Dr. Ajay Kumar Mallick
Assistant Professor, Department of Computer Science and Engineering · National Institute of Technology, Hamirpur
Research Interests
(1) Content based video retrieval. (2) Privacy in image retrieval. (3) Digital Watermarking. (4) Image Classification. (5) Computer Vision. (6) Machine Learning in Video Processing (7) applied Artificial Intelligence in Image and Video Processing
Biography
Ajay Kumar Mallick completed his B.E in Computer Science and Engineering from the University Institute of Technology, which is affiliated to the University of Burdwan and MTech in Computer Science and Engineering (CSE) from Indian Institute of Technology (Indian School of Mines), Dhanbad, India in 2013 and 2015, respectively. He completed his Ph.D. degree from the Department of CSE, Indian Institute of Technology (Indian School of Mines), Dhanbad, India. Presently, he works as Assistant Professor in the Department of CSE, National Institute of Technology, Hamirpur, India. He possesses membership in many technical professional organizations such as IEEE Membership (student) and life membership to the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI). His research area and interest include content based video retrieval, image processing, and image watermarking, machine learning. He has published articles in many internationally reputed Journals and conferences.
Education
B.E – University Institute of Technology, Burdwan, India MTech - Indian Institute of Technology (Indian School of Mines), Dhanbad, India PhD - Indian Institute of Technology (Indian School of Mines), Dhanbad, India
Recent Scopus Publications
- A comprehensive survey of content based image retrieval schemes: advancements, challenges, and future directions
- Fusion of Handcrafted and Deep Convolution Networks Learned Features for Image Retrieval
- AttentiveFP: An Attention-Guided Deep Learning Approach for Fingerprint Liveness Detection
- Fusion of Deep Cross Block Stage and YOLOv9 for Enhanced Small Object Detection in Aerial Imagery
- Revolutionizing Tomato Agriculture: Leaf Disease Detection Using CNN and Its Variants
Links
- ORCID https://orcid.org/0000-0002-4770-9506
- Google Scholar https://scholar.google.com/citations?user=tNA_6VYAAAAJ
- Scopus https://www.scopus.com/authid/detail.uri?authorId=57190793824