dc.contributor.author | Nordmo, Tor-Arne Schmidt | |
dc.contributor.author | Kvalsvik, Ove | |
dc.contributor.author | Kvalsund, Svein Ove | |
dc.contributor.author | Hansen, Birte | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | Hicks, Steven | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Johansen, Håvard D. | |
dc.contributor.author | Riegler, Michael Alexander | |
dc.date.accessioned | 2022-11-24T11:23:04Z | |
dc.date.available | 2022-11-24T11:23:04Z | |
dc.date.issued | 2022-06-02 | |
dc.description.abstract | FishAI: Sustainable Commercial Fishingis the second chal-lenge at theNordic AI Meetfollowing the successful MedAI,which had a focus on medical image segmentation and trans-parency in machine learning (ML)-based systems. FishAI fo-cuses on a new domain, namely, commercial fishing and howto make it more sustainable with the help of machine learning.A range of public available datasets is used to tackle three spe-cific tasks. The first one is to predict fishing coordinates tooptimize catching of specific fish, the second one is to createa report that can be used by experienced fishermen, and thethird task is to make a sustainable fishing plan that provides aroute for a week. The second and third task require to someextend explainable and interpretable models that can provideexplanations. A development dataset is provided and all meth-ods will be tested on a concealed test dataset and assessed byan expert jury. | en_US |
dc.identifier.citation | Nordmo, Kvalsvik, Kvalsund, Hansen, Halvorsen, Hicks, Johansen, Johansen, Riegler. Fish AI: Sustainable Commercial Fishing Challenge. Nordic Machine Intelligence (NMI). 2022;2(2) | en_US |
dc.identifier.cristinID | FRIDAID 2046492 | |
dc.identifier.doi | 10.5617/nmi.9657 | |
dc.identifier.issn | 2703-9196 | |
dc.identifier.uri | https://hdl.handle.net/10037/27523 | |
dc.language.iso | eng | en_US |
dc.relation.ispartof | Nordmo, T.A.S. (2023). Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry. (Doctoral thesis). <a href=https://hdl.handle.net/10037/29768>https://hdl.handle.net/10037/29768</a>. | |
dc.relation.journal | Nordic Machine Intelligence (NMI) | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2022 The Author(s) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.subject | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551 | en_US |
dc.subject | VDP::Technology: 500::Information and communication technology: 550::Computer technology: 551 | en_US |
dc.title | Fish AI: Sustainable Commercial Fishing Challenge | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |