Smart Agriculture: An Expert System for Tomato Plant Disease Diagnosis - Sheta, Alaa; Elashmawi, Walaa H.; Othman, Emad S. (ISBCom 2025, 2026)
Tomato plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses. Early and accurate diagnosis is crucial for effective disease management. This paper describes the design and implementation of expert systems for tomato disease detection using the CLIPS (C Language Integrated Production System) platform. The tool is designed to help farmers and agronomists accurately identify diseases affecting tomato crops by simulating knowledge from professional experts. We carefully developed a set of rules to distinguish leaf blight symptoms from those of other tomato diseases and provided recommendations to minimize crop losses and maximize yields. The expert system was developed using a forward-chaining inference engine, and its performance was evaluated through a set of real-world test cases, demonstrating a high level of accuracy and consistency in decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Keywords: Blight; CLIPS; Consistency (knowledge bases); Expert System; Expert system; Inference engine; Knowledge Base; Plant Disease Diagnosis; Plant disease; Production (economics); Set (abstract data type); Tomato Diseases; dept.csc

Hybrid evolutionary neural network models for gold price prediction - Sheta, Alaa; Elashmawi, Walaa; Aljahdali; Rauch, Peter; Othman, Emad (2025 26th International Arab Conference on Information Technology (ACIT), 2025)
Keywords: Artificial neural network; Evolutionary algorithm; Feature (linguistics); Genetic algorithm; Hybrid neural network; dept.csc

Utilizing various machine learning techniques for diabetes mellitus feature selection and classification - Sheta, Alaa; Elashmawi, Walaa H.; Al-Qerem, Ahmad; Othman, Emad S. (International Journal of Advanced Computer Science and Applications (IJACSA), 2024)
Diabetes mellitus is a chronic disease affecting over 38.4 million adults worldwide. Unfortunately, 8.7 million were undiagnosed. Early detection and diagnosis of diabetes can save millions of people’s lives. Significant benefits can be achieved if we have the means and tools for the early diagnosis and treatment of diabetes since it can reduce the ratio of cardiovascular disease and mortality rate. It is urgently necessary to explore computational methods and machine learning for possible assistance in the diagnosis of diabetes to support physician decisions. This research utilizes machine learning to diagnose diabetes based on several selected features collected from patients. This research provides a complete process for data handling and pre-processing, feature selection, model development, and evaluation. Among the models tested, our results reveal that Random Forest performs best in accuracy (i.e., 0.945%). This emphasizes Random Forest’s efficiency in precisely helping diagnose and reduce the risk of diabetes.
Keywords: dept.csc; peer-reviewed

Hybrid Evolutionary Neural Network Models for Gold Price Prediction - Sheta, Alaa; Elashmawi, Walaa H.; Aljahdali, Sultan; Rauch, Peter; Othman, Emad S. (ACIT 2025 - 26th International Arab Conference on Information Technology, Conference Proceedings, 2025)
Gold serves as a safe-haven asset, an inflation hedge, and a store of value, among other economic purposes. History has a wealth of information about its connections to important macroeconomic and financial variables. We compare three supervised methods for one-day GLD (SPDR Gold Shares) prediction using cross-Asset drivers: SPX (S&P 500 Index), USO (United States Oil Fund), SLV (iShares Silver Trust), and EUR/USD (Euro-U.S. Dollar exchange rate). To fill this gap: (i) a linear regression baseline, (ii) a feedforward neural network (FFNN) trained by a Genetic Algorithm (GA), and (iii) an FFNN optimized through Particle Swarm Optimization (PSO). We use standard scaling, a held-out train/test split, and R2, Variance Accounted For (VAF), MSE, RMSE, MAE, and GA/PSO convergence curves to check how the optimizer works with daily data from 2008 to 2018. Empirically, PSO-FFNN does better than both the linear baseline and GA-FFNN. It has the best generalization (for example, Test MSE 38.99, R 0.929). This implies that (1) GLD possesses exploitable nonlinear structure concerning these predictors, and (2) PSO traverses the FFNN search space more effectively than GA in our context. The findings validate the application of evolutionary training for reliable and accurate gold price forecasts, with implications for risk management and strategic asset allocation. © 2025 IEEE.
Keywords: Gold prices; dept.csc; evolutionary algorithms; financial forecasting; neural networks; prediction models

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.
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

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. (5th International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2025, 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.
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