Malay Kishore Dutta

Dr. A.P.J. Abdul Kalam Technical University, Amity University

Papers

Total Citations

H-Index

About

Malay Kishore Dutta is a leading researcher at the intersection of computer vision, deep learning, and intelligent automation, with a strong focus on safety-critical applications. His work is distinguished by its practical impact, particularly in railway infrastructure monitoring and advanced robotics. Dutta's most cited paper, "Classification of railway level crossing barrier and light signalling system using YOLOv3" (2020, 16 citations), demonstrates his pioneering use of real-time object detection to enhance transportation safety, addressing a critical need for automated surveillance at level crossings. This contribution showcases his ability to translate cutting-edge deep learning models into deployable solutions for public infrastructure. More recently, his research has expanded into explainable artificial intelligence (XAI) for robotics, as evidenced by "Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI" (2024, 15 citations). This work not only advances robotic motion planning but also ensures transparency in AI-driven decisions, a crucial step for human-robot collaboration. With a growing citation record, Dutta's contributions are shaping the future of intelligent systems, making him a key figure for students and researchers interested in applied AI, safety engineering, and interpretable machine learning.