Ammar Hawbani

University of Science and Technology of China

Papers

Total Citations

H-Index

About

Ammar Hawbani is a leading researcher at the intersection of artificial intelligence, cybersecurity, and autonomous systems. His work spans two pivotal domains: enhancing digital security through deep learning and advancing collaborative robotics for Industry 4.0. In cybersecurity, Hawbani’s most-cited paper, "CAPTCHA Recognition Using Deep Learning with Attached Binary Images" (2020, 40 citations), tackles the critical challenge of distinguishing humans from bots. By applying deep learning to text-based CAPTCHA recognition, his research strengthens web defenses against automated attacks, directly impacting how platforms secure user interactions. Complementing this, Hawbani explores the frontier of industrial automation in "Autonomous Multi-Robot Collaboration in Virtual Environments to Perform Tasks in Industry 4.0" (2022, 7 citations). Here, he addresses the complex coordination of multiple robots, enabling them to dynamically generate actions from shared goals—a key enabler for efficient, scalable automation in smart factories. Together, these contributions demonstrate Hawbani’s dual focus: fortifying digital trust while engineering the collaborative intelligence that will power tomorrow’s autonomous industries. His work offers practical solutions for both securing online systems and optimizing multi-robot teamwork, making him a notable figure in applied AI and robotics.