Published Papers :

• 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, Springer Nature , Vol. 55, pg 434-446. [Link]
• Chakraborty, T., Chakraborty, A. K., & Murthy, C. A. (2019). A nonparametric ensemble binary classifier and its statistical properties. Statistics & Probability Letters, Elsevier, Vol. 149, pg 16-23. [Link]
• Chakraborty, T., Chattopadhyay, S., & Ghosh, I. (2019). Forecasting dengue epidemics using a hybrid methodology. Physica A: Statistical Mechanics and its Applications, Elsevier, Vol. 527, pg . [Link]
• 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, Elsevier (Accepted)
• ​Chakraborty, T. (2019). Imbalanced Ensemble Classifier for learning from imbalanced business school data set. ​International Journal of Mathematical, Engineering and Management Sciences (Accepted)

Submitted Papers:

• Chakraborty, T., & Chakraborty, A. K. (2018). Superensemble Classifier for Improving Predictions in Imbalanced Datasets. (Under Revision)
• Chakraborty, T., & Chattopadhyay, S., & Chakraborty, A. K. (2018). Radial basis neural tree model for improving waste recovery process in a paper industry. (Under Revision)
• Chakraborty, T., Chakraborty, A. K., & Murthy, C. A. (2019). Consistency of Perceptron Trees. (Under Review)
• Chakraborty, T., Chakraborty, A. K., & Mansoor, Z. (2019). A hybrid regression model for water quality prediction. (Under Review)
• ​Chakraborty, T. (2019). Strong Universal Consistency and Rate of Convergence of Fast Trained Deep Feedforward Networks. (Under Review)