Prediction of functional properties of nano TiO2 coated cotton composites by artificial neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F21%3A00008876" target="_blank" >RIV/46747885:24210/21:00008876 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24620/21:00008876

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-021-91733-y" target="_blank" >https://www.nature.com/articles/s41598-021-91733-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-021-91733-y" target="_blank" >10.1038/s41598-021-91733-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of functional properties of nano TiO2 coated cotton composites by artificial neural network

Popis výsledku v původním jazyce

This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties (p95%) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

Název v anglickém jazyce

Prediction of functional properties of nano TiO2 coated cotton composites by artificial neural network

Popis výsledku anglicky

This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties (p95%) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

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

11

Strana od-do

—

Kód UT WoS článku

000663784500005

EID výsledku v databázi Scopus

2-s2.0-85109314725