Stock Market Price Forecasting Using Metaheuristic Search Algorithms: A Comparative Analysis - Sheta, A.L.A.A.; Abdel-Raouf, A.M.A.L. (International Journal of Computing, 2024)
Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor's 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models. © (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.
Keywords: Algorithm; Computer science; Econometrics; Economics; Engineering; Mathematical optimization; Mathematics; Metaheuristic; Particle swarm optimization; Stock (firearms); Stock market; Stock market forecasting; Stock market index; computational intelligence; crow search algorithm; dandelion optimizer; dept.csc; gray wolf optimizer; metaheuristic search algorithms; particle swarm optimizer; 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.
Keywords: 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.
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

Fear of Missing Out’s (FoMO) relationship with moral judgment and behavior - McKee, Paul C.; Senthilnathan, Ithika; Budnick, Christopher J.; Bind, Marie-Abèle; Antonios, Imad; Sinnott-Armstrong, Walter (PLOS ONE, 2024)
Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.
Keywords: Behavior; Interpersonal relationships; Machine learning; Memory recall; Moral philosophy; Personality; Religion; Social media; dept.csc; dept.psy; peer-reviewed

Barcode-less Fruits Classification Using Deep Learning - Abdel-raouf, Amal; Sheta, Alaa; Baareh, AbdelKarim; Rausch, Peter (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
Keywords: Classification; Convolution Neural Network; Fruit Detection; dept.csc; peer-reviewed

Heart disease diagnosis using decision trees with feature selection method - Sheta, A.; El-Ashmawi, W.; Baareh, A. (International Arab Journal of Information Technology, 2024)
The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease. © 2024, Zarka Private University. All rights reserved.
Keywords: Machine learning; classification; decision tree; dept.csc; diagnosis; heart disease; peer-reviewed

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

Computer networks: data communications, internet and security - Elahi, Ata; Cushman, Alex (Springer International Publishing, 2024)
​The hands-on textbook covers both the theory and applications of data communications, the Internet, and network security technology, following the ACM guideline for courses in networking. The content is geared towards upper undergraduate and graduate students in information technology, communications engineering, and computer science. The book is divided into three sections: Data Communications, Internet Architecture, and Network Security. Topics covered include flow control and reliable transmission; modulation, DSL, cable modem, and FTTH; Ethernet and Fast Ethernet; gigabit and 10 gigabit Ethernet; and LAN interconnection devices, among others. The book also covers emerging topics such as IPv6 and software defined networks. The book is accompanied with a lab manual which uses Wireshark, Cisco Packet Tracer, and virtual machines to lead students through simulated labs.
Keywords: Communications Channels and Media; Communications Networks; Computers / Information Theory; Computers / Networking / Hardware; Data Communications; Internet Protocols; Language Arts & Disciplines / Library & Information Science / General; Network Security; SCSU-student; Technology & Engineering / Electrical; Technology & Engineering / Telecommunications; Wireless Local Area Network (WLAN); dept.csc