معرفی
Mahmoud Bekhit is a Lecturer at the Peter Faber Business School within the Faculty of Law and Business. His research focuses on machine learning applications in optimization, IoT systems, and data security. He specializes in predictive modeling for transportation systems, real-time video transmission, and network function virtualization. His work addresses challenges in healthcare technology, structural health monitoring, and financial sector innovations.
Key research interests include optimization algorithms (e.g., genetic algorithms, jellyfish search), ensemble learning techniques, and transfer learning for cross-domain problems. He has contributed to projects such as bike-sharing demand forecasting, surgical tele-education systems, and energy-efficient IoT protocols.
Bekhit has published 15+ peer-reviewed articles in journals like Mathematical Biosciences and Engineering and IEEE Access, with additional conference contributions. His work spans interdisciplinary domains including sports science (karate performance analysis), marine data prediction, and financial technology surveys.
Notable collaborations involve teams addressing VNF resource allocation, hybrid cloud security for healthcare, and AI-driven fraud detection. His research emphasizes practical solutions for industry challenges through data-driven methodologies.
