没有确的类别,和法医枪支识别
1SEP Forensic Consultants, 296 Washington Ave., Memphis, TN 38103, USA.
Forensic science international
|June 6, 2023
概括
在法医枪支检查中估计错误率是具有挑战性的,特别是没有确的结果. 本研究提出了使用神经网络和信息理论的新方法,以更好地衡量模式证据分析的准确性.
科学领域:
- 法医科学 法医科学 法医科学
- 模式 证据分析 分析.
- 机器学习应用 机器学习应用
背景情况:
- 最近的讨论凸显了对枪支检查等法医学科的误差率估计的挑战.
- 总统科学技术顾问委员会 (PCAST) 报告批评了许多法医领域缺乏错误率研究.
- 在包括"不确定的"类别的学科中测量错误率方面缺乏共识,例如枪支和工具标记检查.
研究的目的:
- 探索法医模式证据分析中测量错误率的方法,特别是当使用"不确定的"类别时.
- 将二进制决策模型中的错误率计算适应科学领域,并得出一系列结论.
- 介绍一个模型系统,用于检查在这些领域的各种错误指标的性能.
主要方法:
- 开发和训练了不同复杂度的三个神经网络,以对弹药盒上的喷射器标记轮进行分类.
- 利用来自枪支的喷射器痕迹作为错误率分析的模型系统.
- 讨论了一种基于的方法来评估分类与基本真理的相似性,适用于具有不确定的结果的尺度.
主要成果:
- 神经网络被训练来分类射出器标记模式,作为错误度量评估的测试台.
- 该研究展示了一种模型系统,用于分析法医模式分析中的性能指标.
- 讨论了一种基于的方法,用于评估不确定的结果的系统中的分类准确性.
结论:
- 该研究为评估法医学科的错误率提供了一个框架,这些学科包含了"不确定的"结论.
- 提出的方法提供了一个更细致的方法来评估错误率超越二元决策模型.
- 这些发现有助于正在进行的关于提高法医证据分析可靠性和客观性的科学讨论.
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