Dr. Anima Pramanik

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Dr. Anima Pramanik

Assistant Professor

B.Tech, M.Tech, PhD, PDF
animapramanik@tsm.ac.in

Biography
Publications
Courses
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Biography

Dr. Anima is currently serving as an Assistant Professor in the Systems & Analytics area at Thiagarajar School of Management (TSM), Madurai, India (August 2025 – present). Prior to joining TSM, She worked as a Research Associate in the Information Systems area at the Indian Institute of Management Ahmedabad (IIMA) and served as a Visiting Scientist at the Indian Statistical Institute (ISI), Kolkata.

Dr. Anima earned a Ph.D. from the Department of Industrial and Systems Engineering, Indian Institute of Technology (IIT) Kharagpur. He holds an M.Tech in Mechatronics Engineering from the National Institute of Technical Teacher’s Training and Research (NITTTR), Kolkata, and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology, West Bengal, India. His research interests include Computer Vision, Intelligent Transportation Systems, and Recommendation Systems. He has served as a reviewer for 25 peer-reviewed top-tier international journals and has been a member of the IISE professional body. She has also participated in academic collaborations and international conferences and has traveled to Thailand (2022 & 2023) and Singapore (2024) in connection with research and scholarly activities.

Publications

Pramanik, A., Sarker S., Sarkar S., & Pal S.K. (2025). Real-time fall detection on roads using transfer learning-based granulated Bi-LSTM. Knowledge-Based Systems, 113038.
Pramanik, A., Sarker S., Sarkar S., & Bose I. (2024). FGI-CogViT: Fuzzy Granule-based Interpretable Cognitive Vision Transformer for Early Detection of Alzheimer’s Disease using MRI Scan Images. Information Systems Frontier, 1-35.
Pramanik, A., Sarkar S., & Pal, S. K. (2023). Video Surveillance-based fall detection system using object-level feature thresholding and Z-numbers. Knowledge Based Systems, 280, 110992.
Sarkar, S., Pramanik, A., & Maiti, J. (2023). An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk prediction. Engineering Applications of Artificial Intelligence, 117, 105515.
Pramanik, A., Pal, S. K., Maiti, J., & Mitra, P. (2022). Traffic Anomaly Detection and Video Summarization using Spatio-Temporal Rough Fuzzy Granulation using Z-numbers. IEEE Transactions on Intelligent Transportation Systems.
Sarkar, S., Ejaz, N., Maiti, J. & Pramanik, A. (2022). An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis. Neural Computing & Applications, 34, 9661-9687.
Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2021). COVID-19 outbreak: A data-driven optimization model for allocation of patients. Computers & Industrial Engineering, 161, 107675.
Pramanik, A., Sarkar, S., Maiti, J., & Mitra, P. (2021). RT-GSOM: Rough tolerance growing self-organizing map. Information Sciences, 566, 19-37.
Pramanik, A., Sarkar, S., & Maiti, J. (2021). A real-time video surveillance system for traffic pre-events detection. Accident Analysis & Prevention, 154, 106019.
Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. (2021). Deep learning in multi-object detection and tracking: state of the art. Applied Intelligence, 51(9), 6400-6429.
Pramanik, A., Pal, S. K., Maiti, J., & Mitra, P. (2021). Granulated RCNN and multiclass deep sort for multi-object detection and tracking. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(1), 171-181.
Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2020). Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data. Safety science, 125, 104616.

Courses

Corporate Finance

others

Professional Certifications:

Memberships: Institute of Industrial & Systems Engineers (IISE)
Workshops Organized/Co-ordinated/Resource person for FDP/MDP

Conferences

Sarkar, S., & Pramanik, A. (2023, March). Quantifying data imbalance using Exponential f-Divergence. In 2023 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) (pp. 403-408). IEEE.

Pramanik, A., Venkatagiri, K., Sarkar, S., & Pal, S. K. (2022, December). Deep Network-based Slow Feature Analysis for Human Fall Detection. In The International Conference on Computational Modelling, Simulation and Optimization (ICCMSO-2022), Bangkok, Thailand. IEEE.

Pramanik, A., Djeddi, C., Sarkar, S., & Maiti, J. (2020, October). Region proposal and object detection using HoG-based CNN feature map. In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) (pp. 1-5). IEEE.
Pramanik, A., Gorai, A., Sarkar, S., & Gupta, P. (2018, December). A Novel Feature Extraction-based Human Identification Approach using 2D Ear Biometric. In 2018 IEEE Applied Signal Processing Conference (ASPCON) (pp. 168-172). IEEE.