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机器学习在分类拉丁美洲水同位素值方面的潜力及其对法医来源的影响

Thomas A Delgado¹, Richard C Tillquist², Eric J Bartelink¹

¹California State University, Chico, Department of Anthropology, California State University, 400 West First Street, Chico, CA 95929-0400, United States.

Forensic science international

|November 16, 2025

概括

人工智能 (AI) 算法可以通过分析同位素数据来预测起源地区来改善已故移民的识别. 人工神经网络实现了83%的准确性,为法医调查提供了一个有前途的工具.

科学领域:

法医科学法医科学法医科学
计算生物学计算生物学
地质化学地质化学

背景情况:

美国 - 墨西哥边境是一个致命的移民走廊,环境因素使移民识别复杂化.
同位素分析为确定原产地和旅行历史提供了潜力,但缺乏强大的拉丁美洲数据集.
现有的方法在法医应用中受到数据稀缺的阻碍.

研究的目的:

开发和评估人工智能 (AI) 算法用于拉丁美洲的同位素分类.
推出IsoPredict,这是一个用于使用同位素数据预测移民来源的原型应用程序.
为了比较六个AI算法的性能,用于法医同位素分析.

主要方法:

来自拉丁美洲的互插同位素数据被用于训练和测试AI模型.
评估了6个AI算法:人工神经网络,随机森林,支持向量机,决策树,k-最近邻居和高斯的天真贝叶斯.
模型的性能是根据对原产地预测的同位素数据分类的准确性进行评估的.

主要成果:

人工神经网络在从同位素数据中预测原产地方面达到最高准确率 (83%).
随机森林 (77%) 和支持矢量机 (76%) 模型也表现出强的表现.
高斯纳夫贝叶斯 (Gaussian Naïve Bayes) 的准确度最低 (49%),这表明它对此数据集的具体实施存在局限性.

结论:

人工智能,特别是人工神经网络,显示出显著的潜力,可以增强对象分类,用于移民的法医识别.
这项研究强调了为拉丁美洲开发更全面,更具法律相关性的同位素数据集的必要性.
进一步开发数据集和人工智能算法可以提高同位素分析的准确性和实用性,用于识别失踪人员和支持人道主义工作.

关键词:

人工神经网络的人工神经网络拉丁美洲拉丁美洲拉丁美洲机器学习机器学习移民移民移民稳定的同位素是稳定的.美国 - 墨西哥边境

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