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dc.contributor.authorJaiswal, Amar
dc.contributor.authorYigzaw, Kassaye Yitbarek
dc.contributor.authorØzturk, Pinar
dc.date.accessioned2023-02-14T08:35:22Z
dc.date.available2023-02-14T08:35:22Z
dc.date.created2022-07-19T19:11:37Z
dc.date.issued2022
dc.identifier.citationIEEE Access. 2022, 10 75458-75471.en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/3050566
dc.description.abstractCase-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility.en_US
dc.description.abstractF-CBR: An Architecture for Federated Case-Based Reasoningen_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectCase-Based Reasoningen_US
dc.subjectCase-Based Reasoningen_US
dc.subjectBeslutningsstøtteen_US
dc.subjectDecision supporten_US
dc.subjectKnowledge federationen_US
dc.subjectKnowledge federationen_US
dc.titleF-CBR: An Architecture for Federated Case-Based Reasoningen_US
dc.title.alternativeF-CBR: An Architecture for Federated Case-Based Reasoningen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.subject.nsiVDP::Kunnskapsbaserte systemer: 425en_US
dc.subject.nsiVDP::Knowledge-based systems: 425en_US
dc.source.pagenumber75458-75471en_US
dc.source.volume10en_US
dc.source.journalIEEE Accessen_US
dc.identifier.doi10.1109/ACCESS.2022.3188808
dc.identifier.cristin2038819
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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