Evidence map›Paper›PMID 35463225›Full record

ArticleComputational intelligence and neuroscience2022

A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning.

Li-Li Yin, Yi-Wen Qin, Yuan Hou, Zhao-Jun Ren

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Li-Li YinBeijing Technology and Business University, Beijing, China.ORCID https://orcid.org/0000-0002-3242-7143
Yi-Wen QinNorth Borneo University College, Kota Kinabalu, Sabah, Malaysia.ORCID https://orcid.org/0000-0002-9128-0508
Yuan HouBeijing Technology and Business University, Beijing, China.ORCID https://orcid.org/0000-0002-1257-2490
Zhao-Jun RenBeijing Technology and Business University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, there are widespread financing difficulties in China's trade circulation industry. Supply chain finance can provide financing for small- and medium-sized enterprises in China's trade circulation industry, but it will produce financing risks such as credit risks. It is necessary to analyze the causes of the risks in the supply chain finance of the trade circulation industry and measure these risks by establishing a credit risk assessment system. In this article, a supply chain financial risk early warning index system is established, including 4 first-level indicators and 29 third-level indicators. Then, on the basis of the supply chain financial risk early warning index system, combined with the method of convolution neural network, the supply chain financial risk early warning model of trade circulation industry is constructed, and the evaluation index is measured by the method of principal component analysis. Finally, the relevant data of trade circulation enterprises are selected to make an empirical analysis of the model. The conclusion shows that the supply chain financial risk early warning model and risk control measures established in this article have certain reference value for the commercial circulation industry to carry out supply chain finance. It also provides guidance for trade circulation enterprises to deal with supply chain financial risks effectively.

Indexed as

IndustryNeural Networks, ComputerChinaRisk Assessment

Identifiers

PMID35463225
PMCPMC9033324

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.