Forecasting river flow in the USA: a comparison between auto-regression and neural network non-parametric models - Baareh, Abdel Karim M.; Sheta, Alaa F.; Khnaifes, Khaled Al (Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, 2006)
Keywords: dept.csc; ⛔ No DOI found

Medical image analysis for brain tumor classification using CNN architecture - Sheta, Alaa; Elashmawi, Walaa H.; Karim Baareh, Abdel; Rausch, Peter; Othman, Emad S. (Proceeding - 12th International Conference on Information Technology: Innovation Technologies, ICIT 2025, 2025)
Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing methods expensive and inefficient. Deep Learning (DL) is a promising approach for developing automated systems to diagnose or segment brain tumors with high accuracy in less time. Within Deep Learning, Convolutional Neural Networks (CNNs) are potent tools for image classification tasks. This is achieved through a series of specialized layers, including convolution layers that identify patterns within images, pooling layers that summarize these patterns, fully connected layers that ultimately classify the image, and a feedforward layer to produce the output class. This study employed a CNN to classify brain tumors in T1-weighted contrast-enhanced images with various image resolutions, including 30×30, 50×50, 70×70, 100×100, and 150×150 pixels. The model successfully distinguished between three tumor types: glioma, meningioma, and pituitary. The CNN's impressive accuracy on training data reached up to 86.38% for image resolution (30×30) and 94.64% for higher resolution (150×150). This indicates its potential as a valuable tool in real-world brain tumor classification tasks. © 2025 IEEE.
Journal Abbreviation: Proceeding - Int. Conf. Inf. Technol.: Innov. Technol., ICIT
Keywords: Brain Tumor; Brain tumor; Classification; Contextual image classification; Convolutional Neural Networks; Convolutional neural network; Deep learning; MRI; Pattern recognition (psychology); Pooling; Population; Rendering (computer graphics); dept.csc

Hybrid PSO–ELM-Based Clinical Decision Support for Heart Disease Diagnosis - Sheta, Alaa; Ahmed, Sara Elsir; Elashmawi, Walaa H.; Baareh, Abdel Karim M.; Pahnehkolaee, Nastaran D. (2026 2nd International Conference on Computational Intelligence Approaches and Applications (ICCIAA), 2026)
Cardiovascular diseases (CVDs) are still one of the foremost causes of death globally. An urgent need for an accurate premature diagnosis is essential for efficient treatment and avoidance. The goal of this research is to introduce our initial idea for developing a hybrid intelligent diagnosis model that integrates the Extreme Learning Machine (ELM) with Particle Swarm Optimization (PSO) to enhance diagnosis accuracy for heart disease. To address the problems of random initialization, the PSO algorithm is used to optimize the ELM’s input biases and weights. Experiments using the Cleveland Heart Disease dataset evaluated the performance of traditional ELM and the proposed PSO-ELM model. Results indicate that integrating PSO for parameter tuning yields significant improvements across evaluation metrics. The traditional ELM recorded an accuracy of 0.8595 on the training set and 0.8525 on the test set, while the PSO-ELM model achieved up to 0.9174 in training and 0.8852 in testing. Enhancements were also noted in precision, recall, and F1-score, with the F1-score increasing from 0.8768 for traditional ELM to over 0.92 for several PSO configurations.
Keywords: Cardiovascular diseases; Classification; Diseases; Equations; Extreme Learning Machine; Heart; Heart Disease; Hybrid Learning; Machine Learning; Machine learning; Machining; Modeling; Optimization; Particle Swarm Optimization; Printing; Training; dept.csc

Evolutionary design of a PID controller using metaheuristics search algorithms - Reddy, V.; Reddy, V.; Suresh, Karnati; Sheta, Alaa; Hassan, Walaa; Djellal, Adel; Baareh, Abdel (Journal of Computing and Communication, 2025)
Proportional-Integral-Derivative (PID) controllers are prominent due to their superior functionality and ease of use. However, optimizing their parameters presents a significant challenge. Adjusting parameters must be done carefully and cautiously because improper calibration can compromise the system’s stability. Although classic tuning techniques, such as the Ziegler-Nichols (ZN), are frequently employed, their efficiency is restricted due to the intricate and ever- changing nature of the systems, often leading to parameter settings that could be more optimal. Therefore, the need for a more accurate parameter-tuning technique is urgent. Various optimization strategies are used to fine-tune parameters with more precision. These methods include Gray Wolf Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These methods are applied to fine-tune the PID parameters for a Direct Current (DC) motor to achieve optimal performance, and a comparative analysis of the results is conducted. Various fitness functions encompass performance metrics such as rise time, overshoot, peak time, settling time, and mean square error (MSE). These metrics are incorporated into the corresponding optimization approaches to quantitatively assess the controller’s performance. Various test cases have been utilized and the GA outperforms other algorithms ranging from 17% to 28% where rise time, settling time, and MSE are significant in the fitness function.
Keywords: Algorithm; Artificial intelligence; Computer science; Control (management); Control engineering; Control theory (sociology); Engineering; Evolutionary algorithm; Mathematical optimization; Mathematics; Metaheuristic; PID controller; Temperature control; dept.csc

ES-SVM Hyperparameter Optimization for Non-Invasive Coronary Heart Disease Classification - Sheta, Alaa; Elashmawi, Walaa H.; Surani, Salim; Baareh, Abdel Karim M.; Rauch, Peter; Othman, Emad S. (Int. Mobile, Intell., Ubiquitous Comput. Conf., MIUCC, 2025)
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.
Journal Abbreviation: Int. Mobile, Intell., Ubiquitous Comput. Conf., MIUCC
Keywords: CAD; Classification; Coronary Heart Disease; Coronary angiography; Coronary heart disease; Diagnostic accuracy; Evolutionary Strategies; Gold standard (test); Hyperparameter; Machine Learning; Non-Invasive Screening; Support Vector Machine; Support vector machine; dept.csc

A Comparative Statistical Analysis of Machine Learning Models for Suspicious Email Detection - Sheta, Alaa; Pahnehkolaee, Nastaran Davudi; Braik, Malik; Baareh, Abdel Karim M.; Elashmawi, Walaa H.; Othman, Emad S. (Journal of Computing Science and Engineering, 2025)
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.
Keywords: Classification; Machine learning; Random forest classifier; Security; Suspicious email; dept.csc