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Faculty Directory Sayantani Das
Sayantani Das

Sayantani Das

Assistant Professor

[email protected]
Vidwan Profile Google Scholar

Academic Information

M.Tech, Jadavpur University,2024

Research Areas

Artificial Intelligence Machine Learning Deep Learning Computer Vision

Research Interests

My research interests lie in Artificial Intelligence with a focus on Machine Learning and Deep Learning techniques for Computer Vision applications. I am particularly interested in developing data-driven models for image analysis, pattern recognition, and visual classification tasks. My work aims to apply deep neural networks to solve real-world problems by improving accuracy, robustness, and efficiency of vision-based systems. I am also interested in exploring emerging architectures and optimization techniques to enhance the performance of intelligent visual systems.

Courses Taught

Artificial Intelligence & Machine Learning, Digital Forensics, Operating Systems, Neural Network & Deep Learning

Positions Held

Assistant Professor, Industry experience: WIPRO: 1 year, Machine Learning & Deep Learning Intern: WEBEL:6 months

Publications

1. Aluminium Induced Vapor Phase Stain Etch Method for Generating Porous Silicon Structure(2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), Published, Publisher:IEEE, Publication date
2020/9/5)

2. Markov Model based Treatment Path Prediction of Patients with Severe Mental Disorder( 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE): Published, Publisher: IEEE, Publication date
2024/5/16)

3. Empowering Crop Disease Detection with RGB Image Analysis: A Comprehensive Deep Learning Framework.(Published on 2025-03-06, International Journal of Advances in Science, Engineering and Technology(IJASEAT))

4. Enhancing Brain Tumor Diagnosis with Ensemble Deep Learning Technique for Improved Accuracy( Accepted & Presented)(AISC 2025)

5. Attention-Enhanced Deep Learning Framework with Edge-Aware Preprocessing for Ground-Based Cloud Classification( ICDMIS 2025)

6. TriNetDerm: An Ensemble Deep Learning Framework for Robust Skin Cancer Classification(ICRCICN 2025)

7. Explainable AI for Fuel and Energy Consumption: A Meta-Deep Learning Approach for Sustainable Vehicle Analytics" for the book "Internet of Vehicles: Scope and Application of Artificial Intelligence-Based Technologies," part of the Springer Transactions on Computer Systems and Networks series.( Accepted)