Papers
5
Total Citations
49
H-Index
4
About
Sushil Kumar is a researcher pushing the boundaries of high-dimensional information processing, with a core focus on quaternionic neural networks and their real-world applications. His work addresses a critical challenge in modern engineering: designing intelligent systems capable of efficiently handling multi-dimensional data. Kumar’s most significant contribution is his comprehensive 2023 review on the advancement of high-dimensional neural networks in the quaternionic domain, which has already garnered 19 citations, establishing it as a key reference in the field. He has also made notable strides in practical implementations, including an improved block matching algorithm for motion estimation in video sequences, applied to robotics (15 citations), and foundational work on learning machines with quaternionic domain neural networks for applications in communication, control, computer vision, and biometrics. His 2018 systematized review on cognitive robotics further demonstrates his interest in creating machines that can think and reason like humans. Through his research, Kumar is helping to bridge the gap between theoretical high-dimensional neural architectures and tangible engineering solutions, making him a notable voice in the advancement of intelligent, robust systems.
Research Focus
Key Achievements
Top Papers
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- 4Cognitive Robotics in Artificial Intelligence6 citations · 2018
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