Extrapolating Continuous Color Emotions Through Deep Learning
| dc.contributor.author | Ram,V. | |
| dc.contributor.author | Schaposnik,L.P. | |
| dc.contributor.author | Konstantinou,N. | |
| dc.contributor.author | Volkan,E. | |
| dc.contributor.author | Papadatou-Pastou,M. | |
| dc.contributor.author | Manav,B. | |
| dc.contributor.author | Mohr,C. | |
| dc.contributor.other | Interior Architecture and Environmental Design | |
| dc.contributor.other | 06. Faculty of Art and Design | |
| dc.contributor.other | 01. Kadir Has University | |
| dc.date.accessioned | 2024-10-15T19:42:03Z | |
| dc.date.available | 2024-10-15T19:42:03Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | By means of an experimental dataset, we use deep learning to implement an RGB (red, green, and blue) 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 (type-m individuals) typically associate a given emotion with darker colors, while females (type-f individuals) associate it 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. © 2020 authors. Published by the American Physical Society. | en_US |
| dc.description.sponsorship | Directorate for Mathematical and Physical Sciences, MPS, (1749013) | en_US |
| dc.identifier.citationcount | 7 | |
| dc.identifier.doi | 10.1103/PhysRevResearch.2.033350 | |
| dc.identifier.issn | 2643-1564 | |
| dc.identifier.scopus | 2-s2.0-85113520218 | |
| dc.identifier.uri | https://doi.org/10.1103/PhysRevResearch.2.033350 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/6512 | |
| dc.language.iso | en | en_US |
| dc.publisher | American Physical Society | en_US |
| dc.relation.ispartof | Physical Review Research | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | [No Keyword Available] | en_US |
| dc.title | Extrapolating Continuous Color Emotions Through Deep Learning | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Manav, Banu | |
| gdc.author.scopusid | 57221150141 | |
| gdc.author.scopusid | 55652378300 | |
| gdc.author.scopusid | 55927872000 | |
| gdc.author.scopusid | 57192306774 | |
| gdc.author.scopusid | 18537242000 | |
| gdc.author.scopusid | 7801349425 | |
| gdc.author.scopusid | 56426551700 | |
| gdc.bip.impulseclass | C4 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.access | open access | |
| gdc.coar.type | text::journal::journal article | |
| gdc.description.department | Kadir Has University | en_US |
| gdc.description.departmenttemp | Ram V., Milton High School, Milton, 30004, GA, United States; Schaposnik L.P., Department of Mathematics, Statistics and Computer Science, University of Illinois, Chicago, 60607, IL, United States; Konstantinou N., Department of Rehabilitation Sciences, Faculty of Health Sciences, Cyprus University of Technology, Limassol, 3036, Cyprus; Volkan E., Department of Psychology, Cyprus International University, Nicosia, 99258, Cyprus; Papadatou-Pastou M., National and Kapodistrian University of Athens, Athens, 157 72, Greece; Manav B., Kadir Has University, Faculty of Art and Design, Department of Interior Architecture and Environmental Design, Kadir Has Caddesi, Cibali-İstanbul, 34083, Turkey; Jonauskaite D., Institute of Psychology, University of Lausanne, Lausanne, 1015, Switzerland; Mohr C., Institute of Psychology, University of Lausanne, Lausanne, 1015, Switzerland | en_US |
| gdc.description.issue | 3 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.volume | 2 | en_US |
| gdc.identifier.openalex | W3101842064 | |
| gdc.oaire.accesstype | GOLD | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 6.0 | |
| gdc.oaire.influence | 2.8450529E-9 | |
| gdc.oaire.isgreen | true | |
| gdc.oaire.keywords | FOS: Computer and information sciences | |
| gdc.oaire.keywords | Computer and Information Sciences | |
| gdc.oaire.keywords | Computer Science - Machine Learning | |
| gdc.oaire.keywords | QC1-999 | |
| gdc.oaire.keywords | FOS: Physical sciences | |
| gdc.oaire.keywords | Associations | |
| gdc.oaire.keywords | Quantitative Biology - Quantitative Methods | |
| gdc.oaire.keywords | Machine Learning (cs.LG) | |
| gdc.oaire.keywords | Age | |
| gdc.oaire.keywords | colour; emotion; machine learning; neural network | |
| gdc.oaire.keywords | Biological Physics | |
| gdc.oaire.keywords | Preferences | |
| gdc.oaire.keywords | Quantitative Methods | |
| gdc.oaire.keywords | Physics - Biological Physics | |
| gdc.oaire.keywords | Quantitative Methods (q-bio.QM) | |
| gdc.oaire.keywords | Physics | |
| gdc.oaire.keywords | Deep learning | |
| gdc.oaire.keywords | Neural network | |
| gdc.oaire.keywords | Quantitative Biology | |
| gdc.oaire.keywords | Biological Physics (physics.bio-ph) | |
| gdc.oaire.keywords | FOS: Biological sciences | |
| gdc.oaire.keywords | Computer Science | |
| gdc.oaire.keywords | Natural Sciences | |
| gdc.oaire.popularity | 7.2990725E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0501 psychology and cognitive sciences | |
| gdc.oaire.sciencefields | 05 social sciences | |
| gdc.openalex.fwci | 1.444 | |
| gdc.openalex.normalizedpercentile | 0.8 | |
| gdc.opencitations.count | 9 | |
| gdc.plumx.mendeley | 18 | |
| gdc.plumx.newscount | 1 | |
| gdc.plumx.scopuscites | 13 | |
| gdc.scopus.citedcount | 13 | |
| relation.isAuthorOfPublication | d11f6424-2d69-45f8-9d15-5faa04038be1 | |
| relation.isAuthorOfPublication.latestForDiscovery | d11f6424-2d69-45f8-9d15-5faa04038be1 | |
| relation.isOrgUnitOfPublication | 0a9f6edd-770d-4882-af28-4100f62c19f0 | |
| relation.isOrgUnitOfPublication | 8fe856d9-ed4d-45d5-8d18-d900eb9c3bff | |
| relation.isOrgUnitOfPublication | b20623fc-1264-4244-9847-a4729ca7508c | |
| relation.isOrgUnitOfPublication.latestForDiscovery | 0a9f6edd-770d-4882-af28-4100f62c19f0 |