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"Baareh, Abdel Karim M."

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Baareh, A. K. M., Sheta, A. F., & Khnaifes, K. A. (2006). Forecasting river flow in the USA: a comparison between auto-regression and neural network non-parametric models. Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, 7–12. https://dl.acm.org/doi/abs/10.5555/1369472.1369474

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Sheta, A., Pahnehkolaee, N. D., Braik, M., Baareh, A. K. M., Elashmawi, W. H., & Othman, E. S. (2025). A Comparative Statistical Analysis of Machine Learning Models for Suspicious Email Detection. Journal of Computing Science and Engineering, 19(4), 117–134. https://doi.org/10.5626/JCSE.2025.19.4.117

The increasing volume of suspicious emails, commonly known as spam, has created a critical need for more reliable and robust anti-spam filters. These suspicious emails can be dangerous and can lead to the loss of personal information, underscoring the necessity for an effective spam filtering system. The application of machine learning methods has enhanced system security and improved the detection of suspicious messages. This research evaluates the effectiveness of seven machine learning algorithms for classifying suspicious email messages: random forest, support vector machine, artificial neural network, decision tree, gradient boosting classifier, and k-nearest neighbor. The primary focus of this evaluation is the accuracy achieved by each algorithm in identifying spam emails. Our analysis revealed that the random forest algorithm outperformed the other evaluated algorithms in terms of accuracy for spam email classification, achieving a remarkable 95%. The accuracy percentages of the various methods ranged from 88% to 93%. Copyright 2025. The Korean Institute of Information Scientists and Engineers.
Sheta, A., Elashmawi, W. H., Surani, S., Baareh, A. K. M., Rauch, P., & Othman, E. S. (2025). ES-SVM Hyperparameter Optimization for Non-Invasive Coronary Heart Disease Classification. Int. Mobile, Intell., Ubiquitous Comput. Conf., MIUCC, 57–62. https://doi.org/10.1109/MIUCC66482.2025.11196848

Coronary heart disease (CHD) is the leading global cause of death, making early detection essential. While coronary angiography is the diagnostic gold standard, its invasive nature poses risks, and non-invasive symptom-based methods often lack accuracy. Machine learning-powered computer-aided diagnostic systems can effectively address challenges in clinical decisionmaking. This work presents an Evolutionary Strategy-optimized Support Vector Machine (ES-SVM) model for classifying CHD based on non-invasive test results and patient characteristics. Using the Coronary Heart Disease dataset, the proposed ESSVM demonstrated significant precision and F1-scores, as well as the accuracy of the proposed model. The results indicate that SVM performance can be significantly enhanced through evolutionary hyperparameter tuning, resulting in a reliable, noninvasive diagnostic tool for initial CAD screening and supporting early intervention techniques. © 2025 IEEE.

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