DeustoTeka Examinando por Autor "Mohammed, Mazin Abed"

Examinando por Autor "Mohammed, Mazin Abed"

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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.
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.
COVID-DeepNet: hybrid multimodal deep learning system for Improving COVID-19 pneumonia detection in chest X-ray images
(Tech Science Press, 2021-02-05) Al-Waisy, Alaa S.; Mohammed, Mazin Abed ; Al-Fahdawi, Shumoos; Maashi, Mashael S. ; García-Zapirain, Begoña; Abdulkareem, Karrar Hameed ; Mostafa, Salama A.; Kumar, Nallapaneni Manoj; Le, Dac-Nhuong
Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and followup. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.
A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging
(MDPI, 2022-05-10) Mahmoudi, Ramzi; Benameur, Narjes ; Mabrouk, Rania; Mohammed, Mazin Abed; García-Zapirain, Begoña; Bedoui, Mohamed Hédi
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative; however, segmenting infected regions from CT slices encounters many challenges. Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training.
Detection of focal and non-focal electroencephalogram signals using fast walsh-hadamard transform and artificial neural network
(MDPI AG, 2020-09-01) Prasanna, J.; Subathra, M.S.P.; Mohammed, Mazin Abed; Maashi, Mashael S.; García-Zapirain, Begoña; Sairamya, N.J.; George, Thomas S.
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.
Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach
(MDPI AG, 2021-01-11) Awan, Mazhar Javed ; Mohd Rahim, Mohd Shafry ; Salim, Naomie; Mohammed, Mazin Abed; García-Zapirain, Begoña; Abdulkareem, Karrar Hameed
The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
Fully automatic segmentation of gynaecological abnormality using a new Viola–Jones model
(Tech Science Press, 2021) Hussein, Ihsan Jasim; Burhanuddin, M.A.; Mohammed, Mazin Abed; Elhoseny, Mohamed; García-Zapirain, Begoña; Maashi, Marwah Suliman; Maashi, Mashael S.
One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer database contains 250 images, while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq. Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images. The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%. By contrast, the segmentation result of the proposed system in the ovarian tumor data set was 79.2%. In the classification results, we achieved 95.43% accuracy, 92.20% sensitivity, and 97.5% specificity when we used the breast cancer data set. For the ovarian tumor data set, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity.
Innovative artificial intelligence approach for hearing-loss symptoms identification model using machine learning techniques
(MDPI, 2021-05-12) Abd Ghani, Mohd Khanapi; Noma, Nasir G.; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; García-Zapirain, Begoña ; Maashi, Mashael S.; Mostafa, Salama A.
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
A multi-agent deep reinforcement learning approach for enhancement of COVID-19 CT image segmentation
(MDPI, 2022-02-18) Allioui, Hanane; Mohammed, Mazin Abed ; Benameur, Narjes ; Al-Khateeb, Belal ; Abdulkareem, Karrar Hameed ; García-Zapirain, Begoña; Damasevicius, Robertas ; Maskeliunas, Rytis
Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary. The use of cooperative agents in mask extraction increases the efficiency of automatic image segmentation. Hence, we introduce a new mask extraction method that is based on multi-agent deep reinforcement learning (DRL) to minimize the long-term manual mask extraction and to enhance medical image segmentation frameworks. A DRL-based method is introduced to deal with mask extraction issues. This new method utilizes a modified version of the Deep Q-Network to enable the mask detector to select masks from the image studied. Based on COVID-19 computed tomography (CT) images, we used DRL mask extraction-based techniques to extract visual features of COVID-19 infected areas and provide an accurate clinical diagnosis while optimizing the pathogenic diagnostic test and saving time. We collected CT images of different cases (normal chest CT, pneumonia, typical viral cases, and cases of COVID-19). Experimental validation achieved a precision of 97.12% with a Dice of 80.81%, a sensitivity of 79.97%, a specificity of 99.48%, a precision of 85.21%, an F1 score of 83.01%, a structural metric of 84.38%, and a mean absolute error of 0.86%. Additionally, the results of the visual segmentation clearly reflected the ground truth. The results reveal the proof of principle for using DRL to extract CT masks for an effective diagnosis of COVID-19.
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%.
Novel crow swarm optimization algorithm and selection approach for optimal deep learning COVID-19 diagnostic model
(Hindawi Limited, 2022-08-13) Mohammed, Mazin Abed; Al-Khateeb, Belal ; Yousif, Mohammed ; Mostafa, Salama A.; Kadry, Seifedine ; Abdulkareem, Karrar Hameed ; García-Zapirain, Begoña
Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.
A novel fuzzy parameterized fuzzy hypersoft set and riesz summability approach based decision support system for diagnosis of heart diseases
(MDPI, 2022-06-22) Rahman, Atiqe Ur; Saeed, Muhammad ; Mohammed, Mazin Abed; Jaber, Mustafa Musa; García-Zapirain, Begoña
Fuzzy parameterized fuzzy hypersoft set (∆-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (∆-set) and Riesz Summability. After investigating some properties and aggregations of ∆-set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of ∆-set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.
Parametric methods for the regional assessment of cardiac wall motion abnormalities: comparison study
(Tech Science Press, 2021-06-04) Benameur, Narjes; Mohammed, Mazin Abed ; Mahmoudi, Ramzi ; Arous, Younes; García-Zapirain, Begoña ; Abdulkareem, Karrar Hameed; Bedoui, Mohamed Hédi
Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.
Rise of deep learning clinical applications and challenges in omics data: a systematic review
(MDPI, 2023-02-01) Mohammed, Mazin Abed ; Abdulkareem, Karrar Hameed ; Dinar, Ahmed M.; García-Zapirain, Begoña
This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks
(King Saud bin Abdulaziz University, 2023-10-01) Lakhan, Abdullah ; Abdul Lateef, A.A.; Abd Ghani, Mohd Khanapi ; Abdulkareem, Karrar Hameed ; Mohammed, Mazin Abed ; Nedoma, Jan ; Martinek, Radek ; García-Zapirain, Begoña
The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.
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.

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