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A Platform for Human-Machine Information Data Fusion

  • Migdat HodžićEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 59)

Abstract

The overall goal in this concept paper is to present an innovative and rigorous mathematical methodology and an expert self learning data fusion and decision platform, which is scalable and effective for a variety of applications. This includes (i) Interface design that incorporates the understanding of how both machines and humans fuse soft and hard data and information, and (ii) Forming a shared perception and understanding of the environment between the human and the machine, which supports human decisions and reduces human soft and decision making errors. With this paper we continue our research on Uncertainty Balance Principle (UBP) which is at the core of our soft hard data fusion and decision making strategy. The proposed methodology can be employed in the context of Artificial Intelligence (AI) and Machine Learning (ML) applications, such as banking risk assessment, Block Chain peer to peer systems, ecological and climate modeling, social sciences, econometrics, as well as defense applications such as battle management.

Keywords

Human machine data fusion Probabilistic data Possibilistic data 

References

  1. 1.
    Jenkins, M.P., Gross, G.A., Bisantz, A.M., Nagi, R.: Towards context aware data fusion: Modeling and integration of situationally qualified human observations to manage uncertainty in a hard [plus] soft fusion process. Inf. Fusion 21, 130–144 (2015)CrossRefGoogle Scholar
  2. 2.
    Hall, D.L., McNeese, M.D., Hellar, D.B., Panulla, B.J., Shumaker, W.: A cyber infrastructure for evaluating the performance of human centered fusion. In: Proceedings of the 12th International Conference on Information Fusion, pp. 1257–1264. IEEE (2009)Google Scholar
  3. 3.
    Nachouki, G., Quafafou, M.: Multi-data source fusion. Inf. Fusion 9, 523–537 (2008)CrossRefGoogle Scholar
  4. 4.
    Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14, 28–44 (2013)CrossRefGoogle Scholar
  5. 5.
    Smirnov, A., Levashova, T., Shilov, N.: Patterns for context-based knowledge fusion in decision support Systems. Inf. Fusion 21, 114–129 (2015)CrossRefGoogle Scholar
  6. 6.
    Wozniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)CrossRefGoogle Scholar
  7. 7.
    Kurc, T., Janies, D.A., Johnson, A.D., Langella, S., Oster, S., Hastings, S., Habib, F., Camerlengo, T., Ervin, D., Catalyurek, U.V., Saltz, J.H.: An XML-based system for synthesis of data from disparate databases. J. Am. Med. Inform. Assoc. 13(3), 289–301 (2006)CrossRefGoogle Scholar
  8. 8.
    Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic, Theory and Applications. Van Nostrand Reinhold (1985)Google Scholar
  9. 9.
    Belman, R.E., Zadeh, L.A.: Decision making in a fuzzy environment. Manag. Sci. 17(4), B141–B164 (1970)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Durrant-Whyte, H.: Multi Sensor Data Fusion. University of Sydney (2001)Google Scholar
  11. 11.
    Zadeh, L.A.: Fuzzy sets as basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sumari, A.D.W., Ahmad, A.S.: Design and implementation of multi agent-based information fusion system for decision making support. ITB J. ICT 2(1), 42–63 (2008)CrossRefGoogle Scholar
  13. 13.
    Hodzic, M.: Fuzzy to random uncertainty alignment. SEJSC 5(1), 58–67 (2016)Google Scholar
  14. 14.
    Hodzic, M.: Uncertainty balance principle. PEN 4(2), 17–32 (2016)CrossRefGoogle Scholar
  15. 15.
    Hodzic, M.: Soft to hard data transformation using uncertainty balance principle. In: International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, IAT 2017. Advanced Technologies, Systems, and Applications II, pp. 785–809. Springer, January 2018Google Scholar
  16. 16.
    Brkić, S., Hodžić, M., Djanić, E.: Fuzzy logic model of soft data analysis for corporate client credit risk assessment in commercial banking. ICEI, Tuzla, Bosnia and Herzegovina, December 2017Google Scholar
  17. 17.
    Leon-Garcia, A.: Probability, Statistics, and Random Processes for Electrical Engineering. Pearson, London (2008)Google Scholar
  18. 18.
    IBM: Global Technology Outlook (2012). www.IBM.com
  19. 19.
    Dubois, D., Prade, H.: Possibility Theory. Plenum, New York (1988)CrossRefGoogle Scholar
  20. 20.
    Dubois, D., Prade, H.: Possibility theory and its applications: where do we stand? IRIT-CNRS, Universit´e Paul Sabatier, 31062 Toulouse Cedex 09, France (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.International University of SarajevoSarajevoBosnia and Herzegovina

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