Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F21%3A00008875" target="_blank" >RIV/46747885:24410/21:00008875 - isvavai.cz</a>

Alternative codes found

RIV/46747885:24620/21:00008875

Result on the web

<a href="https://www.nature.com/articles/s41598-021-93108-9.pdf" target="_blank" >https://www.nature.com/articles/s41598-021-93108-9.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-021-93108-9" target="_blank" >10.1038/s41598-021-93108-9</a>

Result language

angličtina

Original language name

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites

Original language description

This paper presents a new hybrid approach for the prediction of functional properties i.e., selfcleaning efciency, antimicrobial efciency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artifcial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an efective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an efective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input–output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efciency, antimicrobial efciency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically signifcant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.

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

2021

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

11

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

13

Pages from-to

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UT code for WoS article

000687302800066

EID of the result in the Scopus database

2-s2.0-85109314725