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Advanced Nanomaterials for Detection of CBRN pp 327–338Cite as

Visual Analytics in Machine Training Systems for Effective Decision

Part of the NATO Science for Peace and Security Series A: Chemistry and Biology book series (NAPSA)

Abstract

The approaches to the formation, development of a formal and mental model based on the use of visual analytics are proposed. It is based on the description of model building technologies. An example of information technology that allows getting a formal model based on the transformation of the mental model through the space of formalized universal forms is given. This allows the model to be used in a different usage and execution environment. Model development is carried out using loops the improvement of the base model or transforming the use of the model from another runtime. An example of equipment and tools for the construction and transformation of models is demonstrated.

Keywords

  • Visual analytics
  • Classification
  • Mental model
  • Formal model
  • Visualization
  • Decision making

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Author information

Authors and Affiliations

  1. V.M. Glushkov Institute of Cybernetics, Taras Shevchenko National University, Kyiv, Ukraine

    Iu. Krak & K. Kruchynin

  2. Khmelnitsky National University, Khmelnitsky, Ukraine

    O. Barmak & E. Manziuk

  3. Bogolyubov Institute for Theoretical Physics, Kyiv, Ukraine

    S. P. Kruchinin

Authors
  1. Iu. Krak
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  2. K. Kruchynin
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  3. O. Barmak
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  4. E. Manziuk
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  5. S. P. Kruchinin
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Editor information

Editors and Affiliations

  1. J. Stefan Institute, Ljubljana, Slovenia

    Prof. Janez Bonča

  2. Bogolyubov Inst. f. Theor. Physics, Nat. Ukrainian Academy of Science, Kiev, Ukraine

    Prof. Sergei Kruchinin

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Krak, I., Kruchynin, K., Barmak, O., Manziuk, E., Kruchinin, S.P. (2020). Visual Analytics in Machine Training Systems for Effective Decision. In: Bonča, J., Kruchinin, S. (eds) Advanced Nanomaterials for Detection of CBRN. NATO Science for Peace and Security Series A: Chemistry and Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-2030-2_25

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