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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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Compared Insights on machine-learning Anomaly Detection for Process Control Feature

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Computers, Materials & Continua 2022年第11期73卷 4033-4049页

作者： Ming Wan Quanliang Li Jiangyuan Yao Yan Song Yang Liu Yuxin Wan School of Information Liaoning UniversityShenyang110036China School of Computer Science and Technology Hainan UniversityHaikou570228China School of Physics Liaoning UniversityShenyang110036China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016China Department of Electrical Engineering and Information Technology Karlsruhe Institute of TechnologyKarlsruhe76131Germany

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber ***,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature *** a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning *** on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching ***,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming *** the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively *** experimental results can clearly explain that SVM(Support Vector machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

关键词： Anomaly detection machine-learning algorithm process control feature qualitative and quantitative comparisons

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