Nuclear Fuel Cycle–related R&D Classification for Implementing the IAEA's Additional Protocol

13 Pages Posted: 2 Apr 2021

See all articles by Seungmin Lee

Seungmin Lee

affiliation not provided to SSRN

Wonjong Song

affiliation not provided to SSRN

Jae-Suk Yang

Korea Advanced Institute of Science and Technology (KAIST)

Date Written: March 19, 2021

Abstract

Novel methodologies for classifying scientific articles related to the nuclear fuel cycle have been developed using machine learning to discover declarable activities under the additional protocol of the International Atomic Energy Agency. In this study, the relationships between articles and their lists of references or authors were analyzed using a network to examine the resultant features. By comparing the original network and a randomly rewired network based on the original data, we show that article topics and lists of references or authors form clusters in a projected bipartite network, indicating that lists of references or authors can be employed as independent variables for classification. The topics of scientific articles were classified using the lists of article authors, lists of references, and abstract word counts. Notably, decision-tree classifiers and logistic regression exhibit high F1_score and recall. Furthermore, to improve classifier performance, ensemble classifiers were applied based on the abovementioned single classifiers. The combined classifiers with logistic regression based on author lists as an independent variable showed a particularly high recall value when the subject of an article was distinguished. This classification method could contribute to a better understanding for determining and monitoring nuclear fuel cycle–related R&D to achieve safeguard objectives.

Keywords: Nuclear safeguards, Safeguards inspections, machine learning, classification methods, nuclear fuel cycle

Suggested Citation

Lee, Seungmin and Song, Wonjong and Yang, Jae-Suk, Nuclear Fuel Cycle–related R&D Classification for Implementing the IAEA's Additional Protocol (March 19, 2021). Available at SSRN: https://ssrn.com/abstract=3807792 or http://dx.doi.org/10.2139/ssrn.3807792

Seungmin Lee

affiliation not provided to SSRN

Wonjong Song

affiliation not provided to SSRN

Jae-Suk Yang (Contact Author)

Korea Advanced Institute of Science and Technology (KAIST) ( email )

291 Daehak-ro
Yuseong-gu
Daejeon, 34141
Korea, Republic of (South Korea)

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