Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Farooq, Assad; Sarwar, Muhammad Ilyas; Ashraf, Muhammad Azeem; Iqbal, Danish; Hussain, Azmat; Malik, Samander Ali

doi:10.1515/aut-2018-0024

Cited by 8 publications

(5 citation statements)

References 21 publications

(21 reference statements)

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“…Farook et al used ANN for cotton fibre maturity. The results showed low error for ANN 20 . Unal et al used ANN and regression for single jersey knitted fabrics to predict air permeability and bursting strength of knit structures.…”

Section: Introductionmentioning

confidence: 87%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

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Section: Introductionmentioning

confidence: 87%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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“…They selected various fibre characteristics as an input variables and analyzed fibre maturity as an output variable. The simulation results showed that ANN predicted cotton fibre maturity with small error 18 . In another study, Farooq et al proposed ANN to predict the change of shade of dyed knitted fabrics that would happen after finishing application.…”

Section: Introductionmentioning

confidence: 95%

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Amor

Noman

Petrů

2021

Sci Rep

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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 ($$p<0.1 e^{-10} $$ p < 0.1 e - 10 ) compared to MLR. The correlation coefficient of MLP model ($$>95\%$$ > 95 % ) 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.

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“…Therefore, many researcher worked with the prediction of fibre maturity. Farook et al proposed that ANN algorithms are excellent prediction tools in order to predict cotton fibre maturity [ 23 ]. They selected various fibre characteristics as an input variables and analysed fibre maturity as an output variable.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Cited by 8 publications

References 21 publications

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Classification of Textile Polymer Composites: Recent Trends and Challenges

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