Food safety risk intelligence early warning based on support vector machine

Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning

Guest editors: Ekaterina Isaeva and Álvaro Rocha

Article type: Research Article

Authors: Zhang, Yu^{a; b; *}

Affiliations: [a] China Research Center for Emergency Management, Wuhan University of Technology, Wuhan, China | [b] Hubei Collaborative Innovation Center for Early Warning and Emergency Response Technology, Wuhan, China

Correspondence: [*] Corresponding author. Yu Zhang, E-mail: [email protected].

Abstract: From the perspective of China’s food safety management practices, effective early warning and intervention of food safety risks in the food industry chain will greatly reduce the possibility of food safety accidents, thereby improving the safety and stability of people’s social life. This study builds a food safety risk intelligence early warning model based on support vector machines. First, it discusses the process of food safety risk intelligence early warning, classifies warning indicators of warning indicators, and sets the principles of early warning. Secondly, based on the theory of support vector machine technology, an intelligence early warning model for food safety risks is constructed, and the collection and processing of sample data are explained. Finally, based on the analysis results of the early warning model, the results are discussed to verify the effectiveness and accuracy of the early warning model.

Keywords: Support vector machine, food safety, risk, intelligence early warning

DOI: 10.3233/JIFS-179774

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 6957-6969, 2020