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http://hdl.handle.net/11452/26038
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DC Field | Value | Language |
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dc.contributor.author | Akkurt, İskender | - |
dc.contributor.author | Günoǧlu, Kadir | - |
dc.contributor.author | Tekin, Huseyin Ozan | - |
dc.contributor.author | Demirci, Zehra Nur | - |
dc.contributor.author | Yeǧin, Gültekin | - |
dc.date.accessioned | 2022-04-25T09:09:45Z | - |
dc.date.available | 2022-04-25T09:09:45Z | - |
dc.date.issued | 2012-06 | - |
dc.identifier.citation | Akkurt, 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.issn | 1728-4554 | - |
dc.identifier.uri | http://ijrr.com/browse.php?mag_id=37&slc_lang=en&sid=1 | - |
dc.identifier.uri | http://hdl.handle.net/11452/26038 | - |
dc.description.abstract | Background: 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.iso | en | en_US |
dc.publisher | Ijrr-Iranian Journal Radiation Res | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Atıf Gayri Ticari Türetilemez 4.0 Uluslararası | tr_TR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Radiology, nuclear medicine & medical imaging | en_US |
dc.subject | Ann | en_US |
dc.subject | Egs | en_US |
dc.subject | Photon fluence | en_US |
dc.title | Estimation of bremsstrahlung photon fluence from aluminum by artificial neural network | en_US |
dc.type | Article | en_US |
dc.identifier.wos | 000314275400009 | tr_TR |
dc.identifier.scopus | 2-s2.0-84864093054 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü. | tr_TR |
dc.contributor.orcid | 0000-0003-2245-8461 | tr_TR |
dc.identifier.startpage | 63 | tr_TR |
dc.identifier.endpage | 65 | tr_TR |
dc.identifier.volume | 10 | tr_TR |
dc.identifier.issue | 1 | tr_TR |
dc.relation.journal | Iranian Journal of Radiation Research | en_US |
dc.contributor.buuauthor | Demir, Nilgün | - |
dc.contributor.researcherid | AAH-3156-2021 | tr_TR |
dc.relation.collaboration | Yurt içi | tr_TR |
dc.subject.wos | Radiology, nuclear medicine & medical imaging | en_US |
dc.indexed.wos | SCIE | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.wos.quartile | Q4 | en_US |
dc.contributor.scopusid | 7006874016 | tr_TR |
dc.subject.scopus | Compressive Strength; High Performance Concrete; Concrete Mixtures | en_US |
dc.subject.emtree | Aluminum | en_US |
dc.subject.emtree | Article | en_US |
dc.subject.emtree | Artificial neural network | en_US |
dc.subject.emtree | Bremsstrahlung photon fluence | en_US |
dc.subject.emtree | Electron | en_US |
dc.subject.emtree | Electron gamma shower simulation | en_US |
dc.subject.emtree | Measurement | en_US |
dc.subject.emtree | Photon | en_US |
dc.subject.emtree | Radiation energy | en_US |
dc.subject.emtree | Radiological parameters | en_US |
dc.subject.emtree | Simulation | en_US |
dc.subject.emtree | Thickness | en_US |
Appears in Collections: | Scopus Web of Science |
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Demir_vd_2012.pdf | 448.07 kB | Adobe PDF | View/Open |
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