Processing scintillation gamma-ray spectra by artificial neural network

Shahabinejad, H.; Vosoughi, Naser; Saheli, Fereshte

doi:10.1007/s10967-020-07239-w

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Although the library least square method provides good results, the occurrence of negative values for elemental concentrations was reported as its main drawback [15], as also noted in other studies [16;17]. To address this issue in the full spectrum analysis of gamma-ray spectra, the nonnegative least square method [18][19][20] and arti cial intelligence techniques such as hybrid fuzzy-genetic algorithms, particle swarm optimization and arti cial neural networks [16; [21][22][23] have been exploited in similar applications in recent years. Currently, a step-by-step non-negative least square method is being used to analyze gamma-ray spectra obtained from RRTNIS, a topic which is not discussed here.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Integrating Tagged Neutron Inspection with Explainable AI for Threat Material Identification

Shahabinejad,

Sudac,

Nad

et al. 2024

Preprint

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“…Another application was demonstrated through the work of Varley et al They worked on an environmental protection application in which they developed an ANN model capable of successfully discriminating between natural radioactivity and the radioactivity emitted from 226 Ra mixed with the natural radioactivity spectrum [11]. ANNs also have been used in processing the gamma spectra obtained from a scintillation detector [12]. ANNs have been also used in neutron depth profile calculations to predict the boron concentration in a thin film layer from the alpha spectrum emitted due to the (n, alpha) reaction [13].…”

Section: Introductionmentioning

confidence: 99%

Determination of the full energy peak efficiency of HPGe detector for varying soil compositions and Marinelli beaker dimensions using Artificial Neural Networks

Hossny

Salama²,

Mageed³

et al. 2022

J. Inst.

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Natural radioactivity measurement is studying the radiation emitted naturally from any nature's components such as air, soil, water, etc. This field of study plays an important role in public health and safety management. Scientists and engineers can calculate the radiation doses received by inhabitants of a certain area by measuring the natural radioactivity in the same area. One of the main issues facing the standard measurement procedure is determining the appropriate volume of the sample to be taken from the area under investigation. The volume of the investigated sample affects the detection efficiency of the HPGe detector due to the self-shielding effect. In this work we show how using artificial neural networks can help in choosing the proper dimensions of the Marinelli beaker to be used in the detection process. We used the various compositions of soil as a case study. Results showed that artificial neural networks were capable of finding the relation between soil composition, Marinelli beaker dimensions, and gamma energy as inputs and the HPGe detection efficiency as output. The developed model's regression was 99% compared to the detection efficiency calculated using MCNP. This will help to have a better prediction of the detection efficiency of natural radioactivity measurements.

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Rapid quantitative elemental analysis using artificial neural network for case study of Isfahan Miniature Neutron Source Reactor

Asgari

Hosseini

2022

J Radioanal Nucl Chem

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Processing scintillation gamma-ray spectra by artificial neural network

Cited by 8 publications

References 30 publications

Integrating Tagged Neutron Inspection with Explainable AI for Threat Material Identification

Integrating Tagged Neutron Inspection with Explainable AI for Threat Material Identification

Determination of the full energy peak efficiency of HPGe detector for varying soil compositions and Marinelli beaker dimensions using Artificial Neural Networks

Rapid quantitative elemental analysis using artificial neural network for case study of Isfahan Miniature Neutron Source Reactor

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