No abstract

In the present study, the CCC shade sorting method was employed with CMC(2:1) color difference formula on the colorimetric data (CIEL*a* b*) of 37 fabric color sets. The k‐means non‐hierarchical clustering technique was also combined with the CCC shade sorting method to increase its efficiency. The results of this combined method showed a slightly better performance, as compared with the CCC method. Also, a new proposed shade sorting method by the application of principal components analysis (PCA) technique was used to identify and remove the outliers in each of the color sets. The results of separating the outliers showed that although the diameter of group criterion was improved significantly, the number of groups, the number of singleton groups, and the number of groups with low samples were increased considerably. Finally, in a second new proposed shade sorting method, PCA was used as a data reduction tool on the colorimetric data of the 37 color sets. Then, the two first principal components in combination with a k‐means clustering technique were used for the clustering of the samples in each color set. The results of this second new proposed method were found to be similar to the CCC method considering number of group and fabric consumption criteria. The second new proposed method revealed a moderately worse result, with regard to the diameter of group criterion, than the CCC method.

Purpose Artificial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods. Design/methodology/approach In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The first and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction. Findings The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method. Originality value The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.

In the present research by combination of Clemson Colour Clustering (CCC) instrumental shade sorting method and two metaheuristic algorithms, a genetic algorithm (GA) and a particle swarm optimisation (PSO), two new shade sorting methods, called CCCGA and CCCPSO were proposed. Then these proposed methods were applied on 16 well-prepered colour sets made of coloured fabrics and their results were compared using some important performance evaluation factors. The results of the methods were also compared with conventional CCC shade sorting method and a method based on CCC combined with k-means technique (CCCk). The results obtained from various shade sorting methods showed that the CCCGA and CCCPSO methods successfully sorted the coloured fabrics with high efficiency, and their results slightly outperformed the results of the CCC method.

Purpose This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for prediction of silver nanoparticles (AgNPs) and dye concentrations on AgNP-treated silk fabrics. Design/methodology/approach For estimation of the dye and AgNPs concentration using image processing, the silk fabrics were scanned under the condition of 200 pixels per inch. The red green blue (RGB) values of scanned images were obtained after applying the median filter. Then, the relationship between scanner RGB values and dye and AgNPs concentrations were obtained by using artificial intelligence methods such as ANFIS and ANNs. Findings The best result was achieved by the ANFIS system for calculation concentration of dye with 0.07% error and concentration of AgNPs with 0.008 (gr/l) error. The obtained results indicate that the performance of the ANFIS system method is better than the other methods. Originality/value Using a scanner-based artificial intelligence technique for prediction of nanosilver and dye content on silk fabric.