Examinando por Autor "Alhakami, Hosam"

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  • Miniatura
    Ítem
    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.
  • Miniatura
    Ítem
    Voice pathology detection and classification using convolutional neural network model
    (MDPI AG, 2020-05-27) Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A.; Ghani, Mohd Khanapi Abd; Maashi, Mashael S.; García-Zapirain, Begoña; Oleagordia Ruiz, Ibon; Alhakami, Hosam; Al-Dhief, Fahad Taha
    Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrucken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.