dc.contributor.author | Toktamış, Dilek | |
dc.contributor.author | Er, Mehmet Bilal | |
dc.contributor.author | Işık, Esme | |
dc.date.accessioned | 2022-03-19T17:37:00Z | |
dc.date.available | 2022-03-19T17:37:00Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.citation | Toktamis, D., Er, M. B., & Isik, E. (2022). Classification of thermoluminescence features of the natural halite with machine learning. Radiation Effects and Defects in Solids, 1-12. | en_US |
dc.identifier.issn | 1042-0150 | en_US |
dc.identifier.issn | 1029-4953 | en_US |
dc.identifier.uri | https://doi.org/10.1080/10420150.2022.2039927 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12899/709 | |
dc.description.abstract | Radiation dosimeters are used to measure the absorbed radiation
dose of any living organism during the time intervals. They include
defective crystals that store radiation until they are stimulated. Thermoluminescence (TL) is a way to see the absorbed dose of the
dosimeters. The irradiated crystal is heated up to 500°C to reveal the
absorbed dose as a luminescence light. The TL dosimetric properties of natural halite (rock-salt) crystals extracted from Meke crater
lake in Konya, Turkey, were investigated in this study. Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest
Neighbor (K-NN) were also examined utilizing machine learning for
categorization of TL characteristics. According to the experimental
output, the TL glow curve has two main peaks located at 100 and
270°C with good dosimetric properties. In the three classifiers, SVM
has the biggest accuracy and precision. High training-low testing
and results from normalized data give the best accuracy, precision,
sensitivity and F-score. | en_US |
dc.language.iso | en | en_US |
dc.publisher | TAYLOR & FRANCIS | en_US |
dc.relation.ispartof | Radiation Effects and Defects in Solids | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Halite | en_US |
dc.subject | Thermoluminescence | en_US |
dc.subject | Machine learning | en_US |
dc.title | Classification of thermoluminescence features of the natural halite with machine learning | en_US |
dc.type | Preprint | en_US |
dc.authorid | 0000-0002-6179-5746 | en_US |
dc.department | MTÖ Üniversitesi, Darende Meslek Yüksekokulu, Tıbbi Hizmetler ve Teknikler Bölümü | en_US |
dc.institutionauthor | Işık, Esme | |
dc.identifier.doi | 10.1080/10420150.2022.2039927 | |
dc.identifier.startpage | 1 | en_US |
dc.identifier.endpage | 12 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-85125469506 | en_US |
dc.identifier.scopusquality | Q3 | en_US |
dc.identifier.wos | WOS:000758633600001 | en_US |
dc.identifier.wosquality | Q4 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |