KATHIRAVAN SRINIVASAN (SMIEEE) is an Associate Professor in the School of Computer Science and Engineering at Vellore Institute of Technology (VIT), Vellore, India. He was previously working as a Faculty/Lecturer in the Department of Computer Science and Information Engineering and as the Deputy Director of the Office of International Affairs at National Ilan University, Taiwan. He received his Ph.D. in Information and Communication Engineering, M.E. in Communication Systems Engineering (Distinction), and B.E. in Electronics and Communication Engineering from Anna University, Chennai, India. In 2016, he received the Best Service Award as the Deputy Director at the Office of International Affairs, National Ilan University. He is presently serving as the Editor of IEEE Future Directions and KSII Transactions on Internet and Information Systems (TIIS), PeerJ Computer Science and Associate Editor for IEEE Access, IET Networks, Elsevier Array, and Journal of Internet Technology. He is the guest editor for MDPI Algorithms, Future Internet, Journal of Mobile Multimedia, International Journal of Distributed Sensor Networks, and Mobile Information Systems. He was awarded the IEEE Access Outstanding Associate Editor – 2020 and 2019. His research interests include Machine Learning, Artificial Intelligence, Deep learning, Communication Systems & Networks, Computer Vision & Multimedia, Data Analytics, and Feature Engineering.
A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification
2022 | Frontiers Media S.ABiometric Authentication for Intelligent and Privacy-Preserving Healthcare Systems
2022 | HindawiLeveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions
2022 | Multidisciplinary Digital Publishing InstituteA Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks
2022 | Multidisciplinary Digital Publishing InstituteA Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques
2022 | Multidisciplinary Digital Publishing Institute