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Titlebook: Computer Analysis of Images and Patterns; 20th International C Nicolas Tsapatsoulis,Andreas Lanitis,Andreas Panay Conference proceedings 20

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樓主: Auditory-Nerve
31#
發(fā)表于 2025-3-26 23:48:56 | 只看該作者
32#
發(fā)表于 2025-3-27 03:25:06 | 只看該作者
,L’opérateur , sur une variété q-concave,o the combined standard bolus calculator treatment and carbohydrate counting. This approach could potentially improve glycaemic control for PwT1D and reduce the burden of carbohydrate and insulin dosage estimations.
33#
發(fā)表于 2025-3-27 08:21:39 | 只看該作者
34#
發(fā)表于 2025-3-27 11:52:08 | 只看該作者
35#
發(fā)表于 2025-3-27 17:22:15 | 只看該作者
36#
發(fā)表于 2025-3-27 18:16:13 | 只看該作者
37#
發(fā)表于 2025-3-27 22:44:37 | 只看該作者
Highly Crowd Detection and?Counting Based on?Curriculum Learning this paper we formulate the problem in terms of point detection and we propose a novel training strategy, especially devised for point detection networks. The baseline architecture we use is Point to Point Network (P2PNet), that have shown impressing accuracy results in both localization and crowd
38#
發(fā)表于 2025-3-28 02:25:38 | 只看該作者
Race Bias Analysis of?Bona Fide Errors in?Face Anti-spoofingce bias in face anti-spoofing. In this paper, we present a systematic study of race bias in face anti-spoofing with three key features: we focus on the classifier’s bona fide errors, where the most significant ethical and legal issues lie; we analyse both the scalar responses of the classifier and i
39#
發(fā)表于 2025-3-28 10:02:11 | 只看該作者
Fall Detection with?Event-Based Data: A Case Studyolutions lack the ability to combine low-power consumption, privacy protection, low latency response, and low payload. In this work, we address this gap through a comparative analysis of the trade-off between effectiveness and energy consumption by comparing a Recurrent Spiking Neural Network (RSNN)
40#
發(fā)表于 2025-3-28 12:57:42 | 只看該作者
Towards Accurate and?Efficient Sleep Period Detection Using Wearable Devicesoring sleep. This study investigates methods for autonomously identifying sleep segments base on wearable device data. We employ and evaluate machine and deep learning models on the benchmark MESA dataset, with results showing that they outperform traditional methods in terms of accuracy, F1 score,
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