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Mass excess estimations using artificial neural networks
(Applied Radiation and Isotopes, 2022)
Mass excess knowledge is important to investigate the fundamental properties of atomic nuclei. It is a meaningful and important parameter for the determinations of nucleon binding energy, nuclear reaction Q value, energy ...
Estimation of (n,p) reaction cross sections at 14.5 ∓ 0.5 MeV neutron energy by using artificial neural network
(Applied Radiation and Isotopes, 2021)
The aim of this study is to develop an accurate artificial neural network algorithm for the cross-section of (n,p) reactions at 14.5 ∓0.5 MeV neutron energy which is important to developing materials for fusion reactor ...
A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network
(Modern Physics Letters A, 2021)
In this paper, calculations of the (n,t) reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium ...
Estimations of level density parameters by using artificial neural network for phenomenological level density models
(Applied Radiation and Isotopes, 2021)
The main aim of this study is to develop accurate artificial neural network (ANN) algorithms to estimate level density parameters. An efficient Bayesian-based algorithm is presented for classification algorithms. Unknown ...
Estimations of giant dipole resonance parameters using artificial neural network
(Applied Radiation and Isotopes, 2021)
In this study; Giant Dipole Resonance (GDR) parameters of the spherical nucleus have been estimated by using artificial neural network (ANN) algorithms. The ANN training has been carried out with the Levenberg–Marquardt ...
