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dc.contributor.authorÖzdoğan, Hasan
dc.contributor.authorÜncü, Yiğit Ali
dc.contributor.authorKaraman, Onur
dc.contributor.authorŞekerci, Mert
dc.contributor.authorKaplan, Abdullah
dc.date.accessioned2021-01-12T07:05:23Z
dc.date.available2021-01-12T07:05:23Z
dc.date.issued2021
dc.identifier.citationÖzdoğan, H., Üncü, Y. A., Karaman, O., Şekerci, M., & Kaplan, A. (2021). Estimations of giant dipole resonance parameters using artificial neural network. Applied Radiation and Isotopes, 169.en_US
dc.identifier.issn0969-8043
dc.identifier.urihttp://hdl.handle.net/20.500.12566/613
dc.description.abstractIn 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 feed-forward algorithm in order to provide fast convergence and stability in ANN training and experimental data, taken from Reference Input Parameter Library (RIPL). R values of the system have been found as 0.99636, 0.94649, and 0.98318 for resonance energy, full width half maximum, and resonance cross-section, respectively. Obtained results have been compared with the GDR parameters which are taken from the literature. To validate our findings, newly acquired GDR parameters were then replaced with the existing GDR parameters in the TALYS 1.95 code and 142-146Nd(γ, n)141-145Nd reaction cross-sections have been calculated and compared with the experimental data taken from the literature. As a result of the study, it has been shown that ANN algorithms can be used to calculate the GDR parameters in the absence of the experimental data.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherApplied Radiation and Isotopesen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectPhoto-nuclear interactionen_US
dc.subjectFoto-nükleer etkileşimtr_TR
dc.subjectGDR parametersen_US
dc.subjectGDR parametreleritr_TR
dc.subjectArtificial neural networken_US
dc.subjectYapay sinir ağlarıtr_TR
dc.subjectLevenberg–marquardten_US
dc.subjectTALYS 1.95en_US
dc.titleEstimations of giant dipole resonance parameters using artificial neural networken_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.scopus2-s2.0-85098762449
dc.identifier.volume169
dc.contributor.orcid0000-0001-6127-9680 [Özdoğan, Hasan]
dc.contributor.abuauthorÖzdoğan, Hasan
dc.contributor.YOKid116763 [Özdoğan, Hasan]
dc.contributor.ScopusAuthorID55123312600 [Özdoğan, Hasan]
dc.identifier.PubMedID33423020
dc.identifier.doihttps://doi.org/10.1016/j.apradiso.2020.109581


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