This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while ...This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while maintaining data quality.We contributed to meeting the challenges of big data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)algorithm.Thus,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.展开更多
With the popularity of the internet,users hope to better protect their privacy while obtaining network services.However,in the traditional centralized authentication scheme,identity information such as the user's ...With the popularity of the internet,users hope to better protect their privacy while obtaining network services.However,in the traditional centralized authentication scheme,identity information such as the user's private key is generated,stored,and managed by the network operator.Users can't control their identity information,which will lead to a great threat to the privacy of users.Based on redactable blockchain,we propose a fine-grained and fair identity authentication scheme for mobile networks.In our proposed scheme,the user's identity information is generated and controlled by the users.We first propose a notion of score chameleon hash(SCH),which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services.We propose another notion of self-updating secret sharing(SUSS),which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor.Experimental results show that,compared with the immutable blockchain Bitcoin,the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions,and can be adopted with a small additional overhead.展开更多
文摘This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while maintaining data quality.We contributed to meeting the challenges of big data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)algorithm.Thus,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.
基金supported by the Natural Science Foundation of Shanghai(20ZR1419700 and 22ZR1481000)Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(HNTS2022011)。
文摘With the popularity of the internet,users hope to better protect their privacy while obtaining network services.However,in the traditional centralized authentication scheme,identity information such as the user's private key is generated,stored,and managed by the network operator.Users can't control their identity information,which will lead to a great threat to the privacy of users.Based on redactable blockchain,we propose a fine-grained and fair identity authentication scheme for mobile networks.In our proposed scheme,the user's identity information is generated and controlled by the users.We first propose a notion of score chameleon hash(SCH),which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services.We propose another notion of self-updating secret sharing(SUSS),which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor.Experimental results show that,compared with the immutable blockchain Bitcoin,the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions,and can be adopted with a small additional overhead.
