Research Scholar at Kalinga Institute of Industrial Technology, India📖
Dr. Pritam Chakraborty is a dedicated researcher in computer vision, image segmentation, and autonomous vehicle technology, specializing in deep learning and machine learning applications. Currently pursuing his Ph.D. under the Visvesvaraya PhD Scheme (MeitY, Govt. of India) at Kalinga Institute of Industrial Technology, his work focuses on real-time image segmentation for autonomous vehicles in unstructured environments. His research contributions extend to medical imaging, game theory, and AI-driven healthcare predictions.
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Education Background🎓
- Ph.D. in Information Technology (Ongoing) – Kalinga Institute of Industrial Technology (2023 – Present)
- Topic: Image segmentation for autonomous vehicles in unstructured environments
- Integrated Postgraduate (B.Tech + M.Tech) in Information Technology – Indian Institute of Information Technology, Gwalior (2018 – 2023)
- Thesis: Semantic Segmentation using Modified Deeplab V3 Plus for Autonomous Vehicles
- Higher Secondary (Science) – Bidhan Chandra Institution (2016 – 2018)
Dr. Chakraborty has been actively involved in academic research, data-driven AI applications, and deep learning innovations. His expertise spans machine learning, neural networks, and game theory-based AI modeling. He has contributed to multiple high-impact journal publications and IEEE conference proceedings, presenting novel AI frameworks for real-time segmentation, medical diagnostics, and autonomous driving technologies. His work integrates AI-driven decision-making models, stroke prediction, and computer vision advancements for real-world applications.
Her research interests include:
- Autonomous Vehicles & Image Segmentation (Deep Learning for Real-time Road Analysis)
- Medical AI & Predictive Analytics (Stroke Prediction & Hemorrhage Detection)
- Machine Learning & Game Theory in Healthcare
- Convolutional Neural Networks (CNNs) & Pyramid Networks for Image Processing
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Publications Top Notes 📄
1. OptiSelect and EnShap: Integrating Machine Learning and Game Theory for Ischemic Stroke Prediction
- Authors: P. Chakraborty, A. Bandyopadhyay, S. Parui, S. Swain, P.S. Banerjee, T. Si, …
- Journal: PLOS One
- Status: Communicated
- DOI: 10.21203/rs.3.rs-3841050/v1
- Year: 2024
- Summary: This paper presents the integration of machine learning and game theory for predicting ischemic stroke, exploring how these techniques can enhance diagnostic accuracy in medical predictions.
2. IndiRTS: Real-Time Segmentation for Autonomous Vehicles for Indian Conditions
- Authors: P. Chakraborty, A. Bandyopadhyay, R. Ghosh, R. Sarkar
- Journal: SN Computer Science
- Volume: 6, Issue 2
- Pages: 1-13
- Year: 2025
- DOI: 10.1007/s42979-025-00788-z
- Summary: This research proposes IndiRTS, a real-time image segmentation model for autonomous vehicles tailored for Indian driving conditions, focusing on improving the safety and efficiency of self-driving cars in challenging environments.
3. Predicting Stroke Occurrences: A Stacked Machine Learning Approach with Feature Selection and Data Preprocessing
- Authors: P. Chakraborty, A. Bandyopadhyay, P.P. Sahu, A. Burman, S. Mallik, …
- Journal: BMC Bioinformatics
- Volume: 25, Issue 1
- Article: 329
- Year: 2024
- Summary: This paper introduces a stacked machine learning model for stroke occurrence prediction, incorporating feature selection and data preprocessing to enhance the model’s diagnostic reliability.
4. PyramidNet: Image Segmentation Model for Autonomous Vehicles for Indian Conditions
- Authors: P. Chakraborty, A. Bandyopadhyay
- Conference: 10th IEEE International Conference on Electronics, Computing, and Communication Technologies (CONECCT)
- Location: IISc Bangalore
- Year: 2024
- Summary: The paper discusses the development of PyramidNet, an image segmentation model specifically designed for autonomous vehicles operating under Indian environmental conditions, improving vehicle navigation and road safety.
5. Automated Detection of Intracranial Hemorrhage using Convolutional Neural Networks
- Authors: P. Chakraborty, A. Bandyopadhyay, M. Misra, P. Gupta, T.H. Sardar, …
- Conference: 2024 IEEE AITU: Digital Generation
- Pages: 20-26
- Year: 2024
- DOI: 10.1109/IEEECONF61558.2024.10585483
- Summary: This work explores the use of convolutional neural networks (CNNs) for the automated detection of intracranial hemorrhage, showcasing the application of deep learning techniques in medical diagnostics.
Conclusion
Dr. Pritam Chakraborty is a highly deserving candidate for the Best Researcher Award, thanks to his innovative research, strong academic record, and interdisciplinary expertise. His work has the potential to transform the fields of autonomous driving and medical AI, and with some additional focus on scaling and global visibility, he will undoubtedly continue to make game-changing contributions.