Record link: 
http://hdl.handle.net/20.500.12708/212697
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Title: 
Machine Learning and Knowledge Extraction : 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings
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Citation: 
Holzinger, A., Kieseberg, P., Cabitza, F., Campagner, A., Tjoa, A. M., & Weippl, E. (Eds.). (2023). Machine Learning and Knowledge Extraction : 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings (Vol. 14065). Springer. https://doi.org/10.1007/978-3-031-40837-3
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Publisher DOI: 
10.1007/978-3-031-40837-3
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Publication Type: 
Proceedings - Full Paper Book
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Language: 
English
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Editors: 
Holzinger, Andreas 
Kieseberg, Peter 
Cabitza, Federico 
Campagner, Andrea 
Tjoa, A Min 
Weippl, Edgar 
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Organisational Unit: 
E194-01 - Forschungsbereich Software Engineering 
E194-04 - Forschungsbereich Data Science 
E056-19 - Fachbereich Precision Livestock Farming 
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Series: 
Lecture Notes in Computer Science 
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ISBN: 
978-3-031-40836-6
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Volume: 
14065
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Date (published): 
2023
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Number of Pages: 
320
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Publisher: 
Springer
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Peer reviewed: 
Yes
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Keywords: 
artificial intelligence; computer networks; computer science; computer systems; computer vision; cyber-insurance; risk management; risk modelling; Application of social network analysis techniques; Security and Privacy; interactive Machine Learning; Automatic User Interface Generation; Semantic Web; Internet of Everything; User Interface Ontologies; Commercial Exchange; Natural Language Generation; Sentiment Analysis; Text classification; Metric Spaces
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Abstract: 
This volume LNCS-IFIP constitutes the refereed proceedings of the 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 in Benevento, Italy, during August 28 – September 1, 2023. The 18 full papers presented together were carefully reviewed and selected from 30 submissions. The conference focuses on integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security.
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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Appears in Collections:
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