Development of Machine Learning based Method and Technology for Food Supply Chain Anomaly Detection

Abstract

The complexity of the food supply chain leads to frequent food security incidents which not only causes social unrest but also directly or indirectly endangers people's health and life Therefore the maintenance of food safety has become an important factor for countries all over the world With the rise of Data Science Machine Learning and Blockchain the ideal of system intelligence has gradually been realized This study looks forward to solving food safety problems through the support of intelligent systems to thereby promote human welfare This study designed a "food safety monitoring and management model" based on the concept of data science For this "food safety monitoring and management mode" the functional requirements of its system are analyzed and the functional architecture of the "safe food protection system" is planned and designed with reference to the concept of blockchain and the principles of machine learning According to the functional framework define the needs of data analysis design the "food safety inspection data model" use machine learning technology to analyze the impact factors and abnormal patterns of food anomalies to build anomaly detection mechanism In order to verify the validity and correctness this study uses public data to detect First detect supply and sales anomalies Since anomaly detection focuses on picking out as many anomalies as possible the recall rate is used as an validation index The final model's recall rate increases from 0 75027 to 0 86638 Then it detects the abnormality of the supplier's equipment and also uses public data to detect the error rate of the model from 0 006077 to 0 004112 The above models are all evaluated with validation index and the validation index can reflect the accuracy of the model so it can verify the effectiveness of the anomaly detection methods and techniques in this study

Date of Award	2020
Original language	English
Supervisor	Yuh-Min Chen (Supervisor)