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All Journal International Journal of Electrical and Computer Engineering
Muawia A. Elsadig
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Breast cancer detection using machine learning approaches: a comparative study Muawia A. Elsadig; Abdelrahman Altigani; Huwaida T. Elshoush
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp736-745

Abstract

As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier.
An ensemble model to detect packet length covert channels Muawia A. Elsadig; Ahmed Gafar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5296-5304

Abstract

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.
Co-Authors Abdelrahman Altigani Ahmed Gafar Huwaida T. Elshoush