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Titlebook: Creative Solutions for a Sustainable Development; 21st International T Yuri Borgianni,Stelian Brad,Pavel Livotov Conference proceedings 202

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書目名稱Creative Solutions for a Sustainable Development
副標題21st International T
編輯Yuri Borgianni,Stelian Brad,Pavel Livotov
視頻videohttp://file.papertrans.cn/240/239536/239536.mp4
叢書名稱IFIP Advances in Information and Communication Technology
圖書封面Titlebook: Creative Solutions for a Sustainable Development; 21st International T Yuri Borgianni,Stelian Brad,Pavel Livotov Conference proceedings 202
描述.This book constitutes the refereed proceedings of the 21st International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2021, held virtually in September 2021 and sponsored by IFIP WG 5.4...The 28 full papers and 8 short papers presented were carefully reviewed and selected from 48 submissions. They are organized in the following thematic sections: inventiveness and TRIZ for sustainable development; TRIZ, intellectual property and smart technologies; TRIZ: expansion in breadth and depth; TRIZ, data processing and artificial intelligence; and TRIZ use and divulgation for engineering design and beyond...Chapter ‘Domain Analysis with TRIZ to Define an Effective “Design for Excellence’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com..
出版日期Conference proceedings 2021
關鍵詞artificial intelligence; business models; computer-aided design; electronic commerce; hci; human-computer
版次1
doihttps://doi.org/10.1007/978-3-030-86614-3
isbn_softcover978-3-030-86616-7
isbn_ebook978-3-030-86614-3Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2021
The information of publication is updating

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Dauerfestigkeit von Welle-Nabe-Verbindungen,algorithms on patents while using a very low volume of annotated data. The effectiveness of the method will be analyzed in a task of extracting contradictions from patents, which is a complex task that cannot be performed using unsupervised techniques.
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Patent Specialization for Deep Learning Information Retrieval Algorithmsalgorithms on patents while using a very low volume of annotated data. The effectiveness of the method will be analyzed in a task of extracting contradictions from patents, which is a complex task that cannot be performed using unsupervised techniques.
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