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Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network algorithm

Computers, Materials & Continua 2021年第7期68卷 805-819页

作者： Fawaz Waselallah alsaade Theyazn H.H.aldhyani mosleh hmoud al-adhaileh College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi Deanship of E-Learning and Distance Education King Faisal University Saudi Arabia Al-AhsaSaudi Arabia

The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity,and developing a system to identify COVID-19 in its early stages will save millions of *** study applied support vector machine(SVM),k-nearest neighbor(K-NN)and deep learning convolutional neural network(CNN)algorithms to classify and detect COVID-19 using chest X-ray *** test the proposed system,chest X-ray radiographs and CT images were collected from different standard databases,which contained 95 normal images,140 COVID-19 images and 10 SARS *** scenarios were considered to develop a system for predicting *** the first scenario,the Gaussian filter was applied to remove noise from the chest X-ray radiograph images,and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray *** segmentation,a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting *** features were processed using SVM and *** the second scenario,a CNN transfer model(ResNet 50)was used to detect *** system was examined and evaluated through multiclass statistical analysis,and the empirical results of the analysis found significant values of 97.14%,99.34%,99.26%,99.26%and 99.40%for accuracy,specificity,sensitivity,recall and AUC,***,the CNN model showed significant success;it achieved optimal accuracy,effectiveness and robustness for detecting COVID-19.

关键词： Machine-learning algorithm recognition system COVID-19 convolutional neural network

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Human-Animal Affective Robot Touch Classification Using Deep Neural Network

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Computer Systems Science & Engineering 2021年第7期38卷 25-37页

作者： Mohammed Ibrahim Ahmed al-mashhadani Theyazn H.H.aldhyani mosleh hmoud al-adhaileh alwi M.Bamhdi Mohammed Y.alzahrani Fawaz Waselallah alsaade Hasan alkahtani al-Iraqia University College of EducationComputer DepartmentBaghdadIraq Community College of Abqaiq King Faisal UniversityAl-AhsaSaudi Arabia Deanship of E-learning and Distance Education King Faisal UniversityAl-AhsaSaudi Arabia UMM al-Qura University College of ComputingMakkahSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahaSaudi Arabia College of Computer Science and Information Technology King Faisal UniversityAl-AhsaSaudi Arabia

Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and *** novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning *** proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge *** dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor *** leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is *** show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same ***,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification.

关键词： Touch gesture recognition touch gesture classification deep learning

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Deep Learning and Holt-Trend algorithms for Predicting Covid-19 Pandemic

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Computers, Materials & Continua 2021年第5期67卷 2141-2160页

作者： Theyazn H.H.aldhyani Melfi alrasheed mosleh hmoud al-Adaileh Ahmed Abdullah alqarni Mohammed Y.alzahrani Ahmed H.alahmadi Community College of Abqaiq King Faisal UniversityAl HofufSaudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl HofufSaudi Arabia Deanship of E-Learning and Distance Education King Faisal UniversityAl HofufSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahahSaudi Arabia Department of Computer Science and Information Taibah UniversityMadinahKingdom of Saudi Arabia

The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its ***,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of *** this study,a deep learning algorithm and a Holt-trend model are proposed to predict the *** Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death *** real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and *** results suggest that the LSTM models show better performance in predicting the cases of coronavirus *** measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed *** empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three *** far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain ***,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain *** empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these *** findings provide better insights regarding the future of Covid-19 this pandemic in *** results were obtained by applying time series models,which

关键词： Deep learning algorithm holt-trend prediction Covid-19 machine learning

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