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Optimal Time Segments for Stress Detection

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dc.contributor.authorSharma, Nandita
dc.contributor.authorGedeon, Tamas (Tom)
dc.date.accessioned2015-12-10T23:22:14Z
dc.date.issued2013
dc.date.updated2016-02-24T10:58:13Z
dc.description.abstractSome response signals being modeled for humans over some time segments may not be relevant for analysis and modeling. These signals could contribute to reducing the quality of patterns captured by models, inefficient processing and may impose huge demands
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/1885/66445
dc.publisherSpringer
dc.sourceLecture Notes in Computer Science (LNCS)
dc.subjectKeywords: Analysis and modeling; Hybrid classifier; Physical signal; Physiological signals; Storage resources; Stress modeling; Stress recognition; Time segments; Data mining; Genetic algorithms; Optimization; Pattern recognition; Physiological models; Physiology; genetic algorithms; physical signals; physiological signals; stress modeling; support vector machines; time segments
dc.titleOptimal Time Segments for Stress Detection
dc.typeJournal article
local.bibliographicCitation.issue2013
local.bibliographicCitation.lastpage433
local.bibliographicCitation.startpage421
local.contributor.affiliationSharma, Nandita, College of Engineering and Computer Science, ANU
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.authoruidSharma, Nandita, u4306724
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080602 - Computer-Human Interaction
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1284
local.identifier.citationvolume7988
local.identifier.doi10.1007/978-3-642-39712-7_32
local.identifier.scopusID2-s2.0-84881258275
local.type.statusPublished Version

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