Back propagation neural network analysis for the detection of explosives based on tagged neutron

Гонг, Ке; Xiao, Shujun; Jing, Shi-Wei; Zheng, Y.

doi:10.1007/s10967-020-07321-3

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…A crucial aspect of neural networks is that they learn from data by adjusting the weights of connections through algorithms during the training process. Over time, several neural networks architectures have been developed and applied for different applications[26][27][28][29][30][31][32][33], notably including Feed Forward Neural Network…”

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confidence: 99%

Integrating Tagged Neutron Inspection with Explainable AI for Threat Material Identification

Shahabinejad,

Sudac,

Nad

et al. 2024

Preprint

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Here we present an innovative approach for detecting threat materials within a sealed container by integrating tagged fast neutron activation analysis with Explainable Artificial Intelligence (XAI). Two AI models, a Feed-Forward Neural Network (FFNN) and a Convolutional Neural Network (CNN), were developed to analyze the emitted gamma rays to identify materials like explosives and drugs based on depth profiles of carbon, nitrogen, and oxygen concentrations. XAI was applied to make the models' decision-making process transparent. The method is adaptable to various spectrometric analyses. We demonstrate its effectiveness using data obtained by the Rapidly Relocatable Tagged Neutron Inspection System (RRTNIS), which is irreplaceable for inspecting sealed cargo containers, despite challenges such as variable material placement, background noise, and shielding effects. Our approach successfully locates and categorizes threat materials, both alone and within surrounding materials, at various locations within sealed cargo containers.

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confidence: 99%