dc.date.accessioned | 2022-12-02T16:05:45Z | |
dc.date.available | 2022-12-02T16:05:45Z | |
dc.date.created | 2022-08-28T15:46:24Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Nordmo, 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.uri | http://hdl.handle.net/10852/97861 | |
dc.description.abstract | Sustainable 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.language | EN | |
dc.publisher | Universitetet i Oslo | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Fish AI: Sustainable Commercial Fishing | |
dc.title.alternative | ENEngelskEnglishFish AI: Sustainable Commercial Fishing | |
dc.type | Journal article | |
dc.creator.author | Nordmo, Tor-Arne Schmidt | |
dc.creator.author | Kvalsvik, Ove | |
dc.creator.author | Kvalsund, Svein Ove | |
dc.creator.author | Hansen, Birte | |
dc.creator.author | Halvorsen, Pål | |
dc.creator.author | Hicks, Steven | |
dc.creator.author | Johansen, Dag | |
dc.creator.author | Johansen, Håvard D. | |
dc.creator.author | Riegler, Michael Alexander | |
cristin.unitcode | 185,15,33,0 | |
cristin.unitname | NORA - Research Consortium | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 2046492 | |
dc.identifier.bibliographiccitation | info: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.jtitle | Nordic Machine Intelligence (NMI) | |
dc.identifier.volume | 2 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 1 | |
dc.identifier.endpage | 3 | |
dc.identifier.pagecount | 3 | |
dc.identifier.doi | https://doi.org/10.5617/nmi.9657 | |
dc.subject.nvi | VDP::Datateknologi: 551 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 2703-9196 | |
dc.type.version | PublishedVersion | |