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The purpose of this paper is to derive the relationship between color schemes and emotion to serve for designers and consumers.

The three attributes of hue, brightness and saturation of the selected sample color are analyzed, and the Semantic Differential (SD) method is used for the emotional evaluation of color schemes, and data obtained from the emotional evaluation of color schemes is analyzed by using Excel software for mean statistics and SPSS software for factor analysis and cluster analysis.

From the results of the factor analysis, three main factors that affect the feeling of the color scheme can be extracted: “personality”, “gender” and “fashion”. Color emotions can be achieved by changing the level of color saturation and brightness, the cold and warmth of the hue and the way of color combination.

Since it takes a long time to fill out the questionnaire, the number of valid questionnaires collected is a little less and the research data is limited. In addition, some problems are not taken into account such as geography and so on, so the results of the statistical analysis are not very precise and further research is needed.

It can provide information of emotional color schemes for designers and consumers, and based on the SD method, an emotional color matching questionnaire is designed and statistical analysis is conducted to establish the relationship between emotion and color schemes.

Based on the fashion color sample and color harmony theory, the color matching rules and color matching schemes are designed independently.

This study aimed to Simultaneous matching of color and antimicrobial properties of silk fabric treated with silver nanoparticle. The antimicrobial finishing using silver nanoparticles (AgNPs) is one of the most important finishing processes in the textile industry. Color matching is widely applied in the textile industry, but there has been a need for the prediction of AgNPs concentration for the matching of dyed silver-treated samples.

In this research, the silk fabrics were dyed with various concentrations of C.I. Acid Red 359 dye at 0.5, 1, 1.5 and 2 per cent (w/w). The dyed fabrics were then coated with AgNPs in several concentrations at 0.015, 0.030, 0.050, 0.100 and 0.250 ml/l. The prediction of dye and AgNPs concentrations were evaluated using single constant color matching and artificial neural network techniques.

The obtained results indicate that the accuracy of dye concentration prediction, as well as AgNPs concentration prediction, was improved by using a neural network method. Also, the correlation between actual and predicted dye and AgNPs concentrations in the best neural networks is more than the single constant color matching method.

Simultaneous antibacterial and color matching of nanosilver-treated fabric is novel. This method achieved acceptable accuracy for antibacterial and color matching.

This study aims to identify how restaurants can effectively initiate communication via social media to promote ethical dining behaviors. This research investigates the psychological mechanism of how the matching effect of color and a sustainability activity influence customer attitude toward a restaurant and the role of perceived credibility and green image.

Two experimental studies were conducted. Study 1 used a 2 food source (non-sustainable vs sustainable) × 2 color consistency (inconsistent vs consistent) factorial design (n = 231). Study 2 used a 2 food origin (world-famous vs locally renowned) × 2 color consistency (inconsistent vs consistent) factorial design (n = 220).

The results indicate that the matching effect from the marketing effect of sustainability significantly promotes customer attitudes and visit intentions when background color is consistent. An unexpected matching effect was found between a non-sustainable restaurant using world-famous food with its associated color. This research demonstrates a moderation effect of credibility and a mediation effect of green image to explain the ethical decision-making process for customers.

The findings provide suggestions for restaurant marketers to effectively advertise sustainability initiatives and practices using color as a marketing tool via social media.

This research is one of the earliest studies to investigate the effect of color consistency with primary information to demonstrate how consumers respond to restaurant sustainability in social media messages using local food.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Computer Colour Matching of printing inks may be introduced by the simple definition:

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.

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.

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.

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.

The deficiency of the mapping between fashion color (FoCo) value and linguistic color expression causes the difficulty of machine-based fashion understanding tasks that are heavily associated with color matching. The purpose of this paper is to propose the FoCo system and construct it with four steps, in order to bridge this gap.

The color distribution in HSB color space is analyzed to estimate the rough number of color categories. Similar color values are grouped to obtain the initial HSB value range for each color category. The intra-category color differences are calculated to determine their final HSB value ranges and Pantone color is used for fine-tuning.

With practical applications in mind, the FoCo system is designed as a hierarchical structure with three layers.

The FoCo system is designed as a hierarchical structure with three layers: color units for color matching-related tasks, color categories for style analysis tasks and color tones for color recognition tasks. Extensive experiments demonstrate the effectiveness of the FoCo system.

Colour is that part of the electromagnetic spectrum that is perceived by the human eye to form what we know as ‘light’. Other forms of radiation such as UV and radio waves do not form part of the visible spectrum and are perceived by other parts of the human sensory system (UV will burn, radio waves can be converted into sound). The full electromagnetic spectrum spans wavelengths from 10−5 to 1013 nanometers, with visible light forming only a small fraction of this between about 400–700 nanometers. The colour of an object as perceived by the human eye depends on the proportion of each wavelength of light reflected, and on the nature of the illumination.

Precise colour matching, a regular requirement for architects, major retailers, designers and clothes buyers, is now much more convenient with Macbeth's portable viewing booth. Easy to carry and quick to erect, the Judge II can be used for matching colours, evaluating colour quality and appraising colour uniformity in almost any environment. The unit's extremely rugged construction also allows it to double as a fixed booth.

Color Maker is a computer simulation of mixing colorants to generate a resultant true colour on screen. It can be described as the infinite shade card. It has obvious advantages to offer the paint coatings and printing ink industries, which are defined. However, applications can be made for Color Maker in all colour‐using industries. An example is given of its development as an accurate tool in determining true colours on pharmaceutical tablet coatings. Here, it evaluates the colour on the tablet in a three‐dimensional rendering of the colour, and produces what can be termed a three‐dimensional shade card.