Rakesh Kumar Mahendran

About

Dr.Rakesh Kumar Mahendran is an Assistant Professor & Coordinator-Research, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India. He is actively involved in qualitative and quantitative research in the area of Science, Engineering specializing in Artificial Intelligence of Medical Things. His active research area is in Deep Learning with possible applications in the field of Biomedical Applications includes Medical Imaging, EEG and ECG, Neural Networks, Computer-Aided Diagnosis, Clinical applications- Outcome/disease prediction/detection, Machine Learning, Health and Medical Informatics. He received M.E degree in Computer Science Engineering with Specialization in Networks from Anna University, India, in 2015; and Ph.D. in Deep Learning for Cardiovascular Disease Prediction from Anna University in 2023, India and also, He is functioning as a Managing Editor in International Journal of Wireless and Ad Hoc Communication (IJWAC), Associate Editor in Journal of Intelligent Systems and Internet of Things (JISIoT), Human Research in Rehabilitation (HRR), Cardiometry and also, he functioning as Program Committee Member in several conferences like AIHC 2023-IEEE-IRI 2023, DaMi 2022, MLT 2021, BDIoT 2021, BMLI 2020.etc.

Employment

• Saveetha Engineering College Associate Professor
  2025 - Present
• Rajalakshmi Engineering College Assistant Professor
  2023 - 2025

Education

• Vel Tech Multitech Dr.Rangarajan Dr.Sakuthala Engineering College M.E- CSE Networks
  2013 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (61)

• AI-Driven Dynamic Allocation and Management Optimization for EV Charging Stations
  IEEE Transactions on Intelligent Transportation Systems 2026 DOI: 10.1109/TITS.2025.3579924
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• C2DEEP-OT: Utilizing Multi-Agent Deep Reinforcement Learning Algorithm and Optimized Attentive Transformer Network for Cervical Cancer Detection
  Information Sciences 2026 DOI: 10.1016/j.ins.2025.123047
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• Cry-P3CNN: audio signal processing based neonatal cry classification using improved reinforcement learning
  Multimedia Tools and Applications 2026 DOI: 10.1007/s11042-026-21780-y
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• HEAL-LLMF: A hierarchical explainable AI framework using LLM fusion for multi-modal and personalized healthcare decision making
  Information Systems 2026 DOI: 10.1016/j.is.2026.102696
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• Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models
  Frontiers in Computational Neuroscience 2026 DOI: 10.3389/fncom.2026.1808274
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• Tri-STANet: An advanced framework integrating dual axis transformer and morphological encoding for enhanced AS detection
  Expert Systems with Applications 2026 DOI: 10.1016/j.eswa.2025.129372
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• VITAL-DR: Vision transformer adaptive learning for diabetes retinopathy (DR) detection and classification from fundus image
  Biomedical Signal Processing and Control 2026 DOI: 10.1016/j.bspc.2026.110567
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• MORA-Net: Multi-Organ Relational Attention Network for Abdominal Organs Segmentation from MRI images
  3rd International Conference on Emerging Research in Computational Science Icercs 2025 2025 DOI: 10.1109/ICERCS65898.2025.11580473
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• Fed-IoMT-Block: A Privacy-Preserving Framework for Secure Federated Learning in Consumer-Centric Internet of Medical Things
  IEEE Transactions on Consumer Electronics 2025 DOI: 10.1109/TCE.2025.3582794
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• MedHub - LLM-based Healthcare System
  Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2025 2025 DOI: 10.1109/ICAISS61471.2025.11042126
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