Machine Learning For Networking

Machine Learning For Networking Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Machine Learning For Networking book. This book definitely worth reading, it is an incredibly well-written.

Machine Learning in Complex Networks

Thiago Christiano Silva,Liang Zhao
Machine Learning in Complex Networks Book PDF

Author : Thiago Christiano Silva,Liang Zhao
Publisher : Springer
Page : 331 pages
File Size : 47,5 Mb
Release : 2016-01-28
Category : Computers
ISBN : 9783319172903

Get Book

Machine Learning in Complex Networks by Thiago Christiano Silva,Liang Zhao Pdf

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Machine Learning for Networking

Éric Renault,Paul Mühlethaler,Selma Boumerdassi
Machine Learning for Networking Book PDF

Author : Éric Renault,Paul Mühlethaler,Selma Boumerdassi
Publisher : Springer
Page : 400 pages
File Size : 52,7 Mb
Release : 2019-05-10
Category : Computers
ISBN : 9783030199456

Get Book

Machine Learning for Networking by Éric Renault,Paul Mühlethaler,Selma Boumerdassi Pdf

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.

Neural Networks and Deep Learning

Charu C. Aggarwal
Neural Networks and Deep Learning Book PDF

Author : Charu C. Aggarwal
Publisher : Springer
Page : 497 pages
File Size : 43,7 Mb
Release : 2018-08-25
Category : Computers
ISBN : 9783319944630

Get Book

Neural Networks and Deep Learning by Charu C. Aggarwal Pdf

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems

K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching
Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems Book PDF

Author : K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching
Publisher : CRC Press
Page : 296 pages
File Size : 42,9 Mb
Release : 2021-09-14
Category : Technology & Engineering
ISBN : 9781000441819

Get Book

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching Pdf

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.

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

Nur Zincir-Heywood,Marco Mellia,Yixin Diao
Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Book PDF

Author : Nur Zincir-Heywood,Marco Mellia,Yixin Diao
Publisher : John Wiley & Sons
Page : 402 pages
File Size : 51,8 Mb
Release : 2021-09-03
Category : Technology & Engineering
ISBN : 9781119675518

Get Book

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning by Nur Zincir-Heywood,Marco Mellia,Yixin Diao Pdf

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

Selma Boumerdassi,Éric Renault,Paul Mühlethaler
Machine Learning for Networking Book PDF

Author : Selma Boumerdassi,Éric Renault,Paul Mühlethaler
Publisher : Springer Nature
Page : 498 pages
File Size : 46,9 Mb
Release : 2020-04-19
Category : Computers
ISBN : 9783030457785

Get Book

Machine Learning for Networking by Selma Boumerdassi,Éric Renault,Paul Mühlethaler Pdf

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 in Social Networks

Manasvi Aggarwal,M.N. Murty
Machine Learning in Social Networks Book PDF

Author : Manasvi Aggarwal,M.N. Murty
Publisher : Springer Nature
Page : 121 pages
File Size : 45,5 Mb
Release : 2020-11-25
Category : Technology & Engineering
ISBN : 9789813340220

Get Book

Machine Learning in Social Networks by Manasvi Aggarwal,M.N. Murty Pdf

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.

Neural Networks with R

Giuseppe Ciaburro,Balaji Venkateswaran
Neural Networks with R Book PDF

Author : Giuseppe Ciaburro,Balaji Venkateswaran
Publisher : Packt Publishing Ltd
Page : 270 pages
File Size : 50,6 Mb
Release : 2017-09-27
Category : Computers
ISBN : 9781788399418

Get Book

Neural Networks with R by Giuseppe Ciaburro,Balaji Venkateswaran Pdf

Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

Machine Learning for Networking

Éric Renault,Selma Boumerdassi,Paul Mühlethaler
Machine Learning for Networking Book PDF

Author : Éric Renault,Selma Boumerdassi,Paul Mühlethaler
Publisher : Springer Nature
Page : 386 pages
File Size : 47,7 Mb
Release : 2021-03-02
Category : Computers
ISBN : 9783030708665

Get Book

Machine Learning for Networking by Éric Renault,Selma Boumerdassi,Paul Mühlethaler Pdf

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

Éric Renault,Selma Boumerdassi,Paul Mühlethaler
Machine Learning for Networking Book PDF

Author : Éric Renault,Selma Boumerdassi,Paul Mühlethaler
Publisher : Springer Nature
Page : 171 pages
File Size : 44,9 Mb
Release : 2022-03-22
Category : Computers
ISBN : 9783030989781

Get Book

Machine Learning for Networking by Éric Renault,Selma Boumerdassi,Paul Mühlethaler Pdf

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.

Developing Networks using Artificial Intelligence

Haipeng Yao,Chunxiao Jiang,Yi Qian
Developing Networks using Artificial Intelligence Book PDF

Author : Haipeng Yao,Chunxiao Jiang,Yi Qian
Publisher : Springer
Page : 248 pages
File Size : 47,5 Mb
Release : 2019-04-26
Category : Technology & Engineering
ISBN : 9783030150280

Get Book

Developing Networks using Artificial Intelligence by Haipeng Yao,Chunxiao Jiang,Yi Qian Pdf

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.

Machine Learning Modeling for IoUT Networks

Ahmad A. Aziz El-Banna,Kaishun Wu
Machine Learning Modeling for IoUT Networks Book PDF

Author : Ahmad A. Aziz El-Banna,Kaishun Wu
Publisher : Springer Nature
Page : 71 pages
File Size : 47,6 Mb
Release : 2021-05-29
Category : Technology & Engineering
ISBN : 9783030685676

Get Book

Machine Learning Modeling for IoUT Networks by Ahmad A. Aziz El-Banna,Kaishun Wu Pdf

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.

Special Topics in Information Technology

Angelo Geraci
Special Topics in Information Technology Book PDF

Author : Angelo Geraci
Publisher : Springer Nature
Page : 150 pages
File Size : 55,8 Mb
Release : 2021-02-26
Category : Technology & Engineering
ISBN : 9783030624767

Get Book

Special Topics in Information Technology by Angelo Geraci Pdf

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Machine Learning for Future Wireless Communications

Fa-Long Luo
Machine Learning for Future Wireless Communications Book PDF

Author : Fa-Long Luo
Publisher : John Wiley & Sons
Page : 490 pages
File Size : 44,9 Mb
Release : 2020-02-10
Category : Technology & Engineering
ISBN : 9781119562252

Get Book

Machine Learning for Future Wireless Communications by Fa-Long Luo Pdf

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Machine Learning for Networking

Éric Renault (Computer scientist),Paul Mühlethaler,Selma Boumerdassi
Machine Learning for Networking Book PDF

Author : Éric Renault (Computer scientist),Paul Mühlethaler,Selma Boumerdassi
Publisher : Unknown
Page : 388 pages
File Size : 46,6 Mb
Release : 2019
Category : Machine learning
ISBN : 3030199460

Get Book

Machine Learning for Networking by Éric Renault (Computer scientist),Paul Mühlethaler,Selma Boumerdassi Pdf

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.