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Methodology of Learning Curve Analysis for Development of Incoming Material Clustering Neural Network

  • Boris Onykiy
  • Evheniy Tretyakov
  • Larisa Pronicheva
  • Ilya Galin
  • Kristina Ionkina
  • Andrey Cherkasskiy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)

Abstract

This paper describes the methodology of learning curve analysis for development of incoming material clustering neural network. This methodology helps to understand deeply the learning curve adequate level and to bring learning curve structure to the relevant one of the thematic scope of incoming materials. The methodology is based on visual analysis and comprises the building of directed graphs in order to identify data templates. As the battlefield for material clustering the Nuclear Infrastructure Development Section (NIDS) of the International Atom Energy Agency (IAEA) is selected as the support from NIDS’ experts had been available during the research. Some of the challenges the NIDS faces are data aggregation for Country Nuclear Infrastructure Profiles (CNIP) and data assessment after Nuclear Infrastructure Review Missions (INIR).

Keywords

Neural network Material clustering Learning curve Visual analysis 

Notes

Acknowledgments

This work was supported by Competitiveness Growth Program of the Federal Autonomous Educational Institution of Higher Professional Education National Research Nuclear University MEPhI (Moscow Engineering Physics Institute).

References

  1. 1.
    Onykiy, B., Suslina, A., Ionkina, K., Ananieva, A., Pronicheva, L., Artamonov, A., Tretyakov, E.: Agent technologies for polythematic organizations information-analytical support. Procedia Comput. Sci. 88, 336–340 (2016)CrossRefGoogle Scholar
  2. 2.
    Ananieva, A.G., Artamonov, A.A., Galin, I.U., Tretyakov, E.S., Kshnyakov, D.O.: Algoritmizatiom of search operations in multiagent information-analytical systems. J. Theor. Appl. Inf. Technol. 81(1), 11–17 (2015)Google Scholar
  3. 3.
    Artamonov, A., Leonov, D., Nikolaev, V., Onykiy, B., Pronicheva, L., Sokolina, K., Ushmarov, I.: Visualization of semantic relations in multi-agent systems. Sci. Vis. 6(3), 68–76 (2014)Google Scholar
  4. 4.
    Hu, Y.: Efficient, high-quality force-directed graph drawing. Math. J. 10(1), 37–71 (2005)MathSciNetGoogle Scholar
  5. 5.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 304(5), 35–43 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Boris Onykiy
    • 1
  • Evheniy Tretyakov
    • 1
  • Larisa Pronicheva
    • 1
  • Ilya Galin
    • 1
  • Kristina Ionkina
    • 1
  • Andrey Cherkasskiy
    • 1
  1. 1.National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)MoscowRussia

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