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Titlebook: Big Data and Security; 5th International Co Yuan Tian,Tinghuai Ma,Muhammad Khurram Khan Conference proceedings 2024 The Editor(s) (if appli

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51#
發(fā)表于 2025-3-30 11:32:59 | 只看該作者
52#
發(fā)表于 2025-3-30 13:28:25 | 只看該作者
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發(fā)表于 2025-3-30 20:34:25 | 只看該作者
1865-0929 organized in topical sections as follows:?..Part One:?Big Data & New Method and?Artificial Intelligence & Machine Learning..Part Two:?Data Technology & Network Security and?IoT Security & Privacy Protection..978-981-97-4389-6978-981-97-4390-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
54#
發(fā)表于 2025-3-31 00:01:42 | 只看該作者
The Development of Metalinguistic Abilityhis issue, the method called asymptotic PINNs (A-PINNs) is proposed, which combines the prior knowledge provided by the Shishkin mesh with domain decomposition methods to solve SPDEs effectively. Numerical results indicate that our method shows superiority in handling the singularly perturbed property of SPDEs.
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發(fā)表于 2025-3-31 02:00:08 | 只看該作者
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發(fā)表于 2025-3-31 08:58:51 | 只看該作者
Big Data and Security978-981-97-4390-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
57#
發(fā)表于 2025-3-31 11:17:49 | 只看該作者
The Development of Metalinguistic Abilityon models have been implemented end-to-end and achieve remarkable performance. To achieve better results on the regions of non-textures, boundaries, and tiny details, it is necessary to effectively combine global context information. However, current models rely on intricate cascade structures or st
58#
發(fā)表于 2025-3-31 16:34:34 | 只看該作者
The Development of Metalinguistic Abilityand phenomena defined by partial differential equations (PDEs). However, PINNs fail to solve PDEs with special properties, such as singularly perturbed differential equations (SPDEs). SPDEs tend to have boundary layers, where the value of the solution increases or decreases drastically. To address t
59#
發(fā)表于 2025-3-31 20:51:11 | 只看該作者
60#
發(fā)表于 2025-4-1 01:00:47 | 只看該作者
https://doi.org/10.1007/978-3-642-74124-1ess to increase productivity. Automating the defect detection process using deep learning such as the YOLO (You Only Look Once) algorithm has shown remarkable performance in object detection tasks. Further integrating the YOLO algorithm with BADGE (Batch Active learning by Diverse Gradient Embedding
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