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This study aims to enhance the accuracy and efficiency of nuclide identification methods using advanced algorithms.

June 3, 2026Nuclear TechniquesOpen Access

Nuclide identification method based on neighborhood rough set and KNN classification

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Randomized trial demonstrates high accuracy in nuclide identification using NRS and KNN in portable devices, implying improved detection efficiency.

This study aims to enhance the accuracy and efficiency of nuclide identification methods using advanced algorithms.
Applied principal component analysis for dimensionality reduction of gamma spectrum data.
Utilized neighborhood rough set theory for optimizing the feature subset by removing redundancies.
Employed K-nearest neighbor classifier for efficient identification on reduced dimensions.
Achieved an average identification accuracy of 98.5% with a neighborhood radius of δ=0.2.
Processing time for a single identification was within 140 ms.
Confirmed superior performance across varied scenarios, including mixed-nuclide conditions.

Chen et al. (2026) studied this question.

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June 3, 2026Nuclear TechniquesOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates high accuracy in nuclide identification using NRS and KNN in portable devices, implying improved detection efficiency.

This study aims to enhance the accuracy and efficiency of nuclide identification methods using advanced algorithms.
Applied principal component analysis for dimensionality reduction of gamma spectrum data.
Utilized neighborhood rough set theory for optimizing the feature subset by removing redundancies.
Employed K-nearest neighbor classifier for efficient identification on reduced dimensions.
Achieved an average identification accuracy of 98.5% with a neighborhood radius of δ=0.2.
Processing time for a single identification was within 140 ms.
Confirmed superior performance across varied scenarios, including mixed-nuclide conditions.

Chen et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark