Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24620%2F22%3A00009516" target="_blank" >RIV/46747885:24620/22:00009516 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-4360/14/5/937" target="_blank" >https://www.mdpi.com/2073-4360/14/5/937</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/polym14050937" target="_blank" >10.3390/polym14050937</a>

Result language

angličtina

Original language name

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

Original language description

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2 ) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

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

10404 - Polymer science

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

Polymers

ISSN

2073-4360

e-ISSN

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Volume of the periodical

14

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

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UT code for WoS article

000772931000001

EID of the result in the Scopus database

2-s2.0-85125592632