Tanujit Chakraborty

Publications (year-wise archive)

Historical page. Kept for the record. The current page is linked from the menu.

The maintained list is the current publications page. This page keeps the older year-wise list as it was printed.

Publications (Year-wise):

  1. Please refer to my https://scholar.google.co.in/citations?user=sJOm-voAAAAJ&hl=en Google Scholar Profile, https://www.researchgate.net/profile/Tanujit-Chakraborty-2 RGate Profile, and https://github.com/ctanujit 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. [ https://arxiv.org/abs/2312.05878 Paper | https://github.com/7shraddha/SkewPNN Code] [41] Sadhukhan, P., Chakraborty, T. Footprints of Data in a Classifier Model: The Privacy Issues and Their Mitigation through Data Obfuscation. [ https://arxiv.org/abs/2407.02268 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. [ https://arxiv.org/abs/2403.06999 Paper | https://github.com/nliulab/Survival-Benchmark 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. [ https://arxiv.org/abs/2407.04465 Paper | https://github.com/ctanujit/CBurr 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. [ https://arxiv.org/abs/2311.14359 Paper | https://cran.r-project.org/web/packages/countts 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. [ https://arxiv.org/pdf/2312.04502.pdf 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. [ https://arxiv.org/pdf/2401.00249.pdf Paper | https://github.com/ctanujit/FEWNet 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. [ https://arxiv.org/abs/2404.05482v1 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. [ https://iopscience.iop.org/article/10.1088/2632-2153/ad1f77 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. [ https://onlinelibrary.wiley.com/doi/10.1111/exsy.13654 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. [ https://aisel.aisnet.org/amcis2024/dsa/dsa/21/ Paper] [29] Sadhukhan, P., Chakraborty, T., Sengupta, K. (2024). Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. AMCIS. [ https://aisel.aisnet.org/amcis2024/security/security/38/ 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. [ https://doi.org/10.1016/j.neunet.2023.05.049 Paper | https://github.com/mad-stat/Epicasting Code | https://medium.com/@madhurima.panja/epicasting-package-in-r-epidemic-forecasting-made-easy-dcdaffd694b Medium Post | https://cran.r-project.org/web/packages/epicasting/index.html 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. [ https://doi.org/10.1111/anzs.12386 Paper | https://github.com/gaurikamat/Bayesian-Neural-Tree 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. [ https://doi.org/10.1038/s41598-023-49343-3 Paper | https://github.com/Rahisha-Thottolil/RidgeGAN 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. [ https://doi.org/10.1016/j.chaos.2023.113124 Paper | https://github.com/mad-stat/XEWNet Code] [24] Panja, M., Chakraborty, T., Kumar, U., & Hadid, A. (2023). Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting. ICONIP. [ https://link.springer.com/chapter/10.1007/978-981-99-8178-6_35 Paper | https://github.com/mad-stat/PARNN 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. [ https://openreview.net/attachment?id=OQXCc21rgM&name=pdf Paper | https://github.com/mad-stat/VPINN 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. [ https://doi.org/10.1007/s11071-021-07099-3 Paper | https://github.com/arinjita9/COVID-19-Forecasting-by-TARNN- 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. [ https://ieeexplore.ieee.org/document/9933754 Paper | https://github.com/ctanujit/nonscalefree 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. [ https://doi.org/10.1515/ms-2022-0016 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. [ https://arxiv.org/abs/2209.03945 Paper | https://github.com/CapWidow/W-Transformer Code] [18] Elabid, Z.^, Chakraborty, T., Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. In IEEE ICMLA. [ https://arxiv.org/abs/2209.04259 Paper | https://github.com/Zelabid/KdL 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. [ https://link.springer.com/chapter/10.1007/978-3-030-72834-2_29 Paper | https://github.com/indrajitg-r/Forecasting-COVID-19-cases Code] [16] Chakraborty, T., Kumar, U. Loss Function. In Encyclopedia of Mathematical Geosciences, Springer. [ https://doi.org/10.1007/978-3-030-26050-7_187-2 Paper | https://github.com/ctanujit/loss 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. [ https://doi.org/10.1063/5.0074213 Paper | https://github.com/arnob-r/OEDL 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. [ https://link.springer.com/article/10.1007/s13278-021-00751-1 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. [ https://doi.org/10.1142/S1793962321410014 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. [ https://link.springer.com/article/10.1007%2Fs10614-020-10040-2 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 [ https://ieeexplore.ieee.org/document/9533747 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. [ https://doi.org/10.1145/3430984.3431021 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. [ https://doi.org/10.1016/j.chaos.2020.109850 Paper | https://github.com/indrajitg-r/COVID 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. [ https://ieeexplore.ieee.org/document/9194340 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. [ https://onlinelibrary.wiley.com/doi/full/10.1002/asmb.2473 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. [ https://www.tandfonline.com/doi/full/10.1080/23737484.2020.1740065 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. [ https://doi.org/10.1016/j.physa.2019.121266 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. [ https://doi.org/10.1016/j.cam.2019.05.013 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. [ https://www.sciencedirect.com/science/article/pii/S0167715219300276 Paper] [2] Chakraborty, T., Chakraborty, A. K., Mansoor, Z. (2019). A hybrid regression model for water quality prediction. Opsearch, Vol. 56, pg. 1167-1178. [ https://link.springer.com/article/10.1007/s12597-019-00386-z 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. [ https://link.springer.com/article/10.1007/s12597-017-0329-2 Paper]