Mohamed Hany Sallam

Mohamed Hany Sallam is affiliated with the Arab Academy for Science, Technology and Maritime Transport (AASTMT), a regional university operated by the Arab League with campuses primarily in Egypt. He works at the College of Maritime Transport and Technology at AASTMT, where he collaborates with a team of researchers focused on applying artificial intelligence and machine learning technologies to various engineering challenges, including deception detection systems.

Sallam's research in polygraph science centers on integrating modern machine learning and multimodal sensor fusion technologies with traditional psychophysiological deception detection methods. His 2025 study presented a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature) plus demographic data, collected from 49 unique subjects, and developed a polygraph-based lie detection system utilizing multimodal sensor fusion. His Random Forest machine learning model achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58%), and k-Nearest Neighbor (83%). The research team also publicly released their multimodal polygraph dataset, contributing valuable resources to the deception detection research community.

As part of a collaborative research team at AASTMT, Sallam works closely with Assistant Professor Omar Osama Mohamed Shalash, who holds a Ph.D. in Biomedical Engineering from the University of Strathclyde, UK, and specializes in robotics and artificial intelligence, along with Ahmed Métwalli, an Assistant Lecturer specializing in AI and Data Science with a research portfolio spanning multidisciplinary AI applications. This team represents a new generation of researchers applying computational intelligence to traditional forensic psychophysiology, bridging the gap between established polygraph methodologies and emerging artificial intelligence techniques for deception detection.