標(biāo)題: Titlebook: Developing Networks using Artificial Intelligence; Haipeng Yao,Chunxiao Jiang,Yi Qian Book 2019 Springer Nature Switzerland AG 2019 machin [打印本頁] 作者: 娛樂某人 時(shí)間: 2025-3-21 17:26
書目名稱Developing Networks using Artificial Intelligence影響因子(影響力)
書目名稱Developing Networks using Artificial Intelligence影響因子(影響力)學(xué)科排名
書目名稱Developing Networks using Artificial Intelligence網(wǎng)絡(luò)公開度
書目名稱Developing Networks using Artificial Intelligence網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Developing Networks using Artificial Intelligence被引頻次
書目名稱Developing Networks using Artificial Intelligence被引頻次學(xué)科排名
書目名稱Developing Networks using Artificial Intelligence年度引用
書目名稱Developing Networks using Artificial Intelligence年度引用學(xué)科排名
書目名稱Developing Networks using Artificial Intelligence讀者反饋
書目名稱Developing Networks using Artificial Intelligence讀者反饋學(xué)科排名
作者: 膽小懦夫 時(shí)間: 2025-3-21 22:35
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 作者: 鑒賞家 時(shí)間: 2025-3-22 03:57 作者: 波動 時(shí)間: 2025-3-22 08:22
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作者: 符合國情 時(shí)間: 2025-3-22 11:46 作者: 植物茂盛 時(shí)間: 2025-3-22 15:12 作者: 植物茂盛 時(shí)間: 2025-3-22 17:56
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作者: TRACE 時(shí)間: 2025-3-23 01:17
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作者: 外形 時(shí)間: 2025-3-23 01:58
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 作者: 亞當(dāng)心理陰影 時(shí)間: 2025-3-23 09:36
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作者: Eeg332 時(shí)間: 2025-3-23 10:52 作者: indoctrinate 時(shí)間: 2025-3-23 15:44
Intelligence-Driven Networking Architecture,ware defined network facilitates separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, in this chapter, we propose NetworkAI, a new network architecture exploit作者: EXALT 時(shí)間: 2025-3-23 21:19 作者: obligation 時(shí)間: 2025-3-24 00:27 作者: 音樂等 時(shí)間: 2025-3-24 05:16
Intelligent Network Resource Management, placement in cloud computing. In this chapter, we propose a reinforcement learning based dynamic attribute matrix representation (RDAM) algorithm for virtual network embedding. The RDAM algorithm decomposes the process of node mapping into the following three steps: (1) static representation of sub作者: 協(xié)定 時(shí)間: 2025-3-24 09:10
Intention Based Networking Management,y eliminating manual configurations. It allows a user or administrator to send a simple request—using natural language—to plan, design and implement/operate the physical network which can improve network availability and agility. For example, an IT administrator can request improved voice quality fo作者: 山崩 時(shí)間: 2025-3-24 14:15 作者: 廢墟 時(shí)間: 2025-3-24 17:43
2366-1186 ches 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 learni作者: BROOK 時(shí)間: 2025-3-24 20:04 作者: 安裝 時(shí)間: 2025-3-25 00:33 作者: AVANT 時(shí)間: 2025-3-25 05:46
Intelligence-Driven Networking Architecture,NetworkAI implements a network state upload link and a decision download link to accomplish a closed-loop control of the network and builds a centralized intelligent agent aiming at learning the policy by interacting with the whole network.作者: generic 時(shí)間: 2025-3-25 10:55 作者: 消毒 時(shí)間: 2025-3-25 14:29 作者: inconceivable 時(shí)間: 2025-3-25 16:29 作者: Entirety 時(shí)間: 2025-3-25 21:21
Book 2019vestigate 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 t作者: 大約冬季 時(shí)間: 2025-3-26 00:44 作者: 導(dǎo)師 時(shí)間: 2025-3-26 06:53
https://doi.org/10.1007/978-3-030-15028-0machine learning; software-defined network; reinforcement learning; deep learning; intrusion detection; l作者: triptans 時(shí)間: 2025-3-26 10:23
Springer Nature Switzerland AG 2019作者: 冥想后 時(shí)間: 2025-3-26 16:04
Hans-Dietrich Dahnke,Regine Otto, economic operation and society. However, burgeoning megatrends in the information and communication technology (ICT) domain are urging the Internet for pervasive accessibility, broadband connection and flexible management, which call for potential new Internet architectures. The original design ta作者: gonioscopy 時(shí)間: 2025-3-26 17:33
Hans-Dietrich Dahnke,Regine Ottoware defined network facilitates separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, in this chapter, we propose NetworkAI, a new network architecture exploit作者: 亞當(dāng)心理陰影 時(shí)間: 2025-3-26 22:47 作者: 故意釣到白楊 時(shí)間: 2025-3-27 03:02 作者: 復(fù)習(xí) 時(shí)間: 2025-3-27 07:58 作者: 淘氣 時(shí)間: 2025-3-27 10:17
Zu G.s geologischer Forschung nach 1800y eliminating manual configurations. It allows a user or administrator to send a simple request—using natural language—to plan, design and implement/operate the physical network which can improve network availability and agility. For example, an IT administrator can request improved voice quality fo作者: Mobile 時(shí)間: 2025-3-27 17:15
Schriften zur Landschaftsmalereit of NetworkAI, which is a novel paradigm that applying machine learning to automatically control a network. NetworkAI employs reinforcement learning and incorporates network monitoring technologies such as the in-band network telemetry to dynamically generate control policies and produces a near-op作者: TAIN 時(shí)間: 2025-3-27 18:07
Developing Networks using Artificial Intelligence978-3-030-15028-0Series ISSN 2366-1186 Series E-ISSN 2366-1445 作者: 支柱 時(shí)間: 2025-3-28 00:19 作者: URN 時(shí)間: 2025-3-28 02:26
