Abstract
This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%–12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.
Recommended Citation
S. M. Galib et al., "A Comparative Study of Machine Learning Methods for Automated Identification of Radioisotopes using NaI Gamma-ray Spectra," Nuclear Engineering and Technology, vol. 53, no. 12, pp. 4072 - 4079, Elsevier, Dec 2021.
The definitive version is available at https://doi.org/10.1016/j.net.2021.06.020
Department(s)
Nuclear Engineering and Radiation Science
Publication Status
Open Access
Keywords and Phrases
Artificial neural network; Gamma-ray spectroscopy; Nuclear security; Nuclear threat detection; Radioisotope identification; Real-time processing
International Standard Serial Number (ISSN)
2234-358X; 1738-5733
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Dec 2021
