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dc.date.accessioned2022-12-02T16:05:45Z
dc.date.available2022-12-02T16:05:45Z
dc.date.created2022-08-28T15:46:24Z
dc.date.issued2022
dc.identifier.citationNordmo, Tor-Arne Schmidt Kvalsvik, Ove Kvalsund, Svein Ove Hansen, Birte Halvorsen, Pål Hicks, Steven Johansen, Dag Johansen, Håvard D. Riegler, Michael Alexander . Fish AI: Sustainable Commercial Fishing. Nordic Machine Intelligence (NMI). 2022, 2(2)
dc.identifier.urihttp://hdl.handle.net/10852/97861
dc.description.abstractSustainable Commercial Fishing is the second challenge at the Nordic AI Meet following the successful MedAI, which had a focus on medical image segmentation and transparency in machine learning (ML)-based systems. FishAI focuses on a new domain, namely, commercial fishing and how to make it more sustainable with the help of machine learning. A range of public available datasets is used to tackle three specific tasks. The first one is to predict fishing coordinates to optimize catching of specific fish, the second one is to create a report that can be used by experienced fishermen, and the third task is to make a sustainable fishing plan that provides a route for a week. The second and third task require to some extend explainable and interpretable models that can provide explanations. A development dataset is provided and all methods will be tested on a concealed test dataset and assessed by an expert jury.
  
 artificial intelligence; machine learning; segmentation; transparency; medicine
dc.languageEN
dc.publisherUniversitetet i Oslo
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleFish AI: Sustainable Commercial Fishing
dc.title.alternativeENEngelskEnglishFish AI: Sustainable Commercial Fishing
dc.typeJournal article
dc.creator.authorNordmo, Tor-Arne Schmidt
dc.creator.authorKvalsvik, Ove
dc.creator.authorKvalsund, Svein Ove
dc.creator.authorHansen, Birte
dc.creator.authorHalvorsen, Pål
dc.creator.authorHicks, Steven
dc.creator.authorJohansen, Dag
dc.creator.authorJohansen, Håvard D.
dc.creator.authorRiegler, Michael Alexander
cristin.unitcode185,15,33,0
cristin.unitnameNORA - Research Consortium
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin2046492
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Nordic Machine Intelligence (NMI)&rft.volume=2&rft.spage=&rft.date=2022
dc.identifier.jtitleNordic Machine Intelligence (NMI)
dc.identifier.volume2
dc.identifier.issue2
dc.identifier.startpage1
dc.identifier.endpage3
dc.identifier.pagecount3
dc.identifier.doihttps://doi.org/10.5617/nmi.9657
dc.subject.nviVDP::Datateknologi: 551
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2703-9196
dc.type.versionPublishedVersion


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