Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stage...Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was developed.The first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it vertically.The second stage builds a custom convolutional neural network model using several pre-trained ImageNet.Finally,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory efficiency.Several experiments were done using different techniques and parameters.Accordingly,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 detection.This paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.展开更多
At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these metho...At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).展开更多
Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment.With the increasing demand for mobile...Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment.With the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests.This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices.Meanwhile,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources.This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm.The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user preferences.The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance.The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS.The relationships among concepts in the ontology are considered to improve the recall and precision ratio.After the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user interface.The interface of the invocation process is changed according to the WS description document.An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.展开更多
COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.T...COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.展开更多
基金This work was supported by the National Research Foundation of Korea-Grant funded by the Korean Government(Ministry of Science and ICT)-NRF-2020R1A2B5B02002478).There was no additional external funding received for this study.
文摘Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was developed.The first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it vertically.The second stage builds a custom convolutional neural network model using several pre-trained ImageNet.Finally,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory efficiency.Several experiments were done using different techniques and parameters.Accordingly,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 detection.This paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.
基金Taif University Researchers Supporting Project Number(TURSP-2020/126),Taif University,Taif,Saudi Arabia.
文摘At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)the Soonchunhyang University Research Fund.
文摘Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment.With the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests.This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices.Meanwhile,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources.This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm.The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user preferences.The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance.The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS.The relationships among concepts in the ontology are considered to improve the recall and precision ratio.After the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user interface.The interface of the invocation process is changed according to the WS description document.An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.
文摘COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.