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Prediction of the activity concentrations of 232Th, 238U and 40K in geological materials using radial basis function neural network

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Abstract

In this paper, three individual models and one generalized radial basis function neural network (RBFNN) model were developed for the prediction of the activity concentrations of primordial radionuclides, namely, 232Th, 238U and 40K. To achieve this, gamma spectrometry measurements of 126 different geological materials were used in the development of the RBFNN models. The results indicated that individual and generalized RBFNN models are quite efficient in predicting the activity concentrations of 232Th, 238U and 40K of geological materials.

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Acknowledgements

The authors are grateful Dr. Zsolt Revay, Editor-in-Chief of Journal of Radioanalytical and Nuclear Chemistry, and two anonymous reviewers for their constructive criticism leading to extensive improvement in the revised manuscript.

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Authors and Affiliations

  1. Department of Physics, Faculty of Science, Dokuz Eylul University, 35390, Izmir, Turkey

    Selin Erzin

  2. Institute of Nuclear Sciences, Ege University, 35100, Izmir, Turkey

    Gunseli Yaprak

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  1. Selin Erzin
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  2. Gunseli Yaprak
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Correspondence to Selin Erzin.

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Erzin, S., Yaprak, G. Prediction of the activity concentrations of 232Th, 238U and 40K in geological materials using radial basis function neural network. J Radioanal Nucl Chem 331, 3525–3533 (2022). https://doi.org/10.1007/s10967-022-08438-3

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  • DOI: https://doi.org/10.1007/s10967-022-08438-3

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