Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in th...Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic situations.To handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified configurations.Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server.Data deduplication also saves network bandwidth.In this paper,a new cloud-based big data security technique utilizing dual encryption is proposed.The clustering model is utilized to analyze the Deduplication process hash function.Multi kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption procedure.The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is used.This security process involving dual encryption with the optimization model develops the productivity mechanism.In this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques.The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.展开更多
Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral ...Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.展开更多
文摘Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic situations.To handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified configurations.Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server.Data deduplication also saves network bandwidth.In this paper,a new cloud-based big data security technique utilizing dual encryption is proposed.The clustering model is utilized to analyze the Deduplication process hash function.Multi kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption procedure.The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is used.This security process involving dual encryption with the optimization model develops the productivity mechanism.In this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques.The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.
文摘Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly increased.Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis.The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials.Presently,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high performance.In this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC model.Initially,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier results.Next,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML map.In addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class labels.An extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University dataset.The obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.
