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Titlebook: Developing Networks using Artificial Intelligence; Haipeng Yao,Chunxiao Jiang,Yi Qian Book 2019 Springer Nature Switzerland AG 2019 machin

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發(fā)表于 2025-3-21 17:26:06 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Developing Networks using Artificial Intelligence
編輯Haipeng Yao,Chunxiao Jiang,Yi Qian
視頻videohttp://file.papertrans.cn/270/269773/269773.mp4
叢書名稱Wireless Networks
圖書封面Titlebook: Developing Networks using Artificial Intelligence;  Haipeng Yao,Chunxiao Jiang,Yi Qian Book 2019 Springer Nature Switzerland AG 2019 machin
描述.This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book...?Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today‘s network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box appr
出版日期Book 2019
關鍵詞machine learning; software-defined network; reinforcement learning; deep learning; intrusion detection; l
版次1
doihttps://doi.org/10.1007/978-3-030-15028-0
isbn_ebook978-3-030-15028-0Series ISSN 2366-1186 Series E-ISSN 2366-1445
issn_series 2366-1186
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 22:35:11 | 只看該作者
Zu G.s geologischer Forschung nach 1800djustment of network traffic distribution, increase detection accuracy and reduce the false negative rate. Finally, we propose an end-to-end IoT traffic classification method relying on deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates
板凳
發(fā)表于 2025-3-22 03:57:00 | 只看該作者
地板
發(fā)表于 2025-3-22 08:22:16 | 只看該作者
Zu G.s geologischer Forschung nach 1800 system. We firstly propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score o
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發(fā)表于 2025-3-22 11:46:06 | 只看該作者
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發(fā)表于 2025-3-22 15:12:56 | 只看該作者
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發(fā)表于 2025-3-22 17:56:48 | 只看該作者
Intelligent Network Control, and propose a novel Quality of Service (QoS) enabled load scheduling algorithm based on reinforcement learning to solve the problem of complexity and pre-strategy in the networks. In addition, we present a Wireless Local Area Networks (WLAN) interference self-optimization method based on a Self-Org
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發(fā)表于 2025-3-23 01:17:27 | 只看該作者
Intention Based Networking Management, system. We firstly propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score o
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發(fā)表于 2025-3-23 01:58:30 | 只看該作者
Conclusions and Future Challenges, becomes substantially important to strengthen the management of data traffic in networks. As a critical part of massive data analysis, traffic awareness plays an important role in ensuring network security and defending traffic attacks. Moreover, the classification of different traffic can help to
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發(fā)表于 2025-3-23 09:36:08 | 只看該作者
Book 2019pproaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box appr
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