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dc.rights.licenseCC0 1.0 Universal*
dc.contributor.authorÐuretanović Simona-
dc.contributor.authorJakovljević, Marija-
dc.contributor.authorMilošković, Aleksandra-
dc.contributor.authorRadojković, Nataša-
dc.contributor.authorRadenković, Milena-
dc.contributor.authorSimić, Vladica-
dc.contributor.authorMaguire, Ivana-
dc.date.accessioned2023-11-06T09:24:52Z-
dc.date.available2023-11-06T09:24:52Z-
dc.date.issued2023-
dc.identifier.isbn9788682172024en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/19293-
dc.description.abstractUniform Manifold Approximation and Projection (UMAP) is a nonlinear dimension reduction technique based on manifold learning. It is specifically designed to achieve a balance between the global and local structure when embedding data points. We applied this method to morphometric features in populations of the noble crayfish, a freshwater species recognized as both a keystone species and an ecosystem engineer, as well as an indicator of good water quality, with unquestionable cultural and economic value to humans. Our results show that the CLL parameter most contributed to the differences and grouped the investigated specimens into seven clusters, along with ROL and ABL parameters. The parameters associated with the claws also exhibited a considerable influence on differentiation.en_US
dc.language.isoenen_US
dc.publisherUniversity of Kragujevac, Institute for Information Technologiesen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.source2nd International Conference on Chemo and BioInformatics-
dc.subjectmachine learningen_US
dc.subjectUniform Manifold Approximation and Projection (UMAP)en_US
dc.subjectfreshwater crayfishen_US
dc.titleApplied machine learning in exploring key features of crayfish populationsen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.identifier.doi10.46793/ICCBI23.184DJen_US
dc.type.versionPublishedVersionen_US
Налази се у колекцијама:Faculty of Science, Kragujevac

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