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Titlebook: Service-Oriented Computing – ICSOC 2021 Workshops; AIOps, STRAPS, AI-PA Hakim Hacid,Monther Aldwairi,Hai Dong Conference proceedings 2022 S

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书目名称Service-Oriented Computing – ICSOC 2021 Workshops
副标题AIOps, STRAPS, AI-PA
编辑Hakim Hacid,Monther Aldwairi,Hai Dong
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Service-Oriented Computing – ICSOC 2021 Workshops; AIOps, STRAPS, AI-PA Hakim Hacid,Monther Aldwairi,Hai Dong Conference proceedings 2022 S
描述This book constitutes the selected papers from the scientific satellite events held in conjunction with the19th International Conference on Service-Oriented Computing, ICSOC 2021. The conference was held Dubai, United Arab Emirates in November 2021..This year, these satellite events were organized around three main tracks, including a workshop track, a demonstration track, and a tutorials track. . ..The ICSOC 2021 workshop track consisted of the following three workshops covering a wide range of topics that fall into the general area of service computing. .. ..•          International Workshop on Artificial Intelligence for IT Operations (AIOps)..•          3rd Workshop on Smart Data Integration and Processing (STRAPS 2021)..•          International Workshop on AI-enabled Process Automation (AI-PA 2021).
出版日期Conference proceedings 2022
关键词artificial intelligence; computer hardware; computer networks; computer science; computer security; compu
版次1
doihttps://doi.org/10.1007/978-3-031-14135-5
isbn_softcover978-3-031-14134-8
isbn_ebook978-3-031-14135-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2022
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Say No2Ads: Automatic Advertisement and Music Filtering from Broadcast News Contentose ., an autoregressive deep convolutional neural network (CNN) model that is trained on over 500 h of human annotated training samples to remove ads and music from broadcast content. . reached very high performance results in our tests, achieving 97% and 95% in precision and recall on detecting ad
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Automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networksode in the graph, we use two methods of word embedding. We then use graph neural networks to extract graph features. From the graph and image features, we obtain our output vector (set of class probabilities). The proposed approach is evaluated using ., ., ., and . performance measures on two public
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Human-in-the-Loop Optimization for Artificial Intelligence Algorithms in prior research of a comparable scale. The proposed software can optimise and monitor the AI-powered product process and outputs to involve people directly in the optimisation loop to identify and avoid likely and diverse failures. The Local Interpretable Model-agnostic Explanations (LIME) heatma
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