Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor

dc.authorid OZBEY, Mehmet Furkan/0000-0002-5813-3514
dc.authorscopusid 36015912400
dc.authorscopusid 57219871456
dc.authorscopusid 56011415300
dc.authorwosid OZBEY, Mehmet Furkan/GLU-8252-2022
dc.contributor.author Yerlikaya-Ozkurt, Fatma
dc.contributor.author Ozbey, Mehmet Furkan
dc.contributor.author Turhan, Cihan
dc.contributor.other Energy Systems Engineering
dc.contributor.other Industrial Engineering
dc.contributor.other Mechanical Engineering
dc.date.accessioned 2024-10-06T11:17:09Z
dc.date.available 2024-10-06T11:17:09Z
dc.date.issued 2025
dc.department Atılım University en_US
dc.department-temp [Yerlikaya-Ozkurt, Fatma] Atilim Univ, Fac Engn, Dept Ind Engn, Ankara, Turkiye; [Ozbey, Mehmet Furkan] Atilim Univ, Grad Sch Nat & Appl Sci, Dept Mech Engn, Ankara, Turkiye; [Turhan, Cihan] Atilim Univ, Fac Engn, Dept Energy Syst Engn, Ankara, Turkiye en_US
dc.description OZBEY, Mehmet Furkan/0000-0002-5813-3514 en_US
dc.description.abstract Psychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Ozbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation "r" between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model. en_US
dc.description.sponsorship Scientific and Technological Research Council of Turkiye (TUBITAK) [120M890] en_US
dc.description.sponsorship This work was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK) [Project Number: 120M890]. en_US
dc.description.woscitationindex Social Science Citation Index
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.newideapsych.2024.101124
dc.identifier.issn 0732-118X
dc.identifier.issn 1873-3522
dc.identifier.scopus 2-s2.0-85204602369
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1016/j.newideapsych.2024.101124
dc.identifier.volume 76 en_US
dc.identifier.wos WOS:001322573800001
dc.identifier.wosquality Q2
dc.institutionauthor Yerlikaya Özkurt, Fatma
dc.institutionauthor Özbey, Mehmet Furkan
dc.institutionauthor Turhan, Cihan
dc.institutionauthor Yerlikaya Özkurt, Fatma
dc.institutionauthor Özbey, Mehmet Furkan
dc.institutionauthor Turhan, Cihan
dc.language.iso en en_US
dc.publisher Pergamon-elsevier Science Ltd en_US
dc.relation.ispartof New Ideas in Psychology en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 5
dc.subject Adaptive thermal comfort en_US
dc.subject Human behaviour en_US
dc.subject Psychology en_US
dc.subject Profile of mood states (POMS) en_US
dc.subject Multivariate adaptive regression splines (MARS) en_US
dc.title Modeling the Mood State on Thermal Sensation With a Data Mining Algorithm and Testing the Accuracy of Mood State Correction Factor en_US
dc.type Article en_US
dc.wos.citedbyCount 5
dspace.entity.type Publication
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