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M. Vijaya Prabhagar

Assistant Professor, IT Systems & Analytics

Dr. M. Vijaya Prabhagar is an Assistant Professor of IT systems and Analytics at the Indian Institute of Management (IIM) Jammu. Before joining IIM Jammu, he worked as an Assistant Professor at SRM University Andhra Pradesh. He received his Doctoral degree in Data Analytics and Machine Learning from the National Institute of Technology, Tiruchirappalli. He also holds an M.E. degree in Industrial Engineering from Thiagarajar College of Engineering, Madurai, Tamilnadu. He is a passionate academician and researcher in Machine learning and Deep Learning, with more than two years of accomplished experience in teaching and research. His research interests predominantly revolve around the Performance Improvement of Advanced Machine learning Techniques like Self-Organizing Map (SOM), Artificial Neural Networks (ANN), Random Forest, Long Short Term Memory (LSTM) and Rough k-means Clustering. He has published research articles in reputed international journals like Neural Computing and Applications, Soft Computing, Multimedia Tools and Applications, Benchmarking: An International Journal and Business Process Management. 

Ph.D. – National Institute of Technology, Tiruchirappalli.
M.E. (Industrial Engineering) – Thiagarajar College of Engineering, Madurai.
B.E. (Mechanical Engineering) – Alagappa Chettiar Government College of Engineering & Technology, Karaikudi.

IIM Jammu (Current Employer)
SRM University, Andhra Pradesh (Ex-Employer)

  • Murugesan, V., V, V. . & N, V. (2024). Does ESG disclosure really influence the firm performance? Evidence from India. The Quarterly Review of Economics and Finance, (ABDC,SCI/SSCI/ESCI), DOI : https://doi.org/10.1016/j.qref.2024.03.008
  • Murugesan, V. (2023). ’Impact of new seed and performance criteria in proposed rough k-means clustering. Multimedia Tools and Applications, (SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1007/s11042-023-14414-0
  • Viswanathan, ., Murugesan, P., Murugesan, V. & Vilvanathan, L. (2023). An Application to Rate Banks using a New Variant of Agglomerative Clustering Algorithm. International Journal of Business Performance Management, (SCI/SSCI/ESCI,Scopus), DOI : Accepted
  • Ramachandran, R., Babu, V. & Murugesan, V. (2023). Human Resource Analytics Revisited: A Systematic Literature Review of adoption, global acceptance and implementation. Benchmarking: An International Journal, (ABDC,SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1108/BIJ-04-2022-0272
  • R, R., Babu, V. & Murugesan, V. (2022). The Role of Blockchain technology in the Process of Decision-making in Human Resource Management: A Review and Future Research Agenda. Business Process Management, 29(1), 116-139. (ABDC,SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1108/BPMJ-07-2022-0351
  • Murugesan, V. & M, P. (2021). Some measures to impact on the performance of Self-Organizing map. Multimedia Tools and Applications, 80(17), 26381-26409. (SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1007/s11042-021-10912-1
  • Murugesan, V. & M, P. (2020). Development of new agglomerative and performance evaluation models for classification. Neural Computing and Applications, 32(7), 2589-2600. (SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1007/s00521-019-04297-4
  • Murugesan, V. & M, P. (2020). A new initialization and performance measure for rough k-means clustering. Soft Computing, 24(15), 11605-11619. (SCI/SSCI/ESCI,Scopus), DOI : https://doi.org/10.1007/s00500-019-04625-9
  • Murugesan, V., M, P., Takahashi, C., Kundu, S. & Narayanan, T., (2019). Boron-doped graphene quantum dots: an efficient photoanode for a dye-sensitized solar cell. New Journal of Chemistry, 43(36), 14313-14319. (SCI/SSCI/ESCI), DOI : https://doi.org/10.1039/C9NJ00052F
  • V., Murugesan, V. & N, V. (2023). “Forecasting of Environmental Social and Governance Disclosures and Firm Performance using dynamic panel regression and LSTM models – Review. Role of Capital Markets for Sustainable Growth of Economy NISM, India Mumbai:
  • V., R. & V. (2023). Development of Modified Random Forest Algorithm to predict the Employee Attrition. International Conference on Emerging Trends in Operations and Analytics (ICETOA) T A Pai Management Institute (TAPMI) Karnataka:
  • R, R., Babu, V., Murugesan, V. & (2023). 'Blockchain Fragmented Clusters for Advancing HR Saliency: The Case of India Book , 'Emerging Issues and Trends in Indian Business and Economics (pp. 90-120 ). World Scientific, Singapore

Big Data Analytics

Cloud and IoT Analytics


Business Analytics

Business Intelligence & Data Modelling

Information Systems for Managers

Emerging Technology in Business Management

Executive Program in Financial Analytics