Papers
10
Total Citations
141
H-Index
5
About
Anis Sakly is a leading researcher in intelligent systems, specializing in the intersection of computational intelligence, robotics, and control theory. His work focuses on developing novel optimization algorithms—particularly Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO)—to enhance autonomous navigation and decision-making in complex environments. His most cited paper (32 citations) introduces a TLBO-adaptive neuro-fuzzy controller for mobile robot navigation in unknown settings, while his follow-up work (31 citations) advances a unified fuzzy logic controller for two-wheeled robots. Sakly has also made significant contributions to robust control theory, establishing stability criteria for uncertain switched Takagi-Sugeno fuzzy systems with time-varying delays (25 citations). His expertise extends to image processing, where he pioneered an efficient multi-level thresholding method combining modified PSO with Otsu’s method (24 citations). With over 140 total citations across his portfolio, Sakly’s work demonstrates remarkable breadth—from real-time FPGA architectures for medical image segmentation to machine learning applications in agriculture for evapotranspiration prediction. His research consistently bridges theoretical advances with practical implementations, making him a notable figure in intelligent systems engineering.
Research Focus
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- 10Robust FPGA Hardware Architecture of DPSO Multilevel Image Segmentation2 citations · 2017