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Titlebook: Artificial Intelligence XXXVII; 40th SGAI Internatio Max Bramer,Richard Ellis Conference proceedings 2020 Springer Nature Switzerland AG 20

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41#
發(fā)表于 2025-3-28 17:07:54 | 只看該作者
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發(fā)表于 2025-3-28 21:44:13 | 只看該作者
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發(fā)表于 2025-3-28 23:19:52 | 只看該作者
Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper) on the domain and also a searching strategy is critical. Obtaining such qualities can be challenging for students since they are still learning about various domains and might not be familiar with the domain-specific keywords. In this paper, we are proposing a framework that aims to assist students
44#
發(fā)表于 2025-3-29 03:54:40 | 只看該作者
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發(fā)表于 2025-3-29 09:07:32 | 只看該作者
Overlap Training to Mitigate Inconsistencies Caused by Image Tiling in CNNsted by the graphics processing unit (GPU) resources, image tiling and stitching countermeasure have been applied for most megapixel images, that is, cutting images into overlapping tiles as CNN input, and then stitching CNN outputs together. However, we found that stitched (i.e. recovered) predictio
46#
發(fā)表于 2025-3-29 13:27:19 | 只看該作者
The Use of Max-Sat for Optimal Choice of Automated Theory RepairsBelief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the . of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones
47#
發(fā)表于 2025-3-29 15:45:43 | 只看該作者
48#
發(fā)表于 2025-3-29 20:25:50 | 只看該作者
Personalised Meta-Learning for Human Activity Recognition with Few-Dataprohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains
49#
發(fā)表于 2025-3-30 00:54:48 | 只看該作者
50#
發(fā)表于 2025-3-30 04:08:37 | 只看該作者
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