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.
Highlights
This paper presents a new hybrid approach for the prediction of functional properties i.e., selfcleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric
The proposed algorithm was based on two different optimization modules i.e., the first module is called extreme learning machine (ELM) classifier whereas, the later is called a group of stacked autoencoders.The ELM module optimized by a new optimizer known as grey wolf optimizer (GWO), to determine the number of neurons and weights to get more accuracy while detection and classification
We predicted the functional properties of nano TiO2 coated cotton fabric using the optimized multilayer perceptron (MLP) model with crow search algorithm (MLP-CSA)
Summary
This paper presents a new hybrid approach for the prediction of functional properties i.e., selfcleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. ANN is an excellent approach that has been widely used for the prediction of various properties of textile materials where it has proven its effectiveness and potential, such as: prediction of the tensile properties of even and uneven yarns extracted from polyester-cotton blend[21]; prediction of the warp and weft yarns crimp in woven barrier fabrics[22]; prediction of antimicrobial performance of chitosan/AgCl-TiO2 coated fabrics[23]; prediction of core spun yarn strength, elongation and r upture[24]; prediction of cotton fibre[25]; prediction the change of shade of dyed knitted fabrics[26]; prediction of coatings process on textile fabrics[27]; and prediction of thermal resistance of wet knitted fabrics[28] These mentioned work reveal that the most common type of ANN algorithm used in textile industry is multilayer perceptron MLP29,30. The results demonstrated that the proposed model ANN-PSO provided better results than feedforward ANN
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