Examinando por Autor "Mutlag, Ammar Awad"

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    Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods
    (Institute of Electrical and Electronics Engineers Inc., 2020-06-08) Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Al-Waisy, Alaa S.; Mostafa, Salama A.; Al-Fahdawi, Shumoos; Dinar, Ahmed M.; Alhakami, Wajdi; Baz, Abdullah; Al-Mhiqani, Mohammed Nasser; Alhakami, Hosam; Arbaiy, Nureize; Maashi, Mashael S.; Mutlag, Ammar Awad; García-Zapirain, Begoña; Torre Díez, Isabel de la
    Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.
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    A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on X-ray images
    (Tech Science Press, 2021) Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; García-Zapirain, Begoña; Mostafa, Salama A.; Maashi, Mashael S.; Al-Waisy, Alaa S.; Subhi, Mohammed Ahmed; Mutlag, Ammar Awad; Le, Dac-Nhuong
    The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor (k-NN), Decision Tree (DT), and CN 2 rule inducer techniques) and deep learning models (e.g., MobileNets V2, ResNet50, GoogleNet, DarkNet and Xception). A large X-ray dataset has been created and developed, namely the COVID-19 vs. Normal (400 healthy cases, and 400 COVID cases). To the best of our knowledge, it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases. Based on the results obtained from the experiments, it can be concluded that all the models performed well, deep learning models had achieved the optimum accuracy of 98.8% in ResNet50 model. In comparison, in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBF accuracy 94% for the prediction of coronavirus disease 2019.
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    Multi-agent systems in fog–cloud computing for critical healthcare task management model (CHTM) used for ECG monitoring
    (MDPI, 2021-10-19) Mutlag, Ammar Awad; Ghani, Mohd Khanapi Abd; Mohammed, Mazin Abed; Lakhan, Abdullah; Mohd, Othman ; Abdulkareem, Karrar Hameed ; García-Zapirain, Begoña
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
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    A new multi-agent feature wrapper machine learning approach for heart disease diagnosis
    (Tech Science Press, 2021-01-12) Elhoseny, Mohamed; Abed Mohammed, Mazin; Mostafa, Salama A.; Abdulkareem, Karrar Hameed; Maashi, Mashael S.; García-Zapirain, Begoña; Mutlag, Ammar Awad; Maashi, Marwah Suliman
    Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%-83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.