Mohd Ibrahim Shapiai
Papers
2
Total Citations
7
H-Index
2
About
Mohd Ibrahim Shapiai’s research lies at the intersection of robotics, machine learning, and intelligent systems, with a particular focus on solving real-world engineering challenges. His work spans from developing practical robotic applications to advancing kernel-based learning algorithms for small-sample problems. Notably, his 2023 study on “Real-Time Crack Classification with Wall-Climbing Robot Using MobileNetV2” (4 citations) demonstrates his ability to integrate deep learning with autonomous inspection systems, addressing critical infrastructure monitoring needs. Earlier foundational work on “Enhanced Weighted Kernel Regression with Prior Knowledge Using Robot Manipulator Problem as a Case Study” (2012, 3 citations) introduced an innovative extension to weighted kernel regression (WKR), showing superior performance over traditional artificial neural networks when learning from limited data—a persistent challenge in robotics and control systems. This contribution to small-sample learning has implications beyond robotics, benefiting fields where data scarcity is common. Shapiai’s research exemplifies a practical, problem-driven approach, bridging theoretical machine learning advances with deployable robotic solutions. His work continues to influence researchers working on efficient learning algorithms and autonomous systems for real-world applications.
Research Focus
Key Achievements
Top Papers
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- 2