Parthasarathy Velusamy
Karpagam Academy of Higher Education, Anna University, Chennai, College of Engineering Guindy, Government College of Technology
0000-0001-6514-3959
About
Prof. Dr. V. Parthasarathy graduated with a Bachelor of Engineering in Electrical and Electronics Engineering from Government College of Technology, Coimbatore. Further, he has completed his post-graduation for Master of Engineering in Computer Science and Engineering from College of Engineering, Guindy, Anna University and Ph.D. in Information and Communication engineering from Anna University, Tamilnadu, India. He is presently working as a Professor & Dean (R&D and Industry Relations) at Karpagam Academy of Higher Education (Deemed to be University), Tamilnadu, India. His research interests include the Internet of Things, Wireless Sensor Network, Biomedical Informatics, Next-Generation Communication Networks, and Artificial Intelligence. He has published over 100 scientific papers in reputed journals and conferences, an Australian patent on sensor networks to his credit, and co-authored a book chapter. He is an Associate Editor/editorial board member and guest editor of international journals. Besides, he is a reviewer in several peer-reviewed journals. He serves/ served in many conferences as Chair / Co-Chair and delivered keynotes. He is a senior member in IEEE and a member of many professional societies such as ISTE, ACM, OSA, etc. He is the recipient of the Indian Government’s Fund for Improvement of Science and Technology (FIST) project and a Co-investigator for the project grant from the Defense Research and Development Organization (DRDO).
Employment
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Karpagam Academy of Higher Education Dean (R&D and Industry Relations)2020 - Present
Education
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Anna University, Chennai Ph.D2006 - 2010
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College of Engineering Guindy M.E (Computer Science and Engineering)
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Government College of Technology B.E (Electrical and Electronics Engineering)
Projects & Funding
Projects & funding information is unavailable.
Publications (85)
- AIM-Net: A Resource-Efficient Self-Supervised Learning Model for Automated Red Spider Mite Severity Classification in Tea Cultivation Save
- ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs Save
- Multi-Disease Recognition in Tea Plants By Evaluating the Performance of Yolo Models Save
- Secure state estimation-based attack-tolerant voltage control for islanded microgrid in energy internet scenario with multiple attacks Save
- A Dynamic Approach to Optimizing Cloud Resource Allocation for Enhanced E-commerce Performance Save
- Dense-BiGRU: Densely Connected Bi-directional Gated Recurrent Unit based Heart Failure Detection using ECG Signal Save
- Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks Save
- Noble metals functionalized reduced graphene oxide as an efficient optical limiter: a combined experimental and theoretical investigation Save
- Virtual resurrection leveraging 3D modeling for digital preservation of an ancient educational institution Save
- WSPNET:Weakly Supervised Learning With Pesudomask For Tea Pest Detection Save