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Human-in-the-loop anomaly detection in industrial data streams
data streams
anomaly detection
explainable artificial intelligence
Poster pokonferencyjny. Article ID: 46
The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment.
dc.abstract.en | The detection of anomalies in an industrial setting remains an important and open challenge for most manufacturing companies. The potential benefits from the utilization of an anomaly detection system are substantial, as deviations from normal operating conditions can cause downtimes, quailty issues or safety hazards. The main requirements for an anomaly detection system include the selection of the machine learning model applicable to streaming data, providing the explanations of the model’s decision and participation of human operator in the learning process of the model. We have proposed the anomaly detection system, which addresses the above challenges and is applicable in industrial environment. | pl |
dc.affiliation | Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Informatyki Stosowanej | pl |
dc.contributor.author | Jakubowski, Jakub | pl |
dc.contributor.author | Bobek, Szymon - 428058 | pl |
dc.contributor.author | Nalepa, Grzegorz - 200414 | pl |
dc.contributor.editor | Gena, Cristina | pl |
dc.contributor.editor | de Russis, Luigi | pl |
dc.contributor.editor | Spano, Davide | pl |
dc.contributor.editor | Lanzilotti, Rosa | pl |
dc.contributor.editor | di Mascio, Tania | pl |
dc.contributor.editor | Prandi, Catia | pl |
dc.contributor.editor | Andolina, Salvatore | pl |
dc.date.accessioned | 2023-10-10T12:13:40Z | |
dc.date.available | 2023-10-10T12:13:40Z | |
dc.date.issued | 2023 | pl |
dc.description.additional | Poster pokonferencyjny. Article ID: 46 | pl |
dc.description.physical | [1-2] | pl |
dc.description.publication | 0,2 | pl |
dc.description.sponsorshipsource | Narodowe Centrum Nauki | pl |
dc.identifier.doi | 10.1145/3605390.3610830 | pl |
dc.identifier.isbn | 979-8-4007-0806-0 | pl |
dc.identifier.project | 2020/02/Y/ST6/00070 | pl |
dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/320842 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.pubinfo | New York : The Association for Computing Machinery | pl |
dc.rights | Dodaję tylko opis bibliograficzny | * |
dc.rights.licence | Bez licencji otwartego dostępu | |
dc.rights.uri | * | |
dc.sourceinfo | liczba autorów 216; liczba stron 416; liczba arkuszy wydawniczych 34,6; | pl |
dc.subject.en | data streams | pl |
dc.subject.en | anomaly detection | pl |
dc.subject.en | explainable artificial intelligence | pl |
dc.subtype | OtherDocuments | pl |
dc.title | Human-in-the-loop anomaly detection in industrial data streams | pl |
dc.title.container | Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, CHItaly 2023, 20-22 September 2023, Torino, Italy | pl |
dc.type | BookSection | pl |
dspace.entity.type | Publication |