Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning
| dc.authorid | 0000-0002-6179-5746 | en_US |
| dc.contributor.author | Işık, Eşme | |
| dc.date.accessioned | 2022-06-30T13:25:54Z | |
| dc.date.available | 2022-06-30T13:25:54Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | MTÖ Üniversitesi, Darende Meslek Yüksekokulu, Tıbbi Hizmetler ve Teknikler Bölümü | en_US |
| dc.description | Isik, Esme, Department of Optician, Malatya Turgut Özal University, Malatya, Turkey | en_US |
| dc.description | Received: 22 February 2022. Revised: 20 April 2022. Accepted: 27 May 2022. | en_US |
| dc.description | © 2022 John Wiley & Sons Ltd. | en_US |
| dc.description | Correspondence, Esme Isik, Department of Optician, MalatyaTurgut Özal University, Malatya, Turkey.Email:esme.isik@ozal.edu.tr | en_US |
| dc.description | Formerly known as:Journal of Bioluminescence and Chemiluminescence | en_US |
| dc.description.abstract | Thermoluminescence (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.citation | Isik, E. Thermoluminescence Characteristic of Calcite with Gaussian Process Regression Model of Machine Learning. Luminescence. 1-7 | en_US |
| dc.identifier.doi | 10.1002/bio.4298 | |
| dc.identifier.endpage | 7 | en_US |
| dc.identifier.issn | 1522-7235 | en_US |
| dc.identifier.issn | 1522-7243 | en_US |
| dc.identifier.pmid | 35641843 | |
| dc.identifier.scopus | 2-s2.0-85131520744 | en_US |
| dc.identifier.scopusquality | Q2 | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://doi.org/10.1002/bio.4298 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12899/1146 | |
| dc.identifier.wos | WOS:000809227300001 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.indekslendigikaynak | PubMed | en_US |
| dc.institutionauthor | Işık, Eşme | |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley | en_US |
| dc.relation.ispartof | Luminescence | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Calcite | en_US |
| dc.subject | Gaussian process regression | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Thermoluminescence | en_US |
| dc.title | Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning | en_US |
| dc.type | Article | en_US |
