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Balajee Jeyakumar

HoD & Associate Professor · Mother Theresa Institute of Engineering and Technology

https://researchid.co/jbbala04
@mtieat.org
46Scopus Publications
726Google Scholar Citations
13Google Scholar h-index
20Google Scholar i10-index

Research Interests

Data Mining, Big Data, Machine Learning, Deep Learning, IoT, Quantum Computing

Biography

Dr. Balajee Jeyakumar is currently serving as Associate Professor and Head of the Department of CSE (Artificial Intelligence) at Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh, India. He completed his Undergraduate degree from University of Madras and his Postgraduate degree from Vellore Institute of Technology,Vellore where he was also awarded his Ph.D. He has published more than 40 research papers in reputed Scopus and SCI-indexed journals and holds over 10 national and international patents. His research interests include Machine Learning, Deep Learning, Internet of Things (IoT), and Big Data Analytics. He actively guides UG and PG students in various academic and research projects, fostering innovation and research excellence in Artificial Intelligence and emerging technologies.

Education

MCA ., MBA., PhD

Recent Scopus Publications

  1. Intelligent Interventions: Practical Applications of Machine Learning for DataDriven Decision-Making in Healthcare
    Machine Learning in Healthcare Data Driven Decisions Predictive Modelling Personalized Medicine, 2026
  2. 6G-Enabled Edge-Enhanced Functional Chain Scheduling for Intelligent Medical Emergency Communication in Smart Healthcare
    IEEE Communications Standards Magazine, 2026
  3. Secure AI-Driven Framework for Predicting Drug Toxicity Using Computational Modeling
    2026 International Conference on ICT and Photonics Ictp 2026 Advancing ICT Photonics for A Smarter Sustainable World Proceedings, 2026
  4. Securing the Future: AI-Powered Weapon Systems, Ethics, and Adversarial Defense
    Robotics in Weaponry Using Machine Learning and Engineering, 2026
  5. Load-Aware Fog-Based Workflow Scheduling for Reliable IIOT Connectivity in Industry 4.0
    IEEE Communications Standards Magazine, 2026

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