Yusman Yusof
Papers
3
Total Citations
21
H-Index
3
About
Yusman Yusof is a pioneering researcher in autonomous systems and artificial intelligence, with a focus on developing self-learning algorithms that enable machines to adapt and respond to dynamic environments without human intervention. His work centers on the intersection of reinforcement learning and weightless neural networks, where he has introduced novel approaches to transform preprogrammed systems into truly autonomous entities capable of real-time behavioral adaptation. Yusof's most cited paper, "Formulation of a lightweight hybrid AI algorithm towards self-learning autonomous systems" (2016, 10 citations), proposes a hybrid AI framework that allows autonomous systems to react and modify their behavior during operation, moving beyond static, preprogrammed responses. His subsequent research, including "Simulation of mobile robot navigation utilizing reinforcement and unsupervised weightless neural network learning algorithm" (2015, 7 citations), demonstrates practical implementations of self-learning algorithms for mobile robot navigation, while his 2017 work on unsupervised weightless neural networks as autonomous state classifiers (4 citations) further refines reinforcement learning by enabling systems to autonomously classify states and discover optimal behaviors through trial-and-error. Yusof's contributions are instrumental in advancing the field of autonomous systems, offering scalable, lightweight solutions that reduce dependency on human expertise and predefined knowledge, with significant implications for robotics, AI, and adaptive control systems.
Research Focus
Key Achievements
Top Papers
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