An Effective Research Method to Predict Human Body Type Using an Artificial Neural Network and a Discriminant Analysis

被引:2
作者
Kim, Namsoon [1 ]
Song, Hwa Kyung [2 ]
Kim, Sungmin [3 ]
Do, Wolhee [1 ]
机构
[1] Chonnam Natl Univ, Dept Clothing & Text, Healthcare Ware Res & Business Dev Ctr, Gwangju 61186, South Korea
[2] Kyung Hee Univ, Dept Clothing & Text, Seoul 02447, South Korea
[3] Seoul Natl Univ, Dept Text Merchandising & Fash Design, Seoul 08826, South Korea
基金
新加坡国家研究基金会;
关键词
Body type; Artificial neural network; Discriminant analysis; Artificial intelligence; Prediction; TENSILE PROPERTIES; FABRICS; PERFORMANCE; REGRESSION; SYSTEM;
D O I
10.1007/s12221-018-7901-0
中图分类号
TB3 [工程材料学]; TS1 [纺织工业、染整工业];
学科分类号
0805 ; 080502 ; 0821 ;
摘要
Artificial intelligence (AI) technology can be an effective solution to decision-making in the apparel field; however, few studies have used the AI-based methodology for categorizing and predicting human body types. The researchers in this study demonstrate the accuracy and effectiveness of the artificial neural network (ANN) as an approach to AI technology to predict women's body types by comparing the predictive accuracy rate between a statistical discriminant analysis and an ANN. It was observed that the ANN (94.7 %) can predict body types more accurately than the discriminant analysis (83.5 %). The ANN is a more effective method than a statistical analysis because the ANN requires fewer input measurement variables than the discriminant analysis to predict body types. Comparing the predictive accuracy rate based on a different number of learning times and input variables, the researchers offer a new methodological guideline to use in future research studies in this field.
引用
收藏
页码:1781 / 1789
页数:9
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