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.

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

Institute of Organic and Polymeric Materials National Taipei University of Technology No 1 Section 3 Zhongxiao East Road Taipei 106 Taiwan

Laboratory of Signal Image and Energy Mastery Electrical Engineering Department University of Tunis ENSIT Tunis 1008 Tunisia

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Noman M.T., Ashraf M.A., Ali A. Synthesis and applications of nano-TiO2: A review. Environ. Sci. Pollut. Res. 2018;26:3262–3291. doi: 10.1007/s11356-018-3884-z. PubMed DOI

Noman M.T., Militky J., Wiener J., Saskova J., Ashraf M.A., Jamshaid H., Azeem M. 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 M.T., Wiener J., Šašková J., Ashraf M.A., Viková M., Jamshaid H., Kejzlar P. In-situ development of highly photocatalytic multifunctional nanocomposites by ultrasonic acoustic method. Pt AUltrason. Sonochem. 2018;40:41–56. doi: 10.1016/j.ultsonch.2017.06.026. PubMed DOI

Noman M.T., Ashraf M.A., Jamshaid H., Ali A. A Novel Green Stabilization of TiO2 Nanoparticles onto Cotton. Fibers Polym. 2018;19:2268–2277. doi: 10.1007/s12221-018-8693-y. DOI

Liu X., Gasco F., Goodsell J., Yu W. Initial failure strength prediction of woven composites using a new yarn failure criterion constructed by deep learning. Compos. Struct. 2019;230:111505. doi: 10.1016/j.compstruct.2019.111505. DOI

Balram D., Lian K.Y., Sebastian N., Al-Mubaddel F.S., Noman M.T. Bi-functional renewable biopolymer wrapped CNFs/Ag doped spinel cobalt oxide as a sensitive platform for highly toxic nitroaromatic compound detection and degradation. Chemosphere. 2022;291:132998. doi: 10.1016/j.chemosphere.2021.132998. PubMed DOI

Chen R., Mihaylova L., Zhu H., Bouaynaya N. A Deep Learning Framework for Joint Image Restoration and Recognition. Circuits Syst. Signal Process. 2020;39:1561–1580. doi: 10.1007/s00034-019-01222-x. DOI

Zazoum B., Triki E., Bachri A. Modeling of Mechanical Properties of Clay-Reinforced Polymer Nanocomposites Using Deep Neural Network. Materials. 2020;13:4266. doi: 10.3390/ma13194266. PubMed DOI PMC

Sebastian N., Yu W.C., Balram D., Al-Mubaddel F.S., Noman M.T. Functionalization of CNFs surface with β-cyclodextrin and decoration of hematite nanoparticles for detection and degradation of toxic fungicide carbendazim. Appl. Surf. Sci. 2022;586:152666. doi: 10.1016/j.apsusc.2022.152666. DOI

Vahid A., Mückschel M., Stober S., Stock A., Beste C. Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control. Commun. Biol. 2020;3:112. doi: 10.1038/s42003-020-0846-z. PubMed DOI PMC

Balram D., Lian K.Y., Sebastian N., Al-Mubaddel F.S., Noman M.T. A sensitive and economical electrochemical platform for detection of food additive tert-butylhydroquinone based on porous Co3O4 nanorods embellished chemically oxidized carbon black. Food Control. 2022;136:108844. doi: 10.1016/j.foodcont.2022.108844. DOI

Sebastian N., Yu W.C., Balram D., Al-Mubaddel F.S., Noman M.T. Nanomolar detection of food additive tert-butylhydroquinone in edible oils based on novel ternary metal oxide embedded β-cyclodextrin functionalized carbon black. Food Chem. 2022;377:131867. doi: 10.1016/j.foodchem.2021.131867. PubMed DOI

Daniel G.G. Artificial Neural Network. In: Runehov A.L.C., Oviedo L., editors. Encyclopedia of Sciences and Religions. Springer; Dordrecht, The Netherlands: 2013. p. 143. DOI

Amor N., Noman M.T., Petrů M. Prediction of functional properties of nano TiO2 coated cotton composites by artificial neural network. Sci. Rep. 2021;11:12235. doi: 10.1038/s41598-021-91733-y. PubMed DOI PMC

Amor N., Noman M.T., Petrů M., Mahmood A., Ismail A. Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites. Sci. Rep. 2021;11:13649. doi: 10.1038/s41598-021-93108-9. PubMed DOI PMC

Lu D., Yu W. Predicting the tensile strength of single wool fibers using artificial neural network and multiple linear regression models based on acoustic emission. Text. Res. J. 2021;91:533–542. doi: 10.1177/0040517520948200. DOI

Gayatri Vineela M., Dave A., Kiran Chaganti P. Artificial Neural Network based Prediction of Tensile Strength of Hybrid Composites. Mater. Today Proc. 2018;5:19908–19915. doi: 10.1016/j.matpr.2018.06.356. DOI

Mishra S. Prediction of Yarn Strength Utilization in Cotton Woven Fabrics using Artificial Neural Network. J. Inst. Eng. (India) Ser. E. 2015;96:151–157. doi: 10.1007/s40034-014-0049-6. DOI

Malik S.A., 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

Altarazi S., Allaf R., Alhindawi F. Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes. Materials. 2019;12:1475. doi: 10.3390/ma12091475. PubMed DOI PMC

Erbil Y., Babaarslan O., İlhami 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

Breuer K., Stommel M. Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database. Fibers. 2021;9:8. doi: 10.3390/fib9020008. DOI

Wang F., Chen Z., Wu C., Yang Y., Zhang D., Li S. A model for predicting the tensile strength of ultrafine glass fiber felts with mathematics and artificial neural network. J. Text. Inst. 2021;112:783–791. doi: 10.1080/00405000.2020.1779167. DOI

Liu F., Ding W., Qiao Y., Wang L. An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power. Front. Struct. Civ. Eng. 2020;14:1299–1315. doi: 10.1007/s11709-020-0712-6. DOI

Amor N., Noman M.T., Petrů M. Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network. Polymers. 2021;13:3104. doi: 10.3390/polym13183104. PubMed DOI PMC

Amor N., Noman M.T., Petrů M. Classification of Textile Polymer Composites: Recent Trends and Challenges. Polymers. 2021;13:2592. doi: 10.3390/polym13162592. PubMed DOI PMC

Mahmood A., Noman M.T., Pechočiaková M., Amor N., Petrů M., Abdelkader M., Militký J., Sozcu S., Hassan S.Z.U. Geopolymers and Fiber-Reinforced Concrete Composites in Civil Engineering. Polymers. 2021;13:2099. doi: 10.3390/polym13132099. PubMed DOI PMC

Noman M.T., Petrů M. Functional Properties of Sonochemically Synthesized Zinc Oxide Nanoparticles and Cotton Composites. Nanomaterials. 2020;10:1661. doi: 10.3390/nano10091661. PubMed DOI PMC

Jeon J.H., Yang S.S., Kang Y.J. 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 E.C., 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

Jierula A., Wang S., OH T.M., Wang P. Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data. Appl. Sci. 2021;11:2314. doi: 10.3390/app11052314. DOI

Briot J.P. From artificial neural networks to deep learning for music generation: History, concepts and trends. Neural Comput. Appl. 2021;33:39–65. doi: 10.1007/s00521-020-05399-0. DOI

Ayres L., Gomez F., Linton J., Silva M., Garcia C. Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal. Chim. Acta. 2021;1161:338403. doi: 10.1016/j.aca.2021.338403. PubMed DOI

Xiao Q., Wang R., Zhang S., Li D., Sun H., Wang L. Prediction of pilling of polyester–cotton blended woven fabric using artificial neural network models. J. Eng. Fibers Fabr. 2020;15:1558925019900152. doi: 10.1177/1558925019900152. DOI

Noman M.T., Petrů M., Amor N., Louda P. Thermophysiological comfort of zinc oxide nanoparticles coated woven fabrics. Sci. Rep. 2020;10:21080. doi: 10.1038/s41598-020-78305-2. PubMed DOI PMC

Meddeb A., Amor N., Abbes M., Chebbi S. A Novel Approach Based on Crow Search Algorithm for Solving Reactive Power Dispatch Problem. Energies. 2018;11:3321. doi: 10.3390/en11123321. DOI

Noman M.T., Amor N., Petrů 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

Balram D., Lian K.Y., Sebastian N., Al-Mubaddel F.S., Noman M.T. Ultrasensitive detection of food colorant sunset yellow using nickel nanoparticles promoted lettuce-like spinel Co3O4 anchored GO nanosheets. Food Chem. Toxicol. 2022;159:112725. doi: 10.1016/j.fct.2021.112725. PubMed DOI

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