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dc.creatorDragović, Snežana D.
dc.creatorOnjia, Antonije
dc.creatorStanković, Srboljub J.
dc.creatorAničin, Ivan V.
dc.creatorBacić, G
dc.date.accessioned2021-03-10T10:25:32Z
dc.date.available2021-03-10T10:25:32Z
dc.date.issued2005
dc.identifier.issn0168-9002
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/814
dc.description.abstractAn artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients (R-2) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium (U-238/Th-232) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the U-238/Th-232 ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy.en
dc.publisherElsevier, Amsterdam
dc.rightsrestrictedAccess
dc.sourceNuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors And
dc.subjectANNen
dc.subjectradionuclidesen
dc.subjectuncertaintyen
dc.subjectmeasurement timeen
dc.subjectsoilen
dc.titleArtificial neural network modelling of uncertainty in gamma-ray spectrometryen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage463
dc.citation.issue2-3
dc.citation.other540(2-3): 455-463
dc.citation.rankM21
dc.citation.spage455
dc.citation.volume540
dc.identifier.doi10.1016/j.nima.2004.11.045
dc.identifier.scopus2-s2.0-17844383973
dc.identifier.wos000228031400025
dc.type.versionpublishedVersion


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