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Titlebook: Recent Trends in Artificial Intelligence and IoT; First International Rajesh Kumar Tiwari,G. Sahoo Conference proceedings 2023 The Editor(

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書目名稱Recent Trends in Artificial Intelligence and IoT
副標題First International
編輯Rajesh Kumar Tiwari,G. Sahoo
視頻videohttp://file.papertrans.cn/824/823401/823401.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Recent Trends in Artificial Intelligence and IoT; First International  Rajesh Kumar Tiwari,G. Sahoo Conference proceedings 2023 The Editor(
描述This book constitutes selected papers presented at the?First International Conference on Artificial Intelligence and?Internet of Things, ICAII 2022, held in Jamshedpur, India. ICAII 2022 has been postponed to April 2023.?.The 23 papers were thoroughly reviewed and selected from the 86 submissions. They are arranged in topical sections on?.?.artificial Intelligence, and Internet of Things..
出版日期Conference proceedings 2023
關鍵詞artificial intelligence; computer security; data security; distributed systems; parallel processing syst
版次1
doihttps://doi.org/10.1007/978-3-031-37303-9
isbn_softcover978-3-031-37302-2
isbn_ebook978-3-031-37303-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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ral and problem oriented way. Recently so called metric or real-time temporal logics have been proposed for the specification of real-time systems, for which not only qualitative but also quantitative temporal properties are very important. In this work we investigate a subset of metric temporal Hor
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Lung Disease Classification Using CNN-Based Trained Models from CXR Imageccuracy to that of three popular pre-trained models, including ResNet-50, Vgg-16, and Vgg-19. The VGG-16, VGG-19 and ResNet-50 CNN-based pre-trained models also achieved an accuracy of 95.76%, 92.84%, and 96.27%, respectively in classification. The results demonstrated that the cascaded feature gene
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Meta-transfer Learning for Contextual Emotion Detection in Face Affirmationbile Net-V2 model with the transfer learning strategy to speed up the generation time and improve the accuracy of emotion detection. In order to improve upon the current method, we have compiled a large CIFE data collection from the relevant literature. One of the best outcomes for emotion detection
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