Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F22%3A00009998" target="_blank" >RIV/46747885:24210/22:00009998 - isvavai.cz</a>

Alternative codes found

RIV/46747885:24620/22:00009998

Result on the web

<a href="https://www.nature.com/articles/s41598-022-10406-6.pdf" target="_blank" >https://www.nature.com/articles/s41598-022-10406-6.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-022-10406-6" target="_blank" >10.1038/s41598-022-10406-6</a>

Result language

angličtina

Original language name

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Original language description

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artifcial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very frst attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10700 - Other natural sciences

Project

<a href="/en/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

16

Pages from-to

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UT code for WoS article

000782844000056

EID of the result in the Scopus database

2-s2.0-85128265065