Titlebook: Advances in Artificial Intelligence and Security; 7th International Co Xingming Sun,Xiaorui Zhang,Elisa Bertino Conference proceedings 2021

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書目名稱Advances in Artificial Intelligence and Security影響因子(影響力)




書目名稱Advances in Artificial Intelligence and Security影響因子(影響力)學(xué)科排名




書目名稱Advances in Artificial Intelligence and Security網(wǎng)絡(luò)公開度




書目名稱Advances in Artificial Intelligence and Security網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Artificial Intelligence and Security被引頻次




書目名稱Advances in Artificial Intelligence and Security被引頻次學(xué)科排名




書目名稱Advances in Artificial Intelligence and Security年度引用




書目名稱Advances in Artificial Intelligence and Security年度引用學(xué)科排名




書目名稱Advances in Artificial Intelligence and Security讀者反饋




書目名稱Advances in Artificial Intelligence and Security讀者反饋學(xué)科排名

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Conference proceedings 2021lume proceedings was carefully reviewed and selected from 1013 submissions. The papers were organized in topical sections as follows:.Part I: artificial intelligence;.Part II: artificial intelligence; big data; cloud computing and security;?.Part III: cloud computing and security;?encryption and cybersecurity;?information hiding;?IoT security.?.

整理

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Conference proceedings 2021ce and Security, ICAIS 2021, which was held in Dublin, Ireland, in July 2021.?.The total of 131 full papers and 52 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1013 submissions. The papers were organized in topical sections as follows:.Part I: artifici

Excise

1865-0929 : artificial intelligence;.Part II: artificial intelligence; big data; cloud computing and security;?.Part III: cloud computing and security;?encryption and cybersecurity;?information hiding;?IoT security.?.978-3-030-78620-5978-3-030-78621-2Series ISSN 1865-0929 Series E-ISSN 1865-0937

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山間窄路

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Philippa D. Darbre,Roger J. B. King article uses the LLVM compilation platform to convert the OpenMP source code into LLVM intermediate code (IR) through Clang, statically and dynamically analyzes the IR, and then obtains the OpenMP performance model to predict the OpenMP execution time and optimal execution time. Based on the modeli

RAG

https://doi.org/10.1007/978-981-19-0197-3m memory artificial neural network proposed in this paper. The results show that the prediction model based on CNN+LSTM is compared with only LSTM algorithm or only classification algorithm. It can improve prediction accuracy, reduce model training time, and is easier to apply in the actual work of