Journal Article

Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain

Li, Haisheng, Hao, Zhihao, Wang, Fan, Mao, Dianhui

2018

Overview

The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Bayes Theorem / Blockchain / Collaboration / Commerce - economics / Commerce - organization & administration / Digital currencies / Engineering / Food Safety / Food Supply / Fuzzy sets / Humans / International conferences / Ontology / Scandals / Sentiment analysis / Stakeholder Participation / Supervision / Supply chains / Support Vector Machine