Yazar "Yesilkanat, Cafer Mert" seçeneğine göre listele

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    Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches
    (Iop Publishing Ltd, 2023) Yesilkanat, Cafer Mert; Akkoyun, Serkan
    With the fission barrier height information, the survival probabilities of super-heavy nuclei can also be reached. Therefore, it is important to have accurate knowledge of fission barriers, for example, the discovery of super-heavy nuclei in the stability island in the super-heavy nuclei region. In this study, five machine learning techniques, Cubist model, Random Forest, support vector regression, extreme gradient boosting and artificial neural network were used to accurately predict the fission barriers of 330 even-even super-heavy nuclei in the region 140 <= N <= 216 with proton numbers between 92 and 120. The obtained results were compared both among themselves and with other theoretical model calculation estimates and experimental results. According to the results obtained, it was concluded that the Cubist model, support vector regression and extreme gradient boosting methods generally gave better results and could be a better tool for estimating fission barrier heights.
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    Generation of fusion and fusion-evaporation reaction cross-sections by two-step machine learning methods
    (Elsevier, 2024) Akkoyun, Serkan; Yesilkanat, Cafer Mert; Bayram, Tuncay
    In order to obtain cross-sections of heavy-ion fusion and fusion-evaporation reactions, artificial neural networks, cubist, random forest, support vector regression, extreme gradient boosting, and multiple linear regression machine learning approaches were used separately in this study. The outcomes from these different methods that are obtained from the training carried out with the existing experimental data in the literature were compared. Furthermore, it has been observed that a two-step process yielded better results for determining the heavy ion reaction cross-sections, after first estimating which approach would be better for which reaction. In this manner, the method for which the cross-section needs to be calculated is determined by the machine learning classification application, and predictions can be made using the machine learning regression application with the determined method. It has been concluded that the obtained results are in harmony with the experimental data and that the methods can be used safely. The obtained results are published on a web page that allows for online calculation of heavy-ion fusion and fusion-evaporation reaction cross-sections.
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    Machine learning predictions for cross-sections of 43,44Sc radioisotope production by alpha-induced reactions on Ca target
    (Elsevier, 2024) Akkoyun, Serkan; Yesilkanat, Cafer Mert; Bayram, Tuncay
    43,44Sc radioisotopes are an alternative to 18F in positron emission tomography. 43,44Sc 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 43,44Sc 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 higherlevel metamodel. We found that the best results were obtained with Bayesian Regularized Neural Network, Support Vector Regression and Stacked Ensemble Learning methods.
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    Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches
    (Springer, 2024) Amrani, Naima; Yesilkanat, Cafer Mert; Akkoyun, Serkan
    This study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14-15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, alpha) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)(2)/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.