Dutta, S., Das, M. An autonomous lightweight model for aerial scene classification under labeled sample scarcity. Appl Intell(2023).
Dutta, S., & Das, M. (2023). Remote sensing scene classification under scarcity of labelled samples—asurvey of the state-of-the-arts. Computers Geosciences, 171, 105295.
Dutta, S., Mukherjee, S., Nag, M., Majumdar, S., & Goyal, G. (2021). Risk stratification of cardiovascular disease in type 2 diabetes using lda and cnn for clinical decision management – a multi-centre study in eastern India. International-Journal-of-Medical-Engineering-and-Informatics-1755-0661. doi:10.1504/IJMEI.2021.10043803.
Das and S. Dutta, “SELFIE: A Semantically-Enhanced Load Forecasting Approach with Indirect Estimate of Spatial Influences,” TENCON 2021 – 2021 IEEE Region 10 Conference (TENCON), Auckland, New Zealand, 2021, pp. 687-692.
Dutta, S., & Das, M. (2021). PReLim: A modeling paradigm for remote sensing image scene classification under limited labeled samples. In 9th international conference on pattern recognition and machine intelligence, December 2021, Kolkata, India, Springer. (Preprint)
Dutta, S., Mukherjee, S., Jana, S., Nag, M., & Majumdar, S. (2021). Dataset annotation on chronicdisease comorbidities study in type 2 diabetes mellitus. In Proceedings of international conference onmachine intelligence and data science applications (pp. 713–725). Springer.
“Inculcating Universal Human Values in Technical Education”, by AICTE, from 19th to 23rd December, 2022.
“Artificial Intelligence in Advanced Machine Learning and Cloud Computing”, by HIT, from 10th to 14th July, 2023.
UG Project Guided
Title: Handwritten Character Recognition System, by Sahban Alam, Rishabh Kumar, Saikat Palit, Rohit Sarkar.