Measurement and prediction of the international reputation of Chinese women’s apparel and accessories

Abstract

Chinese women’s apparel and accessories have been often mentioned by other countries in history and nowadays, and the underlying culture along with these apparel and accessories contributes to this developing world to some extent. The assessment of the international reputation for these kinds of apparel and accessories is important for better understanding the worldwide cultural exchange, globalization, social development and so on; however, it remains unknown. This study presents a work with the overall objective to measure and predict the international reputation of Chinese women’s apparel and accessories. To reach this objective, the study first proposes a data mining procedure to use online query volume data from the Google Ngram corpus to construct a reputation index in order to measure the international reputation of Chinese women’s apparel and accessories. Then, by establishing a series of socio-economic and political determinants associated with the international reputation of Chinese women’s apparel and accessories, several forms of support vector machine based models are developed, aiming to predict their international reputation. There are two contributions out of this work: (1) presenting the first ever large-scale quantitative examination of the trend and measurement of the international reputation of Chinese women's apparel and accessories, which provides a new method to identify the dynamic transition of sociocultural development; (2) constructing a quantitative model to predict the international reputation of Chinese women's apparel and accessories, which generalizes an implication to understand the other sociocultural object and its impact factors.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

“…The breadth of this field is extensive, encompassing investigations such as establishing standardized clothing sizes (Jeyasingh et al ., 2012), identifying factors that influence the insulation properties of garments (Jeyasingh et al ., 2012; Lee et al ., 2019), pinpointing variables that contribute to minimizing waste in glove production (Seçkin et al ., 2019), predicting the types of garments (lower body, upper body, whole body) and their subsets (blouse, dress, etc.) (Jain and Kumar, 2020; Cheng et al ., 2022), crafting a recommender system to assist customers in selecting clothing tailored to their physical attributes and purchasing history (Zhang et al ., 2021), ranking woven fabric defects observed during textile manufacturing (Saeidi et al ., 2013), monitoring yarn quality within a textile firm (Ertuğrul and Aytaç, 2009), modeling an intelligent manufacturing process line, encompassing textile processes from yarn to fabrics and garments (He et al ., 2021a), and exploring fashion trends within the realm of clothing (Shi et al ., 2021); all of which have been cited as noteworthy contributions. Table 2 summarizes research employing data mining methods in the yarns, textiles and clothing industries.…”

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

“…Therefore, achieving effective prediction of apparel fashion colors can bring higher business value to stakeholders and also promote the development of the industry. However, the prerequisite for realizing fashion color prediction for apparel is the effective classification of apparel hues [7]. Based on this, a wide range of domestic and foreign scholars have conducted in-depth research on it.…”

“…In addition, in the final deblurring equation, the classification of each pixel point needs to be achieved based on the corresponding equation, which is expressed as shown in Eq. (7).…”

“…Based on Eq. ( 1) to (7), the proposed SWK-FCM algorithm can automatically classify the color hues of clothing fashions and gradually determine the relevant intervals of color hue values for further processing of the data. Its process in apparel color clustering is similar to Fig.…”

Developing a quantitative relationship towards apparel design transition in China

“…7 During this period of the time, under the impact of the dress code of communism, the public’s aesthetic standards mainly focused on the Chinese tunic suit, military uniforms, and other apparel that symbolized the communist and revolutionary spirit (Figure 1), while cheongsam and colorful apparel design withdrew from the mainstream market. 8 After the reform and opening up in 1987, China’s per capita gross domestic product (GDP) rapidly increased, the Engel coefficient was reduced, and the apparel industry entered a new era. 9 Advanced apparel manufacturing hardware and the innovated production technology for apparel were introduced in China, and thus production efficiency and quality for many kinds of apparel were substantially improved.…”