Husam A. Neamah
Papers
1
Total Citations
10
H-Index
1
About
Husam A. Neamah is a researcher at the forefront of industrial automation and intelligent systems, with a primary focus on optimizing autonomous mobile robots (AMRs) in dynamic manufacturing environments. His work bridges the gap between computational intelligence and real-world industrial applications, particularly through the use of adaptive neuro-fuzzy inference systems (ANFIS) to model and mitigate slippage in AMRs—a critical challenge for precision and efficiency in logistics and production. His most-cited paper, "Optimization Modeling Parameters for Industrial AMR Slippage Using ANFIS System in Dynamic Environment" (2024, 10 citations), introduces a novel framework that enhances robot navigation reliability by integrating fuzzy logic with neural network learning, offering a scalable solution for Industry 4.0 settings. Though early in his career, Neamah’s contributions are already recognized for their practical impact, providing a foundation for smarter, more resilient automation systems. His work is particularly valuable for students and engineers seeking to understand how hybrid AI models can address real-time control problems in complex, unpredictable industrial landscapes.
Research Focus
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Top Papers
- 1