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)


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.


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


  1. 1.
    Vartuzov, V.V., Shkurdota, S.V., Litvinenko, V.I., Fefelov, A.A.: Computerized technology for compiling profiles of narcotic substances. In: Intertechnical System and Resolution of the Problem of Numerical Object 2012, ISRPN, pp. 39–41 (2012)Google Scholar
  2. 2.
    Shkurdota, S.V., Vartuzov, V.V., Litvinenko, V.I., Fefelov, A.A.: Methods of processing chromatograms for obtaining profiles of narcotic substances. In: Intertechnical System and Resolution of the Problem of Numerical Object 2012, ISRPN, pp. 227–229 (2012)Google Scholar
  3. 3.
    United Nations International Drug Control Program. United Nations, Vienna, New York, p. 27 (2004)Google Scholar
  4. 4.
    Bidyuk, P.I., Terentʹyev, O.M., Korshevnyuk, L.O.: Intelektualʹnyy analiz slabostrukturovanykh danykh za dopomohoyu bayyesovykh merezh, No. 3/5-HR, p. 85 (2007). (ukr)Google Scholar
  5. 5.
    Zhurovsʹkyy, M.Z., Bidyuk, P.I., Terentʹev, O.M.: Systemna metodyka pobudovy bayyesovykh merezh. “Naukovi visti” NTUU “KPI”, no. 4, pp. 47–61 (2007). (ukr)Google Scholar
  6. 6.
    Bidyuk, P.I., Terentʹyev, O.M., Korshevnyuk, L.O.: Bayyesovskaya set’ – instrument intellektual’nogo analiza dannykh. In: Problemy upravleniya i informatiki. K.: IKI NANU-NKAU, no.4, pp. 83–92 (2007). (rus)Google Scholar
  7. 7.
    Terentʹev, O.M., Gasanova L.T.: Bayesian networks in credit scoring. In: Second International Conference on Control and Optimization with Industrial Applications (COIA-1008). Institute of Applied Mathematics BSU, Baku, p. 171 (2008)Google Scholar
  8. 8.
    Andreassen, S., Woldbye, M., Falck, B., Andersen, S.K.: MUNIN - a causal probabilistic network for interpretation of electromyographic findings. In: International Joint Conference on Artificial Intelligence - Proceedings Milan, Italy, pp. 366–372 (1987)Google Scholar
  9. 9.
    Beinlich, I.A., Suermondt, H.J., Chavez, R.M., Cooper, G.F.: The ALARM monitoring system. In: 2nd European Conference on Artificial Intelligence in Medicine- Proceedings, London, England, pp. 247–256 (1989)Google Scholar
  10. 10.
    Castillo, E.F., Gutiérrez, J.M., Hadi, A.S.: Sensitivity analysis in discrete Bayesian networks. In: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, no. 27(4), pp. 412–423 (1997)Google Scholar
  11. 11.
    Chavez, R.M., Cooper, G.F.: KNET: Integrating hypermedia and normative Bayesian modeling. In: Uncertainty in Artificial Intelligence 4, North-Holland, Amsterdam, pp. 339–349 (1990)Google Scholar
  12. 12.
    Cheeseman, P., Kelly, M., Taylor, W., Freeman, D., Stutz, J.: Bayesian classification. In: AAAI, St. Paul: MN, pp. 607–611 (1988)Google Scholar
  13. 13.
    Cooper, G.F.: Current research directions in the development of expert systems based on belief networks. In: Applied Stochastic Models and Data Analysis, no. 5, pp. 39–52 (1989)Google Scholar
  14. 14.
    Cheng, J., Druzdzel, M.: AIS-BN: an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks. J. Artif. Intell. Res. JAIR-2000 13, 155–188 (2000)Google Scholar
  15. 15.
    Kayaalp, M., Cooper, G.: A Bayesian network scoring metric that is based on globally uniform parameter priors, pp. 251–258 (2002)Google Scholar
  16. 16.
    Zweig, G.G.: Speech recognition with dynamic bayesian networks: Ph.D. dissertation. University of California, Berkeley, p. 169 (1998)Google Scholar
  17. 17.
    Kayaalp, M.M., Cooper, G.F.: Learning dynamic bayesian network structures from data: Ph.D. dissertation. University of Pittsburgh, p. 203 (2003)Google Scholar
  18. 18.
    Codetta-Raiteri, D., Bobbio, A., Montani, S., Portinale, L.: A dynamic Bayesian network based framework to evaluate cascading effects in a power grid. In: Engineering Applications of Artificial Intelligence, vol. 25\4, pp. 683–697 (2012)Google Scholar
  19. 19.
    Jone, T.B., Darling, M.C., Groth, K.M., Denman, M.R., Luger, G.F.: A dynamic bayesian network for diagnosing nuclear power plant accidents. In: Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pp. 179–184 (2016)Google Scholar
  20. 20.
    Bescos, C., Schmeink, A., Harris, M., Schmidt, R.: Strategies in the use of static and dynamic bayesian networks in home monitoring. In: IEEE Benelux EMBS Symposium, pp. 31–34 (2007)Google Scholar
  21. 21.
    Hulst, I.R.: Modeling physiological processes with dynamic bayesian networks. Thesis Paper. University of Pittsburgh (2006)Google Scholar
  22. 22.
    de Kock, M., Le, H., Tadross, M., Potgeiter, A.: Weather forecasting using dynamic bayesian networks, Technical report, University of Cape Town (2008)Google Scholar
  23. 23.
    Dean, T., Kanazawa, K.: Probabilistic temporal reasoning (1988)Google Scholar
  24. 24.
    Murphy, K.P.: Dynamic bayesian networks: representation, inference and learning. Thesis Paper. University of California, Berkeley (2002)Google Scholar
  25. 25.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., Burlington (1988)zbMATHGoogle Scholar
  26. 26.
    van der Gaag, L.C., Coupé, V.M.: Sensitivity analysis for threshold decision making with Bayesian belief networks. In: Lamma, E., Mello, P. (eds.) AI*IA 99: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, pp. 37–48. Springer, Berlin (1999)Google Scholar
  27. 27.
    D. S. Laboratory: GeNIe & SMILE (1998). Accessed 12 Oct 2017
  28. 28.
    DNV: Det Norske Veritas (2013).
  29. 29.
    Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998)Google Scholar
  30. 30.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. 1–38 (1977)Google Scholar
  31. 31.
    Friedman, N.: The Bayesian structural EM algorithm. In: Fourteenth conference on Uncertainty in Artificial Intelligence (UAI 1998), Madison, Wisconsin, USA, SF, pp. 129–138. Morgan Kaufmann (1998)Google Scholar
  32. 32.
    Zhang, Z., Kwok, J., Yeung, D.: Surrogate maximization (minimization) algorithms for AdaBoost and the logistic regression model. In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), p. 117 (2004)Google Scholar

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

Personalised recommendations