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Partial Replacement of Cement, Fine Aggregates & Coarse Aggregate with fly Ash, Steel Slag & Recycled Aggregates Respectively in Concrete
Dr. A. Kumar
Pages: 1-12 | First Published: 05 Sep 2022
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Abstract
Around the world, a sizable number of people are employed in the construction industry. Today, development is occurring at an alarmingly rapid rate, which has led to an increase in the use of building materials that are already present in nature. The majority of the millions of tonnes of trash produced each year around the world is not recyclable. Additionally, recycling trash uses energy and harms the environment. Furthermore, both the accumulation of trash in the suburbs and the disposal of waste pose serious environmental risks. It is acceptable to accomplish two goals simultaneously by using waste material in the production of concrete, including the addition of advantageous properties. For this undertaking the globe. Today, development is occurring at an alarmingly rapid rate. Experimental findings from a study to determine the compressive strength of concrete with various cement replacements as well as fine and coarse particles were presented. An M-20 grade of concrete (1:1.8:3.16) with a water-cement ratio of 0.5 was developed, per IS-10262-2009. The findings show that cement, fine aggregates, and coarse aggregates can all be replaced with fly ash, steel slag, and recycled aggregates at percentages of 10%, 20%, and 30%, respectively, without compromising the concrete's compressive strength. To ascertain how much the strength will be increased, we are altering the materials one at a time. It stops when it reaches a specific percentage. To find out the mechanical properties of concrete, it will be increased and all of the ingredients will be changed at once.
Keywords: Garbage,steel slag,fly ash, M-20, Recycled aggregates, compressive strength, hazardous

Reference 
1. Mechanical properties of concrete using SiMn slag- J.Saravanan and N.Suganya- International Journal of Engineering InventionseISSSN:2278-7461, p-ISSN: 2319-6491 Volume 4, Issue 9 [May 2015] PP: 07-16. 4. Usage of SiMn slag in concrete as fine and coarse aggregate Indian journal of Engineering and Material Sciences Vol.22, June 2015, pp.339-344.
2. Sudharsan N, Palanisamy T. and Subhash C. Yaragal, ‘Environmental Sustainability of Waste Glass as a Valuable Construction Material – A Critical Review’, Ecology, Environment and Conservation Vol 24, Feb. Suppl. Issue 2018, pp. 335-S342
3. Sudharsan N, Palanisamy T., ‘A Comprehensive Study on Potential Use of Waste Materials in Brick for Sustainable Development’, Ecology, Environment and Conservation, Vol 24, Feb. Suppl. Issue 2018, pp. 343-S347
4. Processing, characterization and erosion wear response of Linz Donawitz(LD) slag filled polypropylene composites Journal of Thermoplastic Composite materials December10, 2014 Pravat Ranjan Pati, Alok Satapathy.
5. Evaluation of the suitability of ground granulated silico-manganese slag in Portland slag cement-Construction and Building Materials.
6. Properties of silicon manganese slag as an aggregate for concrete depending on cooling conditions-Hong-Beom Choi & Jin Man Kim - Journal of Material Cycles and Waste Management (2020).
7. Michale Stacey and Chris Bayliss(2015), “Aluminium and durability : reviewed by inspection and testing”.
8. Sudharsan N, Sivalingam K,” Potential Utilization of Waste Material for Sustainable Development in construction Industry” International Journal of Recent Technology and Engineering, Volume-8 Issue-3, September 2019,pp.3435-3438.
9. Sudharsan N & Saravanaganesh.S, “Feasibility Studies on Waste Glass Powder”, International Journal of Innovative Technology and Exploring Engineering, Volume-8 Issue-8 June, 2019, pp.1644-1647.
10. Mr.Sai Gopi Kamepalli, Ms.Misbah Bashir, Mr.S.Ganesh(2017), “Experimental study of replacement of coarse aggregate with aluminium caps”. 

11. Maciej Szumigała, Łukasz Polus, (2015) Applications of Aluminium and concrete composite structures- Scientific-Technical Conference Material Problems in Civil Engineering.
12. Sudharsan N & Blessy Grant C J, “Comparison of Static Response of Laced Reinforced Concrete Beams With Conventional Reinforced Concrete Beams by Numerical Investigations‟, International Journal of Civil Engineering and Technology, Volume 9, Issue 8, August 2018, pp.700-704.
13. N Sudharsan, C J Blessy Grant, P Murthi, K Poongodi, P Mahesh Kumar, “A Comparative Experimental Investigation on Laced Reinforced Concrete Beam and Conventional Beam under Monotonic Loading”, IOP Conf. Series: Earth and Environmental Science 822 (2021) 012034 (doi:10.1088/1755-1315/822/1/012034)
14. GireeshMailar, SujayRaghavendra N, Sreedhara B.M, Manu D.S, Parameshwar Hiremath,, Jayakesh K, (2016) Investigation of concrete produced using recycled Aluminium dross for hot weather concreting conditions-( Resource Efficient Technologies) Volume 2, Issue 2, Pp.68-80
15. Guidelines for Concrete Mix Design, IS Code 10262 – 2019
16. Mohandas, K & Elangovan, G 2016, ‘Retrofitting of reinforced concrete beam using different resin bonded GFRP laminates’ International Journal of Advanced Engineering Technology, E-ISSN0976-3945, vol. 7 no. 2,
17. Sudharsan N, S Praburanganathan, Yeddula Bharath Simha Reddy & Pavan Kumar P (2022): Interaction of anthracite coal ash and Archis hypogaea shell ash on an innovative brick: an experimental and simulation study, International Journal of Coal Preparation and Utilization, DOI: 10.1080/19392699.2022.2089127.
18. Mohandas, K & Elangovan, G 2016, ‘Retrofitting of reinforced concrete beam using different resin bonded GFRP laminates’ International Journal of Advanced Engineering Technology, E-ISSN0976-3945, vol. 7 no. 2,
19. Mohandas, K & Elangovan, G 2015, ‘Influence of polymeric resins on the enhancement ofstrengthening of reinforced concrete beam with phenolic resin bonded FRP’S’, International Journal of Applied Engineering Research, ISSN0973-4562 vol. 10, no. 5,
20. S. Praburanganathan, N. Sudharsan, Yeddula Bharath Simha Reddy, Chukka Naga Dheeraj Kumar Reddy, L. Natrayan, Prabhu Paramasivam “Force-Deformation Study on Glass Fiber Reinforced Concrete Slab Incorporating Waste Paper”, Advances in Civil Engineering, Volume 2022, Article ID 5343128, pp.1-10 (https://doi.org/10.1155/2022/5343128)
21. Praburanganathan, S., Chithra, S., Divyah, N., Sudharsan, N., Simha, Y. and Vigneshwaran, S. (2022). Value-added waste substitution using slag and rubber aggregates in the sustainable and eco-friendly compressed brick production. Revista de la Construcción. Journal of Construction, 21(1), 5-20. https://doi.org/10.7764/RDLC.21.1.5.

Binary Salp Swarm Optimization Algorithm for Feature Selection with Simulated Annealing
Vaishali .R
Pages: 17-28 | First Published: 05 Sep 2022
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Abstract
Wrapper based Feature Selection is a popular Dimensionality reduction approach that searches for an optimum Feature Subset from a huge dataset which meets the Objective of selection and represents the structure of the entire dataset. Swarm Intelligence based Feature Selection Wrappers are known for their solution exploration and exploitation capability. However when there are multiple objectives to satisfy, wrapper algorithms suffer Pareto optimality problem. The objective of this paper is to overcome Pareto optimality problem and select relevant features with a Hybrid Salp Swarm Optimization Wrapper. The issue is approached with a fuzzy weighted single objective function embedded with classifier rate and Feature Selection ratio as selection factors. For next position selection and to improve solution exploration, Single Solution Simulated Annealing is combined with the Wrapper. Experimental results on 7 UCI benchmark datasets prove that the proposed method outperforms the existing Hybrid Binary Whale Swarm Wrapper in terms of time taken for Feature Selection. Also in terms of Error rate, Feature Reduction Ratio and Fitness measure, the proposed method showcases a decent performance.
Keywords: Feature Selection, Machine Learning, Meta-heuristics, Swarm Intelligence, Simulated Annealing

References
1. Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2), 245–271. https://doi.org/10.1016/S0004-3702 (97)00063-5
2. Blum, C., & Li, X. (2008). Swarm Intelligence in Optimization. In Swarm Intelligence (pp. 43–85). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74089-6_2
3. Cunningham, J. P., & Ghahramani, Z. (2014). Linear Dimensionality Reduction: Survey, Insights, and Generalizations, 16, 2859–2900. http://arxiv.org/abs/1406.0873
4. Dorigo, M., & Birattari, M. (2011). Ant colony Optimization. In Encyclopedia of machine learning (pp. 36–39). Springer.

5. Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016a).
6. Binary ant lion approaches for Feature Selection.
7. Neurocomputing, 213, 54–65. https://doi.org/10.1016/j.neucom.2016.03.101
8. Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016b).
9. Binary grey wolf Optimization approaches for Feature Selection. Neurocomputing, 172, 371–381. https://doi.org/10.1016/j.neucom.2015.06.083
10. Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I. Ala’M, A. Z., Mirjalili, S., & Fujita, H. (2018). An efficient binary Salp Swarm Algorithm with crossover scheme for Feature Selection Problems. Knowledge-Based Systems, 154, 43-67 https://doi.org/10.1016/j.knosys.2018.05.009
11. Forest, O., Rodrigues, D., Pereira, L. A. M., Nakamura, R. Y. M., Costa, K.
A. P., Yang, X., Forest, O. (2014). Expert Systems with Applications a wrapper approach for Feature Selection based on Bat Algorithm. Expert Systems with Applications, 41(5), 2250–2258. https://doi.org/10.1016/j.eswa.2013.09.023
12. Ibrahim, H. T., Mazher, W. J., Ucan, O. N., & Bayat, O. (2018). A grasshopper optimizer approach for Feature Selection and optimizing SVM parameters utilizing real biomedical data sets. Neural Computing and Applications, 1–10.
13. Jain, A., & Zongker, D. (1997). Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153–158. https://doi.org/10.1109/34.574797
14. Jiménez, F., Sánchez, G., García, J. M., Sciavicco, G., & Miralles, L. (2017). Multi-objective evolutionary Feature Selection for online sales forecasting.
15. Neurocomputing, 234(November 2016), 75–92. https://doi.org/10.1016/j.neucom.2016.12.045
16. Jiménez, F., Sánchez, G., & Juárez, J. M. (2014).
17. Multi-objective evolutionary algorithms for fuzzy classification in survival prediction. Artificial Intelligence in Medicine, 60(3), 197–219. https://doi.org/10.1016/j.artmed.2013.12.006
18. Kanan, H. R., & Faez, K. (2008). An improved Feature Selection method based on ant colony Optimization (ACO) evaluated on face recognition system.
19. Applied Mathematics and Computation, 205(2), 716–725.
20. https://doi.org/10.1016/j.amc.2008.05.115
21. Khamees, M., Albakry, A., & Shaker, K. (2018, October). Multi-objective Feature Selection: Hybrid of Salp Swarm and Simulated Annealing Approach. In International Conference on New Trends in Information and Communications Technology Applications (pp. 129-142). Springer, Cham. https://doi.org/10.1007/978-3-030-01653-1_8
22. Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., & Wang, S. (2011). An improved particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 8(2), 191–200.
23. https://doi.org/10.1016/S1672-6529(11)60020-6
24. Mafarja, M. M., Eleyan, D., Jaber, I., Hammouri, A., & Mirjalili, S. (2017). Binary dragonfly algorithm for Feature Selection. In New Trends in Computing Sciences (ICTCS), 2017 International Conference on (pp. 12–17).
25. Mafarja, M., & Mirjalili, S. (2018). Whale Optimization approaches for wrapper Feature Selection. Applied Soft Computing, 62(November), 441–453. https://doi.org/10.1016/J.ASOC.2017.11.006
26. Mirjalili, S. (2015). Moth-flame Optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
27. Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic Optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.
28. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
29. Mirjalili, S., & Lewis, A. (2016). The whale Optimization algorithm. Advances in Engineering Software, 95, 51–67.
30. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
31. Nakamura, R. Y. M., Pereira, L. A. M., Costa, K. A., Rodrigues, D.,
32. Papa, J. P., & Yang, X. S. (2012). BBA: A binary bat algorithm for Feature Selection. Brazilian Symposium of Computer Graphic and Image Processing, 291–297. https://doi.org/10.1109/SIBGRAPI.2012.47
33. Raymer, M. L., Punch, W. F., Goodman, E. D., Kuhn, L. A., & Jain, A. K. (2000). Dimensionality Reduction Using Genetic Algorithms. IEEE Transactions on Control Systems Technology, 4(2), 164–171. https://doi.org/10.1109/TCST.2011.2171964
34. Ahmad, S., Mafarja, M., Faris, H., & Aljarah, I. (2018). Feature Selection using Salp Swarm algorithm with chaos. http://hdl.handle.net/20.500.11889/5516

35. Unler, A., & Murat, A. (2010). A discrete particle Swarm Optimization method for Feature Selection in binary classification problems. European Journal of Operational Research, 206(3), 528–539. https://doi.org/10.1016/j.ejor.2010.02.032
36. Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., & Nalluri, S. (2017, October). Genetic algorithm based Feature Selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset, IEEE, ICCNI 2017 (pp.1-5). https://doi.org/10.1109/ICCNI.2017.8123815
37. Wang, X., Yang, J., Teng, X., Xia, W., & Jensen, R. (2007). Feature Selection based on rough sets and particle Swarm Optimization. Pattern Recognition Letters, 28(4), 459–471.
https://doi.org/10.1016/j.patrec.2006.09.003
38. Yang, X.-S. (2010). Firefly algorithm, stochastic test functions and design Optimization. Int. J.Bio-Inspired Computation 2, 2(2), 78–84. https://doi.org/10.1504/IJBIC.2010.032124
39. Yun, C., Oh, B., Yang, J., & Nang, J. (2011). Feature subset selection based on bio-inspired algorithms. Journal of Information Science and Engineering, 27(5), 1667–1686.
40. Zawbaa, H. M., Emary, E., & Grosan, C. (2016). Feature Selection via chaotic antlion Optimization. PloS One, 11(3), e0150652.
41. Zawbaa, H. M., Emary, E., Parv, B., & Sharawi, M. (2016). Feature Selection approach based on moth-flame Optimization algorithm. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 4612–4617).

Deep Learning for Traffic Prediction: Methods, Analysis, and Future Directions
Ms. Mutyala Keerthi
Pages: 21-26 | First Published: 05 Sep 2022
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Abstract
In an clever transportation gadget, visitors prediction is critical. Accurate visitors forecasting can assist with direction planning, car dispatching, and visitors congestion reduction. Due to the complicated and dynamic spatial-temporal relationships among one of a kind elements in the street community, this trouble is tough to solve. Recently, a considerable quantity of studies paintings has been dedicated to this area, specifically the deep mastering technique, which has extensively stepped forward visitors prediction abilities. The intention of this have a take a observe is to offer a whole evaluation of deep mastering-primarily based totally on visitors prediction algorithms from numerous angles. In particular, we offer a taxonomy and a precis of acknowledged visitors prediction algorithms. Second, we offer a listing of contemporary-day today's methodologies for numerous visitors forecast programs. Third, we acquire and set up normally used public datasets from the literature to make it simpler for different researchers. Furthermore, we adopt thorough experiments to evaluate the overall performance of various methods on a real-global public dataset to offer an assessment and analysis. Finally, we discover the field's unsolved problems.

Reference 
1. J. Zhang, Y. Zheng, D. Qi, R. Li, and X. Yi, “Dnn-based prediction model for spatio-temporal data,” in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016, pp. 1–4.
2. E. Vlahogianni, M. Karlaftis, and J. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transportation Research Part C Emerging Technologies, vol. 43, no. 1, 2014.
3. Y. Li and S. Cyrus, “A brief overview of machine learning methods for short-term traffic forecasting and future directions,” SIGSPATIAL Special, vol. 10, no. 1, pp. 3—-9, 2018. [Online]. Available: https://doi.org/10.1145/3231541.3231544
4. A.M.NagyandV.Simon,“Surveyontrafficpredictioninsmartcities,” Pervasive and Mobile Computing, vol. 50, pp. 148–163, 2018.
5. A. Singh, A. Shadan, R. Singh, and Ranjeet, “Traffic forecasting,” International Journal of Scientific Research and Review, vol. 7, no. 3, pp. 1565–1568, 2019.
6. A. Boukerche and J. Wang, “Machine learning-based traffic prediction modelsforintelligenttransportationsystems,”ComputerNetworks,vol. 181, p. 107530, 2020.
7. I. Lana, J. Del Ser, M. Velez, and E. I. Vlahogianni, “Road traffic forecasting: Recent advances and new challenges,” IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 2, pp. 93–109, 2018.
8. D. A. Tedjopurnomo, Z. Bao, B. Zheng, F. Choudhury, and A. Qin, “A survey on modern deep neural network for traffic prediction: Trends, methods and challenges,” IEEE Transactions on Knowledge and Data Engineering, 2020.
9. J. Ye, J. Zhao, K. Ye, and C. Xu, “How to build a graph-based deep learning architecture in traffic domain: A survey,” arXiv preprint arXiv: 2005.11691, 2020.
10. P. Xie, T. Li, J. Liu, S. Du, X. Yang, and J. Zhang, “Urban flow prediction from spatiotemporal data using machine learning: A survey,” Information Fusion, 2020. [Online]. Available: https: //doi.org/10.1016/j.inffus.2020.01.002
11. B. Williams and L. Hoel, “Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results,” Journal of transportation engineering, vol. 129, no. 6, pp. 664– 672, 2003.
12. E. Zivot and J. Wang, “Vector autoregressive models for multivariate time series,” Modeling Financial Time Series with S-Plus®, Springer New York: New York, NY, USA, pp. 385–429, 2006.
13. R. Chen, C. Liang, W. Hong, and D. Gu, “Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm,” Applied Soft Computing, vol. 26, pp. 435–443, 2015.
14. U. Johansson, H. Bostr¨om, T. L¨ofstr¨om, and H. Linusson, “Regression conformal prediction with random forests,” Machine Learning, vol. 97, no. 1-2, pp. 155–176, 2014.
15. Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Wang, “Traffic flow prediction with big data: A deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2015.
16. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
17. N. Kalchbrenner and P. Blunsom, “Recurrent continuous translation models,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013, pp. 1700–1709.
18. J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” in Proceedings of International Conference on Learning Representations, 2014.
19. D. Rumelhart, G. Hinton, and R. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
20. J. Elman, “Distributed representations, simple recurrent networks, and grammatical structure,” Machine learning, vol. 7, no. 2-3, pp. 195–225, 1991.
21. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997
22. K. Cho, B. Van Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder- decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
23. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776 (2019). https://doi.org/10.1007/s10586-018-1757-3
24. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
25. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
26. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
27. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636. 

28. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
29. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
30. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
31. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
32. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
33. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
34. S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar “Semantic Risk Analysis Model Cancer Data Prediction”, Journal on Science Engineering and Technology, Vol 5 Issue No 2 2018
35. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015

Designing cyber insurance policies: the role of pre-screening and security interdependence
Ms. B. V. Anupama
Pages: 27-32 | First Published: 05 Sep 2022
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Abstract
For cyber hazard transfer, cyber coverage is a ability option. However, it's been proven that it can or might not boom community protection relying at the traits of the underlying surroundings. We're particularly inquisitive about factors of cybersecurity and the way they have an effect on settlement layout. The first is cybersecurity's interconnected individual, wherein one entity's degree of protection is decided now no longer simply through its personal funding and efforts, however additionally through the ones of others withinside the equal eco-machine (i.e., externalities). The 2d is that current breakthroughs in Internet size, paired with gadget gaining knowledge of approaches, now permit us to make specific quantitative tests of protection posture at a enterprise degree. This can be used to behavior a primary protection evaluation, or pre-screening, of a potential client with a purpose to enhance top rate discrimination and coverage formulation. We display that protection interdependency creates a "earnings possibility" for insurers, that is fueled through inefficient attempt tiers exerted through interdependent people that fail to account for hazard externalities. Security pre-screening then allows the insurer to take manipulate of this extra earnings possibility through designing the perfect contracts which incentivize marketers to make bigger their attempt tiers, permitting the insurer to “promote dedication” to interdependent marketers, except insuring their risks. We apprehend situations beneathneath which this sort of contracts results in now no longer simplest upward thrust in earnings for the major, however additionally an upgraded nation of community protection. For cyber hazard transfer, cyber coverage is a ability option. However, it's been proven that it can or might not boom community protection relying at the traits of the underlying surroundings. We're particularly inquisitive about factors of cybersecurity and the way they have an effect on settlement layout. The first is cybersecurity's interconnected individual, wherein one entity's degree of protection is decided now no longer simply through its personal funding and efforts, however additionally through the ones of others withinside the equal eco-machine (i.e., externalities). The 2d is that current breakthroughs in Internet size, paired with gadget gaining knowledge of approaches, now permit us to make specific quantitative tests of protection posture at a enterprise degree. This can be used to behavior a primary protection evaluation, or pre-screening, of a potential client with a purpose to enhance top rate discrimination and coverage formulation. We display that protection interdependency creates a "earnings possibility" for insurers, that is fueled through inefficient attempt tiers exerted through interdependent people that fail to account for hazard externalities. Security pre-screening then allows the insurer to take manipulate of this extra earnings possibility through designing the perfect contracts which incentivize marketers to make bigger their attempt tiers, permitting the insurer to “promote dedication” to interdependent marketers, except insuring their risks. We apprehend situations beneathneath which this sort of contracts results in now no longer simplest upward thrust in earnings for the major, however additionally an upgraded nation of community protection.

References 
1. Insurance Information Institute, “U.S. cyber insurance market demonstrates growth, innovation in wake of high-profile data breaches,”www.iii.org/pressrelease/us-cyber-insurance-marketdemonstrates- growth innovation-in-wake-of-high-profile- databreaches-102015, 2015.
2. B. Johnson, J. Grossklags, N. Christin, and J. Chuang, “Are security experts useful? bayesian nash equilibria for network security games with limited information,” in The 15th European Symposium on Research in Computer Security, (ESORICS), 2010.
3. J. Grossklags, N. Christin, and J. Chuang. Security and insurance management in networks with heterogenous agents. In ACM EC, 2008.
A. Hofmann, “Internalizing externalities of loss prevention through insurance monopoly: an analysis of interdependent risks,” The Geneva Risk and Insurance Review, 2007.
4. M. M. Khalili, P. Naghizadeh, and M. Liu, “Designing cyber insurance policies: Mitigating moral hazard through security prescreening,” in The 7th International EAI Conference on Game Theory fo Networks (Gamenets), 2017.
5. Y. Liu, A. Sarabi, J. Zhang, P. Naghizadeh, M. Karir, M. Bailey, and M. Liu, “Cloudy with a chance of breach: Forecasting cyber security incidents,” in USENIX Security Symposium, 2015.
6. C. Hemenwa, “ABI Research: Cyber insurance market to reach $10B by 2020,” www.advisenltd.com/2015/07/30/abi- research-cyber-insurance-market-to-reach- 10b-by-2020/, 2015.
7. R. A. Miura-Ko, B. Yolken, N. Bambos, and J. Mitchell, “Security investment games of interdependent organizations,” in The 46th Annual Allerton Conference on Communication, Control, and Computing, 2008.
8. R. Bohme and G. Schwartz. Modeling cyber-insurance: Towards a unifying framework. In WEIS, 2010.
9.https://www.javatpoint.com/reinforcement-learning#:~:text=Reinforcement%20Learnin g%20is%20a%20feedback,gets%20negative %20feedback%20or%20penalty.
10. N. Shetty, G. Schwartz, and J. Walrand, “Can competitive insurers improve network security?,” in The Third International Conference on Trust and Trustworthy Computing (TRUST), 2010.
11. N. Shetty, G. Schwartz, M. Felegyhazi, and J. Walrand, “Competitive cyber- insurance and internet security,” Economics of Information Security and Privacy, pp. 229–247, 2010.
12. G. Schwartz, N. Shetty, and J. Walrand, “Cyber-insurance: Missing market driven by user heterogeneity,” 2010.
13. G. A. Schwartz and S. S. Sastry, “Cyber- insurance framework for large scale interdependent networks,” in The Third International Conference on High Confidence Networked Systems, 2014.
14. H. Ogut, N. Menon, and S. Raghunathan, “Cyber insurance and it security investment: Impact of interdependence risk,” in The Workshop on the Economics of Information Security, 2005.
15. Z. Yang and J. C. S. Lui, “Security adoption and influence of cyber-insurance markets in heterogeneous networks,” Performance Evaluation, vol. 74, pp. 1–17, Apr. 2014.
16. J. Kesan, R. Majuca, and W. Yurcik, “The economic case for cyberinsurance,” University of Illinois Legal Working Paper Series uiuclwps-1001, University of Illinois College of Law, 2004.
A. Hofmann, “Internalizing externalities of loss prevention through insurance monopoly: an analysis of interdependent risks,” The Geneva Risk and Insurance Review, 2007.

Evaluating Descriptive Answer Using Machine Learning And Natural Language Processing
T. Sai Kumari
Pages: 33-38 | First Published: 05 Sep 2022
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Abstract
Assessment of the descriptive paper is a tough challenge. It requires lot of man power. In educational institutions a primary hassle is evaluation of descriptive type questions & answers. Using NLP this descriptive answer assessment proper answers and the pupil answer scripts. More marks is probably awarded, if the similarity withinside the answers is more. There are several advantages of using this automated assessment device as it permits in decreasing the time concerned about the resource of the usage of faculty to correct the descriptive papers. The examinations can be accomplished online, the answers can be evaluated right now and it would be beneficial for universities, schools and schools for academic purpose with the resource of the usage of presenting ease to colleges and the examination evaluation cell.
Keyword: Descriptive answer evaluation, machine learning, natural language processing, word2vec.

References 
1. Modeling of answer points to expedite semiautomated Evaluation of descriptive papers”, in proc. Int. Conf. Ieee 8th international advance computing conference (iacc), 2018, pp. 85-9.
2. aloni kadam, priyanka tarachandani, prajakta vetaln and charusheela nehete,”ai based eassessment system”, easychair preprint ,march 18, 2020.
3. Vishal bhonsle, priya sapkal, dipesh mukadam, prof. Vinit raut, “an adaptive approach for descriptive answer evaluation” viva-tech international journal for research and innovation volume 1, issue 2 (2019).
4. J. Muangprathub, s. Kajornkasirat, and a. Wanichsombat, ‘‘document plagiarism detection using a new concept similarity in formal concept analysis,’’ j. Appl. Math., vol. 2021, pp. 1–10, mar. 2021.
5. X. Hu and h. Xia, ‘‘automated assessment system for descriptive questions based on lsi,’’ in proc. 3rd int. Symp. Intell. Inf. Technol. Secur. Informat., apr. 2010, pp. 250–254.
6. M. Kusner, y. Sun, n. Kolkin, and k. Weinberger, ‘‘from word embeddings to document distances,’’ in proc. Int. Conf. Mach. Learn., 2015, pp. 957– 966.
7. C. Xia, t. He, w. Li, z. Qin, and z. Zou, ‘‘similarity analysis of law documents based on word2vec,’’ in proc. Ieee 19th int. Conf. Softw. Qual., rel. Secur. Companion (qrs-c), jul. 2019, pp. 354– 357.
8. H. Mittal and m. S. Devi, ‘‘descriptive evaluation: a comparison of several statistical techniques,’’ appl. Artif. Intell., vol. 32, no. 1, pp. 85–95, jan. 2018.
9. L. A. Cutrone and m. Chang, ‘‘automarking: automatic assessment of open questions,’’ in proc. 10th ieee int. Conf. Adv. Learn. Technol., sousse, tunisia, jul. 2010, pp. 143–147.
10. G. Srivastava, p. K. R. Maddikunta, and t. R. Gadekallu, ‘‘a two-stage text feature selection algorithm for improving text classification,’’ tech. Rep., 2021.
11. H. Mangassarian and h. Artail, ‘‘a general framework for descriptive information extraction from unstructured english text,’’ data knowl. Eng., vol. 62, no. 2, pp. 352–367, aug. 2007.
12. B. Oral, e. Emekligil, s. Arslan, and g. Eryigit, ‘‘information extractionˇ from text intensive and visually rich banking documents,’’ inf. Process. Manage., vol. 57, no. 6, nov. 2020, art. No. 102361.

13. H. Khan, m. U. Asghar, m. Z. Asghar, g. Srivastava, p. K. R. Maddikunta, and t. R.
14. Gadekallu, ‘‘fake review classification using supervised machine learning,’’ in proc. Pattern recognit. Int. Workshops challenges (icpr). New york, ny, usa: springer, 2021, pp. 269–288.
15. S. Afzal, m. Asim, a. R. Javed, m. O. Beg, and t. Baker, ‘‘urldeepdetect: a deep learning approach for detecting malicious urls using semantic vector models,’’ j. Netw. Syst. Manage., vol. 29, no. 3, pp. 1–27, mar. 2021.
16. N. Madnani and a. Cahill, ‘‘automated scoring: beyond natural language processing,’’ in proc. 27th int. Conf. Comput. Linguistics (coling), e. M. Bender, l. Derczynski, and p. Isabelle, eds. Santa fe, nm, usa: association for computational linguistics, aug. 2018, pp. 1099–1109.
17. Z. Lin, h. Wang, and s. I. Mcclean, ‘‘measuring tree similarity for natural language processing based information retrieval,’’ in proc. Int. Conf. Appl. Natural lang. Inf. Syst. (nldb) (lecture notes in computer science), vol. 6177, c. J. Hopfe, y. Rezgui, e. Métais, a. D. Preece, and h. Li, eds. Cardiff, u.k.: springer, 2010, pp. 13–23.
18. G. Grefenstette, ‘‘tokenization,’’ in syntactic wordclass tagging. Springer, 1999, pp. 117–133.
19. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
20. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
21. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
22. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
23. Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
24. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
25. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
26. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
27. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
28. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
29. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
30. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
31. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
32. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
33. S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar” Semantic Risk Analysis Model Cancer Data Prediction” Journal on Science Engineering and Technology Vo1ume 5, No. 02, April 2018

Future Methods Of Managing Privacy And Data Security At The Technical Age-Cashless Society
Anuradha Reddy
Pages: 39-43 | First Published: 05 Sep 2022
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Abstract
Financial transactions in a cashless global are carried out the usage of virtual statistics in place of real banknotes or coins (commonly an digital illustration of cash). [1] Cashless civilizations have existed for the reason that sunrise of human civilization, primarily based totally on barter and different varieties of exchange, and cashless transactions at the moment are possible utilizing credit score playing cards, debit playing cards, cellular bills, and virtual currencies which include bitcoin. However, this newsletter makes a speciality of the term "cashless society" withinside the feel of a motion toward, and implications of, a society wherein coins is changed with the aid of using its virtual equal—in different words, criminal tender (cash) exists, is recorded, and is most effective traded in digital virtual form. A few publications of movement may be coupled to attain a everlasting and applicable result for customers in a cashless society to boom privateness: A new kind of banking carrier that assigns randomised numbers to credit score playing cards, using blockchain to song all man or woman transactions, and a marketing campaign to train and tell key stakeholders approximately safety and privateness dangers so that they have got the gear and historical past expertise to guard their personal statistics earlier than coping with a overseas entity or different 1/3 events (i.e. cybersecurity departments, IT technicians, etc). Both blockchain and card variety randomization are liable to zero-day vulnerabilities, flaws, and ranging ranges of social acceptance. This exploratory observe makes use of a structures evaluation of cashless structures to perceive and have a look at a fixed of social and technical alternatives for a robust cashless gadget that protects customers' privateness even as keeping gadget safety. The statistics amassed and evaluated may be beneficial in uncovering flaws in present day facts integrity and safety procedures. In order to create preemptive countermeasures, it might be useful to study gift and destiny strategies of dealing with privateness and facts safety withinside the contemporary-day age. In a cashless gadget, this studies identifies essential techniques to keep away from the loss of private privateness.
Keywords: cashless, society, privacy, security, data, system

References 
1. “Bitcoin - Open Source P2P Money.” n.d. Accessed December 12, 2019. https://bitcoin.org/en/.
2. Wolters, Timothy. ”‘Carry Your Credit in Your Pocket’: The Early History of the Credit Card at Bank of America and Chase Manhattan.” Enterprise & Society 1.2 (2000): 315-54. Print.
3. Mercer, Christina. n.d. “History of PayPal: 1998 to Now.” Techworld. Accessed December 12, 2019. https://www.techworld.com/picturegallery/ business/history-of-paypal-1998-now-3630386/.
4. Meadows, Donella H., and Diana Wright. Thinking in Systems: a Primer. Chelsea Green Publishing, 2015.
5. Andrew Ferguson, The rise of big data policing: surveillance, race, and the future of law enforcement,” New York; New York University Press,2017.
6. “The Rise of Big Data Policing — TechCrunch.” n.d. Accessed February 5, 2020. https://techcrunch.com/2017/10/22/the-rise-of-bigdata-

7. policing/.
8. Symanovich, Steve. “What Is a VPN?” Official Site,
9. us.norton.com/internetsecurity-privacy-what-is-a-vpn.html.
10. Swan, M. (2015). Blockchain: Blueprint for a New Economy. Sebastopol,
11. CA: OReilly Media, Inc.
12. 2019 Data Breaches - Identity Theft Resource Center. (2020). Retrieved
13. 27 March 2020, from https://www.idtheftcenter.org/2019-data-breaches/
14. ”Leverage Points: Places To Intervene In A System.” The Academy for Systems Change. N. p., 2020. Web. 3 Feb. 2020.
15. ”What’s New In The 2019 Cost Of A Data Breach Report.” Security Intelligence. N. p., 2020. Web. 6 Feb. 2020.
16. Arthur, W. (2018, March 23). Lawsuits may be key to tighter US data privacy rules. Retrieved March 26, 2020, fromhttps://dailybrief.oxan.com/Analysis/DB230635/Lawsuits-may-bekey- to-tighter-US-data-privacy-rules
17. “Leverage Points: Places to Intervene in a System.” The Academy for Systems Change, donellameadows .org/archives/leverage-points-placesto-
18. intervene-in-a-system/.
19. Singh, Simon. The Code Book: the Secrets behind Codebreaking. Ember, 2016.
20. Marwick, Alice E. “How Your Data Are Being Deeply Mined.” The New York Review of Books, www.nybooks.com/articles/2014/01/09/howyour-
21. data-are-being-deeply-mined/.
22. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
23. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
24. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
25. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
26. J. Vignesh, E. Gajendran, Sameeruddin Khan, N. Balakumar and S. R. Boselin Prabhu, “Design and implementation of hybrid cascaded energy Efficient Kogge Stone Adder” ARPN Journal of Engineering and Applied Sciences Vol 12 Issue no 21 2017.
27. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
28. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
29. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
30. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
31. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
32. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
33. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
34. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).

Machine Learning Based Solution for an Effective Credit Card Fraud Detection
Mr. V. Naveen Kumar
Pages: 44-50 | First Published: 05 Sep 2022
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Abstract
Credit card utilization has been growing with the emergence of e-trade and different programs that permit bills to be made on-line. However, whilst credit score card is stolen or any fraudulent interest takes place, it effects in monetary issues to the cardboard holders. It additionally reasons problems to the provider of playing cards. Therefore, it's far crucial to have a mechanism to stumble on fraudulent on-line transactions. In this regard, there exists many answers as located withinside the literature. One such technique is to have ancient transactions divided into fraudulent and non-fraudulent transactions. This ought to assist educate classifiers to stumble on or suspect fraud transactions. These answers focused spending conduct of clients so that you can stumble on opportunity of fraud. In the present device, statistics mining method is accompanied with random forests to version the conduct of ordinary and fraudulent transactions for credit score card fraud detection. The trouble with the version is that it really works best with dataset this is best tuned. If dataset isn't always good, its overall performance is deteriorated. To conquer this trouble, on this project, a characteristic choice set of rules is proposed to beautify the overall performance of classifier. The proposed device additionally could have comparative have a look at with more than one classifiers like Random Forests and SVM to assess the characteristic choice technique.
Keywords: Charge card pressure obvious proof, Random Forest, SVM, incorporate affirmation

Reference 
1. Y. Gmbh and K. G. Co, “Global online payment methods: Full year 2016,” Tech. Rep., 3 2016.
2. Mota, G., Fernandes, J., and Belo, O. (2014). Usage signatures analysis an alternative method for preventing fraud in E-Commerce applications. International Conference on Data Science and Advanced Analytics (pp.203-208). IEEE.
3. Behdad, M., Barone, L., Bennamoun, M., and French, T. (2012). Nature inspired techniques in the context of fraud detection. IEEE Transactions on Systems Man and Cybernetics Part C, 42(6), 1273-1290.
4. Vlasselaer, V. V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., and Snoeck, M., et al. (2015). Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48.
5. Sahin, Y., and Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. Lecture Notes in Engineering and Computer Science, 2188(1).
6. Bhattacharyya, S., Jha, S., Tharakunnel, K., and Westland, J. C. (2011). Data mining for credit card fraud: a comparative study. Decision Support Systems, 50(3), 602-613.
7. Duman, E., and Ozcelik, M. H. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38(10), 13057-13063.
8. Gupta, Shalini, and R. Johari. ” A New Framework for Credit Card Transactions Involving Mutual Authentication between Cardholder and Merchant.” International Conference on Communication Systems and Network Technologies IEEE, 2011:22-26.
9. Seyedhossein, Leila, and M. R. Hashem. ” Mining information from credit card time series for timelier fraud detection.” International Symposium on Telecommunications IEEE, 2011:619-624.
10. Shi, E., Niu, Y., Jakobsson, M., and Chow, R. (2010). Implicit Authentication through Learning User Behavior. International Conference on Information Security (Vol.6531, pp.99-113). Springer-Verlag. 11. Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018). Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). P1-6. 12. Devi Meenakshi, Janani, Gayathri. S and Mrs. Indira. (2019). CREDIT CARD FRAUD DETECTION USING RANDOM FOREST. International Research Journal of Engineering and Technology (IRJET). 6 (3), p1-5.
13. Lakshmi S V S S and Selvani Deepthi Kavila. (2018). Machine Learning for Credit Card Fraud Detection System. International Journal of Applied Engineering Research. 13 (24), p16819-16824.
14. Navanshu Khare and Saad Yunus Sait. (2018). Credit Card Fraud Detection Using Machine Learning Models and Collating Machine Learning Models. International Journal of Pure and Applied Mathematics. 118 (20), p825-838.
15. S. Monika, K. Venkataramanamma, P. Pritto Paul and M. Usha. (2019). Credit Card Fraud Detection using Random Forest Algorithm. International Journal of Research in Engineering, Science and Management. 2 (3), p1-3.
16. Vaishnave Jonnalagadda. (2019). Credit card fraud detection using Random Forest Algorithm. International Journal of Advance Research, Ideas and Innovations in Technology. 5 (2), p1-5.
17. F. Fadaei Noghani and M.- H. Moattar. (2017). Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection. Journal of AI and Data Mining. 5 (2), p235-243.
18. R.Malika. (2017). Fraud Detection using Supervised Learning Algorithms. International Journal of Advanced Research in Computer and Communication Engineering. 6 (6), p1-5.
19. Kuldeep Randhawa and Chu Kiong Loo. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE, p1-8.
20. Yashvi Jain, NamrataTiwari, ShripriyaDubey and Sarika Jain. (2019). A Comparative Analysis of Various Credit Card Fraud Detection Techniques. International Journal of Recent Technology and Engineering. 7 (2), p1-6.
21. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776 (2019). https://doi.org/10.1007/s10586-018-1757-3
22. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
23. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.

24. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
25. V.Senthilkumar , K. Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
26. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
27. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
28. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
29. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
30. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
31. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
32. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
33. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015

An Advanced Review on Human Activity Monitoring Using Artificial Intelligence
A. Venugopal Rao
Pages: 51-64 | First Published: 05 Sep 2022
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Abstract
Human interest following or notoriety appears in human-to-human exchange and social relations. Since it offers data overall the unmistakable check of an individual, their personality, and mental express, it's far trying to take out. Check of human interest progression is proposed as Human interest reputation (HAR). HAR has different applications considering its not amazing use of getting contraptions which works with cells, and camcorders, and its capacity to hold human premium pieces of data. While virtual contraptions and their applications are a focal piece of the time making, the advances in created information (PC based data) have changed the capacity to take out enormous getting data on data for right check and its discernment. This yields an unequaled data on out of nowhere making getting devices, man-made information, and experiences, the three spines of har under one housetop. There are many separate articles posted on the general credits of HAR few have in assessment all the har contraptions on same time, and few have annihilated the effect of making PC based data arranging. In our proposed assessment, an in-force depiction of the three spines of har is given covering the range from 2006 to 2021. Further, the assessment offers the pointers for an improved har plan, its reliable quality, and balance. Five biggest basic openings were: (1) HAR is 3 most focal credits of help with inclining toward contraptions, rehashed data, and applications; (2) HAR values overpowered the clinical benefits making heads or tails of; (three) cream imitated data models are in their early phase and need goliath materials for giving strong regions for the reliable structure. Also, the ones fit models serious areas of strength for basic for goliath for need, enthusiast accuracy, hypothesis, finally, meeting the longings of the endeavors without affection; (four) little craftsmanships changed into picked in flightiness prominent confirmation during moves; and (five) overall around no masterpieces has been finished in picking improvements. We finish: (a) HAR undertaking will uphold in verbalizations of the three spines of modernized contraptions, applications, and the condition of man-made data that. (b) imitated data will give major areas of strength for enormous for serious for a to the har experience withinside what's truly close. In this imaginative signs, we offer an in-power examination of latest and contemporary-day bases on moves concerning human interest type. We support an alluding to for human side interest plans and talk their benefits and obstacles. Particularly, we region human interest class strategy into titanic headings as per whether they use data from amazing modalities or in the end no more. Then, all that about rules is correspondingly analyzed into sub-depictions, which impersonate how they structure human games and what kind of sports practices they'll be spellbound by and large. Besides, we give an entire evaluation of the suitable, uninhibitedly open human side compensation class datasets and coordinate a system the necessities for a first rate human premium noticeable quality dataset. This evaluation article pushes toward typically the contemporary-day improvement made toward sensor-based and video-by and large based totally absolutely human movement check. Three added substances of human development assurance are truly centered around which harden center period, human redirection progress power plans, and endeavors from low-attestation conflicting articulation event.

References
1. Wang, J., Chen, Z., and Wu, Y. (2011b). “Action recognition with multiscale spatio-temporal contexts,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Colorado Springs, CO), 3185–3192.
2. Lichtenauer, J., Valstar, J. S. M., and Pantic, M. (2011). Cost-effective solution to synchronised audio-visual data capture using multiple sensors. Image Vis. Comput. 29, 666–680. doi:10.1016/j.imavis.2011.07.004.
3. Rahmani, H., and Mian, A. (2015). “Learning a non-linear knowledge transfer model for cross-view action recognition,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Boston, MA), 2458–2466.
4. Tian, Y., Sukthankar, R., and Shah, M. (2013). “Spatiotemporal deformable part models for action detection,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Portland, OR), 2642–2649.
5. Jain, M., Jegou, H., and Bouthemy, P. (2013). “Better exploiting motion for better action recognition,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Portland, OR), 2555–2562.
6. Kulkarni, K., Evangelidis, G., Cech, J., and Horaud, R. (2015). Continuous action recognition based on sequence alignment. Int. J. Comput. Vis. 112, 90–114. doi:10.1007/s11263-014-0758-9.
7. Samanta, S., and Chanda, B. (2014). Space-time facet model for human activity classification. IEEE Trans. Multimedia 16, 1525–1535. doi:10.1109/TMM.2014.2326734
8. Jiang, Z., Lin, Z., and Davis, L. S. (2013). A unified tree-based framework for joint action localization, recognition and segmentation. Computer. Vis. Image Understand. 117, 1345–1355. doi:10.1016/j.cviu.2012.09.008.
9. Roshtkhari, M. J., and Levine, M. D. (2013). Human activity recognition in videos using a single example. Image Vis. Comput. 31, 864–876. doi:10.1016/j.imavis.2013.08.005
10. Le, Q. V., Zou, W. Y., Yeung, S. Y., and Ng, A. Y. (2011). “Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Colorado Springs, CO), 3361–3368.
11. Sadanand, S., and Corso, J. J. (2012). “Action bank: a high-level representation of activity in video,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Providence, RI),
1234–1241.

12. Wu, X., Xu, D., Duan, L., and Luo, J. (2011). “Action recognition using context and appearance distribution features,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Colorado Springs, CO), 489–496.
13. Li, R., and Zickler, T. (2012). “Discriminative virtual views for cross-view action recognition,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Providence, RI), 2855–2862.
14. Vrigkas, M., Karavasilis, V., Nikou, C., and Kakadiaris, I. A. (2014a). Matching mixtures of curves for human action recognition. Comput. Vis. Image Understand. 119, 27–40. doi:10.1016/j.cviu.2013.11.007.
15. Yu, G., Yuan, J., and Liu, Z. (2012). “Propagative Hough voting for human activity recognition,” in Proc. European Conference on Computer Vision (Florence), 693–706.
16. Jain, M., Jegou, H., and Bouthemy, P. (2013). “Better exploiting motion for better action recognition,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Portland, OR), 2555–2562.
17. Wang, H., Kläser, A., Schmid, C., and Liu, C. L. (2013). Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103, 60–79. doi:10.1007/s11263-012-0594-8.
18. Ni, B., Moulin, P., Yang, X., and Yan, S. (2015). “Motion part regularization: improving action recognition via trajectory group selection,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Boston, MA), 3698–3706.
19. Gaidon, A., Harchaoui, Z., and Schmid, C. (2014). Activity representation with motion hierarchies. Int. J. Comput. Vis. 107, 219–238. doi:10.1007/s11263-013-0677-1.
20. Raptis, M., Kokkinos, I., and Soatto, S. (2012). “Discovering discriminative action parts from mid-level video representations,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Providence, RI), 1242–1249.
21. Yu, G., and Yuan, J. (2015). “Fast action proposals for human action detection and search,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Boston, MA), 1302–1311.
22. Li, B., Ayazoglu, M., Mao, T., Camps, O. I., and Sznaier, M. (2011). “Activity recognition using dynamic subspace angles,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Colorado Springs, CO), 3193–3200.
23. Matikainen, P., Hebert, M., and Sukthankar, R. (2009). “Trajectons: action recognition through the motion analysis of tracked features,” in Workshop on Video-Oriented Object and Event Classification, in Conjunction with ICCV (Kyoto: IEEE), 514–521.
24. Messing, R., Pal, C. J., and Kautz, H. A. (2009). “Activity recognition using the velocity histories of tracked keypoints,” in Proc. IEEE International Conference on Computer Vision (Kyoto), 104–111.
25. Tran, D., Yuan, J., and Forsyth, D. (2014a). Video event detection: from subvolume localization to spatiotemporal path search. IEEE Trans. Pattern Anal. Mach. Intell. 36, 404–416. doi:10.1109/TPAMI.2013.137.
26. Holte, M. B., Chakraborty, B., Gonzàlez, J., and Moeslund, T. B. (2012a). A local 3-D motion descriptor for multi-view human action recognition from 4-D spatio-temporal interest points. IEEE J. Sel. Top. Signal Process. 6, 553–565. doi:10.1109/JSTSP.2012.2193556.
27. Zhou, Q., and Wang, G. (2012). “Atomic action features: a new feature for action recognition,” in Proc. European Conference on Computer Vision (Firenze), 291–300.
28. Sanchez-Riera, J., Cech, J., and Horaud, R. (2012). “Action recognition robust to background clutter by using stereo vision,” in Proc. European Conference on Computer Vision (Firenze), 332–341.
29. Martinez, Julieta, Michael J. Black, and Javier Romero. "On human motion prediction using recurrent neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2891-2900. 2017.arXiv:1705.02445v1 [cs.CV] 6 May 2017.
30. Li, Chao, Qiaoyong Zhong, Di Xie, and Shiliang Pu. "Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation." arXiv preprint arXiv:1804.06055 (2018).
31. Li, Maosen& Chen, Siheng& Chen, Xu & Zhang, Ya& Wang, Yanfeng & Tian, Qi. (2021). Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence. PP. 1-1. 10.1109/TPAMI.2021.3053765.
32. N. Y. Hammerla, S. Halloran, and T. Ploetz, “Deep, convolutional, and recurrent models for human activity recognition using wearables,” 2016, arXiv:1604.08880. [Online]. Available: http://arxiv.org/abs/1604.08880
33. W. Jiang and Z. Yin, “Human activity recognition using wearable sensors by deep convolutional neural networks,” in Proc. 23rd ACM Int. Conf. Multimedia, Oct. 2015, pp. 1307–1310.

34. F. J. Ordóñez and D. Roggen, “Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition,” Sensors, vol. 16, no. 1, p. 115, 2016.
35. C. Hu, Y. Chen, L. Hu, and X. Peng, “A novel random forests based class incremental learning method for activity recognition,” Pattern Recognit., vol. 78, pp. 277–290, Jun. 2018.
36. H. Qian, S. J. Pan, B. Da, and C. Miao, “A novel distribution-embedded neural network for sensor-based activity recognition,” in Proc. IJCAI, 2019, pp. 5614–5620.
37. M. Zeng et al., “Convolutional neural networks for human activity recognition using mobile sensors,” in Proc. 6th Int. Conf. Mobile Comput., Appl. Services, 2014, pp. 197–205.
38. J. Yang, M. N. Nguyen, P. P. San, X. Li, and S. Krishnaswamy, “Deep convolutional neural networks on multichannel time series for human activity recognition,” in Proc. IJCAI, vol. 15, Buenos Aires, Argentina, 2015, pp. 3995–4001.
39. K. Wang, J. He, and L. Zhang, “Attention-based convolutional neural network for weakly labeled human activities’ recognition with wearable sensors,” IEEE Sensors J., vol. 19, no. 7, pp. 7598–7604, Sep. 2019.
40. Chen, Y., &Xue, Y. (2015, October). A deep learning approach to human activity recognition based on a single accelerometer. In 2015 IEEE international conference on systems, man, and cybernetics (pp. 1488-1492). IEEE.
41. Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications, 59, 235-244.
42. Ignatov, A. (2018). Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. Applied Soft Computing, 62, 915-922.
43. Avilés-Cruz, C., Ferreyra-Ramírez, A., Zúñiga-López, A., &VillegasCortéz, J. (2019). Coarse-fine convolutional deep-learning strategy for human activity recognition. Sensors, 19(7), 1556.
44. Z.S. Abdallah , M.M. Gaber , B. Srinivasan , S. Krishnaswamy , Activity recogni- tion with evolving data streams: areview, ACM Comput. Surv. (CSUR) 51 (4) (2018) 71 .
45. S. Ramasamy Ramamurthy , N. Roy , Recent trends in machine learning for hu- man activity recognition–a survey, Wiley Interdiscip. Rev: Data Min. Knowl. Discov. 8 (4) (2018) e1254 .
46. Tsitsoulis , N. Bourbakis , A first stage comparative survey on vision-based human activity recognition, J. AI Tools 24 (6) (2013) .
47. O.D. Lara , M.A. Labrador , A survey on human activity recognition using wear- able sensors, IEEE Commun. Surv. Tutor. 15 (3) (2012) 1192–1209 .
48. Dernbach, Stefan, Barnan Das, Narayanan C. Krishnan, Brian L. Thomas, and Diane J. Cook. "Simple and complex activity recognition through smart phones." In Intelligent Environments (IE), 2012 8th International Conference on, pp. 214-221. IEEE, 2012.
49. Bayat, Akram, Marc Pomplun, and Duc A. Tran. "A study on human activity recognition using accelerometer data from smartphones." Procedia Computer Science 34 (2014): 450-457.
50. Davis, Kadian, Evans Owusu, Vahid Bastani, Lucio Marcenaro, Jun Hu, Carlo Regazzoni, and LoeFeijs. "Activity recognition based on inertial sensors for Ambient Assisted Living." In Information Fusion (FUSION), 2016 19th International Conference on, pp. 371-378. ISIF, 2016.
51. Farkas, Ioana, and Elena Doran. "ACTIVITY RECOGNITION FROM ACCELERATION DATA COLLECTED WITH A TRI-AXIAL ACCELEROMETER." Acta TechnicaNapocensis. Electronica-Telecomunicatii 52, no. 2 (2011).
52. Schwickert, Lars, Ronald Boos, Jochen Klenk, Alan Bourke, Clemens Becker, and WiebrenZijlstra. "Inertial Sensor Based Analysis of Lie-to-Stand Transfers in Younger and Older Adults." Sensors 16, no. 8 (2016): 1277.
53. Lopez-Nava, Irvin Hussein, and Angelica Munoz-Melendez. "Complex human action recognition on daily living environments using wearable inertial sensors." In Proc. 10th EAI Int. Conf. Pervas. Comput. Technol. Healthcare. 2016.
54. Hamm, Jihun, Benjamin Stone, Mikhail Belkin, and Simon Dennis. "Automatic annotation of daily activity from smartphone-based multisensory streams." In Mobile Computing, Applications, and Services, pp. 328-342. Springer Berlin Heidelberg, 2012.
55. Kaghyan, Sahak, and HakobSarukhanyan. "Accelerometer and GPS sensor combination based system for human activity recognition." In Computer Science and Information Technologies (CSIT), 2013, pp. 1-9. IEEE, 2013.
56. Beth Logan, Jennifer Healey, MatthaiPhilipose, Emmanuel Munguia Tapia, and Stephen Intille. “A long-term evaluation of sensing modalities for activity recognition”. In Proceedings of the 9th international conference on Ubiquitous computing, pages 483–500, Berlin, Heidelberg, 2007. Springer-Verlag.
57. Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S., 2017. A survey on activity detection and classification using wearable sensors. IEEE Sensor. J. 17, 386–403.
58. Cheok, M.J., Omar, Z., Jaward, M.H., 2019. A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybernet. 10, 131–153.

59. Soliman, M.A., Alrashed, M., 2018. Anrfid based activity of daily living for elderly with alzheimer’s. Internet of Things (IoT) Technol. HealthCare 54.
60. G. Xu and F. Huang, “Viewpoint insensitive action recognition using envelop shape,” in Computer Vision--Asian Conference on Computer Vision 2007, pp. 477–486, Springer, Tokyo, Japan, 2007.
61. D. Weinland, R. Ronfard, and E. Boyer, “Free viewpoint action recognition using motion history volumes,” Computer Vision and Image Understanding, vol. 104, pp. 249–257, 2006.View at: Google Scholar
62. M. D. Rodriguez, J. Ahmed, and M. Shah, “Action mach a spatio-temporal maximum average correlation height filter for action recognition,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, Anchorage, AK, USA, 2008
63. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
64. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776 (2019).
65. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
66. Dr.VigneshJanarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.ManojSomeswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
67. V.Senthilkumar , K. Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
68. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
69. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
70. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
71. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
72. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
73. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
74. Umamaheswari, M., &Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
75. Babu, G., &Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
76. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015