In this quantitative correlational study, simulated data were employed to examine artificial-intelligence techniques or, more specifically, artificial neural networks, as they relate to the location prediction of improvised explosive devices (IEDs). An ANN model was developed to predict IED placement, based upon terrain features and objects related to historical IED detonation events, the associated visual and radio-frequency lines of sight of these features and objects, and the volume of target-vehicle traffic during a 24-hour period. The architecture of the model contains a multilayer perceptron network to realize advanced performance. The findings indicate that the model is suitable for IED placement prediction. This research also established that opportunities exist for the development of sophisticated techniques, grounded in AI, that can predict the location of emplaced IEDs.
|Advisor:||Pittman, Jason M.|
|Commitee:||Barker, Helen G., Giulianelli, Lisa C.|
|School Location:||United States -- Maryland|
|Source:||DAI-B 74/09(E), Dissertation Abstracts International|
|Subjects:||Information Technology, Artificial intelligence, Computer science|
|Keywords:||Device emplacement, Improvised explosive devices, Neural networks, RF propagation|
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