Color trend prediction method based on genetic algorithm and extreme learning machine

Wang, Weihao; Liu, Yan; Song, Fanghao; Wang, Yong

doi:10.1002/col.22769

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The issue of sustainable development was comprehensively analyzed through a multi-level perspective system to provide a predictive direction for the strategic transformation of the fashion industry [15][16]. Wang Wei et al constructed a hybrid color trend prediction model based on genetic algorithm and extreme learning machine to address the issue of future color trend prediction, effectively improving prediction accuracy [17].…”

Section: Related Workmentioning

confidence: 99%

Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm

Liu

2023

IJACSA

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In the apparel industry, apparel color is an important factor to enhance the market competitiveness of enterprise products. However, the current prediction samples of clothing fashion color styling information do not incorporate practical cutting-edge fashion information. Therefore, Self-adaptive Weighted Kernel Function (SWK) has been introduced to traditional Fuzzy C-Means (FCM) clustering algorithms. After improvement, the SWK-FCM clustering algorithm is obtained, which enhances the classification ability of fashion colors and hue. Two prediction models have been developed using the finalized data of the International Fashion Color Committee, along with the SWK-FCM clustering algorithm. The models have been tested via experiments to verify their accuracy. The experimental results show that the classification coefficients of SWK-FCM clustering algorithm are 0.9553 and 0.9258 under 5% Gaussian noise. They are higher than those of FCM (0.7063) and FLICM (0.8598). The classification entropy is lower than that of the comparison algorithm, while the same results are presented under other conditions and in the actual experiments. In addition, the overall MSE of the GM (1, 1) prediction model using the final case information is 0.00028, which is close to the order of 10-4. The MSE value of the BP neural network prediction model using the final case information ranges from 0.000529 to 0.011025. Overall, the clustering algorithm of SWK-FCM has good classification performance. Additionally, the GM (1,1) model based on SWK-FCM has better prediction results, which can be effectively applied in practical clothing color classification and popular color prediction.

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Section: Related Workmentioning

confidence: 99%

Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm

Liu

2023

IJACSA

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“…With the gradual segmentation of the wooden door market and the gradual development of new retail, the vast amount of consumer information and user demand has posed a serious challenge to wooden door companies (Wang et al 2021a). Therefore, accurate access to the emotional needs and usage preferences of users with different lifestyles and consumption characteristics, and the successful translation of this information into design elements for wooden doors, are important factors in improving the success rate of product development (Wang et al 2021b). With the advent of the Industry 4.0 era, Computerized Numerical Control (CNC), 3D printing, digital intelligence production, and as other high-tech technologies become mature (Shi and Ling 2022), customised production becomes possible.…”

Section: Introductionmentioning

confidence: 99%

Wooden door preferences based on lifestyle theory and consumer behaviour theory

Zhou

Wang

Kaner

et al. 2023

BioRes

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This paper analyses users with different lifestyles and consumer behaviour, and then segments wooden door users with different consumer preferences, to inspire wooden door companies in their product design and further research on wooden doors. In this paper, a wooden door user research questionnaire was created based on Lifestyle Theory and Consumer Behaviour Theory, and data collection was completed at Nanjing Forestry University. The collected data were analysed using cluster analysis, factor analysis, cross-over analysis, and other analysis methods. The Statistical Package for the Social Sciences (SPSS) was used to simplify the analysis model and process the data, and finally the wooden door users were divided into four categories. The categories were trendy home users, budget-conscious sensible users, basic needs users, and impulsive enjoyable users. Male users were found to be more likely to be “trendy home users” and “impulsive enjoyable users,” while female users were more likely to be “budget-conscious sensible users” and “trendy home users.” For the number of users, the “trendy home users” were the most numerous, followed by the “budget-conscious sensible users.” The age of wooden door users in this study was mainly distributed between 21 to 30 years old, of which “freelancers” accounted for the highest percentage.

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