基于鲁棒主成分分析和改进Apriori算法的食品安全预警系统

Xiaowen Ding, Sheng Xu
{"title":"基于鲁棒主成分分析和改进Apriori算法的食品安全预警系统","authors":"Xiaowen Ding, Sheng Xu","doi":"10.1145/3480651.3480653","DOIUrl":null,"url":null,"abstract":"In response to the frequent food safety incidents in recent years, a risk pre-warning system for food supply chain is proposed to ensure the food quality, This papers builds the food security information pre-warning system use association rules mining technology against the security problems of food production and processing, Monitor the detection data timely and give pre-warn automatically in the whole supply chain. we combines a Robust Principal Component Analysis (RPCA) to obtain better clustering performance and an improved Apriori algorithm to reduces the memory consumption and I/O operations and to shortens the running time. We study of a case of meat producer and the results shows the proposed pre-warning method can identify safety risks efficiently and report the exact warning, when an abnormality is detected by the expert analysis. Experiments verify the correctness of the model and the effectiveness of the algorithm.","PeriodicalId":305943,"journal":{"name":"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Food safety pre-warning system based on Robust Principal Component Analysis and Improved Apriori Algorithm\",\"authors\":\"Xiaowen Ding, Sheng Xu\",\"doi\":\"10.1145/3480651.3480653\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In response to the frequent food safety incidents in recent years, a risk pre-warning system for food supply chain is proposed to ensure the food quality, This papers builds the food security information pre-warning system use association rules mining technology against the security problems of food production and processing, Monitor the detection data timely and give pre-warn automatically in the whole supply chain. we combines a Robust Principal Component Analysis (RPCA) to obtain better clustering performance and an improved Apriori algorithm to reduces the memory consumption and I/O operations and to shortens the running time. We study of a case of meat producer and the results shows the proposed pre-warning method can identify safety risks efficiently and report the exact warning, when an abnormality is detected by the expert analysis. Experiments verify the correctness of the model and the effectiveness of the algorithm.\",\"PeriodicalId\":305943,\"journal\":{\"name\":\"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3480651.3480653\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3480651.3480653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

针对近年来频发的食品安全事件,提出了一种食品供应链风险预警系统,以保障食品质量。本文针对食品生产加工过程中存在的安全问题,利用关联规则挖掘技术构建食品安全信息预警系统,对检测数据进行及时监控,并在全供应链范围内自动预警。我们将鲁棒主成分分析(Robust Principal Component Analysis, RPCA)和改进的Apriori算法相结合,以获得更好的聚类性能,减少内存消耗和I/O操作,缩短运行时间。以某肉类生产企业为例进行研究,结果表明所提出的预警方法能够有效识别安全风险,并在专家分析发现异常时准确预警。实验验证了模型的正确性和算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Food safety pre-warning system based on Robust Principal Component Analysis and Improved Apriori Algorithm
In response to the frequent food safety incidents in recent years, a risk pre-warning system for food supply chain is proposed to ensure the food quality, This papers builds the food security information pre-warning system use association rules mining technology against the security problems of food production and processing, Monitor the detection data timely and give pre-warn automatically in the whole supply chain. we combines a Robust Principal Component Analysis (RPCA) to obtain better clustering performance and an improved Apriori algorithm to reduces the memory consumption and I/O operations and to shortens the running time. We study of a case of meat producer and the results shows the proposed pre-warning method can identify safety risks efficiently and report the exact warning, when an abnormality is detected by the expert analysis. Experiments verify the correctness of the model and the effectiveness of the algorithm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信