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Titlebook: Artificial Intelligence Applications and Innovations; 20th IFIP WG 12.5 In Ilias Maglogiannis,Lazaros Iliadis,Antonios Papale Conference pr

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樓主: 哄笑
21#
發(fā)表于 2025-3-25 03:43:42 | 只看該作者
22#
發(fā)表于 2025-3-25 09:03:44 | 只看該作者
Dynamic Stacking Optimization in?Unpredictable Environments: A Focus on?Crane Schedulings operating on the same girder, multiple arrival and handover stacks, and various stacks in the buffer area. Our approach employs a role-based solver, efficiently planning crane assignments to manage container movements within the terminal. The performance of the solvers is compared with other solve
23#
發(fā)表于 2025-3-25 15:21:35 | 只看該作者
24#
發(fā)表于 2025-3-25 16:39:24 | 只看該作者
Optimizations for?Learning from?Linear Feedback Shift Register Variations with?Artificial Neural Netneeded for learning to predict the outputs of the Geffe generator through the introduction of an ANN pipeline model. Performed experiments display the strength of the approach that is able to maintain up to . accuracy for predicting Geffe outputs while reducing the amount of training bits to the deg
25#
發(fā)表于 2025-3-25 20:37:36 | 只看該作者
Deep Sleep Recognition Based on?CNNs and?Data Augmentation of collecting sleep EEG data in real life, which can also pose unnecessary inconvenience to subjects over long periods, we employed three data augmentation methods to enrich the dataset and introduce more variability under limited data capabilities. In this paper, we explore the application of data
26#
發(fā)表于 2025-3-26 01:15:45 | 只看該作者
Gamified Crowd Management Utilizing AR and?Computer Vision on?the?Edgeital technologies and tools. Among others, the experience is affected by the number of visitors in the exhibition area, making the indoor crowd management a challenging topic which cross cuts several scientific areas..Various methods have been developed to overcome this issue especially in museum ex
27#
發(fā)表于 2025-3-26 04:23:01 | 只看該作者
28#
發(fā)表于 2025-3-26 10:07:52 | 只看該作者
29#
發(fā)表于 2025-3-26 15:53:14 | 只看該作者
Vertical Federated Image Segmentationon is located on separate data silos and it can be difficult for a machine learning engineer to consolidate all of it in a fashion that is appropriate for model development. Additionally, some of these localized data regions may not have access to a labelled ground truth, rendering conventional mode
30#
發(fā)表于 2025-3-26 19:32:28 | 只看該作者
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