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Rapid quantitative elemental analysis using artificial neural network for case study of Isfahan Miniature Neutron Source Reactor

Asgari, A ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1007/s10967-022-08569-7
  3. Publisher: Springer Science and Business Media B.V , 2022
  4. Abstract:
  5. In this study, new method for NAA purposes at 30 kW Isfahan MNSR is suggested. An algorithm based on ANN is proposed to quantitatively predict the unknown elements with no need standard sample. A three-layer feed-forward ANN with back-propagation algorithm has been used to determine concentration of selenium and fluorine in Multiple Sclerosis patients and healthy people blood samples. Predicted concentration of elements show good agreement between new method and experiment results. The correlation coefficient between the experimentally determined and predicted values are 0.99104 and 0.99364, respectively. This method is a rapid and precise approach for elemental analysis. © 2022, Akadémiai Kiadó, Budapest, Hungary
  6. Keywords:
  7. Artificial neural network (ANN) ; Back-propagation algorithm ; Miniature neutron source reactor (MNSR) ; Multiple sclerosis (MS) ; Neutron activation analysis (NAA) ; Fluorine ; Selenium ; Artificial neural network ; Back propagation ; Blood sampling ; Correlation coefficient ; Human ; Multiple sclerosis ; Neutron activation analysis ; Quantitative analysis
  8. Source: Journal of Radioanalytical and Nuclear Chemistry ; Volume 331, Issue 11 , 2022 , Pages 4479-4487 ; 02365731 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10967-022-08569-7