Risk Evaluation Method of Import and Export Goods Based on Fuzzy Reasoning and DeepFM

Yuanyuan Xu,Jiliang Luo,Tao Li,Huijuan Fang,Jianan He,Shiming Lin,Yi-Zhang Jiang

doi:10.1155/2021/2390958

Abstract

At present, the inspection mode of China's import ports is generally manual based on experience, or random inspection by the document review system according to a preset random inspection ratio. In order to improve the detection rate of unqualified goods and realize the best allocation of limited human and material resources of inspection and quarantine institutions, a method composed of fuzzy reasoning, deep neural network, and factorization machine (DeepFM) was proposed for the intelligent evaluation of risk sources of imported goods. Fuzzy reasoning is used to realize the fuzzy normalization of the dataset samples, the DeepFM deep neural network is finally used for training and learning to classify and evaluate the risks of goods. Results of experimental tests on a specific customs import and export dataset verify the effectiveness of the proposed research method.

Highlights

With the rapid development of international trade and logistics, imported goods have higher requirements for port customs clearance [1, 2]. e sharp increase in traded goods poses a challenge to the random inspection of inbound and outbound goods by Chinese customs [3]
In response to the time-consuming and lowefficiency problems of traditional mine-safety-accident data classification, Liu [15] proposed a classification method based on the combination of a long- and short-term memory (LSTM) network and attention mechanism and applied it to the classification of mine-accident levels. e results show that the proposed method improves the accuracy of the classification and achieves good results
Typical deep-learning networks include, e.g., convolutional neural networks (CNNs) [19], deep belief networks (DBNs) [20], and recurrent neural networks (RNNs) [21]. e DeepFM model, like the wide and deep model, is a deep-learning model that is widely used in CTR prediction [22]

Summary

Introduction

With the rapid development of international trade and logistics, imported goods have higher requirements for port customs clearance [1, 2]. e sharp increase in traded goods poses a challenge to the random inspection of inbound and outbound goods by Chinese customs [3]. Cao [14] started from the vagueness and highly nonlinear characteristics of the inspection and quarantine risk assessment itself; used nonstatistical methods for the first time to construct a scientific, reasonable, comprehensive, and objective indicator system; and employed artificial intelligence algorithms as the basis to further propose the use of fuzzy reasoning. A neural network algorithm is used to conduct a risk assessment on specific imported goods, so as to scientifically determine the inspection rate. A risk-assessment method for imported goods based on the combination of fuzzy reasoning and DeepFM is proposed. A large amount of historical declaration and inspection data of imported goods, in this method, key field information is first selected as the characteristic index of cargo risk evaluation according to expert experience. Results of experimental tests on a specific customs import and export dataset verify the effectiveness of the proposed research method

Related Algorithms

Evaluation index factor n

Evaluation list

Conclusions