Note: To access the publications, please click on the paper title. Also, please refer to my Google Scholar Profile.
List of Publications (Area-wise) :
A. Statistical Machine Learning :
[8] Chakraborty, T., Kamat, G., Chakraborty, A. K. (2021). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Accepted.
[7] 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.
[6] 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.
[5] 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.
[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. (Best Student Paper Award Winner at 52nd Annual Convention of ORSI, India)
[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.
[2] Chakraborty, T., Chakraborty, A. K., & Mansoor, Z. (2019). A hybrid regression model for water quality prediction. OPSEARCH, Vol. 56, pg. 1167-1178.
[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. (B.G. Raghavendra Memorial Award Winner from ORSI, India)
B. Applied Time Series Forecasting :
[8] Bhattacharyya, A*., Chakraborty, T.*, & Rai, S. N. (2021). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, Accepted.
[7] 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, Accepted.
[6] Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., & Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. IEEE International Joint Conference on Neural Networks (IJCNN).
[5] 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.
[4] Chakraborty, T., Ghosh, I., Mahajan, T., Arora, T. (2021). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modelling, Control and Drug Development for COVID-19 Outbreak Prevention, Springer.
[3] Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S., & Bhattacharya, S. (2020). Unemployment Rate Forecasting: A Hybrid Approach. Computational Economics, Vol. 57, pg. 183-201.
[2] 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.
[1] 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.
C. Statistical Analysis of Networks :
[3] 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. 1-24.
[2] Chakraborty, T., Das, S., & Chattopadhyay, S. (2021). A New Method for Generalizing Burr and Related Distributions. Mathematica Slovaca, Accepted.
[1] Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. In 8th ACM IKDD CODS and 26th COMAD. (Best Paper Award Winner at ACM CODS-COMAD)
* Both authors contributed equally.
A. Statistical Machine Learning :
[8] Chakraborty, T., Kamat, G., Chakraborty, A. K. (2021). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Accepted.
[7] 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.
[6] 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.
[5] 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.
[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. (Best Student Paper Award Winner at 52nd Annual Convention of ORSI, India)
[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.
[2] Chakraborty, T., Chakraborty, A. K., & Mansoor, Z. (2019). A hybrid regression model for water quality prediction. OPSEARCH, Vol. 56, pg. 1167-1178.
[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. (B.G. Raghavendra Memorial Award Winner from ORSI, India)
B. Applied Time Series Forecasting :
[8] Bhattacharyya, A*., Chakraborty, T.*, & Rai, S. N. (2021). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, Accepted.
[7] 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, Accepted.
[6] Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., & Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. IEEE International Joint Conference on Neural Networks (IJCNN).
[5] 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.
[4] Chakraborty, T., Ghosh, I., Mahajan, T., Arora, T. (2021). Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges. In Modelling, Control and Drug Development for COVID-19 Outbreak Prevention, Springer.
[3] Chakraborty, T., Chakraborty, A. K., Biswas, M., Banerjee, S., & Bhattacharya, S. (2020). Unemployment Rate Forecasting: A Hybrid Approach. Computational Economics, Vol. 57, pg. 183-201.
[2] 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.
[1] 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.
C. Statistical Analysis of Networks :
[3] 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. 1-24.
[2] Chakraborty, T., Das, S., & Chattopadhyay, S. (2021). A New Method for Generalizing Burr and Related Distributions. Mathematica Slovaca, Accepted.
[1] Chattopadhyay, S., Chakraborty, T., Ghosh, K., Das, A. K. (2021). Uncovering patterns in heavy-tailed networks : A journey beyond scale-free. In 8th ACM IKDD CODS and 26th COMAD. (Best Paper Award Winner at ACM CODS-COMAD)
* Both authors contributed equally.
