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https://scidar.kg.ac.rs/handle/123456789/19293
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Поље DC-а | Вредност | Језик |
---|---|---|
dc.rights.license | CC0 1.0 Universal | * |
dc.contributor.author | Ðuretanović Simona | - |
dc.contributor.author | Jakovljević, Marija | - |
dc.contributor.author | Milošković, Aleksandra | - |
dc.contributor.author | Radojković, Nataša | - |
dc.contributor.author | Radenković, Milena | - |
dc.contributor.author | Simić, Vladica | - |
dc.contributor.author | Maguire, Ivana | - |
dc.date.accessioned | 2023-11-06T09:24:52Z | - |
dc.date.available | 2023-11-06T09:24:52Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9788682172024 | en_US |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/19293 | - |
dc.description.abstract | Uniform 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.iso | en | en_US |
dc.publisher | University of Kragujevac, Institute for Information Technologies | en_US |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.source | 2nd International Conference on Chemo and BioInformatics | - |
dc.subject | machine learning | en_US |
dc.subject | Uniform Manifold Approximation and Projection (UMAP) | en_US |
dc.subject | freshwater crayfish | en_US |
dc.title | Applied machine learning in exploring key features of crayfish populations | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Published | en_US |
dc.identifier.doi | 10.46793/ICCBI23.184DJ | en_US |
dc.type.version | PublishedVersion | en_US |
Налази се у колекцијама: | Faculty of Science, Kragujevac |
Датотеке у овој ставци:
Датотека | Опис | Величина | Формат | |
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2nd-ICCBIKG- str 184-187.pdf | 451.65 kB | Adobe PDF | ![]() Погледајте |
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