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Titlebook: Computational Science – ICCS 2018; 18th International C Yong Shi,Haohuan Fu,Peter M. A. Sloot Conference proceedings 2018 Springer Internat

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書目名稱Computational Science – ICCS 2018
副標題18th International C
編輯Yong Shi,Haohuan Fu,Peter M. A. Sloot
視頻videohttp://file.papertrans.cn/234/233077/233077.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Science – ICCS 2018; 18th International C Yong Shi,Haohuan Fu,Peter M. A. Sloot Conference proceedings 2018 Springer Internat
描述.The three-volume set LNCS 10860, 10861 + 10862 constitutes the proceedings of the 18th International Conference on Computational Science, ICCS 2018, held in Wuxi, China, in June 2018...The total of 155 full and 66 short papers presented in this book set was carefully reviewed and selected from 404 submissions. The papers were organized in topical sections named: ..Part I: ICCS Main Track..Part II: Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging ManYcore Systems; Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Data, Modeling, and Computation in IoT and Smart Systems; Track of Data-Driven Computational Sciences; Track of Mathematical-Methods-and-Algorithms for Extreme Scale; Track of Multiscale Modelling and Simulation..Part III: Track of Simulations o
出版日期Conference proceedings 2018
關鍵詞computer networks; data security; artificial intelligence; genetic algorithms; parallel architectures; pa
版次1
doihttps://doi.org/10.1007/978-3-319-93698-7
isbn_softcover978-3-319-93697-0
isbn_ebook978-3-319-93698-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Insider Threat Detection with Deep Neural Networkze feature matrices to detect insider threat. We conduct experiments on a public dataset of insider threats. Experimental results show that our method can successfully detect insider threat and we obtained AUC?=?0.9449 in best case.
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