Muneera Altayeb | Al Ahliyya Amman University (original) (raw)
Papers by Muneera Altayeb
Computer methods in biomechanics and biomedical engineering. Imaging & visualization, Sep 29, 2023
Indonesian journal of electrical engineering and computer science, May 1, 2024
Traffic congestion is a significant issue in cities, impacting the environment, commuters, and th... more Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Apr 1, 2024
The brain is one of the most important vital organs in the human body. It is responsible for most... more 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%).
Indonesian Journal of Electrical Engineering and Informatics, Sep 7, 2023
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Jun 1, 2024
Crack detection plays an essential role in evaluating the strength of structures. In recent years... more 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%).
Bio-Medical Materials and Engineering
BACKGROUND: There are many reasons that could lead to finger joint arthroplasty, and the most fam... more BACKGROUND: There are many reasons that could lead to finger joint arthroplasty, and the most familiar reason is osteoarthritis. Silicone finger joint are the most commonly used implants. However, these implants might fracture with time and cause wear which will lead to chronic inflammation and synovitis for the patient and then implant failure. OBJECTIVE: The aim of this study is to improve the design of the silicone finger joint and simulate the different designs using finite element analysis (FEA) simulation. METHOD: Three different designs were drawn and FEA has been used in this study using Solidworks software. The first design is the silicone finger joint design without any modification, the second one is modified design with added ribs to the junction of distal stem and hinge and the third design was added filler material inside the body of the artificial joint. An axial force with 625 N that was applied on the upper part of the distal stem which is nearly represents the maxi...
Indonesian Journal of Electrical Engineering and Informatics, Feb 28, 2022
Modern monitoring systems or manufacturing machines have a major drawback as they depend on human... more Modern monitoring systems or manufacturing machines have a major drawback as they depend on human operators who can easily get distracted or make mistakes, so a system is needed that can constantly monitor the desired area and make decisions while identifying a pre-trained object. Tracking objects with a camera is critical in any automated monitoring and tracking system. The main goal of this paper is to design and implement a robot that can distinguish objects based on their features, such as color and size, and based on artificial intelligence and image processing algorithms. The robot will analyze the video stream to detect the colored object and specify its location inside the video frame. Using the detected position, the raspberry pi will decide the rotation direction whether it is to the right to the left, or forward until it reaches the object, grabs it and puts it in the robot's pocket. The main controlling unit of the system is the Raspberry Pi, the robot is equipped with a Wi-Fi modem to communicate with the mobile application, which is used to control the robot in two modes: manual mode, where the user can point the robot in any direction either by pressing function button or through voice commands. The second mode is the Automatic mode, where the user can ask the robot to detect an object according to a set of characteristics and grab it without any human intervention and based on a novel digital image processing object-tracking algorithm, the accuracy in voice command mode has reached 95%.
Saetak Today&amp... more Saetak Today's visualization tools are equipped with highly interactive visual aids, which allow analysis and inspection of complex numerical data generated from high-bandwidth data sources such as simulation software, experimental rigs, satellites, scanners, etc. Such tools help ...
Engineering, Technology & Applied Science Research, 2021
The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the... more The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet fe...
International Journal of Electrical and Computer Engineering (IJECE), 2022
Determining and classifying pathological human sounds are still an interesting area of research i... more Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good...
Computer methods in biomechanics and biomedical engineering. Imaging & visualization, Sep 29, 2023
Indonesian journal of electrical engineering and computer science, May 1, 2024
Traffic congestion is a significant issue in cities, impacting the environment, commuters, and th... more Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Apr 1, 2024
The brain is one of the most important vital organs in the human body. It is responsible for most... more 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%).
Indonesian Journal of Electrical Engineering and Informatics, Sep 7, 2023
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Jun 1, 2024
Crack detection plays an essential role in evaluating the strength of structures. In recent years... more 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%).
Bio-Medical Materials and Engineering
BACKGROUND: There are many reasons that could lead to finger joint arthroplasty, and the most fam... more BACKGROUND: There are many reasons that could lead to finger joint arthroplasty, and the most familiar reason is osteoarthritis. Silicone finger joint are the most commonly used implants. However, these implants might fracture with time and cause wear which will lead to chronic inflammation and synovitis for the patient and then implant failure. OBJECTIVE: The aim of this study is to improve the design of the silicone finger joint and simulate the different designs using finite element analysis (FEA) simulation. METHOD: Three different designs were drawn and FEA has been used in this study using Solidworks software. The first design is the silicone finger joint design without any modification, the second one is modified design with added ribs to the junction of distal stem and hinge and the third design was added filler material inside the body of the artificial joint. An axial force with 625 N that was applied on the upper part of the distal stem which is nearly represents the maxi...
Indonesian Journal of Electrical Engineering and Informatics, Feb 28, 2022
Modern monitoring systems or manufacturing machines have a major drawback as they depend on human... more Modern monitoring systems or manufacturing machines have a major drawback as they depend on human operators who can easily get distracted or make mistakes, so a system is needed that can constantly monitor the desired area and make decisions while identifying a pre-trained object. Tracking objects with a camera is critical in any automated monitoring and tracking system. The main goal of this paper is to design and implement a robot that can distinguish objects based on their features, such as color and size, and based on artificial intelligence and image processing algorithms. The robot will analyze the video stream to detect the colored object and specify its location inside the video frame. Using the detected position, the raspberry pi will decide the rotation direction whether it is to the right to the left, or forward until it reaches the object, grabs it and puts it in the robot's pocket. The main controlling unit of the system is the Raspberry Pi, the robot is equipped with a Wi-Fi modem to communicate with the mobile application, which is used to control the robot in two modes: manual mode, where the user can point the robot in any direction either by pressing function button or through voice commands. The second mode is the Automatic mode, where the user can ask the robot to detect an object according to a set of characteristics and grab it without any human intervention and based on a novel digital image processing object-tracking algorithm, the accuracy in voice command mode has reached 95%.
Saetak Today&amp... more Saetak Today's visualization tools are equipped with highly interactive visual aids, which allow analysis and inspection of complex numerical data generated from high-bandwidth data sources such as simulation software, experimental rigs, satellites, scanners, etc. Such tools help ...
Engineering, Technology & Applied Science Research, 2021
The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the... more The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet fe...
International Journal of Electrical and Computer Engineering (IJECE), 2022
Determining and classifying pathological human sounds are still an interesting area of research i... more Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good...