Residential College | false |
Status | 已發表Published |
A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance | |
Tang, Qian1,2; Lu, Yan2; Wang, Bin1; Li, Zhen3,4 | |
2022-04-14 | |
Source Publication | SCIENTIFIC PROGRAMMING |
ISSN | 1058-9244 |
Volume | 2022Pages:6298248 |
Abstract | With the popularity of the Internet, the rise of e-commerce platforms has led to the rapid development of supply chain (SC) financial services in China, and the competitiveness of commercial banks and core enterprises in the supply chain is now gradually increasing, rapidly expanding into an important area of competition between the two. As an emerging force rebounding from the economic downturn, e-commerce platform transactions, with their unique characteristics of informatization, diversification, and convenience, have provided a broad space for Internet SC finance. The article mainly analyzes the risk identification method of e-commerce SC finance, analyzes its risk from the financing process, gives corresponding data support for the matters or processes that may cause financing risk based on DCNN model, and takes Jingdong SC finance as an example and analyzes its main financing methods and risk identification process; based on different experimental comparisons, a multigroup experimental study shows that the accuracy of supply chain finance risk identification using deep convolutional neural network models can reach 95.36%, which demonstrates the effectiveness of the proposed method by providing better performance compared to traditional BP and SVM networks. |
DOI | 10.1155/2022/6298248 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Software Engineering |
WOS ID | WOS:000806209500004 |
Publisher | HINDAWI LTD |
Scopus ID | 2-s2.0-85129078814 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Social Sciences |
Corresponding Author | Li, Zhen |
Affiliation | 1.Faculty of Science and Technology, University of Macau, Taipa, 999078, Macao 2.College of Business, Beijing Institute of Technology, Zhuhai, Guangdong, 519088, China 3.Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, HD1, United Kingdom 4.College of Industrial Automation, Beijing Institute of Technology, Zhuhai, Guangdong, 519088, China |
First Author Affilication | Faculty of Science and Technology |
Recommended Citation GB/T 7714 | Tang, Qian,Lu, Yan,Wang, Bin,et al. A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance[J]. SCIENTIFIC PROGRAMMING, 2022, 2022, 6298248. |
APA | Tang, Qian., Lu, Yan., Wang, Bin., & Li, Zhen (2022). A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance. SCIENTIFIC PROGRAMMING, 2022, 6298248. |
MLA | Tang, Qian,et al."A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance".SCIENTIFIC PROGRAMMING 2022(2022):6298248. |
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