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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Altayeb, Muneera
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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An automated system for classifying types of cerebral hemorrhage based on image processing techniques Arabiat, Areen; Altayeb, Muneera
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.pp1594-1603

Abstract

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).
Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers Altayeb, Muneera; Arabiat, Areen
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.pp3332-3341

Abstract

Crack detection plays an essential role in evaluating the strength of structures. In recent years, the use of machine learning and deep learning techniques combined with computer vision has emerged to assess the strength of structures and detect cracks. This research aims to use machine learning (ML) to create a crack detection model based on a dataset consisting of 2432 images of different surfaces that were divided into two groups: 70% of the training dataset and 30% of the testing dataset. The Orange3 data mining tool was used to build a crack detection model, where the support vector machine (SVM), gradient boosting (GB), naive Bayes (NB), and artificial neural network (ANN) were trained and verified based on 3 sets of features, mel-frequency cepstral coefficients (MFCC), delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC) were extracted using MATLAB. The experimental results showed the superiority of SVM with a classification accuracy of (100%), while for NB the accuracy reached (93.9%-99.9%), and (99.9%) for ANN, and finally in GB the accuracy reached (99.8%).
Enhancing internet of things security: evaluating machine learning classifiers for attack prediction Arabiat, Areen; Altayeb, Muneera
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.pp6036-6046

Abstract

The internet of things (IoT) has contributed to improving the quality of service and operational efficiency in many areas, such as smart cities, but this technology has faced a major dilemma: the problem of cyber-attacks of various types. In this study, we relied on the use of machine learning (ML) and deep learning (DL) techniques to present a proposed model of an intrusion detection system (IDS) for detecting different types of IoT attacks that include ARP_poisoning, DOS_SYN_Hping, MQTT_Publish, NMAP_FIN_SCAN, NMAP_OS_DETECTION, and Thing_Speak. However, the proposed model is built using Orange3 data mining tools. The model consists of random forest (RF), artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) classifiers. On the other hand, the data set that is used was obtained from the Kaggle platform's real-time IoT infrastructure data set, called RT-IoT2022. The data set consists of a huge number of records, which are processed and then reduced to 7,481 records using linear discriminant analysis. In the next stage, the data set is fed to the Orange3 data mining tool, which is divided into 70% of the training dataset and 30% of the test dataset, in addition to using fold-cross validation to increase accuracy and avoid overfitting. Thus, the experimental results showed the superiority of RF with a classification accuracy of (99.9%), while the accuracy in ANN reached (99.8%), (97.8%) in LR, and finally, for SVM, the accuracy reached (92.9%).
Detection and classification of pneumonia using the Orange3 data mining tool Altayeb, Muneera; Arabiat, Areen; Al-Ghraibah, Amani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6894-6903

Abstract

A chest X-ray can convey a lot about a patient's condition. However, it requires a specialized and skilled doctor to determine the type of lung disease with high accuracy. Here comes the role of deep learning techniques (DL) and artificial intelligence (AI) in accelerating the process of detecting lung diseases and classifying them with high precision, which saves time and effort for the patient and the doctor alike. This work presents a proposed model for a machine learning (ML) and AI system to analyze chest X-ray images and categorize them into four cases normal, viral pneumonia, bacterial pneumonia, and coronavirus disease 2019 (COVID-19). The system relies on extracting Mel frequency cepstral coefficient (MFCC) features from a dataset consisting of 4,800 chest X-ray images, and then these features are used to train four basic classifiers based on the data mining tool Orange3, which are adaptive boosting (AdaBoost), decision trees (DTs), gradient boosting (GB), and random forest (RF). The model was tested and evaluated, where the AdaBoost classifier excelled with an accuracy of 100%, followed by RF with an accuracy of 99.5%. Finally, GB and DTs came with a classification accuracy of 98.5%, and 97.2%, respectively.
Co-Authors Al-Ghraibah, Amani Arabiat, Areen Salama, Sanaa