1. Ktisis Cyprus University of Technology
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Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23246
DC FieldValueLanguage
dc.contributor.authorRam, Vishaal-
dc.contributor.authorSchaposnik, Laura P.-
dc.contributor.authorKonstantinou, Nikos-
dc.contributor.authorVolkan, Eliz-
dc.contributor.authorPapadatou-Pastou, Marietta-
dc.contributor.authorManav, Banu-
dc.contributor.authorJonauskaite, Domicele-
dc.contributor.authorMohr, Christine-
dc.date.accessioned2021-10-12T11:54:45Z-
dc.date.available2021-10-12T11:54:45Z-
dc.date.issued2020-11-
dc.identifier.citationPhysical Review Research, 2020, vol. 2, no. 3, articl. no. 033350en_US
dc.identifier.issn26431564-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23246-
dc.description.abstractBy means of an experimental dataset, we use deep learning to implement an RGB extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males typically associate a given emotion with darker colors while females with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofPhysical Review Researchen_US
dc.rights© The Author(s)en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep learningen_US
dc.subjectQuantitative Biologyen_US
dc.subjectQuantitative Methodsen_US
dc.subjectComputer Scienceen_US
dc.subjectBiological Physicsen_US
dc.subjectPhysicsen_US
dc.subjectNeural networken_US
dc.titleExtrapolating continuous color emotions through deep learningen_US
dc.typeArticleen_US
dc.collaborationUniversity of Illinois at Chicagoen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationMilton High Schoolen_US
dc.collaborationNational and Kapodistrian University of Athensen_US
dc.collaborationKadir Has Universityen_US
dc.collaborationUniversity of Lausanneen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsOpen Accessen_US
dc.countryUnited Statesen_US
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.countrySwitzerlanden_US
dc.countryTurkeyen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1103/PhysRevResearch.2.033350en_US
dc.identifier.scopus2-s2.0-85113520218-
dc.identifier.urlhttp://arxiv.org/abs/2009.04519v1-
dc.relation.issue3en_US
dc.relation.volume2en_US
cut.common.academicyear2020-2021en_US
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.languageiso639-1en-
crisitem.journal.journalissn2643-1564-
crisitem.journal.publisherAmerican Physical Society-
crisitem.author.deptDepartment of Rehabilitation Sciences-
crisitem.author.facultyFaculty of Health Sciences-
crisitem.author.orcid0000-0003-4531-3636-
crisitem.author.parentorgFaculty of Health Sciences-
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