Evidence map›Paper›PMID 35571683›Full record

ArticleComputational intelligence and neuroscience2022

The Blockchain Technology Applied in the Development of Real Economy in Jiangsu under Deep Learning.

Wenquan Shi, Qibao Huang

Abstract read
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wenquan ShiFaculty of Economics and Management, Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, China.ORCID https://orcid.org/0000-0002-4298-1455
Qibao HuangAcademic Committee of Chinese Academy of Management Sciences, Beijing 101199, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study focuses on the financing difficulties of small and medium enterprises (SMEs) in China to study the application of blockchain technology in developing the real economy. Deep learning neural network is applied to the vulnerability analysis and detection of smart contracts in blockchain technology by analyzing the connotation of blockchain technology and deep learning. A multiparty joint financial service platform based on blockchain technology is established to help SMEs financing institutions reduce transaction costs, thereby helping them reduce loan interest rates. Finally, Jiangsu Province is studied as a pilot unit. The results show that the Recall and F-score of Bidirectional Neural Network for smart contract vulnerability detection are higher than those of the original neural network. The Recall rate and F-score value of the Wide and Deep model are up to 96.2% and 94.7%, which are higher than those of other vulnerability detection schemes. The Timestamp vulnerability has the highest Recall rate, 94.2%, which can rely on a large amount of valid data to improve detection efficiency. The distribution of financing needs of SMEs in Jiangsu Province from 2020 to 2021 shows that the loan number of SMEs is generally not high. Still, financial institutions and enterprises must spend the same transaction cost. After a technology company in Nanjing made a loan through a blockchain financial service platform, its financing cost decreased by 0.5331%. Blockchain technology has played a great role in the financing process of SMEs, reducing intermediate links and credit costs, and promoting the development of SMEs and the real economy.

Indexed as

BlockchainDeep LearningChinaNeural Networks, ComputerTechnology

Identifiers

PMID35571683
PMCPMC9106490

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.