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Titlebook: Artificial Intelligence on Fashion and Textiles; Proceedings of the A Wai Keung Wong Conference proceedings 2019 Springer Nature Switzerlan

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21#
發(fā)表于 2025-3-25 04:16:56 | 只看該作者
Wai Keung WongPresents recent research devoted to Artificial Intelligence on Fashion and Textiles.Includes Proceedings of the Artificial Intelligence on Textile and Apparel (AITA) Conference 2018 held at the Hong K
22#
發(fā)表于 2025-3-25 09:19:23 | 只看該作者
23#
發(fā)表于 2025-3-25 13:31:54 | 只看該作者
https://doi.org/10.1007/978-3-319-99695-0Artificial Intelligence; Fashion; Apparel; Textile; AITA 2018
24#
發(fā)表于 2025-3-25 18:17:24 | 只看該作者
25#
發(fā)表于 2025-3-25 21:38:48 | 只看該作者
26#
發(fā)表于 2025-3-26 03:34:42 | 只看該作者
Fabric Identification Using Convolutional Neural Network,ages, the network model can efficiently extract discriminative features and achieve a retrieval accuracy of 99.89% on our test set. This performance maintains well when simpler deep architecture is used, but decreases quickly if the contents of fed fabric image are reduced.
27#
發(fā)表于 2025-3-26 05:58:32 | 只看該作者
Conference proceedings 2019nd applications of AI in the fashion and textile industries. It is essential reading for scientists, researchers and R&D professionals working in the field of AI with applications in the fashion and textile industry; managers in the fashion and textile enterprises; and anyone with an interest in the
28#
發(fā)表于 2025-3-26 10:24:43 | 只看該作者
29#
發(fā)表于 2025-3-26 14:13:23 | 只看該作者
Fault Tolerant Computer Architectureulti-objective algorithm that based on a Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results of our experimental study show that the proposed framework can efficiently be used to reduce the cost and time of optimizing the economical problems.
30#
發(fā)表于 2025-3-26 18:37:37 | 只看該作者
Discrete Hashing Based Supervised Matrix Factorization for Cross-Modal Retrieval,zes the correlation between two modalities and discrete cyclic coordinate descent (DCC) method that solves NP-hard problems, which ensures that the hash codes generated in the cross-modal are more accurate and efficient. Experiments on three benchmark data sets show the effectiveness of the proposed method.
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