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Titlebook: Data Science; 7th International Co Jianchao Zeng,Pinle Qin,Zeguang Lu Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 a

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發(fā)表于 2025-3-21 19:03:57 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science
副標題7th International Co
編輯Jianchao Zeng,Pinle Qin,Zeguang Lu
視頻videohttp://file.papertrans.cn/264/263042/263042.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Data Science; 7th International Co Jianchao Zeng,Pinle Qin,Zeguang Lu Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 a
描述This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021..The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis;??social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo..
出版日期Conference proceedings 2021
關鍵詞access control; artificial intelligence; authentication; computer hardware; computer networks; computer s
版次1
doihttps://doi.org/10.1007/978-981-16-5943-0
isbn_softcover978-981-16-5942-3
isbn_ebook978-981-16-5943-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

書目名稱Data Science影響因子(影響力)




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Further topics in competing risksnal neural models obtained impressive performance on standard benchmarks, they often encounter performance degradation when being applied to knowledge-intensive domains like medicine and science. To address this problem and further fill the knowledge gap, we present a simple Evidence-Based Inference
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https://doi.org/10.1057/9780230604285otential public opinion information from online reviews has a certain value for the government to clarify the next work direction. In this paper, the event evolution graph is designed to make COVID-19 network public opinion prediction. The causal relationship was extracted in the network reviews aft
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Competing on Supply Chain Qualityroposed using visual annotation to interpret the internal structure of CNN from the semantic perspective. First, filters are screened in the high layers of the CNN. For a certain category, the important filters are selected by their activation values, frequencies and classification contribution. The
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Competing on Supply Chain Quality provide four types of explanations including logical rules, revealing hidden semantics, sensitivity analysis, and providing examples as prototypes. In this paper, an interpretability method is proposed for revealing semantic representations at hidden layers of CNNs through lightweight annotation by
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