Evidence map›Paper›PMID 37524835›Full record

ArticleScientific reports2023

Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach.

Md Nahid Pervez, Wan Sieng Yeo, Lina Lin, Xiaorong Xiong, Vincenzo Naddeo, Yingjie Cai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

6 authors.

Md Nahid PervezHubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China.
Wan Sieng YeoDepartment of Chemical and Energy Engineering, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009, Miri, Sarawak, Malaysia.
Lina LinHubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China. linalin@wtu.edu.cn.
Xiaorong XiongSchool of Computing, Huanggang Normal University, Huanggang, 438000, China. xiongxiaorong@hgnu.edu.cn.
Vincenzo NaddeoSanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, 84084, Fisciano, Italy. vnaddeo@unisa.it.
Yingjie CaiHubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The typical textile dyeing process calls for a wide range of operational parameters, and it has always been difficult to pinpoint which of these qualities is the most important in dyeing performance. Consequently, this research used a combined design of experiments and machine learning prediction models' method to offer a sustainable and beneficial reactive cotton fabric dyeing process. To be more precise, we built a least square support vector regression (LSSVR) model based on Taguchi's statistical orthogonal design (L

Identifiers

PMID37524835
PMCPMC10390507

What OpenQuestion holds

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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.