Dr. Khaled Fawagreh is a Lecturer in the department of Information Technology at Prince Mohammad Bin Fahd University. Prior joining PMU, Dr. Fawagreh was a Lecturer in the department of Computer Science & Software Engineering at the University of Hail, Saudi Arabia. Dr. Fawagreh also has over six years of industry experience in the areas of document engineering and network management.
Dr. Fawagreh holds a Ph.D. from the school of Computing Science & Digital Media at Robert Gordon University in the UK, an MSc in Computer Science from Dalhousie University in Canada, and a BSc. in Computer Science from York University in Canada. He is a researcher known for his work in the fields of machine learning, data mining, and artificial intelligence. He has contributed to various areas, including ensemble learning, feature selection, and predictive modeling. His research often focuses on developing and improving algorithms for analyzing complex datasets and solving real-world problems.
Key Contributions and Research Interests:
1. Ensemble Learning: Fawagreh has worked on combining multiple models to improve predictive performance, a common technique in machine learning.
2. Feature Selection: He has explored methods for identifying the most relevant features in datasets to enhance model efficiency and accuracy.
3. Applications of AI: His research includes applying machine learning techniques to domains like healthcare, finance, and environmental science.
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PhD, School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK, 2016.
Main research areas: Machine Learning
PhD Thesis: "On Pruning and Feature Engineering in Random Forests".
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Master of Science, Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada, 1995.
MSc Thesis: "An SQL-Like Query Language for Object-Oriented Database Systems".
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Bachelor of Science, Specialized Honors, Computer Science, York University, North York, Ontario, Canada, 1993.
Areas of specialization: Programming Languages, Software Engineering.
Publications
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Latif, Ghazanfar, Jaafar Alghazo, Majid Ali Khan, Ghassen Ben Brahim, Khaled Fawagreh, and Nazeeruddin Mohammad. "Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging." AIMS Mathematics 9, no. 8 (2024): 20539-20571.
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Latif, Ghazanfar, Sherif E. Abdelhamid, Khaled S. Fawagreh, Ghassen Ben Brahim, and Runna Alghazo. "Machine Learning in Higher Education: Students’ Performance Assessment considering Online Activity Logs." IEEE Access (2023).
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Fawagreh, Khaled, and Mohamed Medhat Gaber. "eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning." Big Data and Cognitive Computing 4.4 (2020): 37.
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Fawagreh, Khaled, and Mohamed Medhat Gaber. "Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach." Computing (2020): 1-12.
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Fawagreh, K., Gaber, M. M., & Elyan, E. (2016, September). An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests. In International Conference on Engineering Applications of Neural Networks (pp. 267-282). Springer, Cham.
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Fawagreh, Khaled, Mohamed Medhat Gaber, and Eyad Elyan. "A replicator dynamics approach to collective feature engineering in random forests." In International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 25-41. Springer, Cham, 2015.
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Fawagreh, Khaled, Mohamed Medhat Gaber, and Eyad Elyan. "CLUB-DRF: A clustering approach to extreme pruning of random forests." In International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 59-73. Springer, Cham, 2015.
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Fawagreh, Khaled, Mohamed Medhat Gaber, and Eyad Elyan. "Diversified random forests using random subspaces." In International Conference on Intelligent Data Engineering and Automated Learning, pp. 85-92. Springer, Cham, 2014.
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Fawagreh, Khaled, Mohamed Medhat Gaber, and Eyad Elyan. "Random forests: from early developments to recent advancements." Systems Science & Control Engineering: An Open Access Journal 2, no. 1 (2014): 602-609.
Machine learning, data mining, and artificial intelligence.
