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Understanding the Social and Economic Factors Affecting Adverse Events in an Active Theater of War: A Neural Network Approach

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Advances in Cross-Cultural Decision Making (AHFE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 610))

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Abstract

This study focused on the application of artificial neural networks (ANNs) to model the effect of infrastructure development projects on terrorism security events in Afghanistan. The dataset include adverse events and infrastructure aid activity in Afghanistan from 2001 to 2010. Several ANN models were generated and investigated for Afghanistan and its seven regions. In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANN approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, the developed ANN model provided better prediction accuracy with respect to the MLR approach. The results obtained from this analysis demonstrate that ANNs can predict the occurrence of adverse events according to economic infrastructure aid activity data.

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References

  1. Collins, J.J.: Understanding War in Afghanistan. NDU Press, Washington, DC (2011)

    Google Scholar 

  2. Tarnoff, C.: Afghanistan: US foreign assistance. Library of Congress Washington DC Congressional Research Service (2010)

    Google Scholar 

  3. Child, T.B.: Reconstruction and insurgency: the importance of sector in Afghanistan (2014). http://www.europeanpeacescientists.org/T.B.Child_Stuart%20Bremer%20Winner%202014.pdf

  4. Böhnke, J., Koehler, J., Zürcher, C.: Assessing the Impact of Development Cooperation in North East Afghanistan 2005–2009: Final Report, Evaluation Reports 049 (Bonn: Bundesministerium für wirtschaftliche Zusammen arbeit und Entwicklung) (2010)

    Google Scholar 

  5. Kamrany, N.M.: After the Taliban: nation-building in Afghanistan (review). Middle East J. 63(4), 669–671 (2009)

    Google Scholar 

  6. Wheeler, S., Stolkowski, D.: Development as Counterinsurgency in Afghanistan. Unpublished manuscript (2011)

    Google Scholar 

  7. Cakit, 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)

    Article  Google Scholar 

  8. Çakıt, E., Karwowski, W.: Understanding patterns of infrastructure development in the active war theater of Afghanistan over the period 2002–2010. Proc. Manuf. 3, 3876–3882 (2015)

    Google Scholar 

  9. Çakıt, E., Karwowski, W.: 13 Gaining insight by applying geographical modeling. In: Cohn, J.V., Schatz, S., Freeman, H., Combs, D.J. (eds.) Modeling Sociocultural Influences on Decision Making: Understanding Conflict, Enabling Stability, pp. 243–266. CRC Press, Boca Raton (2016)

    Chapter  Google Scholar 

  10. Stanton, J.: Evolutionary cognitive neuroscience: dual use discipline for understanding & managing complexity and altering warfare. In International Studies Association Conference, Portugal (2007)

    Google Scholar 

  11. LaFree, G., Dugan, L., Xie, M., Singh, P.: Spatial and temporal patterns of terrorist attacks by ETA 1970 to 2007. J. Quant. Criminol. 28(1), 7–29 (2012)

    Article  Google Scholar 

  12. Siebeneck, L.K., Medina, R.M., Yamada, I., Hepner, G.F.: Spatial and temporal analyses of terrorist incidents in Iraq, 2004–2006. Stud. Confl. Terror. 32(7), 591–610 (2009)

    Article  Google Scholar 

  13. Brown, D., Dalton, J., Hoyle, H.: Spatial forecast methods for terrorist events in urban environments. In: Intelligence and Security Informatics, pp. 426–435. Springer, Berlin (2004)

    Google Scholar 

  14. Johnson, S.D., Braithwaite, A.: Spatio-temporal modelling of insurgency in Iraq. In: Frielich, J.D., Newman, G. (eds.) Reducing Terrorism Through Situational Crime Prevention, pp. 9–32. Criminal Justice Press, New York (2009)

    Google Scholar 

  15. Webb, J.J., Cutter, S.L.: The geography of US terrorist incidents, 1970–2004. Terr. Polit. Violence 21(3), 428–449 (2009)

    Article  Google Scholar 

  16. Reed, G.S., Colley, W.N., Aviles, S.M.: Analyzing behavior signatures for terrorist attack forecasting. J. Def. Model. Simul. Appl. Methodol. Technol. 10(2), 203–213 (2013)

    Google Scholar 

  17. Minu, K.K., Lineesh, M.C., John, C.J.: Wavelet neural networks for nonlinear time series analysis. Appl. Math. Sci. 4(50), 2485–2495 (2010)

    MathSciNet  MATH  Google Scholar 

  18. Çakıt, E., Karwowski, W., Bozkurt, H., Ahram, T., Thompson, W., Mikusinski, P., Lee, G.: Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques. Appl. Soft Comput. 25, 204–214 (2014)

    Article  Google Scholar 

  19. Çakıt, E., Karwowski, W.: Fuzzy inference modeling with the help of fuzzy clustering for predicting the occurrence of adverse events in an active theater of war. Appl. Artif. Intell. 29(10), 945–961 (2015)

    Article  Google Scholar 

  20. Çakıt, E., Karwowski, W.: Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection. Artif. Intell. Rev. 1–17. In Press

    Google Scholar 

  21. Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79 (2005)

    Article  Google Scholar 

  22. Tan, Y., Zeng, G., Yu, L.: Energy analysis for an executable program on a single computer based on BP neural network. J. Exp. Theor. Artif. Intell. 25(1), 105–117 (2013)

    Article  Google Scholar 

  23. Çakit, E., Durgun, B., Cetik, O.: A neural network approach for assessing the relationship between grip strength and hand anthropometry. Neural Netw. World 25(6), 603 (2015)

    Article  Google Scholar 

  24. Fard, A.K., Akbari-Zadeh, M.R.: A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting. J. Exp. Theor. Artif. Intell. 26(2), 167–182 (2014)

    Article  Google Scholar 

  25. Sheela, K.G., Deepa, S.N.: Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks. Soft. Comput. 18(3), 607–615 (2014)

    Article  Google Scholar 

  26. NeuroSolutions documentation. http://www.neurosolutions.com/docs/NeuroSolutions.pdf

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Acknowledgments

The authors are grateful for the support of the Office of Naval Research (ONR) under Grant No. 1052339, Complex Systems Engineering for Rapid Computational Socio-Cultural Network Analysis, and the helpful guidance of ONR Program Management and the technical team.

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Authors and Affiliations

  1. Department of Industrial Engineering, Aksaray University, 68100, Aksaray, Turkey

    Erman Çakıt

  2. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816, USA

    Waldemar Karwowski

Authors
  1. Erman Çakıt
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  2. Waldemar Karwowski
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Correspondence to Erman Çakıt .

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Editors and Affiliations

  1. Advanced Technology Labs, Lockheed Martin, Orlando, Florida, USA

    Mark Hoffman

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Çakıt, E., Karwowski, W. (2018). Understanding the Social and Economic Factors Affecting Adverse Events in an Active Theater of War: A Neural Network Approach. In: Hoffman, M. (eds) Advances in Cross-Cultural Decision Making. AHFE 2017. Advances in Intelligent Systems and Computing, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-60747-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-60747-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60746-7

  • Online ISBN: 978-3-319-60747-4

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