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Please use this identifier to cite or link to this item: https://biore.bio.bg.ac.rs/handle/123456789/7450
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dc.contributor.authorSavić-Zdravković, D.en_US
dc.contributor.authorSimović, P.en_US
dc.contributor.authorRadenković, M.en_US
dc.contributor.authorPredić, B.en_US
dc.contributor.authorMilosavljević, A.en_US
dc.contributor.authorStojanović, Katarinaen_US
dc.contributor.authorMilošević, Đ.en_US
dc.date.accessioned2024-11-25T10:16:19Z-
dc.date.available2024-11-25T10:16:19Z-
dc.date.issued2024-06-17-
dc.identifier.urihttps://biore.bio.bg.ac.rs/handle/123456789/7450-
dc.descriptionBook of Abstracts, p. 58en_US
dc.titleIntegrating Deep Learning for Enhanced Bioassessment of Aquatic Invertebrates: A Case Study in Chironomid Ecologyen_US
dc.typeConference Paperen_US
dc.relation.conference22 nd International Symposium on Chironomidae, Serbia, Nišen_US
dc.description.rankM34en_US
dc.coverage.isbn978-86-6275-160-7en_US
item.openairetypeConference Paper-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
crisitem.author.deptChair of Invertebrate Zoology and Entomology-
crisitem.author.orcid0000-0002-1064-792X-
crisitem.author.parentorgInstitute of Zoology-
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