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Important Note : 
1. Please click on the [Paper | Code] to access the paper and code. Also, please refer to my Google Scholar Profile and RGate Profile
2. Classifications of Publications: Journal Publication | Conference Publication | ​Book Chapter | Best Paper Award
3. * : Both authors contributed equally;  ^ : A student/doctoral fellow/mentee of Dr. Tanujit Chakraborty during the time of the work.

​Year-wise Publications :

2023
  • Chakraborty, T., Panja, M., Kumar, U., & Hadid, A. (2023). Probabilistic Autoregressive Neural Network Model for Time Series Forecasting. Under Review. ​[Paper | Code]
  • Panja, M*., Chakraborty, T.*, Kumar, U., & Liu, N. (2023). Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemic. Under Review. ​[Paper | Code]
  • Chakraborty, T., Kamat, G.^, Chakraborty, A. K. (2023). Bayesian Neural Tree Models for Nonparametric Regression. Australian & New Zealand Journal of Statistics, Accepted. [Paper | Code] 
  • 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]

2022
  • 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]  
  • 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]
  • 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]
  • ​​​​Sasal, L.^, Chakraborty, T., & Hadid, A. (2022). W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting. In International Conference on Machine Learning and Applications.​ [Paper | Code]
  • Elabid, Z.^, Chakraborty, T., & Hadid, A. (2022). Knowledge-based Deep Learning for Modeling Chaotic Systems. In International Conference on Machine Learning and Applications.​ [Paper | Code]
  • 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 Book Chapter, Cham. ​​​[Paper | Code]
  • Panja, M.^, Chakraborty, T., Kumar, U. (2022). Data Dictionary. In Encyclopedia of Mathematical Geosciences, Springer Book Chapter, Cham. ​​​[Link]
  • Chakraborty, T., Kumar, U. (2022). Loss Functions. In Encyclopedia of Mathematical Geosciences, Springer Book Chapter, Cham. ​​​[Link]

​2021
  • 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]
  • 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]
  • 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]
  • 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] 
  • Bhattacharyya, A., Pattnaik, M., Chattopadhyay, S., & ​Chakraborty, T. (2021). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. In IEEE International Joint Conference on Neural Networks (IJCNN).​ [Paper]
  •  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. (Best Paper Award Winner at ACM CODS-COMAD) [Paper] ​
​
2020
  • 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]
  • 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]
  • 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]
  • 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
  • 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]
  • 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] (Best Student Paper Award Winner at 52nd Annual Convention of ORSI, India) 
  • Chakraborty, T., Chakraborty, A. K., & Mansoor, Z.^ (2019). A hybrid regression model for water quality prediction. OPSEARCH, Vol. 56, pg. 1167-1178. [Paper]
  • ​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]
​​
2018
  • 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] (B.G. Raghavendra Memorial Award Winner from ORSI, India)​

Contact Me

Dr. Tanujit Chakraborty 
Mathematics and Numerical Sciences Unit, Sorbonne Université. 
Researcher and Advisor at Sorbonne Center for Artificial Intelligence.  

Senior Fellow at Centre for Data Sciences, IIIT Bangalore, India.
​​Email: ctanujit@gmail.com​ 

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​​© 2013 onwards - All rights reserved to Tanujit Chakraborty.​​

  • HOME
  • BIOGRAPHY
  • RESEARCH
    • Research interests
    • PUBLICATIONS
  • TEACHING
    • DATA ANALYTICS (MBA)
    • MULTIVARIATE DATA ANALYTICS (MATH260)
    • MACHINE LEARNING (MATH370)
    • STATISTICAL INFERENCE (MATH350)
    • ANALYSIS (UG LEVEL)
  • MY TALKS
  • RESOURCES
    • LECTURE NOTES
    • BOOKS I WROTE
    • VIDEO LETURES
  • WORKSHOPS
    • WORKSHOP ON ML
    • TOUR OF AI
    • Workshop on Data Analytics
  • STAT & ML LAB
    • STAT & ML LAB
    • APPLY HERE
    • STUDENTS
  • ETC.