Fahad Algarni
Papers
1
Total Citations
16
H-Index
1
About
Fahad Algarni is a researcher whose work sits at the intersection of artificial intelligence, the Internet of Things (IoT), and smart city development. His research focuses on how reinforcement learning—a branch of machine learning where algorithms learn from trial and error—can be harnessed to optimize complex urban systems. In his most-cited work, "Reinforcement Learning Concepts Ministering Smart City Applications Using IoT" (2020), Algarni provides a foundational framework for integrating adaptive AI with IoT sensor networks to improve traffic management, energy distribution, and public safety in urban environments. This paper has garnered 16 citations, reflecting its value as a reference point for scholars exploring autonomous decision-making in smart infrastructure. Algarni’s contributions are particularly notable for bridging theoretical reinforcement learning concepts with practical, scalable IoT deployments—a challenge that remains central to modern urban informatics. His work helps lay the groundwork for cities that can learn and respond in real time, making him a key voice in the ongoing conversation about sustainable, intelligent urban living.
Research Focus
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Top Papers
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