Work place: Amity Institute of Information Technology, Amity University, Kolkata, India
E-mail: ambardutta@gmail.com
Website:
Research Interests: Network Security
Biography
Dr. Ambar Dutta did his B.Sc. (Honors) in Mathematics from Presidency College, Kolkata and MCA and Ph.D. from Jadavpur University, Kolkata. After serving in the Department of Computer Science and Engineering, Birla Institute of Technology, Mesra for 15 years, he currently works as an Associate Professor at Amity Institute of Information Technology, Amity University, Kolkata. Dr. Dutta authored a book and has published more than 50 papers in reputed national/international journals/conferences. His research interests include Image and Video Processing, Data Analytics, Machine Learning, Information Retrieval, Network Security. He is an active reviewer of many reputed journals like Pattern Recognition Letters (Elsevier), Multimedia Tools and Applications (Springer), IET Image Processing etc. He is a senior life member of various professional bodies
By Krishnendra Sankar Ganguly Krishna Sankar Ganguly Ambar Dutta
DOI: https://doi.org/10.5815/ijitcs.2025.05.06, Pub. Date: 8 Oct. 2025
To augment the accuracy of the results of a Time-Series Forecasting problem in the Computational Epidemiology domain of Public Health, to generate an accurate alert in a Real-time Outbreak and Disease Surveillance (RODS) system, namely in the prediction of Malaria incidences, an interdisciplinary approach of data analysis [through Statistical along with Machine-Learning (ML) and Deep-Learning techniques (DL)] has been studied in this research. Two different Non-linear Deep-Learning based techniques, viz., Long Short-Term Memory (LSTM) [a subclass of Recurrent Neural Network (RNN)] & Gated Recurrent Unit (GRU) and two different Non-linear Machine-Learning techniques, viz., Random Forest Regressor & Non-linear Support Vector Machine Regressor are applied in this study to compare against the traditional Statistical-based linear SARIMA model, to forecast a longitudinal data-set of malaria incidences. While SARIMA or other traditional Autoregressive (AR) models, necessitating a smaller number of parameters, undergo limited training and limited prediction power, ML and DL models show profound and persistent performance improvement with better noise-handling/ missing values and perform multi-step forecasts. Moreover, the over-fitting issue can be combated by introducing densely connected residual links in the ML/ DL networks.
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