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

Abstract This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedfo...

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Autores principales:	Nesrine Amor, Muhammad Tayyab Noman, Michal Petru, Aamir Mahmood, Adla Ismail
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4667195e4d204e81b07b31583e24c05d
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spelling	oai:doaj.org-article:4667195e4d204e81b07b31583e24c05d2021-12-02T16:10:38ZNeural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites10.1038/s41598-021-93108-92045-2322https://doaj.org/article/4667195e4d204e81b07b31583e24c05d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93108-9https://doaj.org/toc/2045-2322Abstract This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective 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 effective 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 efficiency, antimicrobial efficiency 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 significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.Nesrine AmorMuhammad Tayyab NomanMichal PetruAamir MahmoodAdla IsmailNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nesrine Amor Muhammad Tayyab Noman Michal Petru Aamir Mahmood Adla Ismail Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
description	Abstract This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective 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 effective 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 efficiency, antimicrobial efficiency 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 significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.
format	article
author	Nesrine Amor Muhammad Tayyab Noman Michal Petru Aamir Mahmood Adla Ismail
author_facet	Nesrine Amor Muhammad Tayyab Noman Michal Petru Aamir Mahmood Adla Ismail
author_sort	Nesrine Amor
title	Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
title_short	Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
title_full	Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
title_fullStr	Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
title_full_unstemmed	Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites
title_sort	neural network-crow search model for the prediction of functional properties of nano tio2 coated cotton composites
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4667195e4d204e81b07b31583e24c05d
work_keys_str_mv	AT nesrineamor neuralnetworkcrowsearchmodelforthepredictionoffunctionalpropertiesofnanotio2coatedcottoncomposites AT muhammadtayyabnoman neuralnetworkcrowsearchmodelforthepredictionoffunctionalpropertiesofnanotio2coatedcottoncomposites AT michalpetru neuralnetworkcrowsearchmodelforthepredictionoffunctionalpropertiesofnanotio2coatedcottoncomposites AT aamirmahmood neuralnetworkcrowsearchmodelforthepredictionoffunctionalpropertiesofnanotio2coatedcottoncomposites AT adlaismail neuralnetworkcrowsearchmodelforthepredictionoffunctionalpropertiesofnanotio2coatedcottoncomposites
_version_	1718384407409065984

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

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