Understanding the social and economic factors affecting adverse events in an active theater of war: A neural network approach
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Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
AHFE International Conference on Cross-Cultural Decision Making (CCDM) -- JUL 17-21, 2017 -- Los Angeles, CA
WOS:000451449700020
WOS:000451449700020
Anahtar Kelimeler
Infrastructure Aid Activity, Adverse Events, Artificial Neural Networks, Multiple Linear Regression
Kaynak
Advances in Cross-Cultural Decision Making , (AHFE 2017)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
610
