Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

dc.authorid0000-0002-6179-5746en_US
dc.contributor.authorIşık, Eşme
dc.date.accessioned2022-06-30T13:25:54Z
dc.date.available2022-06-30T13:25:54Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Darende Meslek Yüksekokulu, Tıbbi Hizmetler ve Teknikler Bölümüen_US
dc.descriptionIsik, Esme, Department of Optician, Malatya Turgut Özal University, Malatya, Turkeyen_US
dc.descriptionReceived: 22 February 2022. Revised: 20 April 2022. Accepted: 27 May 2022.en_US
dc.description© 2022 John Wiley & Sons Ltd.en_US
dc.descriptionCorrespondence, Esme Isik, Department of Optician, MalatyaTurgut Özal University, Malatya, Turkey.Email:esme.isik@ozal.edu.tren_US
dc.descriptionFormerly known as:Journal of Bioluminescence and Chemiluminescenceen_US
dc.description.abstractThermoluminescence (TL) is defined as a luminescence phenomenon that can be detected when an insulator or semiconductor is thermally stimulated. Defective crystals store radiation until they are stimulated. Thermoluminescence is a method of monitoring the absorbed dose of dosimeters. The irradiation crystal is heated to 500°C to display the absorbed dose as a luminescent light. The TL dosimetric properties of calcite obtained from nature were investigated in this study. Machine learning was also examined using Gaussian process regression (GPR) for stimulated TL characteristics. According to the experimental output, the TL glow curve had two main peaks located at 90°C and 240°C with good dosimetric properties. In the four regression models of GPR, the data for the heating rate of 3°C s-1 have the lowest residual.en_US
dc.identifier.citationIsik, E. Thermoluminescence Characteristic of Calcite with Gaussian Process Regression Model of Machine Learning. Luminescence. 1-7en_US
dc.identifier.doi10.1002/bio.4298
dc.identifier.endpage7en_US
dc.identifier.issn1522-7235en_US
dc.identifier.issn1522-7243en_US
dc.identifier.pmid35641843
dc.identifier.scopus2-s2.0-85131520744en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1002/bio.4298
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1146
dc.identifier.wosWOS:000809227300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorIşık, Eşme
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofLuminescenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCalciteen_US
dc.subjectGaussian process regressionen_US
dc.subjectMachine learningen_US
dc.subjectThermoluminescenceen_US
dc.titleThermoluminescence characteristics of calcite with a Gaussian process regression model of machine learningen_US
dc.typeArticleen_US

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