Machine Learning For Networking
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| Author | : Éric Renault |
| Publisher | : Springer |
| Total Pages | : 400 |
| Release | : 2019-05-10 |
| Genre | : Computers |
| ISBN | : 3030199452 |
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.
| Author | : Nur Zincir-Heywood |
| Publisher | : John Wiley & Sons |
| Total Pages | : 402 |
| Release | : 2021-10-12 |
| Genre | : Technology & Engineering |
| ISBN | : 1119675502 |
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.
| Author | : Manasvi Aggarwal |
| Publisher | : Springer Nature |
| Total Pages | : 121 |
| Release | : 2020-11-25 |
| Genre | : Technology & Engineering |
| ISBN | : 9813340223 |
This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.
| Author | : Bernhard Mehlig |
| Publisher | : Cambridge University Press |
| Total Pages | : 262 |
| Release | : 2021-10-28 |
| Genre | : Science |
| ISBN | : 1108849563 |
This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
| Author | : K. Suganthi |
| Publisher | : CRC Press |
| Total Pages | : 285 |
| Release | : 2021-09-13 |
| Genre | : Computers |
| ISBN | : 1000441814 |
This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.
| Author | : Krishna Kant Singh |
| Publisher | : John Wiley & Sons |
| Total Pages | : 272 |
| Release | : 2020-07-08 |
| Genre | : Computers |
| ISBN | : 1119640369 |
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
| Author | : Ahmad A. Aziz El-Banna |
| Publisher | : Springer Nature |
| Total Pages | : 71 |
| Release | : 2021-05-29 |
| Genre | : Technology & Engineering |
| ISBN | : 3030685675 |
This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater’s key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.
| Author | : Éric Renault |
| Publisher | : Springer Nature |
| Total Pages | : 190 |
| Release | : 2023-07-06 |
| Genre | : Computers |
| ISBN | : 3031361830 |
This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning for Networking, MLN 2022, held in Paris, France, November 28–30, 2022. The 12 full papers presented in this book were carefully reviewed and selected from 27 submissions. The papers present novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking.
| Author | : Selma Boumerdassi |
| Publisher | : Springer Nature |
| Total Pages | : 498 |
| Release | : 2020-04-19 |
| Genre | : Computers |
| ISBN | : 3030457788 |
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.
| Author | : Dhruba Kumar Bhattacharyya |
| Publisher | : CRC Press |
| Total Pages | : 364 |
| Release | : 2013-06-18 |
| Genre | : Computers |
| ISBN | : 146658209X |
With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi
