基于软多数投票的监督学习算法的异构集成,用于更可靠地识别基于塑性闪烁体光谱的伽马发射放射性同位素

IF 2.8 3区 物理与天体物理 Q3 CHEMISTRY, PHYSICAL
Khalil Moshkbar-Bakhshayesh
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引用次数: 0

摘要

γ辐射同位素的鉴定在核安全、环境监测和医疗诊断等各种应用中发挥着关键作用。然而,塑料闪烁体的低能量分辨率给精确的放射性同位素鉴定带来了挑战。本研究提出了一种监督学习(HEL)算法的异构集成,包括逻辑回归(LR)、决策树(DT)和k-近邻(k-NN),以提高识别性能。集成方法通过软多数投票(SMV)来利用每个单独算法的优势。分析了Co-60、Cs-137、Na-22和Am-241的实验伽马谱,并将提取的特征用于提高分类精度。此外,本研究扩展了识别含有多种同位素的混合光谱的方法,这是辐射检测中的一个关键挑战。HEL的概率输出能够更精确地表征混合辐射源。基于准确性、精密度、召回率和f1分数的性能评估表明,HEL优于单个算法,对噪声/不确定性具有出色的鲁棒性。结果证实,与单个算法相比,所提出的集合方法显著提高了伽玛发射放射性同位素识别的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heterogeneous ensemble of supervised learning algorithms with soft majority voting for more reliable identification of gamma-emitting radioisotopes based on plastic scintillator spectra
The identification of gamma-emitting radioisotopes plays a critical role in various applications, including nuclear security, environmental monitoring, and medical diagnostics. However, the low energy resolution of plastic scintillators presents challenges in accurate radioisotope identification. This study proposes a heterogeneous ensemble of supervised learning (HEL) algorithms, incorporating logistic regression (LR), decision tree (DT), and k-nearest neighbors (k-NN), to enhance identification performance. The ensemble method leverages the strengths of each individual algorithm through soft majority voting (SMV(. Experimental gamma spectra from Co-60, Cs-137, Na-22, and Am-241 are analyzed, with extracted features applied to improve classification accuracy. Additionally, this study extends the methodology to identify mixture spectra containing multiple isotopes, a crucial challenge in radiation detection. The probabilistic outputs of HEL enable more precise characterization of mixed radiation sources. Performance evaluation, based on accuracy, precision, recall, and F1-score, demonstrated that HEL outperforms individual algorithms, exhibiting superior robustness against noise/uncertainties. The results confirm that the proposed ensemble method significantly enhances the reliability of gamma-emitting radioisotope identification compared to individual algorithms.
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来源期刊
Radiation Physics and Chemistry
Radiation Physics and Chemistry 化学-核科学技术
CiteScore
5.60
自引率
17.20%
发文量
574
审稿时长
12 weeks
期刊介绍: Radiation Physics and Chemistry is a multidisciplinary journal that provides a medium for publication of substantial and original papers, reviews, and short communications which focus on research and developments involving ionizing radiation in radiation physics, radiation chemistry and radiation processing. The journal aims to publish papers with significance to an international audience, containing substantial novelty and scientific impact. The Editors reserve the rights to reject, with or without external review, papers that do not meet these criteria. This could include papers that are very similar to previous publications, only with changed target substrates, employed materials, analyzed sites and experimental methods, report results without presenting new insights and/or hypothesis testing, or do not focus on the radiation effects.
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