbu Malar

Paper Title: High–Significant Ranwar Datamining Algorithm for Biological Data

Abstract: Hand segmentation becomes a challenging task due to uncontrolled environmental conditions,

lighting, rapid motion of the hand and skin colour detection. This paper’s objective is to propose a saliency-

based colour model algorithm for hand segmentation under constrained and non-constrained environments. We

already have colour models for hand segmentation algorithms, but in this work, we are proposing a new model

for the segmentation process. Researchers are actively engaged in hand segmentation to attain natural interaction

with a machine. A secondary objective of this paper is to excel in the region of skin color detection for human-

like interaction between the end user and the computer. Human-computer interaction is achieved by hand

gestures. To make hand gesture identification accurate, we may need to segment the hand from the background.

The proposed work in this paper leads to solving the first problem in human-computer interaction.

Keywords: color map, salient, feature, intensity, saturation.

References:

1. G.Smyth,., “Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments,” Stat. Appl. Genet. Mol. Biol. Vol 3(1) 2014.

2. Mallik S et al., “Integrated analysis of gene expression and genomewide DNA methylation for tumor prediction: An association

rule miningbased approach,” in Proc. IEEE Symp. Comput. Intell. Bioinformat. Comput. Biol. (CIBCB),Singapore,pp. 120–12,2014.

3. M. Anandhavalli, M, Ghose, K Gauthaman, . “Association Rule Mining in Genomics,” Int. J. Comput. Theory Eng., vol 2,

2010,. pp.1793–8201. 4. P.K Vaishali, A Vinayababu. "Application of Data mining and Soft Computing in Bioinformatics." International Journal of

Engineering Research and Applications , ISSN: 2248- 9622, 2014.

5. T Feng, F Murtagh, and M Farid., "Weighted association rule mining using weighted support and significance framework." Proceedings of the ninth ACM SIGKDD International conference on Knowledge discovery and data mining. 2003.

6. Sun, Ke, and Fengshan Bai."Mining weighted association rules without preassigned weights." IEEE Transaction on Knowledge

and Data Engineering”, 2008, 7. R. Agrawal T, Imielinksi ASwami.,”Database mining: a performance perspective.” IEEE Transactions on Knowledge and Data

Engineering”, Vol 5, pp 914–92, 1993 8. M. Angulakshmi. I. NagarajanA “Survey on Multi-Relational Database Based Classification Approaches”, International Journal

of Applied Engineering Research, vol .9, 2014.

9. M. Angulakshmi,. “Big data analytics–A review”. International Journal of Pharmacy and Technology, vol. 8, pp. 4634-4639, 2016.

10. H. Dewangan, M. Angulakshmi,. I. Nagarajan,.”Multiprocessing optimization - Parallel quick sort using open MP”.International

Journal of Pharmacy and Technology. vol.8, pp. 15633-15639, 2016. 11. A.Sharma, P.Patidar, M.Angulakshmi. “Overview of features of smartphone os- android, ios and window phone 8”, International

Journal of Pharmacy and Technology, vol 8(4), pp. 25347-25351, 2016.

12. R.Rathi,. S.Sudha, K. Brindha., M. Angulakshmi, G.Haripriya, M. Teja, “effective evaluation of prediction accuracy using optimation algorithm”, International Journal of Pure and Applied Mathematics, 2017.

13. M. Angulakshmi, G.G. Lakshmi Priya,, “Automatic brain tumour segmentation of magnetic resonance images (MRI) based on

region of interest (ROI).” Journal of Engineering Science and Technology, Taylor & series. Vol. 12, pp. 875-887, 2017.

14. M. Angulakshmi, G. G. Lakshmi Priya. “Automated brain tumour segmentation techniques — A review”. International Journal

of Imaging Systems and Technology.Wiley.vol .27, pp. 66-77, 2017.

15. M. Angulakshmi, G. G Lakshmi Priya. “Walsh Hadamard kernel‐based texture feature for multimodal MRI brain tumour

231-233

https://www.scopus.com/authid/detail.uri?origin=resultslist&authorId=56277853500&zone=

https://www.scopus.com/record/display.uri?eid=2-s2.0-84992344906&origin=resultslist&sort=plf-f&src=s&st1=Angulakshmi.M&st2=&sid=901c47258f8330dddbac7a3c407a0c7c&sot=b&sdt=b&sl=26&s=AUTHOR-NAME%28Angulakshmi.M%29&relpos=3&citeCnt=0&searchTerm=

https://www.scopus.com/sourceid/19700201471?origin=resultslist

segmentation”. International Journal of Imaging Systems and Technology. vol. 16. 28, pp.254-266, 2018.

17. M.Deepa, M. Anand , “Availability Modelling of Fault Tolerant Cloud Computing System”. International Journal of Intelligent

Engineering and Systems,vol.10, pp.154-165, 2017. 18. M. Deepa, M. Anand, “An Approach to Evaluate the Availability of System in Cloud Computing Using Fault Tree Technique”.

International Journal of Intelligent Engineering and Systems, vol.10,pp.245-255, 2017.

19. M. Deepa , M. Anand,“Risk - based availability modelling and reputation management on fault tolerant cloud computing systems”. International Journal of Internet Technology and Secured Transactions- Inderscience.vol.9,pp.37 – 56, 2019.

20. M.Deepa, M. Anand, “Quality of service on performance evaluation- A Survey”. Institute of Integrative Omics and Applied

Biotechnology (IIOAB).Vol.8, pp. 8-13, 2017. 21. M. Anand , M. Deepa “ A Survey on Applications of Grammar formalism In Image Processing”. International Journal of

Applied Engineering Research, Vol.10, pp. 16021-16034, 2015.

22. K. Santhi., C. Priyadarshini. “Efficiently Allocating the Virtual Machines in Cloud”, International Journal of Applied Engineering Research, vol. 9(3). pp. 387-392, 2014.

23. K. Santhi, R. Saravanan. “Facilitate refined keywords search over encrypted data on cloud”. International Journal Of Pharmacy &

Technology, vol 8(3), pp. 15552-15557, 2016. 24. K.Santhi, R.Patel, “Sheds: A simple and secure cost efficient data storage in heterogeneous multiple cloud”. International Journal

Of Pharmacy & Technology, vol.8, pp.26058-26065, 2016.

25. K. Santhi, R. Saravanan, “A survey on queueing models for cloud computing,”International Journal Of Pharmacy & Technology, vol 8(2), pp. 3964-3977, 2016.

26. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing in Healthcare System Using Tandem Queues”.International

Journal of Intelligent Engineering and System,vol 10(4). pp.256-264, 2017. 27. K. Santhi, R.. Saravanan, “Performance Analysis of Cloud Computing Bulk Service Using Queueing Models”. International

Journal of Applied Engineering Research, vol 12(7), pp.6487- 6492, 2017.

28. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing Using Batch Queueing Models in Healthcare Systems”.

Research Journal of Pharmacy and Technology,vol 10(10), pp.3331-3336, 2017.

29. K. Santhi, R. Saravanan, “Performance analysis of cloud computing using series of queues with Erlang service”, International

Journal. Internet Technology and Secured Transactions, Vol. 9, pp.147–162, 2019. 30. K.P. Shiva Priya, S. Monisha, R. Keerthiga, M. Lawanya Shri. , "A comparative analysis of classifier algorithm in defect

prediction using cgbr framework", International Journal of Applied Engineering Research , 2015.

31. K.R. Manoj Prabhakar, M. Lawanya Shri, “Implementation of an issue tracking system in private cloud", International Journal of Applied Engineering Research, 2014.

32. K.S. Tarun Kumar, P. Vignesh Kumar, M. Lawanya Shri, " An implementation of storage provisioning in private

cloud",International Journal of Applied Engineering Research, 2014. 33. G. Jothipriya, M. Lawanya Shri, " Database synchronization of mobile-build by using synchronization framework", International

Journal of Engineering and Technology , 2013

34. M. Lawanya Shri, S. Subha ," An implementation of E-learning system in private cloud", International Journal of Engineering andTechnology,2013.

35. M. Lawanya Shri, B. Balusamy, B, S. Subha, Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications. Informatics in Medicine Unlocked, vol 8, pp 42-50, 2017.

36. M. LawanyaShri, S. Subha, B. Balusamy . Energy-Aware Fruitfly Optimisation Algorithm for Load Balancing in Cloud

Computing Environments. International Journal of Intelligent Engineering and Systems, vol 10(1), pp. 75-85, 2017. 37. M. Lawanya Shri, B. Balusamy, S. Subha, Threshold-based workload control for an under-utilized virtual machine in cloud

computing. International Journal of Intelligent Engineering and Systems, vol 9(4),pp 234-241, 2016.

38. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

39. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex

Systems 5.7 (2015): 1-4. 40. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International

Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

41. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

42. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied

46.

Authors: M.Angulakshmi, M.Deepa, M.Lawanyashri, K.Santhi, M.B.Benjulaanbu Malar

Paper Title: Wireless Networking Sensor Security to Monitor Fire in Building

Abstract: With the rapid and fast development in the field of wireless technology, people life nowadays has

undergone a great change. In recent time, comfort and safety of building environment have been become one of

the biggest universal concern. However, fire is one of the greatest threats that has to be consider for building the

safety measures. Based on consideration of this current issue on building security, we have proposed a new

idea for monitoring fire in building using ZigBee network and ZigBee-WiFi gateway. This would transform

ZigBee network into WiFi network. The structure of the proposed system and its advantages over the existing

system is discussed in the paper.

Keywords: Building Fire, Gateway, Wireless networks, ZigBee-WiFi

References: 1. M. Angulakshmi. I. NagarajanA “Survey on Multi-Relational Database Based Classification Approaches”, International Journal

of Applied Engineering Research, vol.9, 2014.

2. M. Angulakshmi,. “Big data analytics–A review”. International Journal of Pharmacy and Technology, vol. 8, pp. 4634-4639,

2016. 3. H. Dewangan, M. Angulakshmi,. I. Nagarajan,.”Multiprocessing optimization - Parallel quick sort using open MP”.International

Journal of Pharmacy and Technology. vol.8, pp. 15633-15639, 2016.

4. A.Sharma, P.Patidar, M.Angulakshmi. “Overview of features of smartphone os- android, ios and window phone 8”, International Journal of Pharmacy and Technology, vol 8(4), pp. 25347-25351, 2016.

5. R.Rathi,.S.Sudha, K. Brindha., M. Angulakshmi, G.Haripriya, M. Teja, “effective evaluation of prediction accuracy using

optimation algorithm”, International Journal of Pure and Applied Mathematics, 2017. 6. M. Angulakshmi, G.G. Lakshmi Priya,, “Automatic brain tumour segmentation of magnetic resonance images (MRI) based on

234-237




region of interest (ROI).” Journal of Engineering Science and Technology, Taylor & series. Vol. 12, pp. 875-887, 2017. 7. M. Angulakshmi, G. G. Lakshmi Priya. “Automated brain tumour segmentation techniques — A review”. International Journal

of Imaging Systems and Technology.Wiley.vol.27, pp. 66-77, 2017.


segmentation”. International Journal of Imaging Systems and Technology. vol.28, pp.254-266, 2018.

9. M.Deepa, M. Anand ,“Availability Modelling of Fault Tolerant Cloud Computing System”. International Journal of Intelligent

Engineering and Systems,vol.10, pp.154-165, 2017.

10. M. Deepa, M. Anand, “An Approach to Evaluate the Availability of System in Cloud Computing Using Fault Tree Technique”.

International Journal of Intelligent Engineering and Systems, vol.10, pp .245-255, 2017. 11. M. Deepa , M. Anand,“Risk - based availability modelling and reputation management on fault tolerant cloud computing

systems”. International Journal of Internet Technology and Secured Transactions- Inderscience.vol.9, pp.37 – 56, 2019.

12. M.Deepa, M. Anand, “Quality of service on performance evaluation- A Survey”. Institute of Integrative Omics and Applied Biotechnology (IIOAB).Vol.8, pp. 8-13, 2017.

13. M. Anand , M. Deepa “ A Survey on Applications of Grammar formalism In Image Processing”. International Journal of Applied Engineering Research, Vol.10, pp. 16021-16034, 2015.

14. K. Santhi., C. Priyadarshini. “Efficiently Allocating the Virtual Machines in Cloud”, International Journal of Applied Engineering

Research, vol. 9(3). pp. 387-392, 2014. 15. K. Santhi, R. Saravanan. “Facilitate refined keywords search over encrypted data on cloud”. International Journal Of Pharmacy &

Technology, vol 8(3), pp. 15552-15557, 2016.

16. K.Santhi, R.Patel, “Sheds: A simple and secure cost efficient data storage in heterogeneous multiple cloud”. International Journal Of Pharmacy & Technology, vol.8, pp.26058-26065, 2016.

17. K. Santhi, R. Saravanan, “A survey on queueing models for cloud computing,”International Journal Of Pharmacy & Technology,

vol8(2), pp. 3964-3977, 2016. 18. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing in Healthcare System Using Tandem Queues”.International

Journal of Intelligent Engineering and System,vol 10(4). pp.256-264, 2017.

19. K. Santhi, R..Saravanan, “Performance Analysis of Cloud Computing Bulk Service Using Queueing Models”. International Journal of Applied Engineering Research, vol 12(7), pp.6487- 6492, 2017.

20. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing Using Batch Queueing Models in Healthcare Systems”.

Research Journal of Pharmacy and Technology,vol10(10), pp.3331-3336, 2017. 21. K. Santhi, R. Saravanan, “Performance analysis of cloud computing using series of queues with Erlang service”, International

Journal. Internet Technology and Secured Transactions, Vol. 9, pp.147–162, 2019.

22. K.P. Shiva Priya, S. Monisha, R. Keerthiga, M. LawanyaShri. , "A comparative analysis of classifier algorithm in defect prediction using cgbr framework", International Journal of Applied Engineering Research , 2015.

23. K.R. ManojPrabhakar, M. LawanyaShri, “Implementation of an issue tracking system in private cloud", International Journal of

Applied Engineering Research, 2014. 24. K.S. Tarun Kumar, P. Vignesh Kumar, M. LawanyaShri, " An implementation of storage provisioning in private

cloud",International Journal of Applied Engineering Research, 2014.

25. G. Jothipriya, M. LawanyaShri, " Database synchronization of mobile-build by using synchronization framework", International Journal of Engineering and Technology , 2013

26. M. LawanyaShri, S. Subha ," An implementation of E-learning system in private cloud", International Journal of Engineering

andTechnology,2013. 27. M. LawanyaShri, B. Balusamy, B, S.Subha, Energy-aware hybrid fruitfly optimization for load balancing in cloud environments

for EHR applications. Informatics in Medicine Unlocked, vol8, pp 42-50, 2017.

28. M. LawanyaShri, S. Subha, B. Balusamy . Energy-Aware FruitflyOptimisation Algorithm for Load Balancing in Cloud

Computing Environments. International Journal of Intelligent Engineering and Systems, vol10(1), pp. 75-85, 2017.

29. M. LawanyaShri, B. Balusamy, S. Subha, Threshold-based workload control for an under-utilized virtual machine in cloud

computing. International Journal of Intelligent Engineering and Systems, vol9(4),pp 234-241, 2016.

47.

Authors: M.Angulakshmi, M.Deepa, M.B.Benjulaanbumalar, K.Santhi, M.Lawanyashri

Paper Title: An Approach for Steganography in Security Systems

Abstract: Techniques for hiding secret data or information play an important role with the rapid growth of

multimedia content transfer and secret communications of data. Steganography is the art of hiding data or

information in such a ways that prevent detection. Steganography is used for transferring hidden data in an

appropriate carrier like audio, video, image etc from one place to other place through public channel. Many

different carrier files can be used like audio, video, TCP/IP header file, but digital images are the most popular

because images are used very frequency on internet now days. For hiding secret data or message in images,

there is a large variety of Steganography techniques available and all of them have respective strong points

and weak points. Different applications used for steganography technique have different requirements. In this

paper we survey different steganography techniques for hiding the secret data and prosed a method by

combining different methods

Keywords: Audio , Image,Steganography, Text, Video,

References: 1. M. Shirali-Shahreza , “A new method for real time steganography”,International Conference on Signal Processing.vol.4 Nov

2006.

2. Y.Ying Chung, fang FeiXu, “Development of video watermarking for MPEG2 video” TENCON-IEEE Region 10 conference,

Nov2006. 3. C. Lu, J. Chen and K. Fan, "Real-time Frame- Dependent Video Watermarking in VLC Domain", Signal Processing: Image

Communication. vol. 20, pp. 624–64, 2005.

4. J. Cummins, P. Diskin, S. Lau ,R. Parlett .“Steganography and digital watermarking” School of Computer Science, The University of Birmingham. 2003.

5. C. Lu, J. Chen, H. M. Liao, and K. Fan, "Real-Time MPEG2 Video Watermarking in the VLC Domain "Proc.of 16th

International Conference on Pattern Recognition, Vol. 2, , pp. 552-555, Aug 2002. 6. M. Angulakshmi. I. NagarajanA “Survey on Multi-Relational Database Based Classification Approaches”, International Journal

of Applied Engineering Research, vol 9, 2014. 7. M. Angulakshmi,. “Big data analytics–Areview”. International Journal of Pharmacy and Technology, vol. 8, pp. 4634-4639,

2016.

8. H. Dewangan, M. Angulakshmi,. I. Nagarajan,.”Multiprocessing optimization - Parallel quick sort using open MP”. International Journal of Pharmacy and Technology. vol. 8, pp. 15633-15639, 2016.

238-241




9. A.Sharma, P.Patidar, M. Angulakshmi. “Overview of features of smartphone OS- android, ios and window phone 8”, International Journal of Pharmacy and Technology, vol 8(4), pp. 25347-25351, 2016.

10. R.Rathi,.S.Sudha, K. Brindha., M. Angulakshmi, G.Haripriya, M. Teja, “effective evaluation of prediction accuracy using

optimation algorithm”, International Journal of Pure and Applied Mathematics, 2017. 11. M. Angulakshmi, G.G. Lakshmi Priya,, “Automatic brain tumour segmentation of magnetic resonance images (MRI) based on

region of interest (ROI).” Journal of Engineering Science and Technology, Taylor & series. vol. 12, pp. 875-887, 2017.

12. M. Angulakshmi, G. G. Lakshmi Priya. “Automated brain tumour segmentation techniques — A review”. International Journal of Imaging Systems and Technology.Wiley. vol .27, pp. 66-77, 2017.


segmentation”. International Journal of Imaging Systems and Technology. vol.28, pp. 254-266, 2018.

14. M.Deepa, M. Anand ,“Availability Modelling of Fault Tolerant Cloud Computing System”. International Journal of Intelligent

Engineering and Systems, vol.10, pp.154-165, 2017. 15. M. Deepa, M. Anand, “An Approach to Evaluate the Availability of System in Cloud Computing Using Fault Tree Technique”.

International Journal of Intelligent Engineering and Systems, vol.10, pp .245-255, 2017. 16. M. Deepa , M. Anand, “Risk - based availability modelling and reputation management on fault tolerant cloud computing

systems”. International Journal of Internet Technology and Secured Transactions- Inderscience. vol.9, pp.37 – 56, 2019.

17. M.Deepa, M. Anand, “Quality of service on performance evaluation- A Survey”. Institute of Integrative Omics and Applied Biotechnology (IIOAB).Vol.8, pp. 8-13, 2017.

18. M. Anand , M. Deepa “ A Survey on Applications of Grammar formalism In Image Processing”. International Journal of

Applied Engineering Research, Vol.10, pp. 16021-16034, 2015. 19. K. Santhi., C. Priyadarshini. “Efficiently Allocating the Virtual Machines in Cloud”, International Journal of Applied Engineering

Research, vol. 9(3). pp. 387-392, 2014.

20. K. Santhi, R. Saravanan. “Facilitate refined keywords search over encrypted data on cloud”. International Journal Of Pharmacy & Technology, vol 8(3), pp. 15552-15557, 2016.

21. K.Santhi, R.Patel, “Sheds: A simple and secure cost efficient data storage in heterogeneous multiple cloud”. International Journal

Of Pharmacy & Technology, vol.8, pp.26058-26065, 2016. 22. K. Santhi, R. Saravanan, “A survey on queueing models for cloud computing,”International Journal Of Pharmacy & Technology,

vol8(2), pp. 3964-3977, 2016.

23. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing in Healthcare System Using Tandem Queues”.International Journal of Intelligent Engineering and System,vol 10(4). pp.256-264, 2017.

24. K. Santhi, R..Saravanan, “Performance Analysis of Cloud Computing Bulk Service Using Queueing Models”. International

Journal of Applied Engineering Research, vol 12(7), pp.6487- 6492, 2017. 25. K. Santhi, R. Saravanan, “Performance Analysis of Cloud Computing Using Batch Queueing Models in Healthcare Systems”.

Research Journal of Pharmacy and Technology,vol10(10), pp.3331-3336, 2017.

26. K. Santhi, R. Saravanan, “Performance analysis of cloud computing using series of queues with Erlang service”, International Journal. Internet Technology and Secured Transactions, Vol. 9, pp.147–162, 2019.

27. K.P. Shiva Priya, S. Monisha, R. Keerthiga, M. LawanyaShri. , "A comparative analysis of classifier algorithm in defect

prediction using cgbr framework", International Journal of Applied Engineering Research , 2015. 28. K.R. ManojPrabhakar, M. LawanyaShri, “Implementation of an issue tracking system in private cloud", International Journal of

Applied Engineering Research, 2014.

29. K.S. Tarun Kumar, P. Vignesh Kumar, M. LawanyaShri, "An implementation of storage provisioning in private cloud", International Journal of Applied Engineering Research, 2014.

30. G. Jothipriya, M. LawanyaShri, "Database synchronization of mobile-build by using synchronization framework", International

Journal of Engineering and Technology , 2013.

31. M. LawanyaShri, S. Subha ," An implementation of E-learning system in private cloud", International Journal of Engineering

andTechnology,2013.

32. M. LawanyaShri, B. Balusamy, B, S. Subha, Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications. Informatics in Medicine Unlocked, vol8, pp 42-50, 2017.

33. M. LawanyaShri, S. Subha, B. Balusamy . Energy- Aware FruitflyOptimisation Algorithm for Load Balancing in Cloud

Computing Environments. International Journal of Intelligent Engineering and Systems, vol10(1), pp. 75-85, 2017. 34. M. LawanyaShri, B. Balusamy, S. Subha, Threshold-based workload control for an under-utilized virtual machine in cloud

computing. International Journal of Intelligent Engineering and Systems, vol9(4),pp 234-241, 2016.

48.

Authors: A.Jothimani, Parimi Prasanth, Shradha Anil, J.Arun Nehru

Paper Title: Facial Expression for Emotion Detection using Deep Neural Networks

Abstract : The imperative research part of the emotion recognition is the analysis of emotional state in the facial

expression. The subject matter of this study is to aid the human – computer interaction more empathetic with the

help of automatic emotion recognition system which will be a great step forward in the robotic field. This study

proposes a novel method for the emotion detection where usage of Face detection using Haar feature-based

cascade classifiers, saliency mapping and CNN architecture are implemented. The facial expression of the

humans from the data set is fed into the Saliency using hyper-complex Fourier Transform (SHFT). The resulting

saliency map has the extracted feature which is given as input to the CNN to perform feature modeling and

output the emotional state of the human. We also exhibit that the proposed saliency model can emphasize on

both minor and major salient regions more accurately than the other saliency models.

Keywords: CNN classification, Emotion detection, Facial expression, feature extraction, Saliency using Hyper-

complex Fourier Transform..

References: 1. Yang, C. Deng, D. Tao, S. Zhang, W. Liu, and X. Gao,“Latent maxmargin multitask learning with skelets for 3-D action

recognition,”IEEE Trans. Cybern., vol. 47, no. 2, pp. 439–448, Feb. 2017.

2. Deng, R. Ji, D. Tao, X. Gao, and X. Li,“Weakly supervised multi-graph learning for robust image reranking,”IEEE Trans. Multimedia, vol. 16, no. 3, pp. 785–795, Apr. 2014.

3. S. Saha, R. Lahiri, A. Konar, B. Banerjee, and A. K. Nagar,“HMM-based gesture recognition system using Kinect sensor for

improvised humancomputer interaction,” in Proc. IJCNN, Anchorage, AK, USA, May 2017, pp. 2776–2783. 4. P. Ekman, ‘‘Strong evidence for universals in facial expressions: A reply to Russell’s mistaken critique,’’ Psychol. Bull., vol.

115, no. 2, pp. 268–287, Mar. 1994.

5. M. F. Valstar, B. Jiang, M. Mehu, M. Pantic, and K. Scherer, ‘‘The first facial expression recognition and analysis challenge,’’ in

Proc. Face Gesture, Santa Barbara, CA, USA, Mar. 2011, pp. 921–926.

6. Y.-L. Tian, T. Kanade, and J. F. Cohn, ‘‘Recognizing action units for facial expression analysis,’’ IEEE Trans. Pattern Anal.

242-248

Mach. Intell., vol. 23, no. 2, pp. 97–115, Feb. 2001. 7. “Rapid Object Detection using a Boosted Cascade of Simple Features” 2001, Paul Viola Michael Jones.

8. Guo, Q. Ma, and L. Zhang, “Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform,” in IEEE

Conf. Computer Vision and Pattern Recognition, 2008. 9. T. Ell, “Quaternion-fourier transforms for analysis of twodimensional linear time-invariant partial differential systems,” in IEEE

Conf. Decision and Control, 2002.

10. T. Ell and S. Sangwine, “hyper-complex Fourier transforms of color images,” IEEE Trans. Image Processing, vol. 16, no. 1, pp. 22–35, 2006.

11. Emotion Recognition Through Facial Gestures - A Deep Learning Approach Shrija Mishra , Geeta Ramani Bala Prasada , Ravi

Kant Kumar, and Goutam Sanyal Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, India.

12. A Survey on Human Face Expression Recognition Techniques I.Michael Revina , W.R. Sam Emmanuel. Reg No. 12417, N.M.

Christian College, Marthandam Affiliated to Manonmaniam Sunadaranar University, Abishekapatti, Tirunelveli – 627012, Tamil Nadu, India Department of Computer Science, N.M. Christian College, Marthandam Affiliated to Manonmaniam Sunadaranar

University, Abishekapatti, Tirunelveli – 627012, Tamil Nadu, India.

13. Abutaleb, “Automatic thresholding of gray-level pictures using two-dimensional entropy,” Computer Vision, Graphics, and Image Processing, vol. 47, no. 1, pp. 22–32, 1989.

14. W. Chen, C. Wen, and C. Yang, “A fast two-dimensional entropic thresholding algorithm,” Pattern Recognition, vol. 27, no. 7,

pp. 885–893, 1994. 15. N. Aifanti, C. Papachristou and A. Delopoulos, ”The MUG Facial Expression Database,” in Proc. 11th Int. Workshop on Image

Analysis for Multimedia Interactive Services (WIAMIS), Desenzano, Italy, April 12-14 2010.

16. I.Michael Revina, W.R. Sam Emmanuel. "A Survey on Human Face Expression Recognition Techniques", Journal of King Saud University - Computer and Information Sciences, 2018.

17. Shrija Mishra, Geeta Ramani Bala Prasada, Ravi Kant Kumar, Goutam Sanyal. "Chapter 2 Emotion Recognition Through Facial

Gestures - A Deep Learning Approach", Springer Nature, 2017.

18. Yuanyuan Ding, Qin Zhao, Baoqing Li, Xiaobing Yuan. "Facial Expression Recognition From Image Sequence Based on LBP

and Taylor Expansion", IEEE Access, 2017.

19. Poursaberi, A., Noubari, H.A., Gavrilova, M., Yanushkevich, S.N., 2012. Gauss – Laguerre wavelet textural feature fusion with geometrical information for facial expression identification. EURASIP J. Image Video Process., 1–13.

20. Bashyal, S., Venayagamoorthy, G.K.V., 2008. Recognition of facial expressions using Gabor wavelets and learning vector

quantization. Eng. Appl.Artif.Intell.21,1056–1064. 21. Taylor, P., Siddiqi, M.H., Ali, R., Sattar, A., Khan, A.M., Siddiqi, M.H., Ali, R., Sattar, A., Khan, A.M., Lee, S., 2014. Depth

camera-based facial expression recognition system using multilayer scheme. IETE Tech. Rev. 31, 277–286

22. Zhang, L., Member, S., Tjondronegoro, D., 2011. Facial expression recognition using facial movement features. IEEE Trans. Affect. Comput. 2, 219–229.

23. Owusu, E., Zhan, Y., Mao, Q.R., 2014. A neural-ada boost based facial expression recognition system. Expert Syst. Appl. 41,

3383–3390. 24. Biswas, S., 2015. An Efficient Expression Recognition Method using Contourlet Transform. Int. Conf. Percept. Mach. Intell. pp.

167–174. 25. Ji, Y., Idrissi, K., 2012. Automatic facial expression recognition based on spatiotemporal descriptors. Pattern Recognit. Lett. 33,

1373–1380.

26. Hernandez-matamoros, A., Bonarini, A., Escamilla-hernandez, E., Nakano-miyatake, M., 2015., A Facial Expression Recognition with Automatic Segmentation of Face Regions. Int. Conf. Intell. Softw. Methodol. Tools, Tech. 529–540. doi: 10.1007/ 978-3-

319-22689-7.

27. Demir, Y., 2014. A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. Neural Comput. Appl. 27, 131–142.

28. Cossetin, M.J., Nievola, J.C., Koerich, A.L., 2016. Facial expression recognition using a pairwise feature selection and

classification approach. IEEE Int. Jt. Conf. Neural Networks, pp. 5149–5155. 29. Dahmane, M., Meunier, J., 2014. Prototype-based modeling for facial expression analysis. IEEE Trans. Multimed. 16, 1574–

1584.

30. Zhang, L., Tjondronegoro, D., Chandran, V., 2014. Random Gabor based templates for facial expression recognition in images with facial occlusion. Neurocomputing 145, 451–464.

31. Hegde, G.P., Seetha, M., Hegde, N., 2016. Kernel locality preserving symmetrical weighted fisher discriminant analysis based

subspace approach for expression recognition. Eng. Sci. Technol. Int. J. 19, 1321–1333. 32. Happy, S.L., Member, S., Routray, A., 2015. Automatic facial expression recognition using features of salient facial patches.

IEEE Trans. Affect. Comput. 6, 1–12.

33. Kumar, S., Bhuyan, M.K., Chakraborty, B.K., 2016. Extraction of informative regions of a face for facial expression recognition. IET Comput. Vis. 10, 567–576.

34. Zhao, G., Pietikäinen, M., 2009. Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern

Recognit. Lett. 30, 1117–1127. 35. Shan, K., Guo, J., You, W., Lu, D., Bie, R., 2017. Automatic Facial Expression Recognition Based on a Deep Convolutional-

Neural-Network Structure. IEEE 15th Int. Conf. Softw. Eng. Res. Manag. Appl. 123–128.

49.

Authors: M.Aishwarya, Linky Rani Rout C, A.Sushrrutha Iyer, Anwar Basha H

Paper Title: Security Implementation using Fingerprint and Face Recognition in Cloud

Abstract: Cloud is the bunch of topographically associated system uncovering data. In cloud we store

information on server side just as on customer side. Executing security become essential on customer side

.Since, we have issue in record framework we need an incredible answer for beat the above notice issue. Client

conduct profiling and fake innovation give and substitute approach to verify information. There are numerous

calculation on client conduct and distraction innovation however nobody address the issue of productively

conveying the imitation record in such a way the interrupt not ready to perceive the contrast among certified and

bait document. we proposed a framework in which we are going to utilize the two innovation for example Client

conduct profiling and bait innovation give.

Keywords: Cloud computing, Data security, User behavior, Decoy technology, Fingerprint authentication, Face

recognition.

References: 1. Cloud Security Alliance, Top Threat to Cloud Computing V1.0, March 2010. 2. S. Muqtyar Ahmed, P. Namratha, C. Nagesh. Prevention Of Malicious Insider In The Cloud Using

249-251

3. Decoy Documents Ajey Singh, Dr. Maneesh Shrivastava Overview of Attacks on Cloud Computing 4. D.Jamil and H. Zaki. Security Issues in Cloud Computing and Countermeasures, International Journal of Engineering Science and

Technology, Vol. 3 No. 4, pp. 2672-2676, April 2011.

5. K. Zunnurhain and S. Vrbsky. Security Attacks and Solutions in Clouds, 2nd IEEE International Conference on Cloud Computing Technology and Science, Indianapolis, December 2010.

6. A. Iglesias, P. Angelov, A. Ledezma, and A. Sanchis, Creating evolving user behavior profiles automatically, IEEE Trans. on

Knowl. and Data Eng., vol. 24, no. 5, pp. 854867, May 2012. 7. F. Rocha and M. Correia, Lucy in the sky without diamonds: Stealing confidential data in the cloud, in Proceedings of the 2011

IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops, ser. DSNW 11. Washington, DC,

USA: IEEE Computer Society, 2011. 8. M. B. Salem and S. J. Stolfo, Modeling user search behavior for masquerade detection, in Proceedings of the 14th international

conference on Recent Advances in Intrusion Detection, ser. RAID11. Berlin, Heidelberg: SpringerVerlag, 2011, pp. 181-200.

9. S. et al, Decoy document deployment for effective masquerade attack detection, in Proceedings of the 8th international conference on Detection of intrusions and malware, and vulnerability assessment, ser. DIMVA11. Berlin, Heidelberg: Springer-Verlag, 201

50.

Authors: Gvicto Sudha George, K.Meenakshi, Almas Begum

Paper Title: Acrse-Ik- An Attribute Based Confidentialityretainingsearchable Encryption Technique using

Interimkeyword for Protected Cloud Storage

Abstract: Data stored in the cloud server is in the encrypted format because the cloud server cannot be held

accountable always. The cloud server makes use of the searchable encryption algorithm to fetch the required

data by avoiding the decryption process. The attribute-based keyword search allows the users to access the data

that they require from the cloud server any time. This method ensures that the rights of the users who access the

server are not disclosed as a public data in the cloud server which is done by generating search key by the user.

But still this method poses a threat to the privacy of the information.To overcome this shortfall, this paper

proposes a new scheme that utilizes short lived keywords. The proposed method uses search tokens generated in

a specific time span to extract ciphertext for the users and also privacy of the generated search tokens are upheld.

The proposed method does not suffer from the chosen keyword attack which is verified by the random oracle

model. Moreover in the proposed method it can be proved that the two parameter time complexity and the

number of attributes are proportional to each other in a linear fashion. Also this scheme is well suited for real

word applications.

Keywords: Cloud Security, Searchable Encryption, Short lived Keyword Search, Secrecy, Access Policy.

References:

1. Kaoru Kurosawa ,Yasuhiro Ohtaki, UC-Secure Searchable SymmetricEncryption,In Proceedings of SpringerInternational

Conference on Financial Cryptography and Data Security,pp 285-298, 2012 . 2. Ji-Jian Chin , Wei-ChuenYau , Kim-Kwang Raymond Choo , MoesfaSoeheilaMohamadGeongSenPohSearchable

Symmetric Encryption: Designs and Challenges, ACM Computing Surveys (CSUR), Volume 50 Issue 3, October 2017

3. H. Sun and S. A. Jafar, The Capacity of Private Information Retrieval, arXivpreprint arXiv:1602.09134, 2016

4. D. Boneh, G. D. Crescenzo, and R. O. et al., “Public key encyrption with keyword search,” in Advances in Cryptology-

EUROCRYPT 2004, ser. 12 LNCS, C. Cachin and J. Camenisch, Eds., vol. 3027. Springer-Verlag, 2004, pp. 506–522

5. Go Ohtake ,ReihanehSafavi-Naini, and Liang Feng Zhang, Outsourcing of Verifiable Attribute-Based Keyword Search , Nordic Conference on Secure IT SystemsNordSec 2017 Springer International Publishing , pp 18-35

6. Wang S, Yao L, Zhang Y ,Attribute-based encryption scheme with multi-keyword search and supporting attribute

revocation in cloud storage, PLoS ONE, 2018,Vol. 13,No.10 7. Feng Tao ,YinXiaoyu , Liu Chunyan An Efficient and Anonymous KP-ABE Scheme with Keyword ,Search Information

Science and Applications 2018, pp.251-258

8. K. Liang and W. Susilo. Searchable attribute-based mechanism with efficient data sharing for secure cloud storage. In IEEE Transactions on Information Forensics and Security 10(9):1981–1992,2015

9. Goyal, V.; Pandey, O.; Sahai, A.; Waters, B., Attribute-based encryption for fine-grained access control of encryption

data. In Proceedings of the 13th ACM Conference on Computer and Communications Security, 2006, pp. 89–98 10. Hui Yin, Jixin Zhang, Lu Ou, Shaolin Liao, Zheng Qin, A Key-Policy Searchable Attribute-Based Encryption Scheme for

Efficient Keyword Search and Fine-Grained Access Control over Encrypted Data, Electronics 2019, Vol.8, No. 3

11. Hui Cui ,Zhiguo Wan ,Robert H. Deng ; Guilin Wang ; Yingjiu L, Efficient and Expressive Keyword Search Over Encrypted Data in Cloud, IEEE Transactions on Dependable and Secure Computing ,2018,Vol.15,Issue.3,pp.409-422

12. H. Wang, X. Dong, and Z. Cao. Multi-value-Independent Ciphertext-Policy Attribute Based Encryption with

http://www.fon.hum.uva.nl/david/massp/2007/timit/train/dr5/fsdc0/, http://www.fon.hum.uva.nl/david/massp/2007/timit/train/dr5/fsdc0/, Shamus,”Multiprecision integer and rational

arithmeticLibrary(Miracle)

252-256

51.

Authors: E Prasanth Raja, R Jebakumar

Paper Title: Inline De-Duplication for Video Streaming

Abstract: As the new technologies for collection of video data has emerged in all fields of work, data collection

has reached its epic heights from past decade. Since the volume of video data is huge, the storage space needed

to store the data also becomes tremendously large. Even though many technologies support the challenges to

process the huge volume of video data, the cost for data storage becomes a drawback. Inline deduplication is

proposedto save the space and to optimize the capacity. It reduces the number of data copies before writing it in

to storage device. In current years, many concepts are introduced to reduce video data volume. Inline

deduplication is used here to reduce memory and to increase the transmission speed of video. The main purpose

of this paper is to survey various techniques and concepts involved indeduplicationin video streaming with its

counter measurements for the past decade.

Keywords: Cloud Security, Searchable Encryption, Short lived Keyword Search, Secrecy, Access Policy

References:

257-261

https://dl.acm.org/author_page.cfm?id=81430645047&coll=DL&dl=ACM&trk=0

https://www.mdpi.com/search?authors=Jixin%20Zhang&orcid=

https://www.mdpi.com/search?authors=Lu%20Ou&orcid=0000-0002-8441-781X

https://www.mdpi.com/search?authors=Shaolin%20Liao&orcid=

https://www.mdpi.com/search?authors=Zheng%20Qin&orcid=

https://orcid.org/0000-0002-5820-2233

https://orcid.org/0000-0003-1319-1224

https://orcid.org/0000-0003-3491-8146

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858

http://www.fon.hum.uva.nl/david/massp/2007/timit/train/dr5/fsdc0/

http://www.fon.hum.uva.nl/david/massp/2007/timit/train/dr5/fsdc0/

1. H. Ye, Z. Wu, R.-W. Zhao, X. Wang, Y.-G. Jiang, and X. Xue, “Evaluating two-stream cnn for video classification,” in

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ser. ICMR ’15. New York, NY, USA: ACM,

2015, pp. 435–442. [Online]. Available: http://doi.acm.org/10.1145/2671188.2749406.

2. G. Costa, G. Manco, and R. Ortale, “An incremental clustering scheme for data de-duplication,” Data Min. Knowl.Discov., vol.

20, no. 1, pp. 152–187, Jan. 2010. [Online]. Available: http://dx.doi.org/10.1007/s10618-009-0155-0

3. M. Baktashmotlagh, M. Harandi, B. C. Lovell, and M. Salzmann, “Discriminative non-linear stationary subspace analysis for

video classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 12, pp. 2353–2366, Dec 2014.

4. Chen Zhu, Xiao Tan, Feng Zhou, Xiao Liu, KaiyuYue, Errui Ding, and Yi Ma “Fine-grained Video Categorization with

Redundancy Reduction Attention” in ECCV,2018. [Online].Available : https://link.springer.com/conferenceeccv

5. K. Soomro, A. R. Zamir, and M. Shah, “UCF101: A dataset of 101 human actions classes from videos in the wild,” CoRR, vol.

abs/1212.0402, 2012. [Online]. Available: http://arxiv.org/abs/1212.0402

6. K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE

Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1904–1916, Sept 2015

7. L. Zhou, Y. C. Wang, J. L. Zhang, J. Wan, and Y. J. Ren, “Optimize block-level cloud storage system with load-balance

strategy,” in 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops PhD Forum, May 2012,

pp. 2162–2167.4

8. [8] X. He, K. Wang, H. Huang, T. Miyazaki, Y. Wang, and S. Guo, “Green resource allocation based on deep reinforcement

learning in content-centric iot,” IEEE Transactions on Emerging Topics in Computing, pp. 1–1, 2018.

9. K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in Proceedings of the

27th International Conference on Neural Information Processing Systems, ser. NIPS’14. Cambridge, MA, USA: MIT Press,

2014, pp. 568–576.

10. H. Liu, S. Liu, X. Meng, C. Yang, and Y. Zhang,“Lbvs: A load

balancingstrategyforvirtualstorage,”in2010InternationalConference on Service Sciences, May 2010, pp. 257–262

11. C. C. Chuang, Y. J. Yu, A. C. Pang, H. W. Tseng, and H. P. Lin, “Efficient multicast delivery for data redundancy minimization

over wirelessdatacenters,”IEEETransactionsonEmergingTopicsin Computing, vol. 4, no. 2, pp. 225–241, April 2016.

12. C. Xu, K. Wang, Y. Sun, S. Guo and A. Zomaya, "Redundancy Avoidance for Big Data in Data Centers: A Conventional Neural

Network Approach," in IEEE Transactions on Network Science and Engineering., pp. 1-1, June 2018

13. MPEG-4 Committee. (2000). Generic Coding of Moving Pictures and Associated Audio Information: Video. ISO/IEC.

14. [14] Team, J. V. (2003). Advanced video coding for generic audiovisualservices.ITU-T Rec. H, 264, 14496-10.

VCEG, I.(2010). Joint Call for proposals on Video Compression Technology. VCEGAM91 and MPEG N,11113,2010

52.

Authors: M. Edison, A. Aloysius

Paper Title: Polarity Detection of Sentiment Scoring Methodusing Dempstershafer Theory

Abstract: Sentiment Analysis (SA) is a big task to measure the people opinion. The aim of the SA is to obtain

the essential viewpoint of text, which could be opinions, blogs, reviews, online rating comments etc. Nowadays,

most of the peoples are familiar for using internet to express their opinions. However, the opinions are classified

in a different way like positive, negative and neutral and assigned score to the sentimentword like +1, -1, and 0

respectively. Nevertheless, the sentiment score have been assigned formally in the existing works. Some of the

works the sentiment scores are assigned based on the threshold value 0.5.In that case, several existing works

were applied this value for polarity detection.The limitation of the existing works given more attention to

compute the score of a sentiment. Therefore, the new algorithm proposed namely Senti_Demp_Score, which

performs to measure the sentiment score based on the DempsterShaper Theory (DST). The DST perform to

calculatethe sentiments in a sentence and sum of all the sentiments with a category, then they converted into

percentage. Concurrently, the percentage values are summed with a category and subtractedthe percentage score

which gives the accuracy of the Senti_Demp_Score is 0.7756 like in percentage 77.56%. Mainly, the algorithm

Senti_Demp_Score has given better solution to detect the polarity of the sentiment and measure the sentiment

score in a right way.

Keywords: Sentiment analysis, Polarity, Dempster Shafer Theory, Sentiment Score.

References:

1. Quan Zou, SifaXie, Ziyu Lin, Meihong Wu and Ying Ju, “Finding the Best Classification Threshold in Imbalanced

Classification”, Big Data Research, Elsevier, Vol. 5, 2016, pp. 2-8.

2. Deepak Singh Tomar, and Pankaj Sharma, “A Text Polarity Analysis Using Sentiwordnet Based an Algorithm”, International

Journal of Computer Science and Information Technologies (IJCSIT), Vol. 7, Issue 1, 2016, pp: 190-193.

3. EsraAkbas, “Opinion Mining on Non-English Short Text”, International Symposium on Methodologies for Intelligent Systems,

Springer, 2017.

4. Mohammad Ehsan Basiri, Ahmad Reza Naghsh-Nilchi, and Nasser Ghasem-Aghaee, “Sentiment Prediction Based on Dempster-

Shafer Theory of Evidence”, Hindawi Publishing Corporation Mathematical Problems in Engineering, 2014,

http://dx.doi.org/10.1155/2014/361201.

5. G. Shafer, “A Mathematical Theory of Evidence”, PrincetonUniversity Press, Princeton, NJ, USA, Vol. 1, 1976.

6. https://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theory.

7. M. Edison and A. Aloysius, “Lexicon based Acronyms and Emoticons Classification of Sentiment Analysis on Big Data”,

International Journal of Database Theory and Application (IJDTA), Vol. 10, Issue 7, 2017, pp. 41-54

8. E. Haddi, X. Liu and Y. Shi “The Role of Text Pre-Processing in Sentiment Analysis”, SciVerseScienceDirect ELSEVIER, 2013,

pp. 26-32.

9. S. Roy, S. Dhar, S. Bhattacharjee and A. Das “A Lexicon based Algorithm for Noisy TextNormalization as Pre-Processing for Sentiment Analysis”, International Journal of Research inEngineering and Technology (IJRET), 2013, pp. 67-70.

10. H. Hamdan, P. Bellot, and F. Bechet. "lsislif: Feature extraction and label weighting for sentimentanalysis in twitter",

Proceedings of the 9th International Workshop on Semantic Evaluation, 2015, pp.568-573.

11. Fuji Ren, and Kazuyuki Matsumoto. "Semi-automatic creation of youth slang corpus and its application to affective computing"

IEEE Transactions on Affective Computing, 2016, pp. 176-189.

12. LU Xing, LI Yuan, WANG Qinglin and LIU Yu “An Approach to Sentiment Analysis of Short Chinese Text Based on SVMs”, 34th Chinese Control Conference (CCC), IEEE, 2015, pp. 9115-9120.

262-266

http://dx.doi.org/10.1155/2014/361201

https://en.wikipedia.org/wiki/Dempster%E2%80%93Shafer_theory

13. F. M. Kundi, S. Ahmed, A. Khan and M. Z. Asghar “Detection and Scoring of Internet Slangs for Sentiment Analysis Using SentiWordNet”, Life Science Journal, 2014, pp. 66-72.

14. S. Huang, W. Han, X. Que and W. Wang “Polarity Identification of Sentiment Words based onEmoticons”, 9th Conference on

Computational Intelligence and Security, (2013), pp. 134-138. 15. G. G. Dayalani “Emoticon based unsupervised sentiment classifier for polarity analysis in tweets”,International Journal of

Engineering Research and General Science, vol. 2, 2014, pp. 438-445.

53.

Authors: Greeshma George, Sheeja Janardhanan

Paper Title: A Numerical and Structural Response of FPSO Under Wave Induced Motions

Abstract: FPSO (Floating Production, Storage and Offloading) is generally a ship used by oil and gas

industry for performing a multitude of tasks and is moored to the ocean bed for the extraction of oils and

hydrocarbons. Due to continuously varying cyclic load, FPSO undergoes progressive and localized structural

deformations leading to fatigue damage. Structural analysis of the FPSO is important to establish the strength

and stability of the structure. A moored FPSO generally has three degrees of freedom viz. heave, roll and pitch,

under following sea conditions. In this paper, a numerical study on the FPSO has been conducted with the help

of CFD (Computational Fluid Dynamics) analysis under calm sea-stateand the subsequent structural response of

the system has been calculated by finite element method. CFD studies have high potential in determining the

effect of any complex fluid loading with reasonable degree of accuracy. Geometric modeling and meshing have

been carried out in ANSYS ICEM CFD. An unstructured grid system has been used here and the prescribed

motions on the hull in heave, roll and pitch have been brought in using user defined function (UDF) module of

the commercial CFD solver, FLUENT. Air water interface has been captured using volume of fluid method. The

lift and drag forces are calculated from the simulations. Fluid forces have been validated against their analytical

counterpart using Linear wave theory and Strip theory. Structural response of 3D hull has been predicted in

ANYS Workbench with the forces determined from the CFD solver as input. Equivalent stress distribution and

total deformations of the structure have been studied using static analysis.Understanding the behavior of

structure under various motions provides an insight and guidance to the design calculations of the FPSO in order

to withstand fatigue loading.

Keywords: FPSO, CFD, Pitch, Heave, Roll, Lift, Drag, Structural Response.

References: 1. Lei yu et al. (2017)” Study on the remaining fatigue life of FPSO based on spectral analysis”, International Conference on Ocean,

Offshore and Arctic Engineering. 2. Yingguang Wang ,(2010)” Spectral fatigue analysis of a ship structural detail – A practical case study, “International journal of

Fatigue 32 (310-317)” 3. Lei Wu, et al (2017) “Interaction and combination method for estimation of still water and wave-induced bending moments on

FPSO”,World congress on Advances in Structural Engineering and Mechanics. 4. DamithaSandaruwanet.al (2010), “A Six Degrees of Freedom Ship Simulation System for Maritime Education”, The

International Journal on Advances in ICT for Emerging Regions 2010 03 (02) :34 – 47. 5. Emil Mathews et.al (2013), “Fatigue life estimation of ship structure “, International Journal of Scientific & Engineering

Research, Volume 4, Issue 5, May. 6. Lars Bergdahl,(2009) “Wave-Induced Loads and Ship Motions”Department of civil and environmental engineeringdivision of

water environment technology, Chalmers university of technology. 7. T. Rajesh Kannahet.al, (2006) “Experimental study on the hydrodynamics of a Floating, Production, Storage, and Offloading”,

Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol 132, No. 1.

267-271

54.

Authors: M.Gunasekar, S.Thilagamani

Paper Title: Towards Sentiment Analysis and Opinion Mining From Multimodal Data

Abstract: The ease accessibility of internet and web application paves way for people to express their opinion

and emotion to the society. Social networks captures the view of people on products, politics, movie etc., the

review given by customers decide the success and leverage the popularity of the product in the market. The

challenges rely on the technology, which are employed to trace information accurately from the available data.

Sentiment analysis on textual data is widely used to assess the customer satisfaction. Sentiment can also be

perceived from the mixture of text, audio, facial expression, visual display etc. This survey defines multimodal

sentiment analysis and review recent methods which adopts mixture of inputs for multimodal sentiment analysis.

This survey outlines the different approaches followed to extract feature from multimodal data.

References:

1. Plutchik, Robert Emotion : a psychoevolutionary synthesis. Harper & Row, New York, 1980.

2. Ekman, P., 1992. An argument for basic emotions. Cognition & emotion, 6(3-4), pp.169-200.

3. Nasukawa, Tetsuya & Yi, Jeonghee. (2003). Sentiment analysis: Capturing favorability using natural language processing. 70-77.

10.1145/945645.945658.

4. Pang, Bo, and Lillian Lee. "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum

cuts." In Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p. 271. Association for

Computational Linguistics, 2004.

5. Denecke, Kerstin. "Using sentiwordnet for multilingual sentiment analysis." In Data Engineering Workshop, 2008. ICDEW 2008.

IEEE 24th International Conference on, pp. 507-512. IEEE, 2008.

6. Bhadane, Chetashri, Hardi Dalal, and Heenal Doshi. "Sentiment analysis: Measuring opinions." Procedia Computer Science 45

(2015): 808-814.

7. Deng, Zhi-Hong, Kun-Hu Luo, and Hong-Liang Yu. "A study of supervised term weighting scheme for sentiment analysis."

Expert Systems with Applications 41, no. 7 (2014): 3506-3513.

8. Saif, Hassan, Yulan He, Miriam Fernandez, and Harith Alani. "Contextual semantics for sentiment analysis of Twitter."

272-274

Information Processing & Management 52, no. 1 (2016): 5-19.

9. Agarwal, Basant, Soujanya Poria, Namita Mittal, Alexander Gelbukh, and Amir Hussain. "Concept-level sentiment analysis with

dependency-based semantic parsing: a novel approach." Cognitive Computation 7, no. 4 (2015): 487-499.

10. Pak, Alexander, and Patrick Paroubek. "Twitter as a corpus for sentiment analysis and opinion mining." In LREc, vol. 10, no.

2010, pp. 1320-1326. 2010.

11. Chikersal, Prerna, Soujanya Poria, and Erik Cambria. "SeNTU: sentiment analysis of tweets by combining a rule-based classifier

with supervised learning." In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 647-

651. 2015.

12. Hu, Xia, Jiliang Tang, Huiji Gao, and Huan Liu. "Unsupervised sentiment analysis with emotional signals." In Proceedings of the

22nd international conference on World Wide Web, pp. 607-618. ACM, 2013.

13. Fernández-Gavilanes, Milagros, Tamara Álvarez-López, Jonathan Juncal-Martínez, Enrique Costa-Montenegro, and Francisco

Javier González-Castaño. "Unsupervised method for sentiment analysis in online texts." Expert Systems with Applications 58

(2016): 57-75.

14. Kontopoulos, Efstratios, Christos Berberidis, Theologos Dergiades, and Nick Bassiliades. "Ontology-based sentiment analysis of

twitter posts." Expert systems with applications 40, no. 10 (2013): 4065-4074.

15. Maas, Andrew L., Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. "Learning word vectors

for sentiment analysis." In Proceedings of the 49th annual meeting of the association for computational linguistics: Human

language technologies-volume 1, pp. 142-150. Association for Computational Linguistics, 2011.

16. Poria, Soujanya, Erik Cambria, Newton Howard, Guang-Bin Huang, and Amir Hussain. "Fusing audio, visual and textual clues

for sentiment analysis from multimodal content." Neurocomputing 174 (2016): 50-59.

17. Bänziger, Tanja, Didier Grandjean, and Klaus R. Scherer. "Emotion recognition from expressions in face, voice, and body: the

Multimodal Emotion Recognition Test (MERT)." Emotion 9, no. 5 (2009): 691.

18. Wagner, Johannes, Elisabeth Andre, Florian Lingenfelser, and Jonghwa Kim. "Exploring fusion methods for multimodal emotion

recognition with missing data." IEEE Transactions on Affective Computing 2, no. 4 (2011): 206-218.

19. Sun, Bo, Liandong Li, Guoyan Zhou, Xuewen Wu, Jun He, Lejun Yu, Dongxue Li, and Qinglan Wei. "Combining multimodal

features within a fusion network for emotion recognition in the wild." In Proceedings of the 2015 ACM on International

Conference on Multimodal Interaction, pp. 497-502. ACM, 2015.

20. Poria, Soujanya, Erik Cambria, Newton Howard, Guang-Bin Huang, and Amir Hussain. "Fusing audio, visual and textual clues

for sentiment analysis from multimodal content." Neurocomputing 174 (2016): 50-59.

21. Munezero, M.D., Montero, C.S., Sutinen, E. and Pajunen, J., 2014. Are they different? Affect, feeling, emotion, sentiment, and

opinion detection in text. IEEE transactions on affective computing, 5(2), pp.101-111.

55.

Authors: Timmana Hari Krishna, C. Rajabhushanam

Paper Title: Breast Cancer Prognosis with Apache Spark Random Forest Pipeline

Abstract: Brest cancer is one of the most common cancers diagnosed in women in western countries. Breast

cancer research and awareness supports the improvements in cancer diagnosis and treatment. Early detection of

Breast cancer improves the survival rates and decreases the number of deaths related to this disease. Recently

Computer concepts are spread across all domains including medical and healthcare. Data science and machine

learning techniques are used in cancer prediction and analysis to get rapid accurate results. The cancer prediction

involves the identification malignant cells from breast cells. Researchers and Pathologists used the several

machine learning algorithms like K-Nearest Neighbors, logistic support vector machine, artificial neural

networks and decision tree in cancer prediction. They did not conclude the feasible method for cancer prediction.

In this paper we propose a scalable, fault tolerant pipeline model that analyses big cancer data in and predicts the

cancerous cells in real time. This model is developed on Apache Spark using Machine Learning Pipeline. In this

paper, we implemented our pipeline using Random Forest algorithm to compare with baseline model in terms of

accuracy and performance.

Keywords: Apache Spark, Machine Learning pipeline, Cancer Prediction, Random Forests

References:

1. T.Hari Krishna and Dr C. Rajabhushanam, ”Mininet Implementation of SDN Towards Network Softwarization”, International

Journal Of Innovative Research In Management, Engineering And Technology, vol. 2, Issue 5, pp.1-4, May 2017,. 2. Dana Bazazeh and Raed Shubair,"Comparative study of machine learning algorithms for breast cancer detection and

diagnosis",5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), 2016

3. Alireza Osareh and Bita Shadgar, "Machine learning techniques to diagnose breast cancer",5th International Symposium on Health Informatics and Bioinformatics (HIBIT), 2010

4. Ahmed F. Seddik and Doaa M. Shawky,"Logistic regression model for breast cancer automatic diagnosis", SAI Intelligent

Systems Conference (IntelliSys), 2015 5. Ganesh N. Sharma, Rahul Dave,Jyotsana Sanadya,Piush Sharma and K. K Sharma,"VARIOUS TYPES AND MANAGEMENT

OF BREAST CANCER: AN OVERVIEW", Journal of Advanced Pharmaceutical Technology & Research, pp.109–126,Apr-Jun

2010 6. Chandresh Arya and Ritu Tiwari, "Expert system for breast cancer diagnosis: A survey", International Conference on Computer

Communication and Informatics (ICCCI), 2016

7. PubMed Forums,"Breast cancer: Overview",Informed Health Online,July 27, 2017. 8. Spark Machine learning documentation site at https://spark.apache.org/docs/2.2.0/ml-pipeline.html

9. PubMed Forums,"Female Breast Cancer (Female Breast Carcinoma): Symptoms"

10. Aiello EJ1, Buist DS, White E, Seger D and Taplin SH, "Rate of breast cancer diagnoses among postmenopausal women with self-reported breast symptoms",The Journal of the American Board of Family Medicine,Dec 2004..

11. Big data wiki Available at https://en.wikipedia.org/wiki/Big_data

12. Spark Documentation site available at https://spark.apache.org/ 13. UCI Breast Cancer Data Set at https://archive.ics.uci.edu/ml/datasets/breast+cancer

14. Mona Botros and Kenneth A Sikaris,"The De Ritis Ratio: The Test of Time", The Clinical Biochemist Reviews, pp.117-130, Nov

2013

275-277

56. Authors: Indra Gandhi R, V.Ponnavaikko

Paper Title: Over Edge Detection Evolution and Challenges on Pattern Recognition

Abstract: Pattern recognition places an important role in partitioning the higher than mentioned issues. Pattern

recognition analysis is complete provided that it acknowledges distorted document, which is deficient within the

present analysis works. In all image pattern recognition, process, analysis and PC vision techniques Edge

detection plays major role. In recent past pattern recognition directly deals pc vision systems, orientation and

intensity info concerning edges as primary input for more process to document identification. This review

provides a summary of the literature on the sting detection ways for pattern recognition.

Keywords: Edge Detection, Pattern Recognition, Character Recognition, Image processing

References:

1. U. Pal, and B.B. Chaudhuri, “Indian script character recognition: A survey”, Pattern Recognition, Vol. 37, no.9, pp. 1887-1899,

2004.

2. R. Plamondon and S. N. Srihari, “On-line and off-line handwritten recognition: a comprehensive survey”, IEEE Transactions on PAMI, Vol. 22(1), pp. 63–84, 2000.

3. D. Huttenlocher, G. Klanderman, and W. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Transactions on

Pattern Analysis and Machine Intelligence, vol. 15, pp. 850–863, 1993. 4. M. C. Shin, D. Goldgof, and K. W. Bowyer, “Comparison of edge detectors using an object recognition task,” Proceedings of the

IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1360–1365, 1999.

5. K. Bowyer, C. Kranenburg, and S. Dougherty, “Edge detector evaluation using empirical ROC curves,” Computer Vision and Image Understanding, vol. 84, no. 1, pp. 77–103, 2001.

6. M. Shin, D. Goldgof, K. Bowyer, and S. Nikiforou, “Comparison of edge detection algorithms using a structure from motion

task,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 31, no. 4, pp. 589–601, 2001. 7. D. R.Martin, C. C. Fowlkes, and J.Malik, “Learning to detect natural image boundaries using local brightness, color, and texture

cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530–549, 2004. 8. R. Moreno, D. Puig, C. Julia, and M. Garcia, “A new methodology for evaluation of edge detectors,” in Proceedings of the 16th

IEEE International Conference on Image Processing (ICIP), 2009, pp. 2157–2160.

9. N. Wu and M. Hwang. "Data Hiding: Current Status and Key Issues," International Journal of Network Security, Vol.4, No.1, pp. 1-9, Jan.2007.

10. C. Chan and L. M. Cheng, "Hiding data in images by simple LSB substitution," Pattern Recognition, pp. 469-474, Mar. 2004.

11. Changa, C. Changa, P. S. Huangb, and T. Tua, "A Novel image Steganographic Method Using Tri-way Pixel-Value Differencing," Journal of Multimedia, Vol. 3, No.2, June 2008.

12. Lai and L. Chang, "Adaptive Data Hiding for images Based on Harr Discrete Wavelet transform," Lecture Notes in Computer

Science, Volume 4319/2006 13. H. H. Zayed, "A High-Hiding Capacity Technique for Hiding Data in images Based on K-Bit LSB Substitution," The 30th

International Conference on Artificial Intelligence Applications (ICAIA - 2005) Cairo, Feb. 2005.

14. H. W. Tseng and C. C. Chnag, "High capacity data hiding in jpeg compressed images," Informatica, vol. 15, no. I, pp. 127-142, 2004.

15. P. Chen, and H. Lin, "A DWT Approach for image Steganography," International Journal of Applied Science and Engineering

2006. 4, 3: 275:290.

16. S. Lee, C.D. Yoo and T. Kalker, "Reversible image watermarking based on integer-to-integer wavelet transform," IEEE

Transactions on Information Forensics and Security, Vol. 2, No.3, Sep. 2007, pp. 321-330.

17. M. Ramani, Dr. E. V. Prasad and Dr. S. Varadarajan, "Steganography Using BPCS the Integer Wavelet Transformed image ", UCSNS International Journal of Computer Science and Network Security, VOL. 7 No.7, July 2007.

18. G. Xuan, J. Zhu, Y. Q. Shi, Z. Ni, and W. Su., "Distortion less data hiding based on integer wavelet transform," IEE Electronic

Letters, 38(25): 1646--1648, Dec. 2002. 19. Jeffrey B. Mulligan NASA Ames Research Center, “Recovery of motion parameters from distortions in scanned images”,

November 20-21, 1997. NASA Publication

20. Baby Sathya S and Rajesh Kumar T, “Real Time Recovery of Text based on FPGA”, International Journal of Emerging Trends in Electricals and Electronics , Vol. 3, Issue 3, May 2013.

21. S.K.Thilagavathy and R.Indra Gandhi, “RECOGNITION OF DISTORTED CHARACTER USING EDGE DETECTION

ALGORITHM”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 4, June 2013.

22. Tanuja K et al,”Handwritten Hindi Character Recognition System Using Edge detection & Neural Network”, International

Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443 ISSN (Online): 2394-7454, Volume-2, Issue-6,May-2015.

23. S.Mahalakshmi1 and Prabha.M.Karani,”STUDY OF EDGE DETECTION TECHNIQUES IN AUTOMATIC LICENSE PLATE

RECOGNITION”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 04, p-ISSN: 2395-0072, Page 1658-1661, Apr -2017.

278-280

57.

Authors: Jennifer .P, A. Muthukumaravel

Paper Title: Indexing on IR System by using Stemming and Stopwords

Abstract: Information retrieval system completely happened through keyword searching and it compromises

with a very large search space as documents to be searched can be of any length and thus time to search in a

whole document is also proportional to length of documents i.e. number of words in all documents. By

shortening this large search space search time can also be lessening. Searching of data relevant to our query is

done by information retrieval system. Keyword searching is the basic idea of this system which tries to solve the

large search space problem as the documents to be searched could be of any length. This means time to search

will increase with length of document. Search time will be reduced by reducing the search space. In this,we are

constructing a method which reduces the searching areawith the help of indexing that takes the help of stemming

methodand knowledge of stopwords. Representation of both, a word andmore than one word are done by

creating Indices using singleconcept. The recall is improved by including domain knowledgeusing ontology

while searching.

References:

1. A Query Formulation Language for the Data Web” - IEEE Transactions on Knowledge And Data Engineering, Mustafa

281-283

Jarrar and Marios D. Dikaiakos, Member, IEEE Computer Society-May-12.

2. “Multiagent Ontology Mapping Framework for the semntic web”-IEEE Transactions on Systems, Man, and Cybernetics—Part A:

Systems and Humans, Miklos Nagy and Maria Vargas-Vera-Jul-11.

3. “Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine”-IEEE

Transactions on Knowledge and Data Engineering, Tamer Ahmed Farrag, Ahmed Ibrahim Saleh, and Hesham Arafat Ali-May-

13.

4. “The History of Information Retrieval Research”-Proceedings of the IEEE,Mark Sanderson and W. Bruce Croft-May-12.

5. “CONCEPT-BASED INDEXING IN TEXT INFORMATION RETRIEVAL” International Journal of Computer Science &

Information Technology (IJCSIT), FatihaBoubekeur and Wassila Azzoug-Feb-13.

6. “Concept-Based Information Retrieval Using Explicit Semantic Analysis”-ACM Transactions on Information Systems, OFER

EGOZI, SHAUL MARKOVITCH, and EVGENIY GABRILOVICH-Apr-11.

7. “Context Based indexing in information Retrieval using BST”-International Journal of Scientific and Research Publications,

NehaMangla, Vinod Jain -Jun-14.

8. “The Information Retrieval Process” Web Information Retrieval, Data-Centric Systems and Applications S.,Ceri et al.,-

2013.

9. “An Effective Pre-Processing Algorithm for Information Retrieval Systems”-International Journal of Database Management

Systems ( IJDMS )-Vikram Singh and Balwinder Saini-Dec-14.

10. “A Novel Algorithm for Fully Automated Ontology Merging Using Hybrid Strategy”- European Journal of Scientific Research,

C.R. Rene Robin, G.V. Uma-Nov-10.

11. “Keyword-based Semantic Retrieval System using Location Information in a Mobile Environment” Proceedings of the 2009

International Symposium on Web Information Systems and Applications (WISA’09), Tae-Hoon Lee, Jung-Hyun Kim, Hyeong-

Joon Kwon and Kwang-Seok Hong-May-09.

12. “Stemming Algorithm to Classify Arabic Documents”Symposium on Progress in Information & Communication Technology,

Marwan Ali.H. Omer, Mashi long-2009.

13. “Design and Development of a Stemmer for Punjabi”International Journal of Computer Applications, Dinesh Kumar, Prince

Rana-Dec-10.

14. “A Study and analysis on Web Information Retrieval System for Distributed Environment”, S. Meenakshi, Dr. R. M. Suresh,

International Journal of Applied Engineering Research, Volume 11, Number 4 (2016) pp 2165-2176

15. “A Comparative Study of Stemming Algorithms”,Anjali Ganesh Jivani, IJCTA, Dec-2011

16. “A survey of Stemming Algorithms for Information Retrieval”, IOSR Journal of Computer Engineering (IOSR-JCE), Brajendra

Singh Rajput, Dr. NilayKhare, June 2015

17. “GRAS-An effective and efficient stemming algorithm for information retrieval”, Jiaul H. Paik, MandarMitra, ACM

Transactions on Information Systems (TOIS), Dec-2011.

58.

Authors: Julian Menezes .R, Julian Menezes .R

Paper Title: Conceivement of a Schema Assimilating a Stronghold in the Cloud Computing

Realm Abstract: Within a quintessential Cloud environment 'n' number of divergent entities, to name a few we have

Hardware Infrastructure, Lines of Codes, Updated Softwares to hover over a particular Hardware, RJ45 Cabling

crimped Ethernet Wires, and the other services which are related to the Cloud come together in unison to

overture 'n' services coupled with Computers, whilst Network of Computers, along with Virtual Private

Networking gives out the pathway required for the provisioning of utilities. The breach in Jericho's Wall,

equivalent in the Cloud demarcates the prosperity of the Cloud in terms of “Presentability of Resources in terms

of Infrastructure, Bandwidth, Storage, etc...as and when it is required by the end-user, and the performance of

the above facilities to place a controllable entity in order to Manage the same". We have a presumption that the

Information Technology as well as a particular firm's certainty resolution provided in this era does not suffice to

conquer the issues arising due to solidness in the Cloud. Our research probes the tasks and affairs of certainty

burdens of the Cloud via variant classic and innovative resolutions. Variant inspections and specific Blueprint

for inculcating various validity ideas, crafts and rule for the Cloud, with a special boom light focus on Platform

as a Service, and Infrastructure as a Service. The Blueprint that which we are going to bring forth is going to be

accepted universally, with independence to the variant Cloud Deployment entities. The aforementioned

statements should provide an Admin with a hold on the Cloud Environment, whilst inculcating a unique

resolution to combat a peril. Based on the data extracted from various trial and error methods, the cost-to-benefit

analysis can be estimated by the Cloud Provider.

Keywords: IAAS, SECURITY, CLOUD COMPUTING, PAAS.

References:

1. Conner, W., Iyengar, A., Mikalsen, T. Rouvellou, I., &Nahrstedt K, (2009) “A Trust Management Framework for Service-

Oriented Environments”, WWW Conference, pp891- 900.

2. Friedman, A. A., & West D. M, (Oct. 2010) “Privacy and Security in Cloud Computing,” Issues in Tech. Innovation.

3. Ristenpart, T. Tromer, E. Shacham, H., & Savage S, (2009) “Hey, you, get off of my cloud:exploring information leakage in

third-party compute clouds,” 16th ACM Conference on Computer and Communications Security, pp199 – 212.

4. Yan, L., Rong, C., & Zhao G, (2009) “Strengthen Cloud Computing Security with Federal Identity Management Using

Hierarchical Identity-Based Cryptography,”CloudCom, pp167–177.

5. Yau, S., S., & Ho G, (2010) “Protection of users' data confidentiality in cloud computing,”2nd Asia-Pacific Symposium on

Internetware.

6. Rivest, R. L., Adleman, L., &Dertouzos, M L, (1978) “On data banks and privacy homomorphisms,” Foundations of Secure

Computation.

7. Gentry C (2009), “Fully Homomorphic Encryption Using Ideal Lattices,” 41st ACM Symposium on Theory of Computing,

284-289

pp169 – 178.

8. Leiba B, (2012) “OAuth Web Authorization Protocol,” IEEE Internet Computing, pp74-77.

9. Ahmed, A.S, (2011) “OpenID authentication as a service in OpenStack,” 7th International Conference on Information

Assurance and Security, pp372-377.

10. Keleta, Y., Eloff, J. H. P., & Venter, H S, (2005) “Proposing a Secure XACML Architecture Ensuring Privacy and Trust,”

Research in Progress Paper, University of Pretoria, http://icsa.cs.up.ac.za/issa/2005/Proceedings/Research/093_Article.pdf

(accessed on 24 Aug, 2012)

11. Xu, M., Wijesekera, D., & Zhang X, (2011) “Runtime Administration of an RBAC Profile for XACML,” IEEE Transactions

on Services Computing, 4, 4, pp286-299.

12. Wang, R., Chen, S., & Wang, X F, (2012) “Signing Me onto Your Accounts through Facebook and Google: A Traffic-Guided

Security Study of Commercially Deployed Single-Sign-On Web Services,” IEEE Symposium on Security and privacy, pp365-

379.

13. Ukil, A.,Sen, J., &Koilakonda S, (2011) “Embedded Security for Internet of Things,” 2nd IEEE National Conference on

Emerging Trends and Applications in Computer Science, pp1-6.

14. Koopman, P, (2004) “Embedded system security,” IEEE Computer, 37, pp795-97.

15. http://www.trustedcomputinggroup.org (accessed on 27 Aug, 2012)

16. http://www.atmel.com (accessed on 27 Aug, 2012)

17. Mather, T., Kumaraswamy, S., &Latif S, (2009) “Cloud Security and Privacy: An Enterprise perspective of Risks and

Compliance,” O'Reilly Media, Inc.

18. https://developers.google.com/google-apps/sso/saml_reference_implementation (accessed on 27 Aug, 2012)

19. http://www.cloudaccess.com/saas-sso (accessed on 27 Aug, 2012)

20. Szefer, J. Lee, R.B. Ruby & B. Lee (2012) “Architectural Support for Hypervisor-Secure Virtualization,” I 7th International

Conference on Architectural Support for Programming Languages and Operating System, pp437 – 450.

21. Ukil, A (2010) "Trust and Reputation Based Collaborating Computing in Wireless Sensor Networks," IEEE International

Conference on Computational Intelligence, Modelling and Simulation, pp464 – 469.

22. Hu Y., Wu W., & Cheng D (2012) “Towards law-aware semantic cloud policies with exceptions for data integration and

protection,” 2nd International Conference on Web Intelligence, Mining and Semantics.

23. Ukil, A (2011) "Secure Trust Management in Distributed Computing Systems," IEEE DELTA, pp116 – 121.

24. Sun D., Chang G., Sun L., Li F., &Wang X, “A dynamic multi-dimensional trust evaluation model to enhance security of

cloud computing environments,” International Journal of Innovative Computing and Applications, vol. 3, Issue. 4, pp 200 –

212. http://support.microsoft.com/kb/257591

59.

Authors: S.Kanimozhi, S.Prabavathi

Paper Title: Human Computer Interaction Based Control Over Home Appliances

Abstract: With the recent advancement in science and technology, various techniques like voice controlled,

android controlled, GSM controlled, Zigbee controlled are used to control home appliances. This paper applies

the virtual reality projection technology to control and automate home appliance. Virtual Reality is a technique

that uses motion sensor to control the home appliance with the aid of computer simulation that simulates

physical presence of the object in the real or imaginary world. The operation performed using virtual reality

illustrates the action of real-world environments using special stereoscopic displays on the computer screen. The

environment is controlled and regulated using additional sensory information captured from the devices. The

information emphasizes an action with real sound through speakers or headsets targeted towards witnesses. A

projection device is an input device whereby the image of a virtual key can be projected onto the surface. It

involves laser, an interference, the diffraction light intensity records and an illumination of the recording. Light

is traced wherever needed and then key is pressed and the image is captured by wireless digital camera. Image is

processed using MATLAB to perform a corresponding task.

References:

1. M. Khalilbeigi, R. Lissermann, M. Mühlhäuser, and J. Steimle,“Xpaaand: Interaction techniques for rollable displays,” in

Proc. ACMCHI, 2011, pp. 2729–2732.

2. D. Wilson, “PlayAnywhere: A compact interactive table top projection- vision system,” in Proc. ACM UIST, 2005, pp.

83–92.

3. H. Benko and A. Wilson, “Depth Touch: Using depth-sensing camera to enable freehand interactions on and above the

interactive surface,” in Proc. IEEE Workshop ITS, vol. 8, 2009.

4. C. Harrison, H. Benko, and A. D. Wilson, “Omni Touch Wearable multi touch interaction everywhere,” in Proc. ACM

UIST, 2011,pp. 441–450.

5. C. Harrison, D. Tan, and D. Morris, “Skin put: Appropriating the body as an input surface,” in Proc. ACM CHI, 2010, pp.

453–462.

6. S. K. Kane, D. Avrahami, J. O. Wobbrock, B. Harrison, A. D. Rea,M. Philipose, and A. LaMarca, “Bonfire: A nomadic

system for hybrid laptop-tabletop interaction,” in Proc. ACM UIST, 2009, pp. 129–138.

290-292

60.

Authors: A. Nedumaran, V. Jeyalakshmi, Dereje Girmu Birru

Paper Title: Link Stability for Energy Aware Efficient Multicast Routing Algorithm using Manet

Abstract: The gathering focused administrations are one of the essential application classes that are tended to

by Mobile Ad hoc Networks (MANETs) as of late. To help such administrations, multicast directing is utilized.

Along these lines, there is a need to configuration steady and dependable multicast routing conventions for

293-297

MANETs to guarantee better parcel conveyance proportion, lower delays and diminished overheads. In this

paper, we propose a work based multicast routing plan that finds stable multicast way from source to recipients.

The multicast work is developed by utilizing course solicitation and course answer bundles with the assistance of

multicast steering data store furthermore, connect soundness database kept up at each node. The steady ways are

discovered dependent on choice of stable sending nodes that have high solidness of link availability. The link

stability is registered by utilizing the parameters; for example, got control, remove between neighboring nodes

and the link quality that is surveyed utilizing bit blunders in a bundle. The proposed plot is mimicked over

countless nodes with wide scope of portability also, the presentation is assessed. Execution of the proposed plan

is looked at with two surely understood work based multicast steering conventions, i.e., On-Demand Multicast

Routing Protocol (ODMRP) and Enhanced On-Demand Multicast Routing Protocol (EODMRP). It is seen that

the proposed plan creates better parcel conveyance proportion, diminished bundle delay and decreased

overheads, (for example, control, memory, calculation, and message overheads).website.

Keywords: MANET, scalable routing, biobjective optimization, link stability, energy consumption

References:

1. Tan, Shuaishuai, Xiaoping Li, and Qingkuan Dong. "Trust based routing mechanism for securing OSLR-based MANET." Ad

Hoc Networks 30 (2015): 84-98. 2. Haas, Zygmunt J., Marc R. Pearlman, and Prince Samar. "The zone routing protocol (ZRP) for ad hoc networks." (2002).

3. Misra, Padmini. "Routing protocols for ad hoc mobile wireless networks." Courses Notes, available at http://www. cis. ohio-state.

edu/~ jain/cis788-99/adhoc_routing/index. html (1999).

4. Thaseen, I. Sumaiya, and K. Santhi. "Performance analysis of FSR, LAR and ZRP routing protocols in MANET." International

Journal of Computer Applications 41.4 (2012). 5. P. Karthika and P. Vidhya Saraswathi, “Content Based Video Copy Detection Using Frame Based Fusion Technique”, J. Adv.

Res. Dyna. Cont. Sys. vol. 9, pp. 885-894, September 2017.

6. Shweta Singh and Arun Kr. Tripathi: “Analysis of Delay and Load Factors in Wired and Wireless Environment”, Second

International Conference on Recent Trends in Science, Technology, Management & Social Development (RTSTMSD-15), International Journal of Science, technology and Management (IJSTM), and ISSN NO: 2321-1938, Dec-2015, pp. 1-8.

7. Mauve, Martin, et al. "Position-based multicast routing for mobile ad-hoc networks." ACM SIGMOBILE Mobile Computing and

Communications Review 7.3(2003): 53-55. 8. Shweta Singh, Anil Kr. Ahlawat, and Arun Kr. Tripathi: “Mechanizing Wireless LAN (WLAN) using Compression Techniques”,

in Free Journal International Journal of Engineering, Applied Science and Technology (IJEAST), ISSN No: 2455-2143, March-

April 2016. 9. R.Ganesh Babu, and Dr.V.Amudha, “A Survey on Artificial Intelligence Techniques in Cognitive Radio Networks”, proceedings

of 1st International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS) in association with Springer Advances in Intelligent Systems and Computing Series, February 23-25, 2018.

10. D. Lumbantoruan and A. Sagala, “Performance Evaluation of OLSR Routing Protocol in Ad Hoc Network”, ARPN Journal of

Engineering and Applied Sciences, vol. 10, no. 3, 2015. A. Nedumaranand V. Jeyalakshmi, “Link Stability Decision Based Routing Protocol for MANET Using HMM Model”,

Asian Journal of Information Technology, vol.15, no.12, pp.1924-1928, 2016.

B. Sørensen, N. Hernández and J. Guerrero, “Easy as Pi: A Network Coding Raspberry Pi Testbed” , Electronics , vol. 5, no. 67, 2016.

11. P. Karthika and P. Vidhya Saraswathi, “Digital Video Copy Detection Using Steganography Frame Based Fusion Techniques”,

Proceedings of the Int. Conf. ISMAC in Computational Vision and Bio-Engineering (ISMAC-CVB), Springer, pp.61-68, January 2019.

12. P. Regis, C. Miley and S. Sengupta, “Multi-hop Mobile Wireless Mesh Network Testbed Development and Measurements”,

International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, no. 3, 2 017. 13. R. Ganesh Babu and V. Amudha, “Allow an Useful Interference of Authenticated Secondary User in Cognitive Radio Networks”,

International Journal of Pure and Applied Mathematics, vol.119, no.16, pp.3341-3354, 2018.

14. N.L. Lakshman, R.U.Khan and R.B.Mishra,"Analysis of Node Density and Pause Time Effects in MANET Routing Protocols using NS-3", International Journal of Computer Network and Information Security, vol.8, no.12, pp.9-17, 2016.

A. Thapliyal and C. Kumar,"Development of Data Acquisition Console and Web Server Using Raspberry Pi for Marine

Platforms", International Journal of Information Technology and Computer Science, vol.8, no.11, pp.46-53, 2016. B. Nedumaran and V. Jeyalakshmi, “CAERP: A Congestion and Energy Aware Routing Protocol for Mobile Ad Hoc

Network”, Indian Journal of Science and Technology, vol.8, no.35, pp.1-6, 2015.

15. P. Karthika and P. Vidhya Saraswathi “A Survey of Content Based Video Copy Detection Using Big Data”, Int. J. Sci. Res. Sci. Tech. vol. 3, pp. 114-118, March-April 2017.

16. R. Ganesh Babu and V. Amudha, “Comparative Analysis of Distributive Firefly Optimized Spectrum Sensing Clustering

Techniques in Cognitive Radio Networks”, Journal of Advanced Research in Dynamical and Control Systems, vol.10, no.9,

pp.1364-1373, 2018.

17. T. Clausen and P. Jacquet, ―Optimized Link State Routing Protocol (OLSR)‖, RFC 3626, IETF Network Working Group,

available online at https://tools.ietf.org/html/rfc3626, January 2018.

61.

Authors: Poovammal E, Madhurima Mukherjee

Paper Title: Enhanced Topic Modeling

Abstract: We belong to an era of digitization where our collective knowledge is continuing to be stored in the

form of electronic texts, i.e. blogs, news, scientific articles, web pages, images, audios, videos, social networks. As

a result, it is getting more complicated to find out what we actually aim for. To handle this situation there is a rising

need for analyzing huge collections of document. Topic modeling is a probabilistic generative modeling that is an

efficient text mining technique for finding the hidden semantic structures of contents. In a natural way, topic

modeling is discovering thematic structure in large volume of data and annotating those according to the structure.

It finally uses those annotations for visualization, organization, summarization and many more purposes. New

models of topic modeling are coming up with advanced inference algorithms. Improvements in algorithms will

allow us to retrieve our required data in more efficient and optimized manner. The domain acts as a central concept

for multiple on-going researches and we wish to add to it by our own survey. In this paper we have discussed about

some methodologies which have been introduced in several papers of topic modeling.

References:

298-301

1. Blei, D. (2012). “Probabilistic topic models.” Communications of the ACM, 55(4), 77– 84. 2. Blei, D. M. (2013). “Probabilistic topic models: Origins and challenges,<http://www.cs.columbia.edu/ blei/talks>.

3. C. Lin, Y. He, R. E. and Ruger, S. (Jun. 2012). “Weakly supervised joint sentiment-topic detection from text.” IEEE Trans. Knowl.

Data Eng., 24(6), 1134–1145. 4. Chang, Y. W. B. S. Y. C. Y. (2009). “Plda: Parallel latent dirichlet allocation for largescale applications.” Springer Verlag Berlin

Heidelberg, 301–314.

5. Chen, Z. and Liu, B. (2014). “Topic modeling using topics from many domains, lifelong learning and big data.” in Proc. 31st Int. Conf. Mach. Learn., 703–711.

6. Chien, J. T. and Wu, M. S. (Jan. 2008). “Adaptive bayesian latent semantic analysis.” IEEE Trans. Audio, Speech, Language Process.,

16(1), 198–207. 7. Chou, T.-C. and Chen, M. C. (2008). “Using incremental plsi for threshold resilient online event analysis.” IEEE Trans. Knowl. Data

Eng., 20(3), 289–299.

8. D. M. Blei, A. Y. N. and Jordan, M. I. (Mar. 2003). “Latent dirichlet allocation.” J. Mach. Learn. Res., 3, 993–1022. 9. Deerwester S, Dumais ST, F. G. L. T. H. R. (1990). “Indexing by latent semantic analysis.” journal of the American Society for

Information Science banner, 41(6), 391– 407.

10. Hanqi Wang, Fei Wu, W. L. Y. Y. X. L. X. L. F. I. and Zhuang, Y. (2017). “Identifying objective and subjective words via topic modeling.” IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2162-237X 2017 IEEE.

11. Hofmann, T. (1999). “Probabilistic latent semantic indexing.” in Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr.,

50–57. 12. M. W. Berry, S. T. D. and Brien, G. W. O. (1995). “Using linear algebra for intelligent information retrieval.” Vol. 37, 573–595.

13. X. Wang, N. M. and McCallum, A. (2006). “Group and topic discovery from relations and their attributes.” Proc. Adv. Neural Inf.

Process. Syst., 18, 1449–1456. 14. Ximing Li, JihongOuyang, Y. L. X. Z. T. T. (February 2015). “Group topic model: organizing topics into groups.” Information

Retrieval Journal, 18, 1–25.

15. Y. Zheng, Y.-J. Z. and Larochelle, H. (2014). “Topic modeling of multimodal data: An autoregressive approach.” in Proc. IEEE

Conf. Comput. Vis. Pattern Recognit., 1370– 1377.

16. Yuepeng ZOU, Ji-hong OUYANG, X. m. L. (2018). “Supervised topic models with weighted words: multi-label document

classification.” Front Inform Technol Electron Eng, 19, 513–523.

62.

Authors: C. Malathy, Mridula Vijendran,Krishna Maneesha Dendukuri

Paper Title: Paper on Facial Manipulation Techniques

Abstract: Facial manipulation is the manipulation of pose, identity, albedo, expression, and texture for creative,

artistic, and aesthetic manipulation in different styles. It’s used in visualizations, videos and image presentations,

and digital media, social media, advertising, restoration and preservation of old and damaged photos or images.

However, it can also be used for hijacking the facial identity of the target video or image, thus, compromising

the integrity of the person in question. This can be accomplished by techniques using supervised convolutional

neural networks, unsupervised generative models and physics-based models. These models aim to transfer traits

and features that are intuitively comprehensible from the source to the target. Another objective could be to give

full control to modify the features in the generated model, in a manner that we can grasp the immediate

correlation to the resultant output. This paper considers the various techniques in deep learning and physics

simulations to achieve facial manipulation.

Keywords: 3D parametric face models, Neural Style Transfer, Generative Adversarial Networks(GAN),

VariationalAutoencoders(VAE), Physics-based facial manipulation

References:

1. Hyeongwoo Kim, Pablo Garrido, AyushTewari and WeipengXu, Justus Thies and Matthias Niessner, Patrick pérez, Christian Richardt, Michael zollhöfer, Christian TheobaltDeep Video PortraitsACM Vol 37, No. 4, Article 163, 2018

2. Hui Ding, Kumar Sricharan, Rama Chellappa ExprGAN: Facial Expression Editing with Controllable Expression Intensity, CoRR 2018

3. Justus Thies, Michael Zollher, Christian Theobalt, Marc Stamminger, Matthias Nießner HeadOn: Real-time Reenactment of

Human Portrait VideosACM Volume 37 Issue 4, August 2018

4. Zhenliang He, WangmengZuo, Senior Member, IEEE, MeinaKan, Member, IEEE, Shiguang Shan, Senior Member, IEEE, and

Xilin Chen, Fellow, IEEE AttGAN: Facial Attribute Editing by Only Changing What You WantIn Proc. Arxiv Jul,2018

5. Alexandru - EugenIchim, PetrKadleček, LadislavKavan, Mark PaulyPhace: Physics-based Face Modeling and AnimationACM

Volume 36, 2017

6. GrigoryAntipov, MoezBaccouche, Jean-Luc Dugelay FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL

NETWORKSICIP,2017

7. -Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman Toward Multimodal

Image-to-ImageTranslation 31st conference NIPS 2017

8. Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. EfrosUnpaired Image-to-Image Translation using Cycle-Consistent

Adversarial NetworksICCV 2017

9. Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing KangVisual Attribute Transfer through Deep Image AnalogyACM

Volume 36 Issue 4, July 2017

10. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. EfrosImage-to-Image Translation with Conditional Adversarial

NetworksCVPR 2017

11. RunzeXu, ZhimingZhou,Weinan Zhang, Yong Yu Face Transfer with Generative Adversarial Network CoRR, 2017

12. Shuchang Zhou, Taihong Xiao, Yi Yang, DieqiaoFeng, Qinyao He, Weiran HeGeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired DataCoRR,2017

302-305

13. TeroKarras, TimoAila, SamuliLaine, AnttiHerva, JaakkoLehtinenAudio-Driven Facial Animation by Joint End-to-End Learning

of Pose and Emotion ACM Vol. 36, No. 4, Article 94, 2017

14. Xinyue Zhu, Yifan Liu, Zengchang Qin, and Jiahong LiEmotion Classification with Data Augmentation Using Generative

Adversarial NetworksCoRR 2017

15. YanivTaigman, Adam Polyak&Lior Wolf Unsupervised Cross-Domain Image Generation ICLR 2017

16. Zhifei Zhang, Yang Song, Hairong QiAge Progression/Regression by Conditional Adversarial AutoencoderCVPR,2017

17. Andrew Brock, Theodore Lim, & J.M. Ritchie, Nick WestonNeural Photo Editing With Introspective Adversarial

NetworksCoRR, 2016

18. GATYS, L. A., ECKER, A. S., AND BETHGE, M. A neural algorithm of artistic styleIn Proc. CVPR. 2016.

19. Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt, Matthias NießnerReal-time Expression Transfer for

Facial ReenactmentArxiv, 2016

20. Justus Thies, Michael Zollhofer, Marc Stamminger,ChristianTheobal,MatthiasNießner Face2Face: Real-time Face Capture and

Reenactment of RGB VideosCVPR 2016

21. Ohad Fried, Eli Shechtman, Dan B Goldman, Adam FinkelsteinPerspective-aware Manipulation of Portrait PhotosACM,

SIGGRAPH 2016

22. Raymond Yeh, ZiweiLiuy, Dan B Goldmanz, AseemAgarwalazSemantic Facial Expression Editing using Autoencoded

FlowCoRR 2016

23. Jose A., Iglesias-Guitian, Carlos Aliaga, Adrian Jarabo, Diego GutierrezA Biophysically-BasedModel of the Optical Properties of

Skin AgingEUROGRAPHICS 2015

24. Justus Thies, Michael Zollhöfer, Matthias Nießner, Levi Valgaerts, Marc Stamminger, Christian Theobalt Real-time Expression

Transfer for Facial ReenactmentACM Transactions on Graphics 2015 (TOG)

25. P. Garrido, L. Valgaerts, H. Sarmadi, I. Steiner, K. Varanasi, P. Pérez, C.TheobaltVDub: Modifying Face Video of Actors for

Plausible Visual Alignment to a Dubbed Audio TrackACM Computer Graphics 2015

26. Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang Deep Learning Face Attributes in the Wild ICCV 2015

27. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, SherjilOzair, Aaron Courville, and YoshuaBengio. Generative adversarial nets.NIPS, pp. 2672–2680. 2014.

28. ZEILER, M., AND FERGUS, R. ,Visualizing and understanding convolutional networksIn Proc. ECCV, 2014.

29. Diederik P Kingma, Max WellingAuto-Encoding Variational BayesCoRR 2013

30. BARNES, C., SHECHTMAN, E., FINKELSTEIN, A., AND GOLDMAN, D. B.Patchmatch: A randomized correspondence

algorithm for structural image editing. ACM Trans. Graph.(Proc. of SIGGRAPH) 28, 3. 2009.

63.

Authors: Padma Priya, S.Muruganantham

Paper Title: Coral Reef Image Classifications

Abstract: Thischapter presents various classification methods for resolving the coral reef which exhibit vital

within-class variations, complicated between-class boundaries and discrepant image clarity. This makes coral

classification a difficult task. In this paper we examine the recent activity of image classification approaches and

techniques. Image classification is a difficult process which depends upon various factors. Here, we deliberate

about the current procedures, obstacles as well as prospects of image classification. This main attention will be

on advanced classification techniques which are used for improving classification accuracy. Additionally, some

important problems relating to classification performance are also discussed. The aim of this paper is to report an

illustrative and comparative study of the most popular feature extraction methods which are generally used for

classification.

Keywords: coral reef, classification, SVM, KNN, Decision Tree, Neural Network.

References:

1. Hughes TP, Baird AH, “climate change, human impacts, and the resilience of coral reefs”, Science.2003 Aug 15. 2. Hoegh-Guldberg O, MumbyPJ.”Coral reefs under rapid climate change and ocean acidification”. Science 2007

3. EzmahamrulAfrenAwalludin, Muhammad SuzuriHitam, “Analysis of coral reefs Distribution using Edge Detection and Bloob

Processing Techniques “International Journal of Interactive Digital Media, January 2013. 4. M.S.A Marcos, Caesar, Soriano, and .Saloma, “Classification of coral reef images from underwater video using neural

networks,”15 October 2005.

5. Paul Anton Letnes, Ingrid Myrnes ansen1,”Underwater hyper spectral classification of deepseacorals exposed to a toxic compound “Jun 14, 2017

6. Mehta.A.Ribeiro.E,”Coral Reef Texture Classification using support Vector Machines”In Proceedings of International Joint

Conference on computer vision, Imaging and computer Graphics Theory and Applications, Barcelona, spain,8-11 March 2007 7. O.Pizarro, P.Rigby,”Towards image-based marine habitat classification”, InProc OCEANS, 2008, pp 1-7

8. Patterson and N.Relles”Autonomous underwater vehicles resurvey bonair” a new tool for coral reef management In ICRS, 2008.

9. M.D.stokes and G.B.Deane,”Automated processing of coral reef benthic images” Limnol, Oceanography, 2009. 10. Z.Guo, L.Zhang, D, Zhang “A completed modelling of local binary pattern operator for texture classification” , IEEE Trans,

Image Process.(9).(16).2010.1657-1663.

306-309

11. Beijborn.PJD.kline, Edmund, ”Automated annotation of coral reef survey images,” in Proc,IEEE conference on computer vision and Pattern Recognition(CVPR) June 2012

12. S.M.Shihavuddin 1, NunoGracias”Image- based Coral Reef Classification and Thematic Mapping”RemoteSensing 5,(2013)1809-

1841 13. Eduardo Tusa, Alan Reynolds “Implementation of a Fast Coral Detector Using a Supervised Machine Learning and Gabor

Wavelet Feature Descriptors”2014 .

14. NurhalisWahidina.b, VincentiusP.”Object based image analysis for coral reef benthic habitat mapping with several classification algorithms ProcediaEnvironmental sciences 24(2015).

15. Mohamed Essayed Elawady,”Sparse coral classification using Deep Convolutional Neural Networks”, 29 Nov 2015

16. Mirceacimpoi, SubhransuMaji, “Deep filter banks for texture recognition and segmentation” in IEEE Conference on Computer Vision and Pattern Recognition 2015

17. Mahmood,” Coral classification with hybrid feature representations, “in Proc IEEE Int.Conf.Image Process 2016, pp519-523.

18. Mahmood A, Bennamoun M “Automatic annotation of coral reefs using deep learning. In OCEANS 2016 Sep 20. 19. Jean-Nicola Blanchet, Sebastian Dery,kate Osborne ”Automated annotation of corals in natural scene images using multiple

texture representations”,PeerJ Preprints,5 May 2016

20. YANG Guoqiang” High Resolution remote sensing classification of coral reef substrate, based on SVM-taken xisha an example”2016.

21. Mahmood, M.Bennamoun “RESFEATS: RESIDUAL NETWOK BASED FEATURES FOR IMAGE CLASSIFICATION”2017.

22. N. Ani Brown Mary,Dejey Dharma “Coral reef image classification employing Improved LDP for feature Extraction” Journal of visual communication and image representation,2017

23. InigoAlonso,”Coral Segmentation: Training Dense Labeling Models with sparse Ground Truth”ICCVW 2017

24. E.A.Awalludin, M.S.Hitam “Modification of canny edge detection for coral reef components estimation distribution from underwater video transect” ICSIPA 2017

25. LianXu, Mohammed Bennamoun “classification of corals in Reflectance and fluorescence Images Using convolutional Neural

Network Representations” ICASSP 2018.

26. Dianpeng Su,Fanlin Yang, ”classification of Coral Reefs in the South china Sea by combining Airborne LiDAR Bathymetry

bottom Waveforms and Bathymetric features” IEEE Transactions on Geoscience and Remote Sensing.2018.

27. Anabel, Gomez-Rios “Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation” Expert System with Applications 2018.

28. Shinya Odagawa “Bottom-Type Classification in Coral Reef Area using Hyper spectral Bottom Index Imagery”IEEE 2017.

29. Ammar Mahmood” Deep Image Representations for Coral Image Classification”IEEE journal of oceanic engineering 2018

30. Karen Simonyan “Very Deep Convolutional Networks for Large-ScaleImage Recognition” ICLR 2015

64.

Authors: P.Arivubrakan, B.Umamaheswari

Paper Title: A Efficient Protocol for an Optimum Performance Level of Energy with A Secure Communication

in Wireless Sensor Network

Abstract: Sensor Networks is the emerging trend of computer science and the dynamic nodes are located in

large number , in order to examine the consumption of energy into everyday activities to make them

effectively communicate and perform the smart operations efficiently, it reduce the end user's need to interact

with computers as well as human activity. Sensor devices are wireless network connected and constantly

available with the transmission range. The advancement in the wireless technology like sensors is developed

in communications and transmission of packets, the wireless sensor networks domain is come into sight and

become the one of the most interesting and emerging areas in the field of research. Sensor networks are secure

enough to develop the sensing and monitoring in a large range of application domains such as healthcare

monitoring and battlefield surveillance. Trustworthy, correctness, flexibility, cost-effectiveness, energy

consumption and ease of exploitation of nodes in large number are the major characteristic of networks. In

order to minimize the consumption of energy, this paper is to enhance the optimum solution for the energy

utilization by using the proposed algorithm and applied in smart environment.

Keywords: Sensor, Smart environment, Research, Optimum Utilization, Energy Conservation.

References:

1. Mulligan Raymond,M.HabibAmmari,Wireless Sensor Network Survey, in Elsevier Computer Networks.

2. Vidhyasagar Potdar, Atif Sharif, Elizabeth change,”A Wireless Sensor Networks-A survey, International Conference on

Advanced Information Networking and Applications Workshops,2009. 3. Raymond , Habib M. Ammari, “Coverage in Wireless Sensor Networks: A Survey”, Macrothink Institute, 2010. 4. E. Mathews and C. Mathew, “Deployment of mobile routers ensuring coverage and connectivity,” International Journal

Computer Network. Communication., vol. 4, no. 1, pp. 175–191, 2012. 5. Gao Jun Fan and ShiYao Jin, “Coverage Problem in Wireless Sensor Network: A Survey”, Journal of Networks, Vol. 5, No. 9,

September 2010. 6. I. E. Korbi and S. Zeadally, “Energy-aware sensor node relocation in mobile sensor networks,” Ad Hoc Network., vol. 16, pp.

247– 265, 2014. 7. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and ECayirci, “Wireless Sensor Networks: A Survey,” in Elsevier Computer

Networks, Volume: 38, Issue:2, 2002. 8. Raymond Mulligan and Habib M. Ammari, “Coverage in Wireless Sensor Networks: A Survey”, Macrothink Institute, ISSN

1943-3581, Vol.2, No.2, 2010. 9. V. Blessy Johanal Selvarasi and A. Aruna Devi, “Sensor Deployment and Scheduling using Optimization”, International Journal

of Science Technology & Engineering (IJSTE), Volume 2, Issue 10, April 2016. 10. Wei Shen and Qishi Wu, “Exploring Redundancy in Sensor Deployment to Maximize Network Lifetime and Coverage,” 8th

Annual IEEE Communications Society Conference on Sensor, Mesh and Ad-hoc Communication and Networks, 2011. 11. X. Bai, S. Kumar, D. Xuan, Z. Yun, and T. H. Lai, “Deploying wireless sensors to achieve both coverage and connectivity,” in

Proc. 7th ACM Int. Symp. Mobile Ad-hoc Network. Computing, 2006, pp. 131– 142. 12. R. Wang, G.L. Xing, Y.F. Zhang, C.Y. Lu, R. Pless, and C. Gill, “Integrated Coverage and Connectivity Configuration in

Wireless Sensor Networks,” Proc. ACM Conf. Embedded Networked Sensor Systems pp. 28-39, 2003. 13. Y. Yang, M. I. Fonoage, and M. Cardei, “Improving network lifetime with mobile wireless sensor networks,” Computer.

Communication, vol. 33, no. 4, pp. 409–419, 2010. 14. Y. Zou and K. Chakrabarty, “A distributed coverage- and connectivity-centric technique for selecting active nodes in wireless

sensor networks,” IEEE Transactions on Computers, vol. 54, no. 8, pp. 978–991, 2005. 15. Ming Zou Shijue Zheng, 2010:Energy balancing routing algorithm based on HGACA in WSNs.Computer Engineering and

310-315

Technology (ICCET), vol. 2, pp. 637 -640. 16. Sarma Dhulipala, V.R., Aarthy V., Chandrasekaran, Dr.R.M., 2010, Energy and Fault Aware Management Framework for

Wireless Sensor Networks” , Springer, Communications in Computer and Information Science. vol. 70, pp. 461-464. 17. Silva, A.,Martins, M., Rocha, B., Loureiro, A., Ruiz, L., Wong H., 2005:Decentralized Intrusion Detection in Wireless Sensor

Networks. Proc. First ACM Workshop Quality of Service and Security in Wireless and Mobile Networks, ,pp. 16-23. 18. Sivaraman, R., Sarma Dhulipala, V.R., Aarthy, V., Kavitha, K., 2009: Energy Comparison and Analysis for Cluster-based

Environment in Wireless Sensor Networks, International Journal of Recent Trends in Engineering. Vol. 2, No. 4, pp. 89-9 .

19. Sonali karegaonkar and Archana Raut,Improving Target coverage and network connectivity of mobile sensor networks,

International of journal of Science and Research IJSR,vol 4,issue no.4,2005.

20. Alka.P.Sawlikar,Z.j.Khan,S.G.Akojwar ,A Power Optimization of Wireless Sensor Networks Using Encryption and Compression Techniques,ACM,DL,ICESC,2014.

21. Sonali kareganokar,Archana Raut,”Improving Target Coverage and Network Connectivity of Mobile Sensor Networks,IJSR,2013.

22. Tian, D., Georganas, N.D,. 2002:A Coverage-Preserved Node Scheduling Scheme for Large Wireless Sensor Networks. Proc.First International Workshop Wireless Sensor Networks and Applications (WSNA), pp. 32-41.

23. P.Arivubrakan,V.R.Sarma Dhulipula, Energy Consumption Heuristics in Wireless sensor networks,International conference on

Computing, Communication and Applications,2012. 24. Arivubrakan P, Sarma Dhulipala V.R, Sentry Based Intruder Detection Technique for Wireless Sensor Networks,Journal of

Artificial Intelligence,2012.

65.

Authors: Paras Tripathi, C.Malathy, M.Prabhakaran

Paper Title: Genetic Algorithms Based Approach for Dental Caries Detection using Back Propagation Neural

Network

Abstract: The detection of dental caries from radiograph is a very challenging task for Dentists, often early

forming caries are overlooked or misclassified. The goal is to assist dentists to detect these caries in early stages

so that the severity of the decay caused by delayed treatment can be avoided. To solve this problem a system is

proposed which is capable of recognizing dental caries from bitewing radiography. The dental caries radiograph

has a certain number of grey level pixels which are a differentiating factor from normal teeth. Therefore, the

system utilizes Local Binary Pattern (LBP) to extract second order statistical texture features. These extracted

features would be utilized by a backpropagation neural network to characterize the severity of caries. The

Hybrid approach will help further to optimize the hyperparameter problem in the neural network and increase

accuracy in prediction.

Keywords: Local Binary Pattern, Backpropagation, Neural Networks, Genetic algorithm.

References:

1. World Health Organisation, the burden of oral disease <http://www.who.int/oral_health/disease_burden/global/en/>.

2. Alazab, Mamoun, Mofakharul Islam, and SitalakshmiVenkatraman. “Towards automatic image segmentation using optimized

region growing technique.” AI 2009: Advances in Artificial Intelligence. Springer, Berlin Heidelberg, 2009. 131-139

3. SHARMILA.M, Dr.R.GANESAN, R.KARTHIKA DEVI “Detection of Dental Plaque using Image Processing” International

Journal of Advanced Information Science and Technology (IJAIST) ISSN: 2319:2682 Vol.18, October 2013. 76-80

4. Oprea, S.; Marinescu, C.; Lita, I.; Jurianu, M.; Visan, D.A.; Cioc, I.B., “Image processing techniques used for dental x-ray image

analysis,”. 31st International Spring Seminar on Electronics Technology on May 2008, doi: 10.1109/ISSE.2008.5276424.

125,129.

5. Rad, AbdolvahabEhsani, MohdShafryMohd Rahim, RoselyKumoi, and AlirezaNorouzi. “Dental x-ray image segmentation and

multiple feature extraction.” Global Journal on Technology 2 (2013). 3109, 3114

6. Ojala, T., Pietikäinen, M., and Harwood, D. (1996), A Comparative Study of Texture Measures with Classification Based on

Feature Distributions. Pattern Recognition 19(3). 51-59.

316-319

66.

Authors: S.Prabavathi, S.Kanimozhi

Paper Title: Data Outflow Discovery using Water Marking

Abstract: Beginning late business hones depend upon far reaching email trade. Email spillages have finished

up being far reaching, and the unprecedented naughtiness caused by such spillages comprises an irritating issue

for affiliations. We take a gander at the running with issue: An information merchant has given dubious

information to a blueprint of to the degree anyone knows put stock in overseers (outcasts). On the off chance that

the information appropriated to outcasts is found in an open/private locale by then spotting the capable party as a

nontrivial undertaking to trader. Generally, this spillage of data is overseen by water checking structure which

requires change of data. In case the watermarked copy is found at some unapproved site then vender can

announce his proprietorship there is annihilating of data spillage in a data distributer has given right data to an

course of action of to the degree anybody knows place stock in experts. A bit of the data are spilled and found in

an unjustified place.We demonstrate a LIME information family history system for information stream

transversely wrapped up assorted zones. By utilizing ignorant exchange, proficient watermarking, and check

local people we make and discrete the information move custom in a pernicious space between two segments.

Around the entire of we play out an exploratory outcome and examination of our system. We make and dissect a

novel reliable information exchange convention between two segments inside a malevolent territory by creating

neglectful exchange, solid Watermarking, and check primitivesThe solitary data is open on social affiliations, or

now- a-days it is likewise accessible on Smartphone is intentionally or then again unexpectedly exchanged to

outcast or programming engineers. Facilitate progressively an information distributer may give orchestrated

information to some trusted in experts or outsiders. In the middle of this technique two or three information is

spilled or exchanged to unapproved put at the entire of we play out a preliminary result and examination of our

structure. We make and investigate a novel careful information exchange convention between two substances

inside a poisonous territory by creating thoughtless exchange, e reasonableness of strategies is damaged as long

320-322

as it isn't conceivable to provably relate the spilled information can't be related with them. Continuously end, at

the point when substances grasp that they can be viewed as responsible for spillage of two or three information,

they will display an unrivaled responsibility towards its required affirmation we formalize this issue of provably

relate the culpable party to the spillages, and work on the data family methods of insight to deal with the issue of

information spillage in various spillage conditions.

Keywords: Data surge, Social condition, Detection framework, Sensitive Data, Fake data .

References:

1. Chronology of data breaches. http://www. privacyrights. org/data-breach. Lime: Data Lineage in the Malicous Environment.

Signals,and Image Processing(IWSSIP2006).Citeseer, 2006, pp. 53–56. 2. P. Papadimitriou and H. Garcia-Molina, “Dataleakage detection,Knowledge and Data Engineering,IEEE Transactions on, vol. 23,

no. 1, pp. 51–63, 2011.

3. Pairing-Based Cryptography Library (PBC),http://crypto.stanford.edu/pbc. 4. I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon,“Secure spread spectrum watermarking for multimedia,Image Processing,

IEEE Transactions on, vol. 6, no.12,pp. 1673–1687, 1997.

5. Bhamare Ghanashyam, Desai Kiran, Khatal Supriya,Mane Vinod, Hirave K.S.,” A Survey Paper on DataLineage in Malicious Environments” Multidisciplinary Journal of Research in Engineering and Technology,Volume 2, Issue 4,Pg.720-724

Chronology of data breaches, http://www.privacyrights.org/data-breach.

67.

Authors: S. Ramachandran,s. Rabiyathul Basariya Paper Title: Genys’Online Marketing Buying Behaviour

Abstract: Majority of youngsters’ having connected online through internet either through computers or by

smart phones. After the entry of Jio in the field of internet, the competition began and the cost of internet service

became much cheaper andnoweveryone can afford the cost. Latest “Times of India” statistics shows around 59%

of internet users are college students/young men. The trend of going to the physical stores to buy the product is

in the decline stage where as the trend of surfing product specification as well as its cost and alternates through

online marketing sites is increasing among youngsters. Since, it is more convenient for them to shop anywhere

and anytime. Shopping can be done 24 x 7 and before buying; review of product performance through social

media and compare its price through varies alternate sites. There is no compulsion to buy the product while

surfingoreven if visited the siteforany number of times. The payment can be made through online transition and

products will be delivered to doorstep.Hence shopping through online become a joyful experience and preferred

by youngsters.

Keywords: Online Marketing, Segmentation, Technology and Buying Behaviour.

References:

1. Journal of Advancements in Research & Technology, Volume 1, Issue 6, November-2012 ISSN 2278-7763. 2. Consumer Perception and Buying Decisions by SyedaQuratulainKazmi.

3. Journal of Marketing and Consumer Research www.iiste.org ISSN 2422-8451 An International Peer-reviewed Journal

Vol.13,2015. 4. https://www.techopedia.com/definition/26363/online-marketing

5. https://usatodaytn.com/blog/what-is-online-marketing

6. https://www.thebalancecareers.com/get-to-know-and-use-aida-39273 7. The McCraw-Hill 36 Hour Course "Online Marketing" by Lorrie Thomas

8. The Impact of Customer Satisfaction on Online Purchasing: A Case Study Analysis in Thailand by TaweeratJiradilok,

SettapongMalisuwan, NavneetMadan, and JesadaSivaraks; Journal of Economics, Business and Management, Vol. 2, No. 1, February 2014 Page 5-11.

9. Customer Satisfaction in Online Shopping: a study into the reasons for motivations and inhibitionsbyRashed Al Karim; IOSR Journal of Business and Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 11, Issue 6 (Jul. - Aug.

2013), PP 13-20 www.iosrjournals.org

10. A Study on Customer Satisfaction towards Online Shopping by P Jayasubramanian, D Sivasakthi, K AnanthiPriya; International Journal of Applied Research 2015;1(8):P 489-495.

11. A Study on Customer Satisfaction on Online Marketing in India by S. Chitra, E. Shobana; International Research Journal of

Management, IT & Social Sciences (IRJMIS) Available online at http://ijcu.us/online/journal/index.php/irjmis Vol. 4 Issue 1,

January 2017, pages: 93~98.

12. Customer satisfaction toward Online Marketing - An empirical study by Dr. M. Rajarajan; International Journal of World

Research, Vol: I Issue XXXIV, October 2016 P 72-78 13. https://www.digitalvidya.com/blog/growth-of-digital-marketing-industry-in-india/

14. https://www.business-standard.com/article/current-affairs/india-is-adding-10-million-active-internet-users-per-month-google-

118062700882_1.html 15. https://www.semrush.com/blog/internet-users-in-india-a-fresh-audience-for-brands/

16. https://www.acewebacademy.com/blog/emergence-digital-marketing-coming-years/

323-325

68.

Authors: P. Rethina Sabapathi, K.P.Kaliyamurthie

Paper Title: The Social Media Content Through Content Quality And Personal Behavior using Data Mining

Techniques

Abstract: Today the social media and internet users are grows exponentially. In social media communication

and connections are plays a very vital role in human life. The internet users are mostly use minimum one of the

social media networks. The users connect to other peer users in digitally and share the information every day.

The information may include different forms like blog, website, links, examples, images, audio, video and other

formats. Understanding the behavior of individual users in the social media is become important. Because of the

information shared with others is includes personal, emotional, official and other contents. This research paper

326-327

https://www.techopedia.com/definition/26363/online-marketing

https://usatodaytn.com/blog/what-is-online-marketing

https://www.digitalvidya.com/blog/growth-of-digital-marketing-industry-in-india/

studies about the behavior of mobile users in a community group. The key need of this study is to build the trust

users in the group and their commutations and connections.

References:

1. Who interacts on the Web?: The intersection of users’ personality and social media use Teresa Correa *, Amber Willard Hinsley,

Homero Gil de ZúñigaCenter for Journalism & Communication Research, School of Journalism, University of Texas at Austin,

USA

2. Characterizing User Behavior in Online Social Networks FabrícioBenevenuto† Tiago Rodrigues† Meeyoung Cha∗Virgílio

Almeida† †Computer Science Department, Federal University of Minas Gerais, Brazil ∗Max Planck Institute for Software

Systems (MPI-SWS), Kaiserslautern/Saarbrücken, Germany 2009

3. A Meta-Analysis of Theories and Topics in Social Media Research Wietske Van Osch Michigan State University

[email protected] Constantinos K. Coursaris Michigan State University [email protected] 2015

4. Emotion -and area-driven topic shift analysis in social media discussions2016 IEEE/ACM International Conference on Advances

in Social Networks Analysis and Mining (ASONAM) (2016)

69.

Authors: A. Selvi

Paper Title: Bundle Detecting From Describing Attackks by using Prevention Techniques

Abstract: A remote medium abandons it defenseless against conscious impedance assaults can be defined as

sticking. An inside information of convention determinations and system insider facts can dispatch low-exertion

sticking assaults that are hard to identify the sticking assault and it can't check. The issue of specific sticking

assaults in remote systems is tended to. The particular sticking assault is characterized as the assault in which the

enemy is dynamic just for a brief span period. This sort of assault is specifically focusing on the messages of

high significance. The benefits of specific sticking regarding system execution downgrade and foe exertion is

displayed in two contextual investigation strategies. Initial a specific assault is executed on TCP. Second the

specific sticking assaults executed on steering. The specific sticking assaults can be clarified by performing

constant bundle characterization at the physical layer. To decrease the particular sticking assault, the four sorts

of plans are joined that can be counteract constant parcel grouping by consolidating the cryptographic natives

with physical-layer traits.

References:

1. T.X. Brown, J.E. James, and A. Sethi, “Jamming and Sensing of Encrypted Wireless Ad Hoc Networks,” Proc. ACM Int’l Symp.

Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 120-130, 2006.

2. A. Chan, X. Liu, G. Noubir, and B. Thapa, “Control Channel Jamming: Resilience and Identification of Traitors,” Proc. IEEE

Int’l Symp. Information Theory (ISIT), 2007.

3. R.Rivest, A.Shamir, and D.Wagner,“Time-Lock Puzzles and Timed-Release Crypto,” technical report, Massachusetts Inst. of

Technology, 1996.

4. W. Xu, W. Trappe, and Y. Zhang, “Anti-Jamming Timing Channels for Wireless Networks,” Proc. ACM Conf. Wireless

Network Security (WiSec), pp. 203-213, 2008.

5. W. Xu, W. Trappe, Y. Zhang, and T. Wood, “The Feasibility of Launching and Detecting Jamming Attacks in Wireless

Networks,” Proc. ACM Int’l Symp. Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 46-57, 2005.

6. Y. Desmedt ,“Broadcast Anti- Jamming Systems,” Computer Networks, vol. 35, nos. 2/3, pp. 223-236, Feb. 2001.

7. K. Gaj and P. Chodowiec , “FPGA and ASIC Implementations of AES,” Cryptographic Engineering, pp. 235-294, Springer,

2009.

8. A. Juels and J. Brainard, “ Client Puzzles : A Cryptographic Counter measure against Connection Depletion Attacks,” Proc.

Network and Distributed System Security Symp. (NDSS), pp. 151-165, 1999.

9. L. Lazos , S. Liu, and M. Krunz, “Mitigating Control-Channel Jamming Attacks in Multi-Channel Ad Hoc Networks,” Proc.

Second ACM Conf. Wireless Network Security, pp. 169-180, 2009.

328-330

70.

Authors: Saranya J, Sushma S. Jagtap, Thamizhmalar D Thaslim Banu M, Vimala Priyadharshini K P

Yamini J Paper Title: Human Detection using Wireless Rescue Robot

Abstract: Recently, all over the world, fire accidents is increasing at higher rates and it is high time to offer

safety support system for the humans residing/working at various places such as hospitals, hotels, cinemas,

high-rise buildings, department stores etc. This paper focuses on implementing wireless rescue robot for every

humans moving in and around the globe. However the existing systems are not powerful enough to prevent the

fire accidents since there is no automatic robot system to monitor humans. Nextly the existing prototype makes

use of only a flame sensor that detects the light emitted by the flames at the place of fire accident and alerts the

surrounding by using fire alarms which simply detects the place of fire and does not save the life of people who

are under risk. Hence to overcome this issues our proposed system includes a raspberry PI which is the heart

of the system and it is attached with the following components such as flame sensor for detecting flames,

Open CV camera for capturing video during fire accidents, GSM module for intimating the location at which

fire accident has taken place to the registered recipient, PIR sensor to monitor the movements of human and

331-334

https://www.computer.org/csdl/proceedings/asonam/2016/2846/00/07752283-abs.html

https://www.computer.org/csdl/proceedings/asonam/2016/2846/00/07752283-abs.html

evaluate the same to check whether it is within sensor range or not , pump motor with relay to splash water

upon sensing of fire by flame sensor and lastly robot chase which holds all the above mentioned modules and it

will move with its motor driver. Finally, implementation results for the proposed system are provided in this

paper.

Keywords: PIR sensor, Raspberry PI, GSM, flame sensor, robot chase, Open CV.

References:

1. Faisal Saeed, Anand Paul, Abdul Rehman,Won Hwa Hong and Hyuncheol Seo,“IoT-Based Intelligent Modeling of Smart Home

Environment for Fire Prevention and Safety” Journal of Sensor & Actuators, March 2018, DOI: 10.3390/jsan7010011

2. K. Muheden, E. Erdem and S. Vançin, "Design and implementation of the mobile fire alarm system using wireless sensor

networks," 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, 2016,

pp. 000243-000246.

3. J. Saranya and J. Selvakumar, "Implementation of children tracking System on android mobile terminals," 2013 International

Conference on Communication and Signal Processing, Melmaruvathur, 2013, pp. 961- 965..

4. [4] K. Priy1, M. Yamin1, S. Pavithra, S.Shalini devi. and Shaik Thasleem Banu, ” RFID BASED AUTOMATED CONTROL

AND DETECTION SYSTEM FOR TRAFFIC VIOLATION”,, Pak. J. Biotechnol. Vol. 13 (special issue on Innovations in

information Embedded and communication Systems) Pp. 379- 382 (2016).

J. Saranya, V.Hemananth, B.KarthicSelvamohan,T.Kumaresan, “Implementation of tracking system for mankind” Proceeding of ICFTICC- 2015.

5. Yuichiro MORI, Hideharu KOJIMA, Eitaro KOHNO,Shinji INOUE, Tomoyuki OHTA, and Yoshiaki KAKUDA, “A Self-

Configurable New Generation Children Tracking System based on Mobile Ad Hoc Networks Consisting of Android Mobile

Terminals” proposed in 2011 tenth International symposium on Autonomous decentralized systems. W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

6. C.R. Lin and M. Gerla, “Distributed clustering for adhoc networks,”IEEE J. sel. Areas Common., Vol.15,

no.7, pp. 1265-1275,1997.

7. Otsason, A. Varshavsky, A. LaMarca, and E. D. Lara,”Accurate GSM Indore Location,” in Proc. Ubiquitous Comput.: 7th Int. Conf. (Ubi- Comp 2005), Tokyo, Japan, pp. 141–158.

71.

Authors: Shalom Irence L. B, F.T. Josh, J. Jency Joseph

Paper Title: Battery Modelling For A Photovoltaic System With Battery Management System Using Fuzzy

Logic Controller

Abstract: The need for energy is increasing day by day. Energy production from conventional fuels reduces

due to the lack of availabilityin fuel. Using fossil fuel and its gases also reduced due to the high occurrence of

pollution. This point increased the demand for harvesting energy from non-conventional sources of energy

such as solar, wind, etc. The abundant source of solar energy can be harvestedto satisfy the growing energy

demand on the various applications requirement.This paperfocuson how solar energy is been harvested and

stored for the continuous supply of energy to the required loaddemand.Mathematicalmodelinganddesignof a

stand-alonePV system with maximum power point tracking (MPPT), boost converter and battery storage

system with BMS is developed. BMS is a system which manages the battery to operate in its safe limits by

continuous measuring of state of charge and the state of health. The fuzzy logic controller is included in the

MATLAB Simulation environment for optimistic switching of charging and discharging switches.

Keywords: PV system designing, battery modeling, maximum power point tracking, fuzzy logic controller, state

of charge(SOC)

References:

1. A. Mahrane, M. Chikh, Z. Smara, “Optimization of energy management of a photovoltaic system by the fuzzy logic technique”,

Energy procedia (2011) 513-521.

2. Bijender Kumar and Neeta Khare and P. K. Chaturvedi “Advanced battery management system using MATLAB/Simulink”, 2015

IEEE International Telecommunications Energy Conference (INTELEC), the year 2015, pages 1-6.

3. K.W.E. Cheng,B.P.Divakar, Hongjie Wu, Kai Ding, and Ho Fai Ho “Battery – Management System (BMS) and SOC

Development for Electrical Vehicles” IEEE transactions on vehicular technology, VOL, 60, NO.1, January 2011.

4. M. Kokila, P. Manimekalai& V. Indragandhi (2018): Design and development of battery management system (BMS) using

hybrid multilevel converter, International Journal of Ambient Energy,DOI:10.1080/01430750.2018.1492440.

5. Chin CS, McBride W. “Design, Modeling and Testing of a Standalone Single Axis Active Solar Tracker using

MATLAB/Simulink”, (2011),Renewable Energy 36(11), 3075-3090.

6. Meng Di Yin, JiaeYoun, jeonghumCho,aDaejin Park “MCU-based battery management system for fast charging of IOT- based

large scale battery-cells”, an international journal of applied engineering research ISSN 0973-4562, Volume 12, Number 7 (2017)

pp. 1329-1333.

7. Purushothaman B K, landauU.“Rapid Charging of lithium-ion batteries using pulsed currents”, Journal of the electrochemical

society, 2006, pp. 533-542.

8. FathiaChekired ,ZoubeyrSmara, AchourMahrane, MadjidChikh, SmailBerkane, “An energy flow management algorithm for a

photovoltaic solar home” March 2017,EnergyProcedia 111: 934-943,DOI: 10.1016/j.egypro.2017.03.256.

9. Md. ShahrukhAnis, BasharatJamil, Md. Azeem Ansari, EvangelosBellos “Generalized models for estimation of global solar

radiation based on sunshine duration and detailed comparison with the existing: A case study for India” Sustainable energy

technologies and assessments 31 (2019), 179-198.

10. Ngan, M. S., & Tan, C. W. “A study of maximum power point tracking algorithms for stand-alone Photovoltaic Systems”. 2011

IEEE Applied Power Electronics Colloquium (IAPEC). doi:10.1109/iapec.2011.5779863.

11. Bahir, L. E., &Hassboun, T. “Accurate Maximum Power Point Tracking Algorithm Based on a Photovoltaic Device Model”.

International Journal of Photoenergy, 2017, 1–10.doi:10.1155/2017/5693941.

335-339

12. Ayop, R., & Tan, C. W. “Design of boost converter based on maximum power point resistance for photovoltaic applications”.

(2018), solar energy, 160, 322–335.doi:10.1016/j.solener.2017.12.016.

13. AsmarashidPonniran, Abdul Fatah Mat said “dc-dc boost converter design for solar electric system” nternational conference on

instrumentation, control & automation ica 2009, 20-22 october 2009,bandungindonesia.

14. F. T. Josh Anju Jolly “Optimization of Energy Management for Charged Storage of a PV System by the Fuzzy Logic Technique”

International Journal of Computer Applications; Volume 63,Issue10,Pages 0975 – 8887,Publisher Foundation of Computer

Science (FCS).

15. Ngan, M. S., & Tan, C. W. “A Study Of Maximum Power Point Tracking Algorithms For Stand-alone Photovoltaic Systems”. 2011 IEEE Applied Power Electronics Colloquium (IAPEC). Doi:10.1109/Iapec.2011.5779863

72.

Authors: Ms. S.l. Sobiya , N. Thangavel

Paper Title: An Impact of Technology on E-Banking Dimension in Banking Sector

Abstract: In Banking sector biggest revolution was taken place because of technology. After the adoption

digitalization in banking sector, the internet banking merge into new concept which helps in faster and more

faster than normal banking activities. Indian economic condition totally depends on the financial market, money

market and capital market. Recent years, Indian banking sector taking out new development and innovation in

banking products and adoption of new model payment system and micro finance process. Reserve Bank of India

renowned the measures and restructuring the rules and regulation in Indian Industry. The main development in

the banking sector recent day with the help of technology is immediate payment service, with the help of mobile

application the money can be transferred immediately without any delay.Empirical study is done to measure the

e-banking dimension which is totally connected with technology for this purpose the data collection is processed

to facilitate the study. Data is collected with the help of questionnaire and the analysis is done. The factor

analysis is used for calculation for that we identified technology based three dimension is very important for

electronic banking system in Chennai. The selected e-banking dimensions which are connected with technology

are convenience, security and website aesthetic. In this paper researcher attempt to find the dimension that

influence e-banking services. This will help the other researcher to do the further research.

Keyword: E-Banking, Technology, Dimension

References:

1. Dixon M. and Nixon B. (2000),“E- banking: Managing your money and transactions online”, SAMS, publishing, P. 244.

2. JyotiranjanHota, 2013, Growth of ATM Industry in India, CSI Communications, February 2013, P 23

3. Kumar, S., &Garg, R. (2012). Service Quality Measurement of Internet Banking: A Customers’ Perspective. National Conference

on Emerging Challenges for Sustainable Business, (pp. 1417-1436).

4. Kaur, J., &Kaur, B. (2013). Determining Internet Banking Service Quality & Customer Satisfaction in India. Tenth AIMS

International Conference on Management, (pp. 2670-2679).

5. Mishra, U. S., Sahoo, K. K., & Mishra, S. (2010). Service Quality Assessment in Banking Industry of India: A Comparative

Study between Public and Private Sectors. European Journal of Social Sciences – Volume 16, 653-669.

6. Munusamy, J., Chelliah, S., &Mun, H. W. (2010). Service Quality Delivery and Its Impact on Customer Satisfaction in the

Banking Sector in Malaysia. International Journal of Innovation, Management and Technology, Vol. 1, No. 4, 398-404.

7. Ma, Z. (2012). Assessing Serviceability and Reliability to Affect Customer Satisfaction of Internet Banking. JOURNAL OF

SOFTWARE, VOL. 7, NO. 7 1601-1608.

8. NitsureRupaRege (2006),”E-Banking: Challenges and Opportunities”, E-Banking in India the Paradigm Shift, The ICFAI

University Press, pp.3-16.

9. Robinson, T. (2000). Internet banking stills not a perfect marriage. Information Week, Vol.17, No. 4, pp.104-10621.

10. Sheshunoff, A. (2000). “Internet banking: an update from the frontiers”. ABA Banking Journal Vol.92, No.1, pp. 51 –55

340-342

73.

Authors: Sumithra M, Asha Abraham, Gracia Nissi

Paper Title: Inferring the Products Realistic Feature Through Data From Users Views In Socialmedia

Abstract: In current trends, online life becomes as an inevitable option, feasible source to remove extensive

scale, heterogeneous item includes in a period and cost-proficient way. One of the difficulties of using social

media data is to educate people with availability of item choices along with related information, for example,

mockery, which represents 22.75% of web based data and can possibly make prediction in the predictive models

that gain from such information sources. For instance, if a client says "I simply love holding up throughout the

day while this tune downloads," a feature extraction model may mistakenly relate a positive estimation of

"adoration" to the mobile phone's capacity to download. While conventional content mining strategies are

intended to deal with all around framed content where item includes are gathered from the mix of words, these

devices would neglect to process these social messages that incorporate understood implicit information

conveyed through the data. In this paper, we propose a technique that empowers users to use understood social

media data by making an interpretation of each verifiable message into its proportional express structure,

utilizing the word simultaneousness organize as a coherence network of word (coward). A case study of Twitter

messages that talk about Smartphone highlights is utilized to approve the proposed technique. The outcomes

from the analysis not just demonstrate that the proposed strategy improves the interpretability of verifiable

messages, yet additionally reveals insight into potential applications in the various fields where this work could

be broadened.

343-350

https://pdfs.semanticscholar.org/3b7b/cd06c868904a7dbd998dd7c4dd2c7eafa247.pdf

References:

1. Tuarob, S, Tucker, C. S. 2015, “Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social

Media Networks” ASME J. Mech. Des., 137(7), p. 071402. 2. Tuarob, S., and Tucker, C. S., 2015, “Quantifying Product Favorability and Extracting Notable Product Features Using Large

Scale Social Media Data,” ASME J. Comput. Inf. Sci. Eng., 15(3), p. 031003.1485–1509.

3. Tuarob, S., and Tucker, C. S., 2015, “A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks,” ASME Paper No. DETC2015-47225.

4. Tuarob, S., and Tucker, C. S., 2014, “Discovering Next Generation Product Innovations by Identifying Lead User Preferences

Expressed Through Large Scale Social Media Data,” ASME Paper No. DETC2014-34767. 5. Tuarob, S., and Tucker, C. S., 2013, “Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale,

Social Media Data,” ASME Paper No. DETC2013-12661.

6. Lim, S., and Tucker, C. S., 2016, “A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data,” ASME J. Mech. Des., 138(6), p. 061403.

7. Tuarob, S., Tucker, C. S., Salathe, M., and Ram, N., 2014, “An Ensemble Het- erogeneous Classification Methodology for

Discovering Health-Related Knowl- edge in Social Media Messages,” J. Biomed. Inf., 49, pp. 255–268. 8. Tuarob, S., Tucker, C. S., Salathe, M., and Ram, N., 2013, “Discovering Health-Related Knowledge in Social Media Using

Ensembles of Heterogeneous Features,” 22nd ACM International Conference on Information & Knowledge Management (CIKM

’13), San Francisco, CA, Oct. 27–Nov. 1, pp. 1685–1690. 9. Lim, S., Tucker, C. S., and Kumara, S., 2017, “An Unsupervised Machine Learning Model for Discovering Latent Infectious

Diseases Using Social Media Data,” J. Biomed. Inf., 66, pp. 82–94.

10. Sakaki,T.,Okazaki,M.,andMatsuo,Y.,2010,“EarthquakeShakesTwitter Users: Real-Time Event Detection by Social Sensors,” 19th International Con- ference on World Wide Web (WWW’10), Raleigh, NC, Apr. 26–30, pp. 851–860.

11. Caragea,C.,McNeese,N.,Jaiswal,A.,Traylor,G.,Kim,H.,Mitra,P.,Wu,D., Tapia, A., Giles, L., Jansen, B., and Yen, J., 2011,

“Classifying Text Messages for the Haiti Earthquake,” Eighth International Conference on Information Sys- tems for Crisis Response and Management (ISCRAM), Lisbon, Portugal, May 8–11.

12. Bollen, J., Mao, H., and Zeng, X., 2011, “Twitter Mood Predict

13. the Stock Market,” J. Comput. Sci., 2(1), pp.1–8. 14. Zhang, X., Fuehres, H., and Gloor, P., 2012, “Predicting Asset Value Through Twitter Buzz,” Advances in Collective

Intelligence 2011, Springer, Berlin, pp. 23–34.

15. Maynard, D., and Greenwood, M. A., 2014, “Who Cares About Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis,” Ninth International Conference on Language Resources and Evaluation (LREC), Rey- kjavik, Iceland, May

26–31, pp.4238–4243.

16. Dey, L., and Haque, S., 2009, “Studying the Effects of Noisy Text on Text Min- ing Applications,” Third Workshop on Analytics for Noisy Unstructured Text Data (AND), Barcelona, Spain, July 23–24, pp. 107–114.

17. Tsur, O., Davidov, D., and Rappoport, A., 2010, “ICWSM-A Great Catchy Name: Semi-Supervised Recognition of Sarcastic

Sentences in Online Product Reviews,” Fourth International Conference on Weblogs and Social Media (ICWSM), Washington, DC, May 23–26, pp.162–169.

18. Davidov, D., Tsur, O., and Rappoport, A., 2010, “Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon,” 14th Conference on Computa- tional Natural Language Learning (CoNLL), Uppsala, Sweden, July 15–16, pp. 107–116.

19. Navigli, R., and Velardi, P., 2005, “Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense

Disambiguation,” IEEE Trans.Pat-Gen., 115(1), p. 3.

20. Gibbs, R. W., and Colston, H. L., 2007, Irony in Language and Thought: A Cognitive Science Reader, Lawrence Erlbaum,

NewYork.

21. Archak, N., Ghose, A., and Ipeirotis, P. G., 2011, “Deriving the Pricing Power ofProductFeaturesbyMiningConsumerReviews,”Manage.Sci.,57(8),pp.

22. Asur, S., and Huberman, B. A., 2010, “Predicting the Future With Social Media,” IEEE/WIC/ACM International Conference on

Web Intelligence and Intelligent Agent Technology (WI-IAT), Washington, DC, Aug. 31–Sept. 3, pp. 492–499. 23. Stone, T., and Choi, S.-K., 2014, “Visualization Tool for Interpreting User Needs From User-Generated Content Via Text Mining

and Classification,” ASME Paper No. DETC2014-34424.

24. Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., and Li, X., 2011, “Comparing Twitter and Traditional Media Using Topic Models,” Advances in Information Retrieval, Springer, Berlin, pp. 338–349.

25. Yajuan, D., Zhimin, C., Furu, W., Ming, Z., and Shum, H. Y., 2012, “Twitter Topic Summarization by Ranking Tweets Using

Social Influence and Content Quality,” 24th International Conference on Computational Linguistics, Mumbai, India, Dec. 8–15, pp. 763–780.

26. Wang, Y., Wu, H., and Fang, H., 2014, “An Exploration of Tie-Breaking for Microblog Retrieval,” Advances in Information

Retrieval, Springer, Cham, Switzerland, pp. 713–719. 27. Tuarob, S., Tucker, C. S., Salathe, M., and Ram, N., 2015, “Modeling Individual-Level Infection Dynamics Using Social

Network Information,” 24th ACM International on Conference on Information and Knowledge Manage- ment, Melbourne,

Australia, Oct. 19–23, pp. 1501–1510.

28. Tuarob, S., and Mitrpanont, J. L., 2017, “Automatic Discovery of Abusive Thai Language Usages in Social Networks,”

International Conference on Asian Digi- tal Libraries, Bangkok, Thailand, Nov. 13–15, pp. 267–278.

29. Thelwall, M., Buckley, K., and Paltoglou, G., 2011, “Sentiment in Twitter Events,” J. Am. Soc. Inf. Sci. Technol., 62(2), pp. 406–418.

30. Thelwall, M., 2017, “The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web With SentiStrength,”

Cyberemotions, Springer, Cham, Switzerland, pp. 119–134. 31. Tuarob, S., Tucker, C. S., Kumara, S., Giles, C. L., Pincus, A. L., Conroy, D. E., and Ram, N., 2017, “How are You Feeling?: A

Personalized Methodology for Predicting Mental States From Temporally Observable Physical and Behav- ioral Information,” J.

Biomed. Inf., 68, pp.1–19. 32. Fox, E., 2008, Emotion Science: Cognitive and Neuroscientific Approaches to Understanding Human Emotions, Palgrave

Macmillan, Basingstoke,UK.

33. Cutting, D., Kupiec, J., Pedersen, J., and Sibun, P., 1992, “A Practical Part-of- Speech Tagger,” Third Conference on Applied Natural Language Processing (ANLC ’92), Trento, Italy, Mar. 31–Apr. 3, pp.133–140.

34. 33.33.333333.Jia, S., Yang, C., Liu, J., and Zhang, Z., 2012, “An Improved Information Fil- tering Technology,” Future Computing,

Communication, Control and Manage- ment, Springer, Berlin, pp.507–512. 35. Tuarob,S.,Mitra,P.,andGiles,C.L.,2012,“ImprovingAlgorithmSearchUsing the Algorithm Co-Citation Network,” 12th

ACM/IEEE-CS Joint Conference on DigitalLibraries(JCDL’12),Washington,DC,June10–14,pp.277–280.

36. Tuarob, S., Bhatia, S., Mitra, P., and Giles, C., 2013, “Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning,” 12th Interna- tional Conference on Document Analysis and Recognition (ICDAR), Washing- ton, DC, Aug.

25–28, pp. 738–742.

37. Evans, D. A., Handerson, S. K., Monarch, I. A., Pereiro, J., Delon, L., and Hersh, W. R., 1998, Mapping Vocabularies Using Latent Semantics, Springer, Boston, MA.

38. Tuarob, S., Pouchard, L. C., and Giles, C. L., 2013, “Automatic Tag Recom- mendation for Metadata Annotation Using

http://dx.doi.org/10.1145/1772690.1772777

https://pdfs.semanticscholar.org/9aa7/b3028b7059c2604228eae3a497f273ecdad6.pdf

http://dx.doi.org/10.1016/j.jocs.2010.12.007

https://gate.ac.uk/sale/lrec2014/arcomem/sarcasm.pdf

http://dx.doi.org/10.1145/1568296.1568314

http://people.seas.harvard.edu/~orentsur/papers/icwsm10.pdf

http://dx.doi.org/10.1109/TPAMI.2005.149

http://dx.doi.org/10.1037/0096-3445.115.1.3

http://dx.doi.org/10.1287/mnsc.1110.1370

http://dx.doi.org/10.1016/j.jbi.2017.02.010

http://dx.doi.org/10.1016/j.jbi.2017.02.010

http://dx.doi.org/10.3115/974499.974523

http://dx.doi.org/10.1145/2232817.2232869

http://dx.doi.org/10.1109/ICDAR.2013.151

Probabilistic Topic Modeling,” 13th ACM/IEEE-CS Joint Conference on Digital Libraries, (JCDL’13), Indianapolis, IN, July 22–26, pp.239–248.

39. Tuarob, S., Pouchard, L., Mitra, P., and Giles, C., 2015, “A Generalized Topic Modeling Approach for Automatic Document

Annotation,” Int. J. Digital Libr., 16(2), pp. 111–128. 40. Cliche,M.,2014,“TheSarcasmDetector:LearningSarcasmFromTweets!,”The

SarcasmDetector,accessedFeb.19,2017,http://www.thesarcasmdetector.com

41. Liu,F.,Liu,F.,andLiu,Y.,2008,“AutomaticKeywordExtractionfortheMeet- ing Corpus Using Supervised Approach and Bigram Expansion,” Spoken Lan- guageTechnologyWorkshop(SLT2008),Goa,India,Dec.15–19,pp.181–184.

42. Martin,S.,Brown,W.M.,Klavans,R.,andBoyack,K.W.,2011,“OpenOrd:An Open-

SourceToolboxforLargeGraphLayout,”SPIEProc.,7868,p.786806. 43. Tuarob, S., Pouchard, L. C., Noy, N., Horsburgh, J. S., and Palanisamy, G., 2012, “Onemercury: Towards Automatic Annotation

of Environmental Science Metadata,” Second International Workshop on Linked Science, Boston, MA, Nov.12.

74.

Authors: Shaik Thasleem Bhanu, Amrish Afsal S R , Avinash T, Sathya.R

Paper Title: S-Band Deployable Rectangular Microstrip Patch Antenna for Cubesat Applications

Abstract: This paper presents the designand simulation of a RMSA with pentagon fractal slot which is operated

in S-band at two bands 2.4 GHz & 3.88GHz. The measurements taken from the simulated and fabricated antenna

show good results for the operating frequencies and enhanced performance in gain, bandwidth and VSWR

parameters. The antenna was fabricated on a 1.6 mm thick FR-4 substrate. To obtain themultiband

characteristics DGS (Defected Ground Structure technique) and fractal geometry has been used.DGS is used to

increase the bandwidth, Fractal slots to obtain multi bands and corporate feed 2x1 array structure has been used

to increase overall Gain. The simulation is conducted with HFSS (High Frequency Structure Simulator).From

the proposed design parameters like return loss and gain are obtained. Being a compact antenna, it has a size,

geometry and characteristics that match with the CubeSat’s structure standards.

Keywords : RMSA, 2.4 GHz, 3.88GHz, FR-4 Epoxy, DGS, HFSS, CubeSat.

References:

1. “Fed Star Super Wideband Antenna” by SarthakSinghal, Amit Kumar Singh ,in Asia-Pacific Microwave Conference In 2016

2. “An H-Fractal Antenna for Multiband Application” by Wei-Chung Weng, Senior Member, IEEE and Chia-Liang Hung. 3. “Dual Band Microstrip Antenna using U and S slots for WLAN Application” by FitriYuliZulkifli,DianRodhiah and

EkoTriptiRahrdjo.

4. Nit in K , Ponniah K, Pradeep M and Ram P.S. , ShaikThasleemBhanu, “Design, Analysis and Gain enhancement of RMSA with Air substrate for WiMAX Applications”, International Journal of Pure and Applied Mathematics, Volume 118 No. 20 2018, 173-

177.

5. Srilakshmi. R,Manikandan. T,ShaikhThasleemBhanu , “Performance Comparison of S-Band Antenna with Series Fed and Corporate Fed Microstrip Array”, International Journal of Engineering & Technology,7 (2.33) (2018) 1036-1039.

6. F. E. Tubal, R. Raad, and K.W. Chin“A Survey and Study of Planar Antennas for Pico-Satellites,” IEEE Trans. ACCESS, vol. 3,

no. 1, pp.2590-2612,Dec.2015.

7. A.Nascetti, E.Pittella, P.Teofilatto, and S.Pisa, “High-gain s-band patch antenna system for earth-observation CubeSat satellites,”

in IEEE antennas and wireless propagation letters, vol. 14,pp. 434 – 437, 2015.

8. S. Gaoetal., “Antennas for modern small satellites” in IEEE Antennas Propagation. Mag., vol. 51, no.4, pp. 40 – 56, Aug. 2009.

9. Sheta and F.Mahmoud, “A widely tunable compact patch antenna,” IEEE Antennas Wireless Propag. Lett., vol. 7, pp. 40–42, 2008.

10. M. Lai, T. W. J. Hsieh, and C.W. S Jeng, “Design of reconfigurable Antennas based on an L-shaped slot and PIN diodes for

compact wireless devices,” IET Microw. Antennas Propag., vol. 3, pp. 47–54, 2009.

351-354

75.

Authors: M.Sowmiya, S.Prabavathi

Paper Title: Symmetric and Asymmetric Encryption Algorithms in Cryptography

Abstract: Security is the most challenging issue in today’s internet world. Internet related applications and its

relevant data are need to be exchanged over the internet increasingly. Hence it is necessary to provide security

for the data to be transmitted. Cryptography is one such category, that provides security for data. The security of

data over internet transmission is achieved through several encryption algorithms developed in Cryptography.

This paper analyzes the performance of various encryption algorithms used in Cryptography and indicates which

is the best algorithm based on some parameters.

Keyword: Security, Cryptography, Encryption.

References:

1. William Stallings, ''Cryptography and Network Security – Principles and Security” 7th Ed, Prentice Hall, 2018.

2. Diaa Salama Abdul. Elminaam, “Performance Evaluation of Symmetric Encryption Algorithms”, International Journal of

Computer Science and Network Security, VOL.8 No.12, December 2008.

3. Ritu Tripathi, “Comparative Study of Symmetric and Asymmetric Cryptography Techniques”, International Journal of

Advance Foundation and Research in Computer (IJAFRC) Volume 1, Issue 6, June 2014. ISSN 2348 – 4853.

4. Monica Agarwal, “A Comparative Survey on Symmetric Key Encryption Techniques”, International Journal of Computer

Science and Engineering (IJCSE), 2012.

5. Dr. Prerna Mahajan, “A Study of Encryption Algorithms AES, DES and RSA for Security” Global Journal of Computer

Science and Technology Network, Web & Security, Volume 13 Issue 15 Version 1.0 Year 2013.

6. E.Surya, “A Survey on Symmetric Key Encryption Techniques”, International Journal of Computer Science &

Communication Networks , Vol 2(4), ISSN:2249-5789.

355-357

76. Authors: Sruthy B.S, S.Muruganantham

http://dx.doi.org/10.1145/2467696.2467706

http://dx.doi.org/10.1007/s00799-015-0146-2

http://www.thesarcasmdetector.com/

http://dx.doi.org/10.1109/SLT.2008.4777870

http://dx.doi.org/10.1117/12.871402

http://ceur-ws.org/Vol-951/paper4.pdf

Paper Title: Machine Learning Thyroid Nodules Classification

Abstract: An overview is presented of thyroid medical image processing literature on thyroid cancer

diagnosis.The main aim of this survey is to introduce for those new to this feild,and a reference for those who

searching for specific literature survey on application.Thyroid cancer is now commonly seen one and main

concern in nowdays due to the risk of malignancies and hyper function.The nodules becomes more malignant if

it is not diagnosied at right time.computer aided detection of thyroid nodules and various image processing

techniques and methods are used for effective and efficient classification of thyroid nodules. Diagnostic imaging

is an important tool in medical science due to the continuous observations of the expert and uncertaninty in

medical knowledge. A thyroid ultrasound is a more commonly used imaging study used to detect and classify

abnormalities of the thyroid gland clearly and correctly. Computerized system is a valuable and beneficial means

for feature extraction and classification of thyroid nodule in order to eliminate false diagnosis and to improve the

diagnostic accuracy. The main aim of this paper is to review existing methods and techniques to the automatic

classification of nodules in thyroid ultrasound images, highlighting the main differences between the used

strategies and also for the diagnosis of Nodules in thyroid ultrasound images with their performance measures.

Keywords: Nodules,Thyroid,Ultrasound,Classification,cancerdiagnosis,,image processing.

References:

1. Chuan-yu-chang, Ming-fang Tsai and shao-jer chin “Classification of thyroid nodules using Support Vector Machines”-2008

2. Robertocarraro, FilippoMolinari, MaurilioPeandra, RobertoGarberoglio”Characterization of thyroid nodules by 3-D contrast

enhanced ultrasound Imaging.

3. Konstantin us k.Delibasis, George k matsopoulos, Panatelas A Asbestos “Computer Aided diagnosis of thyroid malignancy using

an Artificial Immune system classification”-2009

4. U.RajendraAcharya, VinithaSree “Automated Benign and Malignant Thyroid Lessions Characterization and Classification in 3D

Contrast –Enhanced Ultrasound”.

5. B.Gopinath, N.Shanthi “Support vector machine based diagnostic system for thyroid cancer using statistical texture features”-

2013

6. MariaLusiaMontero,OmarZenteno,BenjaminCastaneda,MichaelOelze”Evaluation Of Classification Strategies Using Quantitative

Ultrasound Markers and a Thyroid Cancer Rodent Model”.

7. Jianruri Ding, H.D Cheng, Jianhua Huang” Multiple instance learning with global and local features for thyroid ultrasound image

classification”-2014

8. HANUNG Aid Nugroho, Made Rahmawaty, YuliTriyani,”Texture analysis for classification of thyroid ultrasound images”-2016

9. Handgun Wu,ZhaohongDeng,BingjieZhang,”Classifier model based on machine learning algorithms :Application to differential

diagnosis of suspicious thyroid nodules via sonography”-2016

10. [TianjiaoLiu,ShuainingXie,JingYu,LijuanNiu,WeidongSum, “Classification of thyroid nodules in ultrasound images using deep

model based transfer learning and hybrid features”-2017

11. Yezhu, Zhuang ,Jainfei”An image augmentation method using convolutional network for thyroid nodule classification by transfer

learning”-2017

12. Xueyan Mei, Xiaomeng Dong, Timothy Deyer, Jing0yizeng”Thyroid nodule Benignity prediction by deep feature extraction”-

2017

13. Zulfanahri,Hanung,AdiNugroho, Anan Nughroho,”Classification of thyroid ultrasound images based on shape features analysis”-

2017

14. Muhammad Anjou0 Qureshi,Kub0ilay0 Eksioglu0,”Expert Advice Ensemble for Thyroid disease diagnosis”-2017

15. JianningChi,EktaWalia,P aulBabyn”Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep

Convultional Neural Network”.2017

16. Amir Torah,Miandoab and TahasSamadi,SogandHabibi”Image Processing Techniques for Determining Cold Thyroid Nodules”-

2017

17. FarihahAbdGhani,Nurismah,HusyairiHarunarashid,RadhikaSridharan,”Reliability of the ultrasound classification system of

thyroid nodules in predicting Malignancy”-2018

18. LiDandan,ZhangYakui,DuLinyao,ZhouXiannli,ShenYi”Texture analysis classification of diffuse thyroid disease based on

ultrasound images”-2018

19. Rong Zhang.QiufangLiu,HuiCui,”Thyroid Classification Via New Multi-Channel Feature Association And Learning From Multi-

Modality MRI images”.-2018

20. Jianxiongwang,shuai Li Wenfengsong,HongQin,”Learning from weakly –labelled clinical data for automatic thyroid nodule

classification in ultrasound images-2018.

358-364

77.

Authors: C.Thilagavathi, A.Selvi

Paper Title: Auditing the Shared Data on Cloud using Ring Signatures

Abstract: Using the information organization of cloud computing, the information isn't simply saved, anyway

it is flowed by and large among different clients. Forbiddingly the undaunted idea of data is anchored in cloud

faces preposterous issues in light of the particular issues and flighty bungles. Different techniques are

sufficiently utilized to examining cloud data security without getting to the whole data from the server. The

security of such data isn't guarded amidst open minding. The proposed system recommends an insurance

defending method that empowers a cloud to store data securely. It controls ring imprints to assess attestation

meta-data required to ensure the rightness. The system ensures the insurance of scattered information without

getting to the entire record. The methodology enables security for a couple of exercises in the meantime. The

reenactment work traces viability and furthermore accommodation of scattered information unflinching quality.

365-368

References:

1. B. Wang, B. Li, and H. Li, "Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud," University of Toronto,

Tech. Rep., 2011. [Online]. Available: http://iqua.ece.toronto.edu/~bli/techreports/oruta. Pdf

2. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M.

Zaharia, "A View of Cloud Computing," Communications of the ACM, vol. 53, no. 4, pp. 50-58, April 2010. [3] k. Ren, c. Wang,

and q. Wang, "protection difficulties for people in trendy cloud," IEEE internet registering, vol. 16, no. 1, pp. 69-73, 2012.

3. d. Track, e. Shi, i. Fischer, and u. Shankar, "cloud records coverage for the majority," laptop, vol. Forty-five, no. 1, pp. 39-45,

2012.

4. C. Wang, Q. Wang, K. Ren, and W. Lou, "Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing," in

Proc. IEEE INFOCOM, 2010, pp. 525-533.

5. b. Wang, m. Li, S.S. Chow, and h. Li, "facts integrity verification in cloud database," proc. IEEE conf. Comm. Additionally,

framework safety (cns '13), pp. Ninety-Nine, 2013.

6. r. Rivest, a. Shamir, and l. Adleman, "a technique for acquiring virtual signatures and public-key cryptosystems," comm. Acm,

vol. 21, no. 2, pp. One hundred twenty-126, 1978.

7. the md5 message-manner calculation (rfc1321). Https://tools.Ietf.Org/html/rfc1321, 2014.

8. g. Ateniese, r. Consumes, r. Curtmola, j. Herring, l. Kissner, z. Peterson, and d. Track, "provable information ownership at

untrusted stores," proc. Fourteenth acm conf. Laptop and comm. Safety (ccs '07), pp. 598-610, 2007.

9. h. Shacham and b. Waters, "decreased confirmations of retrievability," proc. Fourteenth int'l conf. Hypothesis and utilization of

cryptology and statistics protection: advances in cryptology (Asia crypt)

78.

Authors: Shivam Sharm , Suryavamsi Saripudi, Sushasukhanya

Paper Title: Prediction of Customer Churn in Telecom Industries

Abstract: Churn Examination is one of the widespread used study on Subscription Oriented Businesses for

analyzingthebehavior and activities of customers in order to predict beforehand which customer is likely to exit

the service agreement.Built on Machine Learning procedures and algorithms it has become very significant for

companies in today’s marketas securing of another client is more costlier than their maintenance.The paper

analyses the relevant studies on Customer Churn Analysis in Telecom Business to present overall information to

readers about the commonly used data mining means, and performance of the methods. Initially, we present the

details about the availability of public datasets and various customer details in each dataset for predicting

customer churn. Then, we compare and contrast various analytical modeling systems and compare their

performances and results. Conclusively, we review what kinds of performance metrics have been used to gauge

the current churn prediction approaches. Examining all these three viewpoints is very critical for developing a

more well-organized churn prediction structure

Keywords: EDA – exploratory data analysisCRM- customer relationship managementLRM- logistic regression

model SVM- support vector machines

References: 1. Dilip Singh Sisodia, SomduttaVishwakarma, AbinashPujahari. "Evaluation of machine learning models for employee churn

prediction" , 2017 International Conference on Inventive Computing and Informatics (ICICI), 2017

2. Jas Semrl, AlexandruMatei. "Churn prediction model for effective gym customer retention" , 2017 International Conference on

Behavioral, Economic, Socio-cultural Computing (BESC), 2017

3. zhenyuchen ;zhiping fan(2011)building comprehensible customer churn prediction model. 4. Preeti K. Dalvi; Siddhi K.Khandge ;AshishDeomore; AdityaBankar ;V. A. Kanade(2016) Analysis of customer churn prediction

in telecom industry 5. Saad Ahmed Qureshi ;AmmarSaleemRehman ; Ali Mustafa Qamar; Aatif Kamal(2013) . Telecommunication subscribers' churn

prediction model using machine learning

6. Adnan Amin ;Changez Khan ;Imtiaz Ali ;Sajid Anwar(2014) Customer Churn Prediction in Telecommunication Industry: With and without Counter

7. Alpaydin, E. (2010). Introduction to Machine Learning. London, England: The MIT Press. 8. Archaux, C., Laanaya, H., Martin, A., &Khenchaf, A. (2004). An SVM based Churn Detector in Prepaid

Mobile Telephony. IEEE .

369-372

79.

Authors: Soumya George, M. Sudheep Elayidom, T. Santhanakrishnan

Paper Title: Semantic Desktop Search Engine using Graph Database

Abstract: The rise of big data with advancement in technology leads to an ever-increasing demand for a

personalized search engine to search the huge amount of data residing in personal computers. A desktop search

engine is used to search files or data in a user’s personal systems. This paper proposes a graph based semantic

desktop search engine, GSDSE that uses the Word Sequence Graph model to store the file details and contents

inside a graph database using full text indexing approach. The main features of GSDSE include content-based

query autosuggestion based on entire query term sequence, link based page ranking, the semantic search of

different query combinations and generation of content based valid search snippet view. To prove the efficiency

and reliability of GSDSE, we conduct a comparsion study between Copernic Desktop search engine and

GSDSE, and the results proved that the proposed system is efficient concerning efficiency and reliability.

Keywords: Desktop search engine, Graph database, Word sequence graph model, Semantic search engine

References:

373-375

1. Joel Lee (2016). 10 Best Free Search Tools for Windows 10. http://www.makeuseof.com/tag/10-best-free-search-tools-windows-10/ 2. Soumya George, M. Sudheep Elayidom and T. Santhanakrishnan (2017). A Novel Sequence Graph Representation for Searching and

Retrieving Sequences of Long Text in the Domain of Information Retrieval. IJSRCSEIT Vol. 4 Issue 2 3. Apache Tika API Usage Examples https://tika.apache.org/1.17/examples.html

4. The Stanford NLP Group. https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/process/DocumentPreprocessor.html

5. Lu, C. T., Shukla, M., Subramanya, S. H., & Wu, Y. (2007, August). Performance evaluation of desktop search engines.

In Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on (pp. 110-115). IEEE.

80.

Authors: Kotteeswaran C, Khanaa V, Rajesh A

Paper Title: Rule Based Matrix Insertion Deletion Scheme for Improved Bio Molecular Computing

Abstract: The problem of RNA editing has been well studied and there are number of approaches discussed

earlier but suffer to achieve higher performance. To improve the performance an rule based approach is

discussed in this paper. The RNA sequence generally consists of DNA sequences being represented as chain of

string values. The malformed sequence has been used to identify the presence of any disease and such

malformation encourages the occurrence of any disease to be happen. The rule based approach reads the RNA

sequence and verifies the presence of tiny sequences specified in the rule set. The presence of malformed

sequence has been deleted using matrix insertion deletion and has been added to produce new sequence of RNA.

The classification is performed based on the RNA Sequence Support measure (RSSM) being estimated towards

different class of sequences. The proposed method improves the performance of bio molecular computing to

support the disease prediction.

Keywords: DNA, RNA, Bio Molecular Computing, RSSM

References:

1. Krithivasan. K “On the ambiguity and complexity measures in Insertion-Deletion Systems”, in International Conference On Bio-

Inspired Models of Network, Information, and Computing Systems (pp. 425-439) Springer, Berlin, Heidelberg. 2010. 2. Sergey Verlan. “On minimal context-free Insertion-Deletion Systems”, Journal of Automata, Languages and Combinatorics, Vol 2

Pp. 317–328, 2007.

3. Cristian S. Calude “Computing with cells and atoms: An introduction to Quantum DNA and Membrane Computing, CRC Press (2001) 4. F. Biegler, “Regulated RNA rewriting: Modelling RNA editing with guided Insertion”,Theoretical Computer Science, Vol.387, Issue 2:

Pp 103-112, 2007

5. Chanda, G “Grammatical methods in computer vision: An overview”, Technical Report, Georgia Institute of Technology, 2004. 6. Hong, P “Gesture modeling and recognition using finite state machines”, In automatic face and gesture recognition, Proceedings.

Fourth IEEE International Conference on, pp:410–415 , 2000

7. Kitani, K.M “ An mdl approach to learning activity grammars” in proceedings of the korea-japan joint workshop on pattern recognition vol. 106, no. 376, pp. 19-24)

8. Ryoo, M. S “Recognition of composite human activities through context-free grammar based representation”, In Computer Vision and

Pattern Recognition, IEEE Computer Society Conference on, volume 2, pp:1709–1718,2006. 9. Ivanov, Y “ Recognition of visual activities and interactions by stochastic parsing” IEEE Transactions on Pattern Analysis and Machine

Intelligence, vol.22, issue8: pp:852–872,2000.

10. Spies, M. “Homologous recombination by RecBCD and RecF pathways”, in the bacterial chromosome (pp. 389-403). American society of microbiology, 2005.

11. Shereda, R.D “A central role for SSB in escherichia coli RecQ DNA helicase function”, Journal of Biological Chemistry, Vol.No 282,

Page: 19247–19258, 2007.

376-379

81.

Authors: G. Sasirekha, S. Kishore Verma, S. Sheik Faritha Begum, J.S.Adeline Johnsana

Paper Title: An Improved Clustering Realized Relational Data Anonymization with Optimal Privacy and Utility

Measures

Abstract: Massive growth of technology results the increased usage of computer in day to day life. Every

user feeds millions of data for every minute. The process of converting this raw data into useful information is

called data mining. The need for preservation of data for its privacy is called Privacy Preserving Data Mining

(PPDM). In recent years privacy preserving data mining has become more crucial because of increased storage

of digital collection of data about users in many of government sectors, corporate, hospitals, banks, etc., This

collection of data contains many sensitive attributes, which reveals their identity of the users by combining the

data’s with publicly available data’s, which had been stolen by hackers. To prevent from this, a protection model

called k-anonymization is introduced. This k-anonymity model preserves the individual identity through

generalization and suppression. Privacy and utility measures are inversely proportional to each other. The need

to maintain a tradeoff between privacy and utility is a vital factor in PPDM. In this paper, CARD (Clustered

Anonymization of Relational Data) is presented to reduce the information loss of utility aware anonymization.

The utility aware anonymization means k-anonymizing the dataset by accounting the two novel factors,

transformation pattern loss (tpl) and null value count having minimum values. This utility aware anonymization

is done for Cell oriented Anonymization (CoA), Attribute oriented Anonymization (AoA) and Record oriented

Anonymization (RoA). CARD proceeds in clustering the given dataset with various benchmarked clustering

algorithms like Simple K-Means (KMeans), Farthest First (FF), Expectation Maximization (EM), Partition

around Medoids (PAM) and Gower method then this clustered data set are subjected to utility aware CoA, AoA

and RoA anonymization approaches. Classification analysis like logistic regression, naïve bayes and random

forest are done on clustered anonymized data set to assess and prove the privacy and utility of the proposed

approach based on Information Loss, Re-Identification Risk and Classification Accuracy of the clustered dataset

before publishing them. Our experimental results prove to be better than the non-clustered anonymization

procedures. Among the five clustering algorithms, In our analysis Gower and Partition around Medoids (PAM)

results give better solution in terms of privacy and utility since PAM and Gower approaches are the best

clustering methods that are capable of clustering mixed data type (numerical and categorical).

380-387

Keywords: K Anonymization, Cell Oriented Anonymization, Attribute Oriented Anonymization, Record

Oriented Anonymization, Partition around Medoids, Gower

References:

1. V. Shyamala Susan and T. Christopher, “An Efficient Anonymization Model (EAM) For Data Publishing Using Optimized Clustering Approach,” International Journal of Pure and Applied Mathematics, 2017, 118(19),2743-2459.

2. G. Poulis, G. Loukides, A. Gkoulalas-Divanis, and S. Skiadopoulos, “Anonymizing data with relational and transaction attributes,” In

Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2013, (pp. 353-369), Springer, Berlin, Heidelberg. O. Abul, F. Bonchi, and M. Nanni, “Anonymization of moving objects databases by clustering and perturbation.”,

Information Systems, 2010, 35(8), 884-910.

3. Hina Vaghashia and Amit Ganatra, “A Survey: Privacy Preservation Techniques in Data Mining,” International Journal of Computer Applications, 2015, (0975 – 8887) , Volume 119 – No.4.

4. R. Mendes and J.P. Vilela, “ Privacy-Preserving Data Mining: Methods, Metrics, and Applications,” IEEE Access, 5, 2017, 10562–

10582.doi:10.1109/access.2017.2706947. 5. Vicen¸c Torra and Guillermo Navarro-Arribas, “Big Data Privacy and Anonymization,” IFIP Advances in information and

communication Technologies, 2017.

6. V. Ciriani, S. D. C. di Vimercati, S. Foresti and P. Samarati, “K-Anonymity. In Security in decentralized data management,” Springer-Verlag, 2007.

7. P. Samarati and L. Sweeney, “Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization

and suppression,” SRI International, 1998 8. Khaled El Emam and Fida Kamal Dankar, “Protecting Privacy Using k-Anonymity,” Journal of the American Medical Informatics

Association, Volume 15 Number 5, September / October 2008.

9. Jun-Lin Lin and Meng-Cheng Wei, “An Efficient Clustering Method For K-Anonymization,” 2008.

10. G.Chitra Ganabathi and P.Uma Maheswari, “Privacy Preserving K-Anonymization Clustering Approach For Reducing Information

Loss,” Asian Journal of Information Technology, 2016, 15 (10) : 1531-1538.

11. J. W. Byun, A. Kamra, E. Bertino and N. Li, “Efficient k-anonymization using clustering techniques,” International Conference on Database Systems for Advanced Applications, (pp. 188-200), Springer, Berlin, Heidelberg, 2007.

12. P. Samarati and L. Sweeney, “Generalizing data to provide anonymity when disclosing information,” in Proc. 17th ACM SIGACT-

SIGMOD- SIGART Symp. on Principles of Database Systems, 1998, p. 188. .

13. Ankit Saroha, “Survey of k-Anonymity”, National Institute of Technology, Rourkela, March 2014.

14. R C. Wong, Li J, Fu A W, et a1, “(α, k)-Anonymity: an enhanced k-anonymity model for privacy-preserving datapublishing,”

Proceedings of the 12th ACM SIGKDD, New York: ACM Press, 2006, pp. 754-759. 15. K. LeFevre et al., “Incognito: Efficient full-domain k-anonymity,” in Proc. 2005 ACM SIGMOD Int. Conf. on Management of Data,

2005, pp. 49–60. 16. A. Machanavajjhala et al., “L-diversity: Privacy beyond k-anonymity,” ACM Trans. on Knowledge Discovery from Data, vol. 1, no. 1,

2007.

17. N. Li et al., “t-closeness: Privacy beyond k-anonymity and l-diversity,” in 23rd Int. Conf. on Data Engineering, 2007, pp. 106–115. . 18. Kishore Verma Samraj, Rajesh Appusamy and Ramya Ravi Shankar, “Utility Enhancement of Deficient Relational Recordset

Anonymization,” International Journal of Intelligent Engineering and Systems, 2018.

19. S. Kishore Verma, A. Rajesh and J.S. Adeline Johnsana, “A Systematic Evaluated Recommendation on Performance Enhancement Factors and Procedures of Relational Data Anonymization,” International Journal of Pure and Applied Mathematics, 2018, Volume

120 No. 5 2018, 1175-1188.]. 21(3). pp. 876—880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar

82.

Authors: M.Shailaja, K.Lokeshwaran, S.Sheik Faritha Begum

Paper Title: Smart Medication Pill Box For Blind People with Pulse Sensor

Abstract: Abstract: Internet of Things (IoT) is an environment which connects all the physical things or

objects through the internet. IoT has many applications such as smart home, smart healthcare, smart city, etc.

Majorly IoT healthcare system is developed for patients, hospitals and healthcare centres that are regularly

taking care of patients and also checks whether the patient has taken the prescribed medicine or not.

Especially, elder people are not having proper care. Recently, people spend most of their money in Hospitals

and medicines only. Hence, it proves our negligence towards our health. Not only that, most of the

people are moving abroad leaving their old parents in their hometowns, so that they cannot take care of their

parent’s health. Not only the elders are suffering but also the visually challenged people are suffering a

lot to intake the medicines properly. A smart pill box can be very helpful if you take many types of

medications each day. I offer a free pill box that has 5 compartments that will hold each day’s worth of

medication. If you order more than 5 prescription medications with our pharmacy, you qualify for a free pill

box. It is easy for the seniors to take pills in a correct time. Medication reminders prevent this from happening.

There is nothing your senior has to read or figure out. They simply need to take the pills in the

compartment after the reminder beeps. If it comes to blind people it’s really a big issue to maintain the

medication schedule and there might be a chance that blind people may take wrong medicines due to

their lack of vision. To overcome all the issues and impossibilities in medication field I propose a project

model where normal people as well as blind people both can be beneficiary.

Keywords: Smart med box, sensors, voice module, dc motor, motor driver, Internet of Things(IOT), flex sensor,

healthcare.

References: 1. B.N.Karthik, L.Durga Parameswari, R. HarshinI, A.Akshaya, “ Survey on IOT & Arduino Based Patient Health Monitoring System”,.

International Journal of Engineering Science and Computing, (April-2017). a. Hussain, R. Wenbi, A. da Silva, M. Nadher and M. Mudhish, "Health and emergency-care platform for the elderly and

disabled people in the Smart City", Journal of Systems and Software , vol. 110, pp. 253-263, (2015).

2. Jagpreet Kaur, J. S. Rana and Rupinder Kaur, “Home Environment ”, Stud Home CommSci,, (2009). 3. PASQUALINI BLASS, A. a,c, GOUVEA DA COSTA, S. E. a,b, BORGES, L. A. C, “Hospital Environmental Performance

Measurement: a bibliometric review of literature” , (1987-2017)

4. Urvashi Sharma, Chetna Chauhan, Himani Sharma, Anjali Sharma, “ARDUINO BASED MEDICINE REMINDER”, (AGUIJET), Vol.

388-395

No. 3, Jul-Dec, (2016) 5. MayureshWaykole, Vatsalya Prakash, Himanshu Singh, Nalini N,”ArduMed - Smart Medicine Reminder for Old People”, International

Journal of Scientific & Engineering Research, Volume 7, Issue 5, May-2016

6. Mohammed AsadFasahate, “ Smart Medicine Box Using IOT”,International Journal of Scientific & Engineering Research, Volume 9,Issue 2, February 2018, ISSN 2229-5518

7. Piyush R.Pawar, Shubham S. Kaikade , “Design of Automatic Smart Medication Dispenser”, International Research Journal of

Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 (Jan-2018) 8. Deshmukh Priyanka.A, “Intelligent Medication System for Visually Impaired Patients”, IJEEDC, ISSN (P): 2320-2084, (O) 2321–

2950 (April-2015)

9. Hsiu-Ling Tsai, Chun Hsiang Tseng, Long-Cian Wang, Fuh-Shyang Juang, “Bidirectional Smart Pill Box Monitored Through Internet And Receiving Reminding Message From Remote Relatives”, IEEE International Conference on Consumer Electronics -

Taiwan (ICCE-TW), (2017)

10. Prashant Salunke, Rasika Nerkar, “IoT Driven Healthcare System for Remote Monitoring of Patients”, International Journal for Modern Trends in Science and Technology Volume: 03, Issue No: 06, ISSN: 2455-3778, (June-2017)

11. International Conference on, “Research on Zigbee wireless communication technology”, Electrical and Control Engineering

(ICECE), (Oct-2011) 12. Major conference on, IEEE“ Biomedical and sensors”, Custom Integrated Circuits Conference (CICC), (Oct-2012)

13. Urvashi Sharma, Chetna Chauhan, Himani Sharma, Anjali Sharma (2016), “Arduino Based Medicine Reminder”, (AGUIJET), Vol. No.

3 14. Hussain, R. Wenbi, A. da Silva, M. Nadher and M. Mudhish, "Health and emergency-care platform for the elderly and

disabled people in the Smart City", Journal of Systems and Software , vol. 110, pp. 253-263, (2015).

15. M. Sedlmayr, H. Prokosch, U. Münch, “Towards smart environments using smart objects,”, Studies in Health Technology and Informatics, vol. 140, pp. 315-317, (2010).

16. M. Beigl, H.-W. Gellersen, and A. Schmidt. Mediacups: Experience with design and use of computer-augmented everyday artifacts.

Network security systems, 35(4):400–405,(1999).

17. Paul Kuwik, "The Smart Medical Health kit ," IEEE POTENTIALS, vol. 24 , pp 22 – 30, Apr-May (2006).

18. D Talbot, Computer Viruses are “Rampant” on Medical Devices in Hospitals. Technical Review, May , (2011).

19. L. Li, L. Xu, A. Jeng, D. Naik, T. Allen, and M. Frontini, “Creation of environmental health information system for public health service,” Information System, vol. 10, pp. 515–523, (2007).

20. Carroll R., Cnossen R., Schnell M., Simons D., "Continua: An Interoperable Personal Healthcare Ecosystem", Pervasive

Computing, may.-jul,. (2006). 21. Shyamal P., Hyung P., Paolo B., Leighton C., Mary R., A review of wearable sensors and systems with application in

rehabilitation, Neuro Engineering and Rehabilitation, (2011).

22. Kosmatos, E.A., Tselikas, N.D. and Boucouvalas, A.C. Integrating RFIDs and Smart Objects into a Unified Internet of Things Architecture. Advances in Internet of Things: Scientific Research, (2011).

23. Wireless Patient Health Monitoring System- Manisha Shelar, Jaykaran Singh, Mukesh Tiwari from R.G.P.V.

24. University, Department of E&TC, S.S.S. I.S.T. International Journal of Computer Application (0975- 8887) ( 2013)