Sen, N., Chattopadhyay, S., Tripathy, R. M., Prasad, D. K., & Sekh, A. A. (2026). Ai-driven disaster management: a comprehensive review of deep learning for social media and geospatial data integration. Artificial Intelligence Review, 1, 3.

Basu, S., Adhikary, D., Ghosh, K., Chattopadhyay, S., Deb, S., Mondal, R., ... & Benito-León, J. (2026). Machine Learning and Deep Learning Frameworks for Human–Virus Protein–Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges. International Journal of Molecular Sciences, 27(13), 6034.

Chakraborty, T., Chattopadhyay, S., Das, S., Naik, S. M., & Hens, C. (2025). A compounded Burr probability distribution for fitting heavy-tailed data with applications to biological networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 35(6).

Yadav, D. K., Chattopadhyay, S., Tripathy, D. P., Mishra, P., & Singh, P. (2025). Enhanced slope stability prediction using ensemble machine learning techniques. Scientific Reports, 15(1), 7302.

Chattopadhyay, S., Chowdhury, A., & Ghosh, K. (2023). Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems. In Recommender Systems (pp. 203-233). CRC Press.

Chakraborty, T., Chattopadhyay, S., Das, S., Kumar, U., & Senthilnath, J. (2022). Searching for heavy-tailed probability distributions for modeling real-world complex networks. IEEE Access, 10, 115092-115107.

Chakraborty, T., Das, S., & Chattopadhyay, S. (2022). A new method for generalizing burr and related distributions. Mathematica Slovaca, 72(1), 241-264.

Chattopadhyay, S., Basu, T., Das, A. K., Ghosh, K., & Murthy, L. C. (2021). Towards effective discovery of natural communities in complex networks and implications in e-commerce. Electronic Commerce Research, 21(4), 917-954.

Chattopadhyay, S., Chakraborty, T., Ghosh, K., & Das, A. K. (2021). Modified Lomax model: A heavy-tailed distribution for fitting large-scale real-world complex networks. Social Network Analysis and Mining, 11(1), 43.

Chattopadhyay, S., & Ganguly, D. (2021). Node2vec with weak supervision on community structures. Pattern Recognition Letters, 150, 147-154.

Chattopadhyay, S., Das, A. K., & Ghosh, K. (2020). Finding patterns in the degree distribution of real-world complex networks: going beyond power law. Pattern Analysis and Applications, 23, 913-932.

Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2020). Radial basis neural tree model for improving waste recovery process in a paper industry. Applied Stochastic Models in Business and Industry, 36(1), 49-61.

Chakraborty, T., Chakraborty, A. K., & Chattopadhyay, S. (2019). A novel distribution-free hybrid regression model for manufacturing process efficiency improvement. Journal of Computational and Applied Mathematics, 362, 130-142.

Chattopadhyay, S., Basu, T., Das, A. K., Ghosh, K., & Murthy, C. A. (2019). A similarity based generalized modularity measure towards effective community discovery in complex networks. Physica A: Statistical Mechanics and its Applications, 527, 121338.

Chakraborty, T., Chattopadhyay, S., & Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, 527, 121266.

Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2018). A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. Opsearch, 55, 434-446.

Chattopadhyay, S., & Murthy, C. A. (2017). Generation of power-law networks by employing various attachment schemes: Structural properties emulating real world networks. Information Sciences, 397, 219-242.

Chattopadhyay, S., Murthy, C. A., & Pal, S. K. (2014). Fitting truncated geometric distributions in large scale real world networks. Theoretical Computer Science, 551, 22-38.

Misra, C. and Chattopadhyay, S. Graph Representation of RaagBase: A Unique Dataset for Hindustani Music, Multimedia Tools and Appliocations

A. Santra, V. Gupta, S. Sahoo, S. Chattopadhyay, “Analysis of Neuropsychiatric Changes and Cognitive Dysfunction in Association with Alzheimer’s Disease”, 2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET), Bhubaneswar, India, 2025, pp 1-7, doi: https://doi.org/10.1109/ICAIET65052.2025.11211032

N. Sen, S. Chattopadhyay, R. M. Tripathy, A. A. Sekh, (2025) “Construction and Analysis of Disaster Image Datasets from Social Media for Open World Few Shot Settings”, 2025 IEEE-International Conference on Artificial intelligence and Emerging Technologies (ICAIET), Bhubaneswar, India, 2025, pp 1-5, doi: https://doi.org/10.1109/ICAIET65052.2025.11211268, IEEE

Chowdhury, A., Chattopadhyay, S., Ghosh, K., & Das, G. (2023, December). NeuroANATOP: An Effective Tool to Assess the Progression of Alzheimers Disease in Human Brain Networks. In 2023 IEEE International Conference on Knowledge Graph (ICKG) (pp. 108-116). IEEE.

Chowdhury, A., Chattopadhyay, S., & Ghosh, K. (2023, November). Analyzing the Progression of Alzheimers Disease in Human Brain Networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (pp. 415-418).

Bhattacharyya, A., Chattopadhyay, S., Pattnaik, M., & Chakraborty, T. (2021, July). Theta autoregressive neural network: A hybrid time series model for pandemic forecasting. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

Chattopadhyay, S., Chakraborty, T., Ghosh, K., & Das, A. K. (2021, January). Uncovering patterns in heavy-tailed networks: A journey beyond scale-free. In Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD) (pp. 136-144).

C. Misra, and S. Chattopadhyay, SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet, In Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research (CMMR 23), Tokyo, Japan, 2023, pp. 257-264.

Chattopadhyay, S., Chowdhury, A., & Ghosh, K. (2023). Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems. In Recommender Systems (pp. 203-233). CRC Press.