Dr. Souvik Sengupta is a Professor in the Department of Computer Science & Engineering in Aliah University. He has served as the Head of the Department during 2021-2023. He has more than 20 years of teaching experience in Computer Science and Engineering. Dr. Sengupta received his PhD from the University of Calcutta and MTech from WBUT. He is a recipient of SERB-TARE project grant and fellowship from the Department of Science and Technology, Govt. of India. His research area includes: Machine Learning, Natural Language Processing, Medical Image Processing, Quantum Compiuting, and Learning Technology.

Conference Papers Published

Selected Conference Papers:
 

1. Haldar, S., Sengupta, S., & Das, A. K. (2025, July). RUBRA: An Agentic AI System for Automatic Short Answer Grading Using LLMs and RAG. In 2025 International Conference on Computing, Intelligence, and Application (CIACON) (pp. 1-6). IEEE.
2. Ghosh, P. S., & Sengupta, S. (2024, November). SentiGrad: A New Hindi-English Code Mixed Sentiment Analysis Dataset with Preliminary Results and Open Challenges. In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon) (pp. 1-5). IEEE.
3. Haldar, S., Sengupta, S., & Das, A. K. (2024, March). A Comparative Assessment of the Contemporary Models for Automatic Short Answer Grading. In International Conference on Computational Intelligence in Pattern Recognition (pp. 583-594). Singapore: Springer Nature Singapore.
4. Mullick, A., & Sengupta, S. (2024, February). Machine Learning-Based Analysis of IoT Healthcare Data-A Review of Contemporary Research. In 2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE) (pp. 1-9). IEEE.
5. Iqbal, M., Ogundokun, R. O., Bhanja, S., Sengupta, S., & Das, A. (2024, January). Machine Learning for Dementia and Alzheimer’s Disease Prediction. In International Conference on Emerging Applications of Information Technology (pp. 271-291). Singapore: Springer Nature Singapore.
6. Sengupta, S., Mandal, M., & Das, A. K. (2024, January). A BERT and Text Graph Neural Network-Based Fusion Model for Automated Short Answer Grading. In International Conference on Emerging Applications of Information Technology (pp. 155-164). Singapore: Springer Nature Singapore.
7. Sengupta, S., & Das, A. K. (2023, November). Automated Mapping of Course Outcomes to Program Outcomes using Natural Language Processing and Machine Learning. In 2023 IEEE 3rd Applied Signal Processing Conference (ASPCON) (pp. 44-48). IEEE.
8. Bhadra, S. R., & Sengupta, S. (2023, January). A Review on Machine Learning and Deep Learning Based Approaches in Detection and Grading of Alzheimer’s Disease. In International Conference on Computational Intelligence in Communications and Business Analytics (pp. 1-13). Cham: Springer Nature Switzerland.
9. Mukherjee, N., & Sengupta, S. (2022). Comparing different preprocessing techniques for the classification tasks in diabetic retinopathy from fundus images. In Proceedings of International Conference on Advanced Computing Applications: ICACA 2021 (pp. 601-612). Springer Singapore.
10. Sengupta, S. (2020). Forecasting the Peak of COVID-19 Daily Cases in India Using Time Series Analysis and Multivariate LSTM.
11. Sengupta, S., & Dasgupta, R. (2015). A VDM-based Approach for Specifying and Testing Requirements of Web-applications. Procedia Computer Science, 46, 774-783.
12. Sengupta, S., & Dasgupta, R. (2014). Towards Developing Requirement Analysis Model of iLMS., INSAE conference , pp 154-162
13. Sengupta, S., & Dasgupta, R. (2013, February). Integration of functional and interface requirements of an web based software: a VDM based formal approach. In Proceedings of IASTED International Conference on Software Engineering (Vol. 10, pp. 2013-796).
14. Sengupta, S., & Dasgupta, R. (2012). Identifying, analysing and testing of software requirements in learning management system. In Proceedings of 7th International Conference on Virtual Learning (ICVL).
15. Sengupta, S., & Dasgupta, R. (2010). A Data Mining Approach to Determine an Efficient Learning Path. In EEE 2010: proceedings of the 2010 international conference on e-learning, e-business, enterprise information systems, & e-government (Las Vegas NV, July 12-15, 2010) (pp. 59-62).

Journal Papers Published

Selected Journal Publication:

1. Mukherjee, N., Sengupta, S., Ahmed, M. N., Yaqoob, S. I., Hussain, M. R., & Zamani, A. T. (2025). Bi-directional Hybrid Attention Feature Pyramid Network for Detecting Diabetic Macular Edema in Retinal Fundus Images. IEEE Access.
2. Bakshi, A., Gangopadhyay, K., Basak, S., De, R. K., Sengupta, S., & Dasgupta, A. (2025). Integrating state-space modeling, parameter estimation, deep learning, and docking techniques in drug repurposing: a case study on COVID-19 cytokine storm. Journal of the American Medical Informatics Association, ocaf035.
3. Haldar, S., Sengupta, S., & Das, A. K. (2025). Personalized Learning Path Recommendation using Graph Reinforcement Learning. Procedia Computer Science, 258, 3480-3489.
4. Bakshi, A., Sengupta, S., De, R. K., & Dasgupta, A. (2025). Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference. Expert Systems with Applications, 259, 125339.
5. Sengupta, S., Sarkar, B., Ajmi, I., & Das, A. (2024). Optimizing dosage in linear accelerator based on predictive analysis of radiation induced skin toxicity using machine learning techniques. Microsystem Technologies, 1-12.
6. Sengupta, S. (2024). Legislative Text Analysis from Judicial Case Reports Using Machine Learning. SN Computer Science, 5(5), 443.
7. Mukherjee, N., & Sengupta, S. (2024). Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey. Multimedia Tools and Applications, 83(14), 43115-43175.
8. Mukherjee, N., & Sengupta, S. (2023). A hybrid cnn model for deep feature extraction for diabetic retinopathy detection and gradation. International Journal on Artificial Intelligence Tools, 32(08), 2350036.
9. Sengupta, S., Pal, S., & Pramanik, P. K. D. (2023). Mapping Learner's Query to Learning Objects using Topic Modeling and Machine Learning Techniques. Scalable Computing: Practice and Experience, 24(4), 909-917.
10. Mukherjee, N., & Sengupta, S. (2023). Comparing deep feature extraction strategies for diabetic retinopathy stage classification from fundus images. Arabian Journal for Science and Engineering, 48(8), 10335-10354.
11. Sengupta, S. (2023). Towards finding a minimal set of features for predicting students’ performance using educational data mining. IJ Modern Education and Computer Science, 3(1), 44-54.
12. Sengupta, S. (2022). Possibilities and challenges of online education in India during the COVID-19 pandemic. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 17(4), 1-11.
13. Sengupta, S., & Dave, V. (2022). Predicting applicable law sections from judicial case reports using legislative text analysis with machine learning. Journal of Computational Social Science, 5(1), 503-516.
14. Datta, S., & Sengupta, S. (2018). A Review on the Adaptive Features of E-Learning. International Journal of Learning and Teaching, 4(4), 277-284.
15. Sengupta, S., & Dasgupta, R. (2017). Architectural design of a LMS with LTSA-conformance. Education and Information Technologies, 22, 271-296.
16. Sengupta, S., & Dasgupta, R. (2017). LTSA conformance testing to architectural design of LMS using ontology. Education and Information Technologies, 22, 3017-3035.
17. Sengupta, S., & Dasgupta, R. (2015). Use of semi-formal and formal methods in requirement engineering of ILMS. ACM SIGSOFT Software Engineering Notes, 40(1), 1-13.
18. Sengupta, S., Mukherjee, B., Bhattacharya, S., & Dasgupta, R. (2012). OLAP based Scaffolding to support Personalized Synchronous e-Learning. International Journal of Managing Information Technology, 4(3), 73.
19. Sengupta, S., Sahu, S., & Dasgupta, R. (2012). Construction of learning path using ant colony optimization from a frequent pattern graph. International Journal of Computer Science Issues, Vol. 8, Issue 6, No 1, 2011,  pp. 314-321.
20. Sengupta, S., Pal, S., & Banerjee, N. (2012). A comparison algorithm to check LTSA Layer 1 and SCORM compliance in e-Learning sites. Journal of Computing, Volume 2, Issue 9, 2010, pp 11-16.
21. Sengupta, S., Chaki, N., & Dasgupta, R. (2012). Learners' Quanta based Design of a Learning Management System. International Journal of Education & Information Technologies, Issue 1, Volume 3, 2009, pp 67-74

Books Published

Book Chapters:

1. Sardar, T. H., Khatun, A., Sengupta, S., Alam, Y., & Ara, T. (2024). Machine learning in the healthcare sector and the biomedical big data: Techniques, applications, and challenges. Big Data Computing, 336-352.
2. Mukherjee, N., & Sengupta, S. (2021). In Search for the Optimal Preprocessing Technique for Deep Learning Based Diabetic Retinopathy Stage Classification from Fundus Images.
3. Sengupta, S., & Dasgupta, R. (2015). Using Semiformal and Formal Methods in Software Design: An Integrated Approach for Intelligent Learning Management System. Applied Computation and Security Systems: Volume Two, 53-65.

Project Works

Funding Agency: Anusandhan National Research Foundation (ANRF)
Project Title: Natural Language Processing and Machine Learning based enhancement in Education Technologies
Funding Cost: 18.3 lacs 
Duration: 36 months [27 Oct 2022 - 26 Oct 2025]
Collaboration: IIEST, Shibpur
Outcome: 
Deliverable 
A software for automated CO-PO mapping with Bloom’s Taxonomy
A novel methodology for automated short answer grading
Publications
Journal: 2
Conference: 5
Patent : 1 (Applied)