Search for Articles:

MECS Press Menu

Krishna Sankar Ganguly

Work place: The Kolkata Municipal Corporation, Kolkata, India

E-mail: dr.k.s.ganguly@gmail.com

Website:

Research Interests:

Biography

Dr. Krishna Sankar Ganguly completed his Bachelor’s degree in Medical Science (MBBS) from North Bengal University, prior to completion of his two Master’s degrees (i) in Management (MBA) from Indira Gandhi National Open University (Govt. of India) and (ii) in Public Health (MPH) from Annamalai University (Tamil Nadu). He later expanded his knowledge through seventeen (17) Diplomas and Certificate Program in Management (PGDHRM & PGDIM), Public Health (DIH & DPH), Statistics (OYPC & BSM) and in several medical disciplines (Sports-Medicine, Rheumatology, Anatomy, Thyroid Disorders, COPD & Asthma, Maternity & Child Health, Optometry, CVD & Stroke, Diabetes, Cancer, Healthcare Quality, etc.) from multiple Universities/ Institutions. He attained a 34 years successful professional career in The Kolkata Municipal Corporation under State Government reaching the upper administrative echelon as Executive Health Officer of Health Department. Simultaneously, he has a proven research career in different capacities (including Visiting Scientist) in Indian Statistical Institute, National Institute for the Orthopedically Handicapped, Jadavpur University, etc., in several scientific arenas of Inter-disciplinary Research, Bio-Engineering, Epidemiology, Malaria, Dengue, PEMF-Therapy, Forecasting, etc. having several original publications.

Author Articles
A Comparative Study of Statistical (SARIMA) Vis-À-Vis Some Traditional Machine-Learning and Deep-Learning Techniques to Forecast Malaria Incidences in Kolkata of India

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.

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals