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Titlebook: Discovery Science; 26th International C Albert Bifet,Ana Carolina Lorena,Pedro H. Abreu Conference proceedings 2023 The Editor(s) (if appli

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書目名稱Discovery Science
副標題26th International C
編輯Albert Bifet,Ana Carolina Lorena,Pedro H. Abreu
視頻videohttp://file.papertrans.cn/282/281050/281050.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 26th International C Albert Bifet,Ana Carolina Lorena,Pedro H. Abreu Conference proceedings 2023 The Editor(s) (if appli
描述This book constitutes the proceedings of the 26th International Conference on Discovery Science, DS 2023, which took place in Porto, Portugal, in October 2023.?The 37 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 133 submissions. They were organized in topical sections as follows: Machine learning methods and applications; natural language processing and social media analysis; interpretability and explainability in AI; data analysis and optimization; fairness, privacy and security in AI; control and spatio-temporal modeling; graph theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection..
出版日期Conference proceedings 2023
關鍵詞computing methodologies; artificial intelligence; machine learning; natural language processing; search
版次1
doihttps://doi.org/10.1007/978-3-031-45275-8
isbn_softcover978-3-031-45274-1
isbn_ebook978-3-031-45275-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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0302-9743 theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection..978-3-031-45274-1978-3-031-45275-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Eignet sich meine Arbeit zur Publikation?ntologies and NLP embedding techniques to link relevant information present in the patient’s clinical notes to the original explanation. Our experiments, involving a human expert, highlight promising performance in correctly identifying relevant information about the diseases of the patients.
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