Dung’s theory of argumentation frameworks (AF) has been applied in many fields of artificial intelligence. The arguments and attack relation are generally partly believed due to the uncertainty in the process of mini...Dung’s theory of argumentation frameworks (AF) has been applied in many fields of artificial intelligence. The arguments and attack relation are generally partly believed due to the uncertainty in the process of mining them. Fuzzy AFs catch uncertainty in AFs by associating fuzzy degrees with the arguments or the attacks. Among the various semantics of fuzzy AFs, the comparative semantics develops and defines Dung’s extensions in the form of fuzzy sets. However, the comparative semantic system only puts forward some basic concepts, and has not been deeply studied in terms of algorithms and properties. This paper studies the comparative semantics of fuzzy AFs based on the Łukasiewicz t-norm in a more in-depth and comprehensive manner. This work is not only a supplement and improvement to comparative semantic in theory, but also beneficial to the calculation and fast identification of its various extensions (based on the Łukasiewicz t-norm).展开更多
Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain ph...Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.展开更多
文摘Dung’s theory of argumentation frameworks (AF) has been applied in many fields of artificial intelligence. The arguments and attack relation are generally partly believed due to the uncertainty in the process of mining them. Fuzzy AFs catch uncertainty in AFs by associating fuzzy degrees with the arguments or the attacks. Among the various semantics of fuzzy AFs, the comparative semantics develops and defines Dung’s extensions in the form of fuzzy sets. However, the comparative semantic system only puts forward some basic concepts, and has not been deeply studied in terms of algorithms and properties. This paper studies the comparative semantics of fuzzy AFs based on the Łukasiewicz t-norm in a more in-depth and comprehensive manner. This work is not only a supplement and improvement to comparative semantic in theory, but also beneficial to the calculation and fast identification of its various extensions (based on the Łukasiewicz t-norm).
基金The authors wish to express their sincere thanks to the Department of Science&Technology,New Delhi,India(Project ID:SR/FST/ETI-371/2014)express their sincere thanks to the INSPIRE fellowship(DST/INSPIRE Fellowship/2016/IF160837)for their financial support.The authors also thank SASTRA Deemed to be University,Thanjavur,India for extending the infrastructural support to carry out this work.
文摘Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.
