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dc.contributor.authorThomopoulos, Stelios C. A.
dc.date.accessioned2025-03-08T00:38:44Z
dc.date.available2025-03-08T00:38:44Z
dc.date.issued2021
dc.date.submitted2024-05-23T10:13:27Z
dc.identifierONIX_20240523__57
dc.identifierhttps://library.oapen.org/handle/20.500.12657/90537
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/173059
dc.description.abstractundefined
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
dc.subject.otherMachine learning
dc.titleChapter Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture
dc.typechapter
oapen.identifier.doi10.5772/intechopen.96209
oapen.relation.isPublishedBy035ecc65-6737-43cf-a13a-6bdf67ce01f4
oapen.relation.isFundedByH2020 European Research Council
oapen.relation.isFundedBy178e65b9-dd53-4922-b85c-0aaa74fce079
oapen.collectionEuropean Research Council (ERC)
oapen.collectionEU collection
oapen.grant.number653879
dc.relationisFundedBy178e65b9-dd53-4922-b85c-0aaa74fce079


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