Mahmood Al-khassaweneh
Papers
2
Total Citations
24
H-Index
2
About
Mahmood Al-khassaweneh is a researcher whose work bridges artificial intelligence and robotics, with a particular focus on pattern recognition and educational technology. His key research areas include deep learning for handwritten numeral recognition and the development of robotic systems for interactive learning. His most cited work, "Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks" (2021), has garnered 22 citations, reflecting its significance in automating numeral detection and classification—a field with broad applications in document processing and automation. This contribution addresses a persistent challenge in pattern recognition, leveraging CNNs to enhance accuracy in multilingual script analysis. Additionally, Al-khassaweneh has explored robotics in education, as seen in his 2019 paper on a ROS-based pedagogical robot for children’s mathematics learning. This work integrates the Robotic Operating System with the Pioneer 3 platform to create an interactive tool that reads printed arithmetic equations and displays results, demonstrating a novel approach to STEM education. Through these efforts, Al-khassaweneh contributes to both foundational AI research and practical, human-centered applications, making his work relevant for students and researchers interested in the intersection of machine learning, robotics, and educational innovation.
Research Focus
Key Achievements
Top Papers
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