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- W2896031916 abstract "Pattern recognition algorithms such as artificial neural networks (NNs) and convolution neural networks (CNNs) are prime candidates to perform automated gamma-ray spectroscopy. The way these models train and operate mimic how trained spectroscopists identify spectra. These models have shown promise in identifying gamma-ray spectra with large calibration drift and unknown background radiation fields. In this work, two algorithms for mixtures of radioisotopes based on NN and CNN are presented and evaluated." @default.
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- W2896031916 date "2020-02-01" @default.
- W2896031916 modified "2023-09-29" @default.
- W2896031916 title "A comparison of machine learning methods for automated gamma-ray spectroscopy" @default.
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- W2896031916 doi "https://doi.org/10.1016/j.nima.2018.10.063" @default.
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