论文标题
食品安全风险预测欧盟数据上使用分类嵌入的深度学习模型预测
Food safety risk prediction with Deep Learning models using categorical embeddings on European Union data
论文作者
论文摘要
世界每天都在变得越来越全球化，人们可以从当地商店中几乎每个国家购买产品。鉴于各个国家 /地区的食品和饲料安全法不同，欧盟于1977年开始注册与交易产品相关的所有违规行为，以确保在食品链中发现对公共卫生的风险时，请确保对信息进行跨境监测和快速反应。该信息作为预防工具也具有巨大的潜力，以警告参与食品安全的参与者并优化其资源。在本文中，用机器学习技术刮除了一组与食物问题有关的数据，以预测未来通知的某些特征，以便采取先发制人的措施。这项工作的新颖性依赖于两个点：使用具有深度学习模型的分类嵌入（多层感知和一维卷积神经网络），以及其应用来解决预测欧盟食品问题的问题。这些模型允许预测几个功能：产品类别，危险类别，最后采取适当的措施。结果表明，该系统可以以74.08％至93.06％的精度预测这些功能。
The world is becoming more globalized every day and people can buy products from almost every country in the world in their local stores. Given the different food and feed safety laws from country to country, the European Union began to register in 1977 all irregularities related to traded products to ensure cross-border monitoring of information and a quick reaction when risks to public health are detected in the food chain. This information has also an enormous potential as a preventive tool, in order to warn actors involved in food safety and optimize their resources. In this paper, a set of data related to food issues was scraped and analysed with Machine Learning techniques to predict some features of future notifications, so that pre-emptive measures can be taken. The novelty of the work relies on two points: the use of categorical embeddings with Deep Learning models (Multilayer Perceptron and 1-Dimension Convolutional Neural Networks) and its application to solve the problem of predicting food issues in the European Union. The models allow several features to be predicted: product category, hazard category and finally the proper action to be taken. Results show that the system can predict these features with an accuracy ranging from 74.08% to 93.06%.
