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A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms

Neurons Behavior Data analysis and Theory(2024)10.51628/001c.127770Source: DataRank Database

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms is a research paper published in Neurons Behavior Data analysis and Theory (2024). On theSindex it has a DataRank of 0. It has been cited 3 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access3 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.48)OA: goldTopics (18)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (6)

Evan Schaffer ORCID,Neeli Mishra ORCID,Nathaniel J Miska ORCID,Liam Paninski ORCID,Matthew R Whiteway ORCID

Live enrichment

PubMed:40843338 PMC:PMC12365852 OpenAlex:https://openalex.org/W4401413035

NINDS NIH HHS

Grant: U19 NS123716

Wellcome Trust

Grant: 216324

Wellcome Trust

FWCI

0.48

Citation Percentile

0.6%

Citation Trend

2022

2026

goldLicense: cc-by

journalhttps://doi.org/10.51628/001c.127770

publisherhttps://doi.org/10.51628/001c.127770

publisherhttps://nbdt.scholasticahq.com/article/127770-a-study-of-animal-action-segmentation-algorithms-across-supervised-unsupervised-and-semi-supervised-learning-paradigms.pdf

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39108294

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40843338

Fields of Study

Zebrafish Biomedical Research ApplicationsAnimal Behavior and ReproductionBat Biology and Ecology Studies

Keywords

Artificial intelligenceComputer scienceMachine learningSegmentationUnsupervised learningParsingSupervised learningConvolutional neural networkSemi-supervised learningGraphical modelPattern recognition (psychology)Artificial neural networkTree (set theory)Deep learningMathematics