Papers
4
Total Citations
148
H-Index
3
About
Abdullah Lakhan is at the forefront of intelligent healthcare systems, pioneering the integration of artificial intelligence, the Internet of Medical Things (IoMT), and federated learning to transform digital medicine. His most influential work, "Consumer-Centric Internet of Medical Things for Cyborg Applications Based on Federated Reinforcement Learning" (2023), has garnered 97 citations and introduces a groundbreaking framework for combining AI-driven robotics with surgical operations, enabling secure, privacy-preserving cyborg applications. Lakhan further advances bio-inspired robotics in his 2021 study (43 citations), where he designs blockchain-fog-cloud architectures to optimize bio-ankle sensors for sports medicine within IoMT environments. His recent contributions extend to assistive technologies, including federated learning-driven IoT and edge cloud networks for smart wheelchair systems (2025), and a novel meta-reinforcement learning framework using Deep Q-Networks and graph convolutional networks for cluster representation in dynamic IoT systems (2025). By addressing critical challenges in data security, real-time processing, and adaptive learning, Lakhan’s work has a tangible impact on patient-centric care, assistive robotics, and autonomous systems. His research consistently bridges theoretical advances with practical, life-enhancing applications, making him a key innovator in the convergence of AI, IoT, and medical technology.
Research Focus
Key Achievements
Top Papers
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