Rate My Professor Ganesh Krishnasamy

Krishnasamy's research specializations encompass computer vision, machine learning, optimization, pattern recognition, manifold learning, semi-supervised learning, and sparse optimization, with particular emphasis on commercial applications in multimedia processing and understanding. His scholarly output includes two books, one book chapter, seven articles, and eight conference papers. Key publications feature the book 'Cohort Intelligence: A Socio-Inspired Optimization Method' (2017, co-authored with Anand J. Kulkarni and Ajith Abraham), which has received 64 citations, and editorial contributions to 'Constraint Handling in Metaheuristics and Applications' (2021, with Anand J. Kulkarni, Efrén Mezura-Montes, Yong Wang, and Amir H. Gandomi). Recent works include 'ActNetFormer: Transformer-ResNet Hybrid Method for Semi-supervised Action Recognition in Videos' (2025), 'GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction' (2025), 'A Comparative Analysis of Deep Learning Models and Gradient Computation for Rally Detection in Badminton Videos' (2025), 'Hessian Unsupervised Extreme Learning Machine' (2024), and 'Histohdr-Net: Histogram Equalization for Single LDR to HDR Image Translation' (2024). His research has accumulated over 370 citations on Google Scholar, underscoring his influence in deep learning, computer vision, and related optimization techniques. Current projects involve machine learning algorithms for computer vision applications, HDR content creation, and no-reference image quality assessment using deep learning models.