Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/26038
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dc.contributor.authorAkkurt, İskender-
dc.contributor.authorGünoǧlu, Kadir-
dc.contributor.authorTekin, Huseyin Ozan-
dc.contributor.authorDemirci, Zehra Nur-
dc.contributor.authorYeǧin, Gültekin-
dc.date.accessioned2022-04-25T09:09:45Z-
dc.date.available2022-04-25T09:09:45Z-
dc.date.issued2012-06-
dc.identifier.citationAkkurt, İ. vd. (2012). "Estimation of bremsstrahlung photon fluence from aluminum by artificial neural network". Iranian Journal of Radiation Research, 10(1), 63-65.en_US
dc.identifier.issn1728-4554-
dc.identifier.urihttp://ijrr.com/browse.php?mag_id=37&slc_lang=en&sid=1-
dc.identifier.urihttp://hdl.handle.net/11452/26038-
dc.description.abstractBackground: As bremsstrahlung photon beam fluence is important parameter to be known in a photonuclear reaction experiment as the number of produced particle is strongly depends on photon fluence. Materials and Methods: Photon production yield from different thickness of aluminum target has been estimated using artificial neural network (ANN) model. Target thickness and incoming electron energy has been used as input in ANN model and the photon fluence was output. Results: The results were estimated using ANN model for three different thickness and compared with the results obtained by EGS (Electron Gamma Shower) simulation. Conclusion: It can be concluded from this work that the bremsstrahlung photon fluence can be obtained using ANN model.en_US
dc.language.isoenen_US
dc.publisherIjrr-Iranian Journal Radiation Resen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAtıf Gayri Ticari Türetilemez 4.0 Uluslararasıtr_TR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRadiology, nuclear medicine & medical imagingen_US
dc.subjectAnnen_US
dc.subjectEgsen_US
dc.subjectPhoton fluenceen_US
dc.titleEstimation of bremsstrahlung photon fluence from aluminum by artificial neural networken_US
dc.typeArticleen_US
dc.identifier.wos000314275400009tr_TR
dc.identifier.scopus2-s2.0-84864093054tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.tr_TR
dc.contributor.orcid0000-0003-2245-8461tr_TR
dc.identifier.startpage63tr_TR
dc.identifier.endpage65tr_TR
dc.identifier.volume10tr_TR
dc.identifier.issue1tr_TR
dc.relation.journalIranian Journal of Radiation Researchen_US
dc.contributor.buuauthorDemir, Nilgün-
dc.contributor.researcheridAAH-3156-2021tr_TR
dc.relation.collaborationYurt içitr_TR
dc.subject.wosRadiology, nuclear medicine & medical imagingen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ4en_US
dc.contributor.scopusid7006874016tr_TR
dc.subject.scopusCompressive Strength; High Performance Concrete; Concrete Mixturesen_US
dc.subject.emtreeAluminumen_US
dc.subject.emtreeArticleen_US
dc.subject.emtreeArtificial neural networken_US
dc.subject.emtreeBremsstrahlung photon fluenceen_US
dc.subject.emtreeElectronen_US
dc.subject.emtreeElectron gamma shower simulationen_US
dc.subject.emtreeMeasurementen_US
dc.subject.emtreePhotonen_US
dc.subject.emtreeRadiation energyen_US
dc.subject.emtreeRadiological parametersen_US
dc.subject.emtreeSimulationen_US
dc.subject.emtreeThicknessen_US
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