Professor at Mutah University, Jordan
Professional Profile
Summary
Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.
Educational Details
Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.
Professional Experience
Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.
Research Interests
His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.
Author Metrics
Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.
Awards and Honors
Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.
Publication Top Notes
1. Deep learning computer vision system for estimating sheep age using teeth images
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Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.
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Journal: Connection Science
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Volume/Issue: 37 (1)
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Article ID: 2506456
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Year: 2025
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Summary:
This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.
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Citations: Not provided
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Access: Details not provided
2. ICT: Iterative Clustering with Training: Preliminary Results
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Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.
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Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)
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Year: 2025
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Summary:
This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.
3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing
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Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.
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Journal: Parasites & Vectors
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Volume/Issue: 18 (1)
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Article Number: 33
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Year: 2025
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Summary:
This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.
4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach
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Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.
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Book/Series: Advances and Applications in Bioinformatics and Chemistry
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Pages: 159-178
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Year: 2024
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Summary:
This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.
5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system
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Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.
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Journal: Infection and Drug Resistance
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Pages: 3225-3240
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Year: 2024
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Summary:
This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.
Conclusion
Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.
His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.