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

  • Volodymyr Lytvynenko
  • Nataliia Savina
  • Jan Krejci
  • Andrey Fefelov
  • Iryna Lurie
  • Mariia VoronenkoEmail author
  • Ivan Lopushynskyi
  • Petro Vorona
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

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.

Keywords

Narcotic substance Profiling Bayesian networks Dynamic bayesian networks Structural learning Parametric learning Sensitivity analysis Validation 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Kherson National Technical UniversityKhersonUkraine
  2. 2.National University of Water and Environmental EngineeringRivneUkraine
  3. 3.Jan Evangelista Purkyne University in Usti nad LabemÚstí nad LabemCzech Republic
  4. 4.Institute of Personnel Training of the State Employment Service of UkraineKyivUkraine

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