A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

Mathew, J., Kshirsagar, R., Abidin, D.Z. et al. (5 more authors) (2023) A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data. Scientific Reports, 13. 9948. ISSN 2045-2322

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Mathew, J.
  • Kshirsagar, R.
  • Abidin, D.Z.
  • Griffin, J.
  • Kanarachos, S.
  • James, J.
  • Alamaniotis, M.
  • Fitzpatrick, M.E.
Copyright, Publisher and Additional Information:

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Characterization and analytical techniques; Mathematics and computing
Dates:
  • Published: 19 June 2023
  • Accepted: 10 June 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield)
The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 27 Jun 2023 09:39
Last Modified: 27 Jun 2023 09:39
Status: Published
Publisher: Nature Research
Refereed: Yes
Identification Number: 10.1038/s41598-023-36832-8
Related URLs:
Open Archives Initiative ID (OAI ID):

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