Soumen Moulik
Papers
1
Total Citations
3
H-Index
1
About
Soumen Moulik is a researcher whose work centers on human activity recognition, a critical area within pervasive computing and artificial intelligence. His most-cited paper, "Human Activity Recognition Using CTAL Model" (2023), introduces a novel framework that leverages contextual temporal attention learning to improve the accuracy and efficiency of identifying human behaviors from sensor data. This contribution addresses key challenges in real-world applications, such as healthcare monitoring and smart environments, where precise activity detection is essential. Although his citation count is still growing—with the CTAL paper garnering three citations to date—Moulik’s work demonstrates a focused effort to advance machine learning models for time-series sensor analysis. His research is notable for its potential to enhance adaptive systems that respond to human actions in real time, making it relevant for students and researchers exploring the intersection of deep learning and ubiquitous computing. As his publication record expands, Moulik’s contributions are poised to influence future developments in activity recognition and context-aware technologies.
Research Focus
Key Achievements
Top Papers
- 1Human Activity Recognition Using CTAL Model3 citations · 2023