Evidence map›Paper›PMID 37467199›Full record

ArticlePloS one2023

The communication path and improvement strategy of symbolic culture of sneaker consumption culture using the big data analysis.

Zhongyuan Lv

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Zhongyuan LvSchool of Law, Hohai University, Nanjing, China.ORCID 0009-0005-2579-5457

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women's Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men's Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.

Indexed as

Artificial IntelligencePhysiological PhenomenaBig DataCommunicationFemaleHumansNeural Networks, Computer

Identifiers

PMID37467199
PMCPMC10355455

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.