Sandeep Kumar Vishwakarma
Research Scholar
J.C. Bose University of Science and Technology, YMCA (Formerly YMCA UST)Research Areas
Biography
Dr. Sandeep Kumar Vishwakarma is an academician, researcher, and educator in the field of Computer Science and Engineering, with more than 15 years of teaching, research, and academic administration experience. He holds a B.Tech. from Veer Bahadur Singh Purvanchal University, M.Tech. in Computer Science & Engineering from the National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, and completed his Ph.D. in Computer Science and Engineering from J.C. Bose University of Science and Technology, YMCA, Faridabad, where his doctoral research focused on "Image and Video Dehazing Using Machine Learning. r. Vishwakarma has authored numerous research papers published in IEEE conferences, international journals, and edited books. His research contributions span topics such as Machine Learning, Artificial Intelligence, Image Processing, Internet of Things, Optical Communication, Computer Vision, and Mobile Commerce Security. He is also the inventor of patents related to AI-based Fraudulent Incoming Call Detection Systems and IoT-Based Wearable Stress Level Detection. A passionate educator, Dr. Vishwakarma has delivered courses on Data Structures, Design and Analysis of Algorithms, Compiler Design, Theory of Automata, Programming Languages, Python Programming, Web Technologies, and Cyber Security. Through his educational initiative University Academy, he has developed online learning resources and video lectures to make technical education accessible to students across India.
Educational Qualifications
his Ph.D. thesis presents advanced machine learning and deep learning techniques for restoring hazy images and videos captured under adverse atmospheric conditions such as haze, fog, smoke, and dust. The research proposes several novel frameworks, including CLDP-VAE, Domain-Adaptive Mixture of Experts (DA-MoE), DeepVideoDehazeNet, and DINO-Dehaze, to enhance image clarity while preserving fine details, colors, and temporal consistency. The proposed methods are evaluated on benchmark datasets and demonstrate superior performance in terms of PSNR, SSIM, and visual quality. The research also integrates dehazing with downstream computer vision applications such as object detection using YOLOv7 and text recognition, making the proposed models suitable for real-world applications including autonomous driving, surveillance, remote sensing, satellite imaging, and intelligent transportation systems.
Completed M.Tech. in Computer Science and Engineering with a specialization in Natural Language Processing (NLP). Possesses expertise in machine learning, artificial intelligence, text processing, and language technologies, with a strong foundation in research and software development.
Career Growth
Project Coordinator, NBA Accreditation Member, ICT Coordinator, ISO Coordinator
HOD, Teaching, Academic, Innovation Centre In charge, Exam Cell, International Conference Secririate Admission Cell, AKTU Counseling In charge
HOD, Teaching, Academic, Innovation Centre In charge, Exam Cell,
Teaching, Guiding projects, Sport Organizing Member, Hostel Warden
Teaching, Member of Sport and Cultural Activity
Teaching, Member of Sport and Cultural Activity
Awards & Honors
- GATE (2008)
- GATE (2018)
- GATE (2019)
- UGC NET (2018)