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Titlebook: Data Analytics and Management in Data Intensive Domains; 22nd International C Alexander Sychev,Sergey Makhortov,Bernhard Thalhei Conference

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發(fā)表于 2025-3-21 19:17:44 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Analytics and Management in Data Intensive Domains
副標(biāo)題22nd International C
編輯Alexander Sychev,Sergey Makhortov,Bernhard Thalhei
視頻videohttp://file.papertrans.cn/263/262692/262692.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Data Analytics and Management in Data Intensive Domains; 22nd International C Alexander Sychev,Sergey Makhortov,Bernhard Thalhei Conference
描述This book constitutes the post-conference proceedings of the 22nd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2020, held in Voronezh, Russia, in October 2020*..The 16 revised full papers and two keynotes were carefully reviewed and selected from 60 submissions. The papers are organized in the following topical sections: data Integration, conceptual models and ontologies; data management in semantic web; data analysis in medicine; data analysis in astronomy; information extraction from text..* The conference was held virtually due to the COVID-19 pandemic..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; communication systems; computational linguistics; computer networks; computer s
版次1
doihttps://doi.org/10.1007/978-3-030-81200-3
isbn_softcover978-3-030-81199-0
isbn_ebook978-3-030-81200-3Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Pragmatic Interoperability and?Translation of Industrial Engineering Problems into Modelling and Sim modelling, which refers to mapping industrial research and development problems onto solutions by modelling and simulation. For associated roles and processes, substantial previous standardization efforts have been carried out by the European Materials Modelling Council (EMMC ASBL). In the present
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EMG and EEG Pattern Analysis for Monitoring Human Cognitive Activity during Emotional Stimulationsifies and a power spectrum shifts to the low-frequency region.An emotional state interpreter based on a neural-like hierarchical structure was used to classify EMG patterns. The classification success was 93%. The authors have determined spectral characteristics and attractors of EEG patterns. The
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