View Profile

Sanjay Chakraborty

Assistant Professor · JIS University

https://researchid.co/sanjaychakraborty
@jisuniversity.ac.in
65Scopus Publications
2076Google Scholar Citations
20Google Scholar h-index
31Google Scholar i10-index

Research Interests

Quantum Computing, Machine learning, Data Mining

Biography

Dr. Sanjay Chakraborty is currently working as an Assistant Professor at JIS University. He completed his Ph.D. thesis at the University of Calcutta. He completed his MTech from the National Institute of Technology, Raipur. He completed his B-Tech from the West Bengal University of Technology. He has published 54 research papers in various international journals, conferences, and book chapters. He has published two international authored books. His areas of interest are Data Mining & machine learning, feature subset selection, and quantum computing. He has a total of 11 years of teaching and research experience. He worked as a reviewer in several international conferences and SCI, SCOPUS journals including BMC Medical Informatics and Decision Making Journal, Scientific Reports Springer Nature, IEEE Transactions on Computational Social Systems, International Journal of Machine Learning and Cybernetics, Concurrency and Computation: Practice and Experience Journal Wiley, Egyptian Informat

Education

B.Tech from W.B.U.T (MAKAUT) in IT, M.Tech from NIT Raipur and Ph.D. (Tech) from University of Calcutta

Recent Scopus Publications

  1. Generative artificial intelligence in fifth-generation education systems: A systematic review
    Engineering Applications of Artificial Intelligence, 2026
  2. Exploring Deep Learning Models for COVID-19 Detection from CT-Scan and X-Ray Images
    Artificial Intelligence in Healthcare Trends Applications and Future Directions, 2026
  3. Advancing delignification in the pulp and paper industry: Multivariate time series forecasting, explainability, and simulation analysis
    Journal of Intelligent Manufacturing, 2026
  4. Scaling transformers for time series forecasting: do pretrained large models outperform small-scale alternatives?
    Artificial Intelligence Review, 2026
  5. Advancing EEG Signal Classification Using Hybrid Deep Learning Architectures and Kolmogorov–Arnold Networks
    Lecture Notes in Networks and Systems, 2026

Links