Bayram, TuncayAkkoyun, SerkanKara, S. OkanVagenas, ECVlachos, DS2019-08-012019-08-0120141742-65881742-6596https://dx.doi.org/10.1088/1742-6596/490/1/012105https://hdl.handle.net/11480/42562nd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE) -- SEP 01-05, 2013 -- Prague, CZECH REPUBLICInvestigations of superheavy elements (SHE) have received much attention in the last two decades, due to the successful syntheses of SHE. In particular, alpha-decay of SHEs has a great importance because most synthesized SHE have alpha-decay and the experimentalists have evaluated the theoretical predictions of the alpha-decay half-life during the experimental design. Because of this, the correct prediction of alpha-decay half-life is important to investigate superheavy nuclei as well as heavy nuclei. In this work, artificial neural networks (ANN) have been employed on experimental alpha-decay half-lives of superheavy nuclei. Statistical modeling of alpha-decay half-life of superheavy nuclei have been found as to be successful.eninfo:eu-repo/semantics/openAccessConference Object49010.1088/1742-6596/490/1/0121052-s2.0-84897007557N/AWOS:000335909300103N/A