Convolutional neural network application for supply-demand matching in Zhuang ethnic clothing image classification.

Jingyi Ji, Yonghua Lao, Lei Huo

Abstract read

In one paragraph

Article in Scientific reports, 2024. 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

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

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

3 authors.

Jingyi JiSchool of Art, South China University of Technology, Guangzhou, 510641, People's Republic of China.

Yonghua LaoNational Engineering Research Center for Tissue Restoration and Reconstruction, School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510641, People's Republic of China.

Lei HuoSchool of Art, South China University of Technology, Guangzhou, 510641, People's Republic of China. henry@scut.edu.cn.

Funding

South China University of Technology S202310561344X

6 · The paper itself

Abstract

This study aims to design a classification technique suitable for Zhuang ethnic clothing images by integrating the concept of supply-demand matching and convolutional neural networks. Firstly, addressing the complex structure and unique visual style of Zhuang ethnic clothing, this study proposes an image resolution model based on supply-demand matching and convolutional networks. By integrating visual style and label constraints, this model accurately extracts local features. Secondly, the model's effectiveness and resolution performance are analyzed through various performance metrics in experiments. The results indicate a significant improvement in detection accuracy at different annotation points. The model outperforms other comparative methods in pixel accuracy (90.5%), average precision (83.7%), average recall (80.1%), and average F

Indexed as

Convolutional neural networkImage classificationSupply–demand matchingVisual styleZhuang ethnic clothing

Identifiers

PMID38858436

DOI10.1038/s41598-024-64082-9

PMCPMC11164973

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.