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Titlebook: Wireless Internet; 16th EAI Internation Leandros A. Maglaras,Christos Douligeris Conference proceedings 2024 ICST Institute for Computer Sc

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樓主: 法庭
21#
發(fā)表于 2025-3-25 06:37:47 | 只看該作者
Optimization of Energy Distribution with Demand Response Control in 6G Next Generation Smart GridsSDN technology coupled with network slicing. As a way to achieve power balancing between power generation and demands, this study offers a unique architecture for a smart grid that makes full use of optimization techniques to rationalize the distribution of energy resources. Performance evaluation shows the optimization of resource consumption.
22#
發(fā)表于 2025-3-25 10:18:21 | 只看該作者
23#
發(fā)表于 2025-3-25 13:34:43 | 只看該作者
24#
發(fā)表于 2025-3-25 18:45:31 | 只看該作者
978-3-031-58052-9ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024
25#
發(fā)表于 2025-3-25 23:27:45 | 只看該作者
Wireless Internet978-3-031-58053-6Series ISSN 1867-8211 Series E-ISSN 1867-822X
26#
發(fā)表于 2025-3-26 03:21:47 | 只看該作者
Machine Learning and Explainable Artificial Intelligence in Education and Training - Status and Tren complex. The research field of eXplainable Artificial Intelligence (XAI) tries to fulfill this need. XAI provides a way to help humans understand how an AI’s predictions and decisions come. The scope of this work is to examine the role of XAI in the field of Education, especially in Educational Data Mining in Vocational Education and Training.
27#
發(fā)表于 2025-3-26 05:48:06 | 只看該作者
28#
發(fā)表于 2025-3-26 08:36:17 | 只看該作者
29#
發(fā)表于 2025-3-26 12:59:24 | 只看該作者
s by the requirement that certain subpictures must be cycles or elementary cycles. We investigate basic properties of context-free cycle and elementary cycle grammars with emphasis on the complexity of the recognition problem. In particular, it is shown that the description complexity is polynomial
30#
發(fā)表于 2025-3-26 18:10:21 | 只看該作者
Mohamed Darqaoui,Moussa Coulibaly,Ahmed Erramiond these two methods that focus on the coarse-grained compatibility modeling, we then devised an unsupervised disentangled graph learning method to uncover the hidden factors affecting the overall compatibility and fulfill the fine-grained compatibility modeling. Moreover, to fully utilize item-att
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