Machine Learning For Networking
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Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher: Springer
Release Date : 2019-05-10
Machine Learning For Networking written by Éric Renault and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-10 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher: Springer Nature
Release Date : 2021-03-02
Machine Learning For Networking written by Éric Renault and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
Machine Learning For Networking
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Author : Fouchal Hacène
language : en
Publisher: Springer Nature
Release Date : 2025-10-12
Machine Learning For Networking written by Fouchal Hacène and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-12 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning for Networking, MLN 2024, held in Reims, France, during November 27–29, 2024. The 14 full papers presented in this book were carefully reviewed and selected from 25 submissions. The International Conference on Machine Learning for Networking (MLN) aims at providing a top forum for researchers and practitioners to present and discuss new trends in machine learning, deep learning, pattern recognition and optimization for network architectures and service.
Machine Learning For Networking
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Author : Selma Boumerdassi
language : en
Publisher: Springer Nature
Release Date : 2020-04-19
Machine Learning For Networking written by Selma Boumerdassi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-19 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, patternrecognition and classi cation for networks, machine learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new innovative machine learning methods, performanceanalysis of machine learning algorithms, experimental evaluations ofmachine learning, data mining in heterogeneous networks, distributed and decentralizedmachine learning algorithms, intelligent cloud-support communications,ressource allocation, energy-aware communications, software de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks.
Machine Learning For Networking
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Author : Éric Renault (Computer scientist)
language : en
Publisher:
Release Date : 2019
Machine Learning For Networking written by Éric Renault (Computer scientist) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.
This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
Developing Networks Using Artificial Intelligence
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Author : Haipeng Yao
language : en
Publisher: Springer
Release Date : 2019-04-26
Developing Networks Using Artificial Intelligence written by Haipeng Yao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-26 with Technology & Engineering categories.
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 approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook.
Ai And Machine Learning For Network And Security Management
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Author : Yulei Wu
language : en
Publisher: John Wiley & Sons
Release Date : 2022-11-08
Ai And Machine Learning For Network And Security Management written by Yulei Wu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-08 with Computers categories.
AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.
Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher: Springer Nature
Release Date : 2022-03-22
Machine Learning For Networking written by Éric Renault and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-22 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Machine Learning for Networking, MLN 2021, held in Paris, France, in December 2021. The 10 revised full papers included in the volume were carefully reviewed and selected from 30 submissions. They present and discuss new trends in in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G systems, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, resource allocation, energy-aware communications, software-defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, and underwater sensor networks.
Communication Networks And Service Management In The Era Of Artificial Intelligence And Machine Learning
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Author : Nur Zincir-Heywood
language : en
Publisher: John Wiley & Sons
Release Date : 2021-10-12
Communication Networks And Service Management In The Era Of Artificial Intelligence And Machine Learning written by Nur Zincir-Heywood and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-12 with Technology & Engineering categories.
COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.
Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher:
Release Date : 2021
Machine Learning For Networking written by Éric Renault and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.