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)

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

Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm - Braik, M.; Sheta, A.; Kovač-Andrić, E.; Al-Hiary, H.; Aljahdali, S.; Elashmawi, W.H.; Awadallah, M.A.; Al-Betar, M.A. (Water, Air, and Soil Pollution, 2024)
Urban air pollution, a combination of industry, traffic, forest burning, and agriculture pollutants, significantly impacts human health, plants, and economic growth. Ozone exposure can lead to mortality, heart attacks, and lung damage, necessitating the creation of complex environmental safety regulations by forecasting ozone concentrations and associated pollutants. This study proposes a hybrid method, RFNN-GOA, combining recurrent fuzzy neural network (RFNN) and grasshopper optimization algorithm (GOA) to estimate and forecast the daily ozone (O3) in specific urban areas, specifically Kopački Rit and Osijek city in Croatia, aiming to improve air quality, human health, and ecosystems. Due to the intricate structure of atmospheric particles, modeling of O3 likely poses the biggest challenge in air pollution today. The dataset used by the proposed RFNN-GOA model for the prediction of O3 concentrations in each explored area consists of the following air pollutants, NO, NO2, CO, SO2, O3, PM10, and PM2.5; and five meteorological elements, including temperature, relative humidity, wind direction, speed, and pressure. The RFNN-GOA method optimizes membership functions’ parameters and the rule premise, demonstrating robustness and reliability compared to other identifiers and indicating its superiority over competing methods. The RFNN-GOA method demonstrated superior accuracy in Osijek city and Kopački Rit area, with variance-accounted for (VAF) values of 91.135%, 83.676%, 87.807%, 79.673% compared to the RFNN method’s corresponding values of 85.682%, 80.687%, 80.808%, 74.202% in both training and testing phases, respectively. This reveals that RFNN-GOA increased the average VAF in Osijek city and Kopački Rit area by over 5% and 8%, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Keywords: Auto-regressive integrated moving average; Grasshopper optimization algorithm; Multiple linear regression; Recurrent fuzzy neural network; dept.csc; peer-reviewed