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NSR database version of May 3, 2024.

Search: Author = C.M.Yesilkanat

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2024AK02      Nucl.Instrum.Methods Phys.Res. B549, 165293 (2024)

S.Akkoyun, C.M.Yesilkanat, T.Bayram

Machine learning predictions for cross-sections of ^{43, 44}Sc radioisotope production by alpha-induced reactions on Ca target

NUCLEAR REACTIONS Ca(α, X)⁴³Sc/⁴⁴Sc, ⁴⁰Ca(α, p), E<50 MeV; analyzed available data; deduced production σ with Bayesian Regularized Neural Network, Support Vector Regression and Stacked Ensemble Learning methods.

doi: 10.1016/j.nimb.2024.165293
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2023YE05      J.Phys.(London) G50, 055101 (2023)

C.M.Yesilkanat, S.Akkoyun

Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches

NUCLEAR STRUCTURE Z=98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120; analyzed available data; deduced the fission barrier height information, the survival probabilities of super-heavy nuclei using five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network.

doi: 10.1088/1361-6471/acbaaf
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