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Volume 55
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Proceeding Paper

Using Machine Learning to Identify Product Styles †

by
Hung-Hsiang Wang
and
Yen-Ling Chen
*
Department of Industrial Design, National Taipei University of Technology, Taipei 106344, Taiwan
*
Author to whom correspondence should be addressed.
†
Presented at the IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, Tainan, Taiwan, 2–4 June 2023.
Eng. Proc. 2023, 55(1), 39; https://doi.org/10.3390/engproc2023055039
Published: 1 December 2023
(This article belongs to the Proceedings of 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability)
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Abstract

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool, was used to develop a model to identify product styles, and the style of classic chairs was determined using the model. Data used to develop the model consisted of 100 images of four styles of chairs such as Windsor, Shaker, Thonet, and Ming. After pre-processing the images using the image filters of WEKA, the images were used to train the model to classify chair styles. The accuracy of the model ranged from 96 to 98%. This validated the performance of the proposed method in classifying the styles of chairs, which helps the design of new chairs.
Keywords: product style; image recognition; machine learning product style; image recognition; machine learning

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MDPI and ACS Style

Wang, H.-H.; Chen, Y.-L. Using Machine Learning to Identify Product Styles. Eng. Proc. 2023, 55, 39. https://doi.org/10.3390/engproc2023055039

AMA Style

Wang H-H, Chen Y-L. Using Machine Learning to Identify Product Styles. Engineering Proceedings. 2023; 55(1):39. https://doi.org/10.3390/engproc2023055039

Chicago/Turabian Style

Wang, Hung-Hsiang, and Yen-Ling Chen. 2023. "Using Machine Learning to Identify Product Styles" Engineering Proceedings 55, no. 1: 39. https://doi.org/10.3390/engproc2023055039

APA Style

Wang, H.-H., & Chen, Y.-L. (2023). Using Machine Learning to Identify Product Styles. Engineering Proceedings, 55(1), 39. https://doi.org/10.3390/engproc2023055039

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