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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/46747885:24620/21:00008875

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

<a href="/cs/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modulární platforma pro autonomní podvozky specializovaných elektrovozidel pro dopravu nákladu a zařízení</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

000687302800066

EID výsledku v databázi Scopus

2-s2.0-85109314725