Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL...Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.展开更多
The quick spread of the CoronavirusDisease(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,whi...The quick spread of the CoronavirusDisease(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),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-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 RBFaccuracy 94% for the prediction of coronavirus disease 2019.展开更多
The distribution characteristics of air voids in ultrathin asphalt friction course(UAFC) samples with different gradations and compaction methods were statistically analyzed using X-ray computed tomography(CT) and ima...The distribution characteristics of air voids in ultrathin asphalt friction course(UAFC) samples with different gradations and compaction methods were statistically analyzed using X-ray computed tomography(CT) and image analysis techniques. Based on the results, compared with the AC-5 sample, the OGFC-5mixture has a higher air void ratio, a larger air void size and a greater number of air voids, with the distribution of internal air voids being more uniform and their shapes being more rounded. The two-parameter Weibull function was applied to fit the gradation of air voids. The fitting results is good, and the function parameters are sensitive to changes in both mineral gradation and compaction method. Moreover, two homogeneity indices were proposed to evaluate the compaction uniformity of UAFC samples. Compared with the Marshall method,the SGC method is more conducive to improve the compaction uniformity of UAFC samples. The compaction method significantly influences the air void distribution characteristics and compaction uniformity of AC-5sample, but has a less significant impact on OGFC-5 sample. The experimental results in the study provides a solid foundation for further explorations on the internal structure and mixture design of UAFC.展开更多
Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an u...Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.展开更多
Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41877267 and 41877260)the Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA13010201).
文摘Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.
文摘The quick spread of the CoronavirusDisease(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),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-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 RBFaccuracy 94% for the prediction of coronavirus disease 2019.
基金Funded by Technology Innovation Demonstration Project of the Transportation Department of Yunnan Province (Science and Technology Education Section of Transport Department of Yunnan Province [2019](No.14)。
文摘The distribution characteristics of air voids in ultrathin asphalt friction course(UAFC) samples with different gradations and compaction methods were statistically analyzed using X-ray computed tomography(CT) and image analysis techniques. Based on the results, compared with the AC-5 sample, the OGFC-5mixture has a higher air void ratio, a larger air void size and a greater number of air voids, with the distribution of internal air voids being more uniform and their shapes being more rounded. The two-parameter Weibull function was applied to fit the gradation of air voids. The fitting results is good, and the function parameters are sensitive to changes in both mineral gradation and compaction method. Moreover, two homogeneity indices were proposed to evaluate the compaction uniformity of UAFC samples. Compared with the Marshall method,the SGC method is more conducive to improve the compaction uniformity of UAFC samples. The compaction method significantly influences the air void distribution characteristics and compaction uniformity of AC-5sample, but has a less significant impact on OGFC-5 sample. The experimental results in the study provides a solid foundation for further explorations on the internal structure and mixture design of UAFC.
文摘Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
