启动一个公共存储库来托管基准数据库,以促进机器学习模型在食品安全方面的开发
Chenhao Qian1, Huan Yang2, Jayadev Acharya2
1Department of Food Science, Cornell, Ithaca, NY, USA.
Journal of food protection
|February 8, 2025
概括
创建了一个新的食品安全数据集公共存储库,以推进人工智能 (AI) 和机器学习 (ML) 在食品安全方面. 本资源旨在克服数据障碍,加快对病原体污染的预测模型的开发.
科学领域:
- 食品安全 食品安全
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 人工智能/机器学习在食品安全领域的发展和部署受到缺乏可访问,精心策划的数据集的阻碍.
- 公共数据库对于开发和验证这一领域的AI/ML模型至关重要.
研究的目的:
- 建立一个对食品安全数据集的公共存储库,以促进AI/ML模型的开发.
- 为培训和验证食品传播病原体的预测模型提供基准数据集和工具.
主要方法:
- 整合和整理了三个现有的关于李斯特菌,沙门氏菌,坎比洛巴克特菌和大肠杆菌污染的数据集.
- 在康奈尔食品安全ML库中公开提供数据集.
- 开发了可定制和选脚本 (LazyPredict) 来训练各种ML模型.
主要成果:
- 康奈尔食品安全ML库现在托管了涵盖土壤,家禽和流域污染的精心策划的数据集.
- 证明了存储库的实用性,用于ML模型培训的脚本.
- 强调需要继续提供数据,以推进预测性食品安全建模.
结论:
- 康奈尔食品安全ML存储库是AI/ML在食品安全方面的重要资源,解决了数据可访问性挑战.
- 鼓励对精心策划的数据集进行进一步的贡献,以扩大预测建模能力.
- 讨论公共数据库和保护隐私的数据共享技术的好处.
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