Mr. Ahmed Farouk Osman

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Lecturer
College of Arts and Science
ahmed.osman@uaqu.ac.ae
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    Biography


    Ahmed Farouk Ibrahim is a Lecturer in the Cybersecurity Program at the College of Arts and Sciences at Umm Al Quwain University (UAE). His teaching and research interests focus on Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, and Cybersecurity. His work particularly explores intelligent systems for real-world applications, including sentiment analysis, computer vision, and AI-driven safety systems.

    Ahmed has contributed to several high-impact research publications in internationally recognized journals such as IEEE Access, Scientific Reports, and Computers, Materials & Continua. His recent research emphasizes advanced deep learning models, including transformer architectures and attention-based CNNs, for real-time driver drowsiness detection and intelligent transportation safety systems.

    Prior to joining UAQU, Ahmed served as an Assistant Lecturer at King Salman International University and Nahda University, where he was actively involved in teaching, academic advising, quality assurance, and learning management systems. He also gained early academic experience as a demonstrator in several institutions, contributing to curriculum delivery and student support.

    Ahmed is currently a Ph.D. researcher in Computer Science at Suez Canal University, specializing in Artificial Intelligence. He holds a Master’s degree in Computer Science from South Valley University, where his thesis focused on Twitter sentiment analysis using hierarchical clustering techniques, and a Bachelor’s degree in Computer Science from Minia University.

    He combines strong academic expertise with practical technical skills in programming, data science, and AI frameworks, and is committed to advancing research and education in intelligent systems and cybersecurity.

    Terminal Degree: M.Sc. in Computer Science, South Valley University (Egypt), 2021.

     

  • Academic Publications


    • (2026). Comprehensive Benchmarking of Attention Enhanced CNNs and Transfer Learning Models for Real Time Driver Drowsiness Detection. IEEE Access.
    • (2026). Efficient Deep Learning Models Integrated with a Smart Web Application for Classifying Heart Diseases Based on ECG Signals. Computers, 15(3), 191.
    • (2025). Driver drowsiness detection using swin transformer and diffusion models for robust image denoising. IEEE Access.
    • (2025). Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach. Scientific Reports, 15(1), 17493.
    • (2022). COVID19 outbreak: A hierarchical framework for user sentiment analysis. Comput. Mater. Contin, 70(2), 2507-2524.
    • (2021). A study of sentiment analysis approaches in short text. Digital Transformation Technology: Proceedings of ITAF 2020 (pp. 143-151), Springer Singapore.
  • He has extensive teaching experience in undergraduate and postgraduate law programs, including Criminal Law, Criminal Procedure, Criminology, Penology, Cybercrime Law, and Advanced Criminal Evidence. He has also supervised numerous master's theses in criminal sciences and related legal fields.

  • No ORCID link provided.