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About

Fahd N. Al‐Wesabi is a researcher at the forefront of artificial intelligence, metaheuristic optimization, and autonomous systems, with a particular focus on brain-computer interfaces (BCI), unmanned aerial vehicle (UAV) networks, and bio-inspired robotics. His most cited work, “Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface” (2022, 19 citations), introduces a novel hybrid framework that enhances EEG-based communication for individuals with movement disabilities, enabling more reliable control of assistive robots. In UAV communications, Al‐Wesabi developed a “Dispersal Foraging Strategy With Cuckoo Search Optimization” (2023, 10 citations) to solve real-time routing challenges in dynamic marine environments, improving autonomous emergency response. His research extends to bio-inspired locomotion, as seen in “Locomotion of Bioinspired Underwater Snake Robots Using Metaheuristic Algorithm” (2022), where he leverages snake-like movement for harsh underwater terrains. More recently, Al‐Wesabi has addressed cybersecurity in Industry 5.0, proposing a feature enhancement model for detecting cyber threats in imbalanced Industrial Internet of Things datasets (2025). With a growing citation impact, his work consistently bridges theoretical optimization algorithms with practical, real-world applications in assistive technology, autonomous navigation, and industrial security.