Classification of Human Mental Stress Levels Using a Deep Learning Approach on the K-EmoCon Multimodal Dataset

Shermadurai, P Thiyagarajan, K

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Field	Value	Language
dc.contributor.author	Shermadurai, P
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.date.accessioned	2025-01-24T05:08:29Z
dc.date.available	2025-01-24T05:08:29Z
dc.date.issued	2024-10
dc.identifier.citation	TRAITEMENT DU SIGNAL, 2024, 41, (5), pp. 2559-2571
dc.identifier.issn	0765-0019
dc.identifier.issn	1958-5608
dc.identifier.uri	http://hdl.handle.net/10453/184186
dc.language	English
dc.publisher	INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
dc.relation.ispartof	TRAITEMENT DU SIGNAL
dc.relation.isbasedon	10.18280/ts.410529
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 02 Physical Sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Classification of Human Mental Stress Levels Using a Deep Learning Approach on the K-EmoCon Multimodal Dataset
dc.type	Journal Article
utslib.citation.volume	41
utslib.for	01 Mathematical Sciences
utslib.for	02 Physical Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-24T05:08:27Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	41
utslib.citation.issue	5

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