Publications (Year-wise):
1. Please refer to my Google Scholar Profile, RGate Profile, and GitHub repository for recent updates
2. Classifications of Publications: Journal Publication | Conference Publication | Book Chapter
3. * Indicates Joint First Authors
1. Please refer to my Google Scholar Profile, RGate Profile, and GitHub repository for recent updates
2. Classifications of Publications: Journal Publication | Conference Publication | Book Chapter
3. * Indicates Joint First Authors
Preprints
[45] Wang, X., Chakraborty, T., Chakraborty, B. Bayesian Machine Learning Methodology for Estimating Optimal Dynamic Treatment Regimes with Ordinal Outcomes.[Paper | Code]
[44] Shukla, P.K, Mandal, P.M., Chakraborty, T. Forecasting Deformation Behavior in Salt Rock: A Deep Learning Approach. [Paper | Code]
[43] Ghosh, A., Das, P., Chakraborty, T., Das, P., Ghosh, D. Forecasting of Cholera Epidemic employing Qualitative Dynamics: A Case Study in Malawi. [Paper | Code]
[42] Naik, S.M.*, Chakraborty, T.*, Hadid, A., Chakraborty, B. Skew Probabilistic Neural Networks for Learning from Imbalanced Data. [Paper | Code]
[41] Sadhukhan, P., Chakraborty, T. Footprints of Data in a Classifier Model: The Privacy Issues and Their Mitigation through Data Obfuscation. [Paper]
[40] Jakhmola, Y., Mishra, N.K., Ghosh, K., Chakraborty, T. Wavelet-based Temporal Attention Improves Traffic Forecasting. [Paper]
[39] Sadhukhan, P., Chakraborty, T. Addressing Imbalance: A Root Cause of Algorithmic Bias in Clinical Data Mining. [Paper]
[38] Wang, Z., Lee, J. W., Chakraborty, T., Ning, Y., Liu, M., Feng, X., Ong, M. E. H., Liu, N. Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission. [Paper | Code]
Under Revisions
[37] Chakraborty, T., Naik, S.M., Chattopadhyay, S,, Das, S. Learning Patterns from Biological Networks: A Compounded Burr Probability Model. Revision from Heliyon, Cell Press. [Paper | Code]
[36] Liu, X., Deliu, N., Chakraborty, T., Bell, L., Chakraborty, B. Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. Revision from Annals of Applied Statistics. [Paper | Code]
[35] Ray, A.*, Chakraborty, T.*, Radhakrishnan, A., Hens, C., Dana, S. K., Ghosh, D., Murukesh, N. Pattern change of precipitation extremes in Bear Island. Revision from Scientific Reports. [Paper]
Published Papers
2024
[34] Sengupta, S.*, Chakraborty, T.*, Singh, S.K. Forecasting CPI inflation under economic policy and geo-political uncertainty. International Journal of Forecasting. [Paper | Code]
[33] Borah, J.*, Chakraborty, T.*, Nadzir, M., Cayetano, M., Benedetto, F. Majumdar, S. WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data. IEEE Sensor Letters. [Paper]
[32] Chakraborty, T., Reddy U., Naik, S.M., Panja, M., Manvitha, B. (2024). Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art. Machine Learning: Science and Technology, Vol. 5, pg. 1-35. [Paper]
[31] Hadid, A.*, Chakraborty, T.*, Busby, D. When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges. Expert Systems. Vol. 41, pg. 1-36. [Paper]
[30] Sadhukhan, P., Sengupta, K., Palit, S., Chakraborty, T. (2024). Knowing the class distinguishing abilities of the features, to build better decision-making models. AMCIS. [Paper]
[29] Sadhukhan, P., Chakraborty, T., Sengupta, K. (2024). Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. AMCIS. [Paper]
2023
[28] Panja, M*., Chakraborty, T.*, Kumar, U., Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemic. Neural Networks, Vol. 165, pg. 185-212. [Paper | Code | Medium Post | R Package]
[27] Chakraborty, T., Kamat, G., Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Vol. 65. [Paper | Code]
[26] Thottolil, R., Kumar, U., Chakraborty, T. (2023). Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model. Scientific Reports. [Paper | Code]
[25] Panja, M*., Chakraborty, T.*, Nadim. Sk., Ghosh. I., Kumar, U., Liu, N. (2023). An ensemble neural network approach to forecast Dengue outbreak based on climatic condition. Chaos, Solitons & Fractals, Vol. 167, pg. 1-14. [Paper | Code]
[24] Panja, M., Chakraborty, T., Kumar, U., & Hadid, A. (2023). Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting. ICONIP. [Paper | Code]
[23] Dutta, A., Panja, M., Kumar, U., Hens, C., & Chakraborty, T. (2023). Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events. NeurIPS - AI4Science. [Paper | Code]
2022
[22] Bhattacharyya, A*., Chakraborty, T.*, Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, Vol. 107, pg. 3025-3040. [Paper | Code]
[21] Chakraborty, T., Chattopadhyay, S,, Das, S., Kumar, U., Jayavelu, S. (2022). Searching for heavy-tailed probability distributions for modeling real-world complex networks. IEEE Access, Vol. 10, 115092-115107. [Paper | Code]
[20] Chakraborty, T., Das, S., Chattopadhyay, S. (2022). A New Method for Generalizing Burr and Related Distributions. Mathematica Slovaca, Vol. 72, pg. 241-264. [Paper | Code]
[19] Sasal, L.^, Chakraborty, T., Hadid, A. (2022). W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. In IEEE ICMLA. [Paper | Code]
[18] Elabid, Z.^, Chakraborty, T., Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. In IEEE ICMLA. [Paper | Code]
[17] Chakraborty, T., Ghosh, I., Mahajan, T., Arora, T. (2022). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modeling, Control and Drug Development for COVID-19 Outbreak Prevention, Vol. 366, pg. 1023-1064, Springer. [Paper | Code]
[16] Chakraborty, T., Kumar, U. Loss Function. In Encyclopedia of Mathematical Geosciences, Springer. [Paper | Code]
2021
[15] Ray, A.*, Chakraborty, T.*, Ghosh, D. (2021). Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events. Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 31, pg. 1-15. [Paper | Code]
[14] 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, Vol. 11, pg. 11-43. [Paper]
[13] Ghosh, I., Chakraborty, T. (2021). An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19. International Journal of Modeling, Simulation, and Scientific Computing, Vol. 12, pg. 1-15. [Paper]
[12] Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S., Bhattacharya, S. (2021). Unemployment Rate Forecasting: A Hybrid Approach. Computational Economics, Vol. 57, pg. 183-201. [Paper]
[11] Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. In IEEE IJCNN [Paper]
[10] Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. In ACM IKDD CODS-COMAD. [Paper]
2020
[9] Chakraborty, T., Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals, Vol. 135, pg. 1-10. [Paper | Code]
[8] Chakraborty, T., Chakraborty, A. K. (2020). Hellinger Net : A Hybrid Imbalance Learning Model to Improve Software Defect Prediction. IEEE Transactions on Reliability, Vol. 70, pg. 481-494. [Paper]
[7] 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, Vol. 36, pg. 49-61. [Paper]
[6] Chakraborty, T., Chakraborty, A. K. (2020). Superensemble classifier for improving predictions in imbalanced data sets. Communications in Statistics - Case Studies and Data Analysis, Vol. 6, pg. 123-141. [Paper]
2019
[5] Chakraborty, T., Chattopadhyay, S., Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, Vol. 527, pg. 1-8. [Paper]
[4] 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, Vol. 362, pg. 130-142. [Paper]
[3] Chakraborty, T., Chakraborty, A. K., Murthy, C. A. (2019). A nonparametric ensemble binary classifier and its statistical properties. Statistics & Probability Letters, Vol. 149, pg. 16-23. [Paper]
[2] Chakraborty, T., Chakraborty, A. K., Mansoor, Z. (2019). A hybrid regression model for water quality prediction. Opsearch, Vol. 56, pg. 1167-1178. [Paper]
2018
[1] 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, Vol. 55, pg. 434-446. [Paper]