Aiiad Albeshri
Papers
6
Total Citations
113
H-Index
6
About
Aiiad Albeshri’s research bridges the gap between theoretical optimization and real-world cyber-physical systems, with a focus on scheduling, robotics, and high-performance computing. His work on wafer fabrication introduced a novel approach to cyclic scheduling for dual-arm cluster tools, addressing the critical challenge of wafer-residency-time constraints by optimizing processing module configurations and robot waiting times—a contribution that has earned 32 citations and offers practical solutions for semiconductor manufacturing. Albeshri also developed the ZAKI and ZAKI+ frameworks, machine learning-based tools that automatically optimize sparse matrix-vector multiplication (SpMV) on distributed memory architectures, achieving up to 22 citations for their impact on smart city and scientific applications. In robotics, he applied tabu search to avoid concave obstacles in source localization and proposed a learning-inspired immune algorithm for multiobjective multirobot maritime patrolling, both published in 2023. Additionally, his SVSL method for human activity recognition uses soft-voting and self-learning to improve accuracy in smart health and surveillance. With over 100 citations across these works, Albeshri’s contributions demonstrate a consistent ability to solve complex, interdisciplinary problems, making him a notable figure in optimization, robotics, and cyber-physical systems research.
Research Focus
Key Achievements
Top Papers
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- 4Using Tabu Search to Avoid Concave Obstacles for Source Location14 citations · 2023
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