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Almazaydeh, Laiali
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
A comparative analysis of convolutional neural networks for breast cancer prediction Al Tawil, Arar; Shaban, Amneh; Almazaydeh, Laiali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3406-3414

Abstract

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.
Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis Tawil, Arar Al; Al-Shboul, Lara; Almazaydeh, Laiali; Alshinwan, Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5894-5905

Abstract

Intrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support vector machine (SVM), extreme gradient boosting (XGBoost), and naive Bayes was highly impressive. Vital accuracy, precision, recall, and F1-score values of 0.984389, 0.984479, 0.984375, and 0.984304, respectively, were achieved by SVM. Notably, XGBoost demonstrated exceptional performance across all metrics, attaining flawless scores of 1.0. naive Bayes demonstrated noteworthy accuracy, precision, recall, and F1-score performance, which were recorded as 0.877392, 0.907171, 0.877007, and 0.876986, respectively. The results of this study emphasize the critical importance of preparation methods in improving the effectiveness of IDS via machine learning. This further demonstrates the potential of particular classifiers to detect and prevent network intrusions efficiently, thereby substantially contributing to cybersecurity measures.
Predictive modeling for breast cancer based on machine learning algorithms and features selection methods Al Tawil, Arar; Almazaydeh, Laiali; Alqudah, Bilal; Zaid Abualkishik, Abedallah; A. Alwan, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1937-1947

Abstract

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
Enhancing Alzheimer’s disease diagnosis through metaheuristic feature selection and advanced classification techniques Al-Tawil, Arar; Al-Muhtaseb, Worood; Almazaydeh, Laiali; Fathi, Hanaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3382-3395

Abstract

A diverse array of diagnostic and detection methods has been developed as a result of the advent of Alzheimer’s disease (AD) as a significant global health issue. This study employs bio-inspired algorithms, such as the parrot optimization algorithm (POA), grey wolf optimizer (GWO), and differential evolution (DE), to identify the most effective feature selection techniques for AD diagnosis. The predictive accuracy of these algorithms was improved by the simple keywords: Alzheimer’s disease optimization classification machine learning metaheuristic mentation of the Alzheimer’s disease Dataset. This was achieved by integrating a personalized fitness function and optimizing parameter settings with decision tree classifiers. To evaluate the algorithms’ effectiveness in machine learning models with population sizes of 30 and 60, precision, recall, accuracy, and F1-score were evaluated at 5, 15, and 30 iterations. The gradient boosting and XGBoost classifiers consistently obtained the highest results, while DE, GWO, and parrot optimization (PO) achieved maximal accuracy rates of 0.94, 0.93, and 0.94, respectively. These findings underscore the efficacy of integrating metaheuristic algorithms with robust classifiers to enhance the predictive accuracy of AD diagnosis. Furthermore, they illustrate that artificial intelligence (AI) algorithms that are operated by biological processes can accurately forecast AD, with the success rates and stability of the proposed methods serving as metrics for evaluating their efficacy.
Co-Authors A. Alwan, Ali Al Tawil, Arar Al-Muhtaseb, Worood Al-Shboul, Lara Al-Tawil, Arar Alqudah, Bilal Alshinwan, Mohammad Fathi, Hanaa Shaban, Amneh Tawil, Arar Al Zaid Abualkishik, Abedallah