Machine learning predictions for cross-sections of ⁴³,⁴⁴Sc radioisotope production by alpha-induced reactions on Ca target
| dc.contributor.author | Akkoyun, Serkan | |
| dc.contributor.author | Yeşilkanat, Cafer Mert | |
| dc.contributor.author | Bayram, Tuncay | |
| dc.date.accessioned | 2024-12-05T12:25:17Z | |
| dc.date.available | 2024-12-05T12:25:17Z | |
| dc.date.issued | 2024 | |
| dc.department | AÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümü | en_US |
| dc.description.abstract | ??,??Sc radioisotopes are an alternative to ¹?F in positron emission tomography. ??,??Sc radioisotopes, which can be generated at low costs by irradiating inexpensive natural Ca with alpha particles, can be produced, and distributed in a central cyclotron facility due to their relatively long half-lives. Since there is limited experimental data on the cross-sections in the literature, in this study, cross-section predictions of the production of ??,??Sc radioisotopes with alpha particles on Ca target were carried out with different machine learning approaches. In order to improve the results, the feature engineering method was applied to the variables of the cross-section predictions. Moreover, predictions have been improved with Stacked Ensemble Learning (SEL) approaches, a complex methodology that leverages the predictive capabilities of multiple underlying models to build a higher-level metamodel. We found that the best results were obtained with Bayesian Regularized Neural Network, Support Vector Regression and Stacked Ensemble Learning methods. | |
| dc.identifier.doi | 10.1016/j.nimb.2024.165293 | |
| dc.identifier.issn | 0168583X | |
| dc.identifier.scopusquality | Q3 | |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.nimb.2024.165293 | |
| dc.identifier.uri | https://hdl.handle.net/11494/5117 | |
| dc.identifier.volume | 549 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier B.V. | en_US |
| dc.relation.ispartof | Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
| dc.subject | Cross-Section | en_US |
| dc.subject | Feature Engineering | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Nuclear Medicine | en_US |
| dc.subject | Sc Radioisotope | en_US |
| dc.title | Machine learning predictions for cross-sections of ⁴³,⁴⁴Sc radioisotope production by alpha-induced reactions on Ca target | en_US |
| dc.type | Article |
