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Titlebook: Artificial Intelligence and Machine Learning; First International Hai Jin,Yi Pan,Jianfeng Lu Conference proceedings 2024 The Editor(s) (if

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樓主: CLOG
41#
發(fā)表于 2025-3-28 16:01:47 | 只看該作者
,A Survey of?Homogeneous and?Heterogeneous Multi-source Information Fusion Based on?Rough Set Theorynsors or sources based on certain standards so as to achieve the required decisions and estimates, and it includes two types of information, homogeneous information and heterogeneous information. MSIF is also referred as multi-sensor information fusion. Rough set theory (RST) provides an effective m
42#
發(fā)表于 2025-3-28 19:01:39 | 只看該作者
43#
發(fā)表于 2025-3-29 02:27:51 | 只看該作者
44#
發(fā)表于 2025-3-29 03:35:53 | 只看該作者
Crafting a Postcritical Compasshis data achieved high levels of accuracy (0.99 for exiting, 0.92 for wandering, and 0.88 for entering). The method is the first to achieve visual action recognition of dol-phins behavior, opening new possibilities for using large datasets in dolphins research.
45#
發(fā)表于 2025-3-29 08:46:39 | 只看該作者
46#
發(fā)表于 2025-3-29 15:04:06 | 只看該作者
47#
發(fā)表于 2025-3-29 19:08:53 | 只看該作者
George W. Noblit,Allison Daniel Anders ultra-high voltage substation energy meter based on regularized deep neural networks (DNN) to enhance metering accuracy. Finally, the effectiveness of this algorithm is validated through simulation experiments.
48#
發(fā)表于 2025-3-29 20:21:37 | 只看該作者
Allison Daniel Anders,George W. Noblitt is calculated by using the dynamic time warping algorithm, and the part with the DTW distance less than the threshold is determined to be copy-move forgery. The experiments on publicly available replicated mobile forgery databases show that the algorithm not only enables precise localization of tampered audio, but also has high robustness.
49#
發(fā)表于 2025-3-30 03:07:19 | 只看該作者
50#
發(fā)表于 2025-3-30 07:31:33 | 只看該作者
RSCC: Robust Semi-supervised Learning with Contrastive Learning and Augmentation Consistency Regulathods of augmentation consistency regularization and symmetric cross-entropy learning. We conduct rich experiments, which show that RSCC achieves state-of-the-art accuracy on multiple datasets, such as CIFAR-10 and CIFAR-100, especially when labeled data is extremely scarce. This underscores its cutting-edge and effective performance.
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