Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan (Innovation in Information Systems and Technologies to Support Learning Research, 2020)
Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.
Keywords: Clustering; Crow Search Algorithm; Flower Pollination Algorithm; Fuzzy C-mean; K-means; dept.csc

River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network - Aljahdali, Sultan; Sheta, Alaa; Turabieh, Hamza (Innovation in Information Systems and Technologies to Support Learning Research, 2020)
Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.
Keywords: Forecasting; Long short-term memory; River Flow; dept.csc

Enhanced cuckoo search algorithm for industrial winding process modeling - Braik, M.; Sheta, A.; Al-Hiary, H.; Aljahdali, S. (Journal of Intelligent Manufacturing, 2022)
Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods. © 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
4 citations (Crossref) [2023-10-31] tex.author_keywords: Cuckoo search algorithm; Industrial winding process; Linear model; Nonlinear model tex.coden: JIMNE tex.document_type: Article tex.source: Scopus
Keywords: Cuckoo search algorithms; Developed model; Industrial systems; Industrial winding process; Learning algorithms; Linear modeling; Model method; Model structures; Non-linear modelling; Nonlinear systems; Optimization; Parameter estimation; Performance; Process-models; Winding; Winding process; dept.csc; peer-reviewed

Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms - Elashmawi, Walaa H.; Djellal, Adel; Sheta, Alaa; Surani, Salim; Aljahdali, Sultan (Diagnostics, 2024)
Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.
Number: 24
Keywords: artificial neural network (ANN); chronic obstructive pulmonary disease (COPD); dept.csc; machine learning (ML); peer-reviewed; random forest classifier (RFC)

Hybrid Evolutionary Neural Network Models for Gold Price Prediction - Sheta, Alaa; Elashmawi, Walaa H.; Aljahdali, Sultan; Rauch, Peter; Othman, Emad S. (ACIT - Int. Arab Conf. Inf. Technol., Conf. Proc., 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.
Journal Abbreviation: ACIT - Int. Arab Conf. Inf. Technol., Conf. Proc.
Keywords: Gold prices; dept.csc; evolutionary algorithms; financial forecasting; neural networks; prediction models

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms - Sheta, Alaa; Braik, Malik; Maddi, Dheeraj Reddy; Mahdy, Ahmed; Aljahdali, Sultan; Turabieh, Hamza (Applied Sciences, 2021)
Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.
14 citations (Crossref) [2023-10-31] Number: 14
Keywords: Crow Search Algorithm; Genetic Algorithms; PID controller; Particle Swarm Optimization; dept.csc; peer-reviewed; quadcopter

Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan; El-Hefnawi, Fatma; Al-Hiary, Heba; Elashmawi, Walaa H. (Neural Computing and Applications, 2024)
Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.
Keywords: Antenna design; Artificial Intelligence; Gain optimization; Grey wolf optimizer; Meta-heuristic algorithms; Multi-objective optimization; dept.csc; peer-reviewed

Comprehensive Evaluation of Machine Learning Techniques for Obstructive Sleep Apnea Detection - Sheta, Alaa; Elashmawi, Walaa H.; Djellal, Adel; Braik, Malik; Surani, Salim; Aljahdali, Sultan; Subramanian, Shyam; Patel, Parth S. (International Journal of Advanced Computer Science and Applications (ijacsa), 2024)
Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.
Number: 12
Keywords: dept.csc; peer-reviewed