Aaron Rasheed Rababaah

Tennessee State University, American University of Kuwait

Papers

Total Citations

H-Index

About

Aaron Rasheed Rababaah is a researcher whose work bridges computer vision, soft computing, and educational technology. His primary research areas include automated visual inspection, pattern recognition, and the integration of physical computing into programming education. His most cited work, "Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion" (2007, 5 citations), addresses the critical challenge of detecting unexploded ordnance (UXO) using a novel fusion of color, texture, and shape classifiers. This contribution demonstrates a practical application of soft-computing techniques for real-world safety and defense. Rababaah has also made notable contributions to pedagogy, as seen in his 2018 case study at the American University of Kuwait, which explores how physical computing and robotics can enhance student engagement and learning outcomes in programming. This work highlights his commitment to innovative, hands-on educational methods that extend beyond traditional screen-based feedback. Through his research, Rababaah has advanced both the technical field of automated visual recognition and the practical realm of STEM education, offering impactful solutions for security and learning.