Advertisement

Detecting Adverse Events in an Active Theater of War Using Advanced Computational Intelligence Techniques

  • Jozef ZuradaEmail author
  • Donghui Shi
  • Waldemar Karwowski
  • Jian Guan
  • Erman Çakıt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

This study investigates the effectiveness of advanced computational intelligence techniques in detecting adverse events in Afghanistan. The study first applies feature reduction techniques to identify significant variables. Then it uses five cost-sensitive classification methods. Finally, the study reports the resulting classification accuracy rates and areas under the receiver operating characteristics charts for adverse events for each method for the entire country and its seven regions. It appears that when analysis is performed for the entire country, there is little correlation between adverse events and project types and the number of projects. However, the same type of analysis performed for each of its seven regions shows a connection between adverse events and the infrastructure budget and the number of projects allocated for the specific regions and times. Among the five classifiers, the C4.5 decision tree and k-nearest neighbor seem to be the best in terms of global performance.

Keywords

Detecting adverse events Active war theater Computational intelligence Soft computing 

Notes

Acknowledgment

This study was supported in part by Grant no. 10523339, Complex Systems Engineering for Rapid Computational Socio-Cultural Network Analysis, from the Office of Naval Research awarded to Dr. W. Karwowski at the University of Central Florida.

References

  1. 1.
    Clancy, J., Crossett, C.: Measuring effectiveness in irregular warfare. Parameters 37(2), 88 (2007)Google Scholar
  2. 2.
  3. 3.
    Stanton, J.: Evolutionary cognitive neuroscience: dual use discipline for understanding & managing complexity and altering warfare. In: International Studies Association Conference, Portugal (2007). SSRN. http://ssrn.com/abstract=1946864
  4. 4.
    Open Source Center (OSC): Afghanistan-Geospatial Analysis Reveal Patterns in Terrorist Incidents 2004–2008, 30 April 2009. http://www.fas.org/irp/dni/osc/afghan-geospat.pdf. Accessed 3 May 2012
  5. 5.
    Berrebi, C., Lakdawalla, D.: How does terrorism risk vary across space and time? An analysis based on the israeli experience. Defense Peace Econ. 18(2), 113–131 (2007)CrossRefGoogle Scholar
  6. 6.
    Reed, G.S., Colley, W.N., Aviles, S.M.: Analyzing behavior signatures for terrorist attack forecasting. J. Defense Model. Simul.: Appl. Methodol. Technol. 10, 1–12 (2011)Google Scholar
  7. 7.
    Inyaem, U., Meesad, P., Haruechaiyasak, C., Tran, D.: Terrorism event classification using fuzzy inference systems. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 7(3), 243–256 (2010)Google Scholar
  8. 8.
    Çakıt, E., Karwowski, W.: Assessing the relationship between economic factors and adverse events in an active war theater using fuzzy inference system approach. Int. J. Mach. Learn. Comput. 5(3), 252–257 (2015)CrossRefGoogle Scholar
  9. 9.
    Çakıt, E., Karwowski, W.: Fuzzy inference modelling with the help of fuzzy clustering for predicting the occurrence of adverse events in an active theater of war. Appl. Artif. Intell. 29, 945–961 (2015)CrossRefGoogle Scholar
  10. 10.
    Çakıt, E., Karwowski, W.: Understanding the social and economic factors affecting adverse events in an active theater of war: a neural network approach. In: Advances in Cross-Cultural Decision Making. Advances in Intelligent Systems and Computing, vol. 610, pp. 215–223. Springer (2017)Google Scholar
  11. 11.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
  12. 12.
    Elkan, C.: The Foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence, vol. 17, no. 1, pp. 973–978. Lawrence Erlbaum Associates Ltd. (2001)Google Scholar
  13. 13.
    Platt, J.: Fast Training of support vector machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds). Advances in Kernel Methods - Support Vector Learning (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jozef Zurada
    • 1
    • 2
    Email author
  • Donghui Shi
    • 3
  • Waldemar Karwowski
    • 4
  • Jian Guan
    • 1
  • Erman Çakıt
    • 5
  1. 1.Department of Computer Information Systems, College of BusinessUniversity of LouisvilleLouisvilleUSA
  2. 2.WSB GdanskGdanskPoland
  3. 3.Department of Computer EngineeringAnhui Jianzhu UniversityHefeiChina
  4. 4.Department of Industrial Engineering and Management SystemsUniversity of Central FloridaOrlandoUSA
  5. 5.Department of Industrial EngineeringAksaray UniversityAksarayTurkey

Personalised recommendations