Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment
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| Publicat a: | JOM vol. 77, no. 1 (Jan 2025), p. 211 |
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| Autor principal: | |
| Altres autors: | , , , , , |
| Publicat: |
Springer Nature B.V.
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s11837-024-06930-7 |2 doi | |
| 035 | |a 3159699230 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 28829 |2 nlm | ||
| 100 | 1 | |a Meng, Liang |u Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia | |
| 245 | 1 | |a Deep Learning-Enhanced Jewelry Material Jadeite Jade Quality Assessment | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Jadeite jade, renowned for its unique texture and cultural significance, stands as the epitome of jade varieties, embodying the latest evolution of China's jade culture. This research endeavors to establish an AI model for precisely screening jadeite quality, employing deep learning techniques to revolutionize jadeite design and detection. The objective is to provide jewelry companies, designers, and customers with an unbiased means of grading and evaluating jadeite quality. We have meticulously curated a database of jadeite images, applied preprocessing techniques, and have harnessed convolutional neural networks (CNN) for feature extraction. The outcomes were promising, with the model achieving notable performance indicators: an accuracy rate of approximately 84.75%, a recall rate of about 84.94%, and an F1 score of roughly 73.76% in jade image classification tasks. These results underscore the model's effectiveness in the assessment of jadeite quality. Incorporating computer-aided technology into jadeite screening foreshadows a transformative era where artificial intelligence seamlessly integrates with traditional jade carving design, signifying a pivotal shift in the industry's landscape. | |
| 651 | 4 | |a United States--US | |
| 651 | 4 | |a China | |
| 653 | |a Deep learning | ||
| 653 | |a Culture | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Jewelry | ||
| 653 | |a Machine learning | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Quality assessment | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Computer vision | ||
| 653 | |a Jewelry industry | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Image classification | ||
| 653 | |a Design | ||
| 653 | |a Computer aided design--CAD | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Designers | ||
| 653 | |a Screening | ||
| 700 | 1 | |a Effendi, Raja Ahmad Azmeer Raja Ahmad |u Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia | |
| 700 | 1 | |a Sun, Wei |u Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia | |
| 700 | 1 | |a Mo, Lili |u Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia | |
| 700 | 1 | |a Rahman, Ahmad Rizal Abdul |u Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia | |
| 700 | 1 | |a Hsu, Yu-lin | |
| 700 | 1 | |a Barron, Deirdre | |
| 773 | 0 | |t JOM |g vol. 77, no. 1 (Jan 2025), p. 211 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159699230/abstract/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3159699230/fulltext/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159699230/fulltextPDF/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |