Mohammed Faisal
Papers
16
Total Citations
1,011
H-Index
11
About
Mohammed Faisal is a prolific researcher whose work spans the intersecting fields of artificial intelligence, robotics, computer vision, and biomedical signal processing. He is perhaps best known for his highly influential 2021 review on deep learning techniques for classifying electroencephalogram motor imagery signals, which has amassed an impressive 558 citations and stands as a foundational reference in brain-computer interface research. Equally significant is his early and enduring contribution to mobile robotics, where his 2013 paper on fuzzy logic-based navigation and obstacle avoidance in dynamic environments has garnered 161 citations, establishing him as a key voice in autonomous robot navigation. Faisal has consistently advanced intelligent control systems through hierarchical fuzzy designs, stereo vision-based navigation, and extended Kalman filter localization techniques. His research portfolio also reflects a strong commitment to agricultural technology, with notable deep learning applications for date fruit classification, maturity detection, and weight estimation — work of particular relevance to Saudi Arabia's agricultural sector. Collectively, his publications demonstrate a rare breadth of expertise, bridging foundational robotics theory with cutting-edge machine learning applications across diverse real-world domains.
Research Focus
Key Achievements
Top Papers
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- 5Human Expertise in Mobile Robot Navigation34 citations · 2017
- 6A Hierarchical Fuzzy Control Design for Indoor Mobile Robot29 citations · 2014
- 7An autonomous stereovision-based navigation system (ASNS) for mobile robots15 citations · 2016
- 8Enhancement of mobile robot localization using extended Kalman filter14 citations · 2016
- 9Robot localization using extended kalman filter with infrared sensor14 citations · 2014
- 10Obstacle avoidance using wall-following strategy for indoor mobile robots12 citations · 2016