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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
11#
發(fā)表于 2025-3-23 12:11:05 | 只看該作者
12#
發(fā)表于 2025-3-23 14:54:57 | 只看該作者
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發(fā)表于 2025-3-23 19:12:04 | 只看該作者
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發(fā)表于 2025-3-23 22:14:31 | 只看該作者
15#
發(fā)表于 2025-3-24 04:13:42 | 只看該作者
Gamified Crowd Management Utilizing AR and?Computer Vision on?the?Edgehm and a prototype system for a museum exhibition which operates at the edge of the network utilizing the mobile devices as execution environments. As part of the prototype, we propose the integration of a series of serious games that are supplementary to the exhibits and have the goal of delaying t
16#
發(fā)表于 2025-3-24 07:51:47 | 只看該作者
KeepOriginalAugment: Single Image-Based Better Information-Preserving Data Augmentation Approachalance between data diversity and information preservation, KeepOriginalAugment enables models to leverage both diverse salient and non-salient regions, leading to enhanced performance. We explore three strategies for determining the placement of the salient region—minimum, maximum, or random—and in
17#
發(fā)表于 2025-3-24 13:36:11 | 只看該作者
Using DCGANs and HOG?+?Patch-Based CNN for Face Spoofing Mitigation biometric samples. We then proposed a HOG?+?Patch-based CNN structure for spoofing mitigation on the generated spoofing datasets. Our proposed CNN model outperforms notable VGG-16 and ResNet-50 models in classification and verification accuracies on our spoofing dataset.
18#
發(fā)表于 2025-3-24 18:48:19 | 只看該作者
19#
發(fā)表于 2025-3-24 21:09:55 | 只看該作者
A Machine Learning Approach for?Points of?Interest Extraction and?Event Classificationsion of routine predictions but also enhances the adaptability of the system to changes in mobility behavior over time. The incorporation of a cognitive module, based on Dynamic Neural Fields (DNF), further allows for personalized predictions regarding the timing, duration, and nature of trips. Vali
20#
發(fā)表于 2025-3-25 00:30:11 | 只看該作者
Controlling Popularity Bias in?Sequential Recommendation Models prioritizes being most correct rather than trying to find a truly fitting recommendation. Popularity bias is a main cause of echo chambers within the current media landscape, which unfortunately has led to less critical thinking and more divisiveness our communities. To counter this issue, we prese
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