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 ([Formula: see text]) compared to MLR. The correlation coefficient of MLP model ([Formula: see text]) 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.

Department of Machinery Construction Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Studentská 1402 2 461 17 Liberec 1 Czech Republic

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Zhang K, et al. Textiles/metal-organic frameworks composites as flexible air filters for efficient particulate matter removal. ACS Appl. Mater. Interfaces. 2019;11:17368–17374. doi: 10.1021/acsami.9b01734. PubMed DOI

Zhang K, et al. Multifunctional textiles/metal-organic frameworks composites for efficient ultraviolet radiation blocking and noise reduction. ACS Appl. Mater. Interfaces. 2020;12:55316–55323. doi: 10.1021/acsami.0c18147. PubMed DOI

Noman MT, Ashraf MA, Ali A. Synthesis and applications of PubMed DOI

Noman MT, Ashraf MA, Jamshaid H, Ali A. A novel green stabilization of DOI

Noman MT, et al. Sonochemical synthesis of highly crystalline photocatalyst for industrial applications. Ultrasonics. 2018;83:203–213. doi: 10.1016/j.ultras.2017.06.012. PubMed DOI

Noman MT, Petru M, Amor N, Yang T, Mansoor T. Thermophysiological comfort of sonochemically synthesized nano PubMed DOI PMC

Ashraf M, Wiener J, Farooq A, Šašková J, Noman M. Development of maghemite glass fibre nanocomposite for adsorptive removal of methylene blue. Fibers Polym. 2018;19:1735–1746. doi: 10.1007/s12221-018-8264-2. DOI

Noman MT, et al. In-situ development of highly photocatalytic multifunctional nanocomposites by ultrasonic acoustic method. Ultrason. Sonochem. 2018;40:41–56. doi: 10.1016/j.ultsonch.2017.06.026. PubMed DOI

Daniel, G. G. Artificial Neural Network, 143–143 (Springer, 2013).

Behera P, Noman MT, Petro M. Enhanced mechanical properties of eucalyptus-basalt-based hybrid-reinforced cement composites. Polymers. 2020 doi: 10.3390/polym12122837. PubMed DOI PMC

Azeem M, Noman MT, Wiener J, Petru M, Louda P. Structural design of efficient fog collectors: A review. Environ. Technol. Innov. 2020;20:101169. doi: 10.1016/j.eti.2020.101169. DOI

Noman MT, Petru M. Effect of sonication and nano PubMed DOI PMC

Malik SA, Farooq A, Gereke T, Cherif C. Prediction of blended yarn evenness and tensile properties by using artificial neural network and multiple linear regression. Autex Res. J. 2016;16:43–50. doi: 10.1515/aut-2015-0018. DOI

Malik SA, Gereke T, Farooq A, Aibibu D, Cherif C. Prediction of yarn crimp in pes multifilament woven barrier fabrics using artificial neural network. J. Text. Inst. 2018;109:942–951. doi: 10.1080/00405000.2017.1393786. DOI

Malik SA, Arain RA, Khatri Z, Saleemi S, Cherif C. Neural network modeling and principal component analysis of antibacterial activity of chitosan/agcl-tio DOI

Malik SA, Kocaman RT, Gereke T, Aibibu D, Cherif C. Prediction of the porosity of barrier woven fabrics with respect to material, construction and processing parameters and its relation with air permeability. Fibres Text. East. Eur. 2018;26:71–79. doi: 10.5604/01.3001.0011.7306. DOI

Almetwally AA, Idrees HM, Hebeish A. Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models. J. Text. Inst. 2014;105:1221–1229. doi: 10.1080/00405000.2014.882043. DOI

Farooq A, et al. Predicting cotton fibre maturity by using artificial neural network. Autex Res. J. 2018;18:429–433. doi: 10.1515/aut-2018-0024. DOI

Farooq A, Irshad F, Azeemi R, Iqbal N. Prognosticating the shade change after softener application using artificial neural networks. Autex Res. J. 2020 doi: 10.2478/aut-2020-0019. DOI

Dashti M, Derhami V, Ekhtiyari E. Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms. J. AI Data Mining. 2014;2:73–78. doi: 10.22044/jadm.2014.187. DOI

Furferi R, Governi L, Volpe Y. Modelling and simulation of an innovative fabric coating process using artificial neural networks. Text. Res. J. 2012;82:1282–1294. doi: 10.1177/0040517512436828. DOI

Kanat ZE, Özdil N. Application of artificial neural network (ann) for the prediction of thermal resistance of knitted fabrics at different moisture content. J. Text. Inst. 2018;109:1247–1253. doi: 10.1080/00405000.2017.1423003. DOI

Ribeiro R, et al. Predicting physical properties of woven fabrics via automated machine learning and textile design and finishing features. In: Maglogiannis I, Iliadis L, Pimenidis E, et al., editors. Artificial Intelligence Applications and Innovations. Cham***: Springer International Publishing; 2020. pp. 244–255.

Noman MT, Petru M, Militký J, Azeem M, Ashraf MA. One-pot sonochemical synthesis of zno nanoparticles for photocatalytic applications, modelling and optimization. Materials. 2020 doi: 10.3390/ma13010014. PubMed DOI PMC

Noman MT, Amor N, Petru M, Mahmood A, Kejzlar P. Photocatalytic behaviour of zinc oxide nanostructures on surface activation of polymeric fibres. Polymers. 2021 doi: 10.3390/polym13081227. PubMed DOI PMC

Taieb AH, Mshali S, Sakli F. Predicting fabric drapability property by using an artificial neural network. J. Eng. Fibers Fabr. 2018 doi: 10.1177/155892501801300310. DOI

Kalkanci M, Sinecen M, Kurumer G. Prediction of dimensional change in finished fabric through artificial neural networks. Tekstil Ve Konfeksiyon. 2018;28:43–51.

Noman M, Petru M, Louda P, Kejzlar P. Woven textiles coated with zinc oxide nanoparticles and their thermophysiological comfort properties. J. Nat. Fibers. 2021;18:1–14. doi: 10.1080/15440478.2020.1870621. DOI

Khan S, et al. Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network. Adv. Compos. Mater. 2019;28:409–423. doi: 10.1080/09243046.2019.1573448. DOI

Erbil Y, Babaarslan O, Ilhami I. A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models. J. Text. Inst. 2018;109:560–568. doi: 10.1080/00405000.2017.1361164. DOI

Malik SA, et al. Analysis and prediction of air permeability of woven barrier fabrics with respect to material, fabric construction and process parameters. Fibers Polym. 2017;18:2005–2017. doi: 10.1007/s12221-017-7241-5. DOI

Wang Z, Di Massimo C, Tham MT, Julian Morris A. A procedure for determining the topology of multilayer feedforward neural networks. Neural Netw. 1994;7:291–300. doi: 10.1016/0893-6080(94)90023-X. DOI

Kalantary S, Jahani A, Jahani R. MLR and ANN approaches for prediction of synthetic/natural nanofibers diameter in the environmental and medical applications. Sci. Rep. 2020;10:1–10. doi: 10.1038/s41598-020-65121-x. PubMed DOI PMC

Xiao Q, et al. Prediction of pilling of polyester-cotton blended woven fabric using artificial neural network models. J. Eng. Fibers Fabr. 2020 doi: 10.1177/1558925019900152. DOI

Jeon JH, Yang SS, Kang YJ. Estimation of sound absorption coefficient of layered fibrous material using artificial neural networks. Appl. Acoust. 2020;169:107476. doi: 10.1016/j.apacoust.2020.107476. DOI

Doran EC, Sahin C. The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines. Text. Res. J. 2020;90:1558–1580. doi: 10.1177/0040517519896761. DOI

Jain AK, Jianchang M, Mohiuddin KM. Artificial neural networks: A tutorial. Computer. 1996;29:31–44. doi: 10.1109/2.485891. DOI

Golnaraghi S, Zangenehmadar Z, Moselhi O, Alkass S. Application of artificial neural network(s) in predicting formwork labour productivity. Adv. Civ. Eng. 2019;2019:1–11. doi: 10.1155/2019/5972620. DOI

Meddeb A, Amor N, Abbes M, Chebbi S. A novel approach based on crow search algorithm for solving reactive power dispatch problem. Energies. 2018 doi: 10.3390/en11123321. DOI

Pianosi F, et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ. Model. Softw. 2016;79:214–232. doi: 10.1016/j.envsoft.2016.02.008. DOI

Noman MT, Petru M. Functional properties of sonochemically synthesized zinc oxide nanoparticles and cotton composites. Nanomaterials. 2020 doi: 10.3390/nano10091661. PubMed DOI PMC

Noman MT, Amor N, Petru M. Synthesis and applications of zno nanostructures (zonss): A review. Crit. Rev. Solid State Mater. Sci. 2021;2:1–44. doi: 10.1080/10408436.2021.1886041. DOI

Noman MT, Petru M, Amor N, Louda P. Thermophysiological comfort of zinc oxide nanoparticles coated woven fabrics. Sci. Rep. 2020;10:1–2. doi: 10.1038/s41598-019-56847-4. PubMed DOI PMC

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