View in EDS

Machine learning-based classification analysis of knowledge worker mental stress.

Saved in:
Bibliographic Details
Title: Machine learning-based classification analysis of knowledge worker mental stress.
Authors: Kim H; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Kim M; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Park K; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Kim J; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Yoon D; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Kim W; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Park CH; Division of Computer Convergence, Chungnam National University, Daejeon, Republic of Korea.
Source: Frontiers in public health [Front Public Health] 2023 Nov 07; Vol. 11, pp. 1302794. Date of Electronic Publication: 2023 Nov 07 (Print Publication: 2023).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 38026368
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine learning-based classification analysis of knowledge worker mental stress.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Kim+H%22">Kim H</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+M%22">Kim M</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+K%22">Park K</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+J%22">Kim J</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Yoon+D%22">Yoon D</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+W%22">Kim W</searchLink>; Mobility UX Research Section, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+CH%22">Park CH</searchLink>; Division of Computer Convergence, Chungnam National University, Daejeon, Republic of Korea.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101616579%22">Frontiers in public health</searchLink> [Front Public Health] 2023 Nov 07; Vol. 11, pp. 1302794. <i>Date of Electronic Publication: </i>2023 Nov 07 (<i>Print Publication: </i>2023).
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article; Research Support, Non-U.S. Gov't
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Editorial+Office%22">Frontiers Editorial Office </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101616579 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2296-2565 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2222962565%22">22962565 </searchLink><i>NLM ISO Abbreviation: </i>Front Public Health <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=38026368
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/fpubh.2023.1302794
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 1302794
    Titles:
      – TitleFull: Machine learning-based classification analysis of knowledge worker mental stress.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kim H
      – PersonEntity:
          Name:
            NameFull: Kim M
      – PersonEntity:
          Name:
            NameFull: Park K
      – PersonEntity:
          Name:
            NameFull: Kim J
      – PersonEntity:
          Name:
            NameFull: Yoon D
      – PersonEntity:
          Name:
            NameFull: Kim W
      – PersonEntity:
          Name:
            NameFull: Park CH
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 07
              M: 11
              Text: 2023 Nov 07
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-electronic
              Value: 2296-2565
          Numbering:
            – Type: volume
              Value: 11
          Titles:
            – TitleFull: Frontiers in public health
              Type: main
ResultId 1