As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of th...As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.展开更多
With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus d...With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured.展开更多
In-network data aggregation is severely affected due to information in transmits attack. This is an important problem since wireless sensor networks (WSN) are highly vulnerable to node compromises due to this attack. ...In-network data aggregation is severely affected due to information in transmits attack. This is an important problem since wireless sensor networks (WSN) are highly vulnerable to node compromises due to this attack. As a result, large error in the aggregate computed at the base station due to false sub aggregate values contributed by compromised nodes. When falsified event messages forwarded through intermediate nodes lead to wastage of their limited energy too. Since wireless sensor nodes are battery operated, it has low computational power and energy. In view of this, the algorithms designed for wireless sensor nodes should be such that, they extend the lifetime, use less computation and enhance security so as to enhance the network life time. This article presents Vernam Cipher cryptographic technique based data compression algorithm using huff man source coding scheme in order to enhance security and lifetime of the energy constrained wireless sensor nodes. In addition, this scheme is evaluated by using different processor based sensor node implementations and the results are compared against to other existing schemes. In particular, we present a secure light weight algorithm for the wireless sensor nodes which are consuming less energy for its operation. Using this, the entropy improvement is achieved to a greater extend.展开更多
文摘As the use of mobile devices continues to rise,trust administration will significantly improve security in routing the guaranteed quality of service(QoS)supply in Mobile Ad Hoc Networks(MANET)due to the mobility of the nodes.There is no continuance of network communication between nodes in a delay-tolerant network(DTN).DTN is designed to complete recurring connections between nodes.This approach proposes a dynamic source routing protocol(DSR)based on a feed-forward neural network(FFNN)and energybased random repetition trust calculation in DTN.If another node is looking for a node that swerved off of its path in this situation,routing will fail since it won’t recognize it.However,in the suggested strategy,nodes do not stray from their pathways for routing.It is only likely that the message will reach the destination node if the nodes encounter their destination or an appropriate transitional node on their default mobility route,based on their pattern of mobility.The EBRRTC-DTN algorithm(Energy based random repeat trust computation)is based on the time that has passed since nodes last encountered the destination node.Compared to other existing techniques,simulation results show that this process makes the best decision and expertly determines the best and most appropriate route to send messages to the destination node,which improves routing performance,increases the number of delivered messages,and decreases delivery delay.Therefore,the suggested method is better at providing better QoS(Quality of Service)and increasing network lifetime,tolerating network system latency.
基金This study is supported by the Tamil Nadu State Council of Science and Technology.
文摘With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured.
文摘In-network data aggregation is severely affected due to information in transmits attack. This is an important problem since wireless sensor networks (WSN) are highly vulnerable to node compromises due to this attack. As a result, large error in the aggregate computed at the base station due to false sub aggregate values contributed by compromised nodes. When falsified event messages forwarded through intermediate nodes lead to wastage of their limited energy too. Since wireless sensor nodes are battery operated, it has low computational power and energy. In view of this, the algorithms designed for wireless sensor nodes should be such that, they extend the lifetime, use less computation and enhance security so as to enhance the network life time. This article presents Vernam Cipher cryptographic technique based data compression algorithm using huff man source coding scheme in order to enhance security and lifetime of the energy constrained wireless sensor nodes. In addition, this scheme is evaluated by using different processor based sensor node implementations and the results are compared against to other existing schemes. In particular, we present a secure light weight algorithm for the wireless sensor nodes which are consuming less energy for its operation. Using this, the entropy improvement is achieved to a greater extend.
