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Dynamic Bayesian Networks in the Problem of Localizing the Narcotic Substances Distribution

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Advances in Intelligent Systems and Computing IV (CSIT 2019)

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Abstract

This paper proposed a methodology for the use of static and dynamic Bayesian networks (BN) in the problems of localizing the distribution of narcotic substances. Methods for constructing the BN structure, their parametric training, validation, sensitivity analysis and “What-if” scenario analysis are considered. A model of dynamic Bayesian networks (DBN) for scenario analysis and prediction of the composition of a narcotic substance has been developed. The model was designed in collaboration with law enforcement officers, as well as forensic experts in the selection and quantification of input and output variables.

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Author information

Authors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko, Andrey Fefelov, Iryna Lurie, Mariia Voronenko & Ivan Lopushynskyi

  2. National University of Water and Environmental Engineering, Rivne, Ukraine

    Nataliia Savina

  3. Jan Evangelista Purkyne University in Usti nad Labem, Ústí nad Labem, Czech Republic

    Jan Krejci

  4. Institute of Personnel Training of the State Employment Service of Ukraine, Kyiv, Ukraine

    Petro Vorona

Authors
  1. Volodymyr Lytvynenko
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  2. Nataliia Savina
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  3. Jan Krejci
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  4. Andrey Fefelov
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  5. Iryna Lurie
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  6. Mariia Voronenko
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  7. Ivan Lopushynskyi
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  8. Petro Vorona
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Corresponding author

Correspondence to Mariia Voronenko .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Lytvynenko, V. et al. (2020). Dynamic Bayesian Networks in the Problem of Localizing the Narcotic Substances Distribution. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_29

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