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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.
Internet of Unmanned Things (IoUT) and Mission-based Networking by Chaker Abdelaziz Kerrache,Carlos Calafate,Abderrahmane Lakas,Mohamed Lahby Pdf
This book discusses the potential of the Internet of Unmanned Things (IoUT), which is considered a promising paradigm resulting in numerous applications including shipment of goods, home package delivery, crop monitoring, agricultural surveillance, and rescue operations. The authors discuss how IoUT nodes collaborate with each other in ad hoc manner through a Line-of-Sight (LoS) link to exchange data packets. Also discussed is how Unmanned Arial Vehicles (UAVs) can communicate with fixed ground stations, with an air traffic controller, or through a Non-Line-of-Sight (NLoS) link with a satellite-aided controller, generally based on preloaded missions. The authors go on to cover how to tackle issues that arise with dissimilar communication technologies. They cover how various problems can appear in inter-UAV and UAV-to-X communications including energy management, lack of security and the unreliability of wireless communication links, and handover from LoS to NLoS, and vice versa. In this book, the editors invited front-line researchers and authors to submit research exploring emerging technologies for IoUT and mission-based networking and how to overcome challenges.
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.
Modeling and Optimization in Software-Defined Networks by Konstantinos Poularakis,Leandros Tassiulas,T .V. Lakshman Pdf
This book provides a quick reference and insights into modeling and optimization of software-defined networks (SDNs). It covers various algorithms and approaches that have been developed for optimizations related to the control plane, the considerable research related to data plane optimization, and topics that have significant potential for research and advances to the state-of-the-art in SDN. Over the past ten years, network programmability has transitioned from research concepts to more mainstream technology through the advent of technologies amenable to programmability such as service chaining, virtual network functions, and programmability of the data plane. However, the rapid development in SDN technologies has been the key driver behind its evolution. The logically centralized abstraction of network states enabled by SDN facilitates programmability and use of sophisticated optimization and control algorithms for enhancing network performance, policy management, and security.Furthermore, the centralized aggregation of network telemetry facilitates use of data-driven machine learning-based methods. To fully unleash the power of this new SDN paradigm, though, various architectural design, deployment, and operations questions need to be addressed. Associated with these are various modeling, resource allocation, and optimization opportunities.The book covers these opportunities and associated challenges, which represent a ``call to arms'' for the SDN community to develop new modeling and optimization methods that will complement or improve on the current norms.
Scalable and Distributed Machine Learning and Deep Learning Patterns by Thomas, J. Joshua,Harini, S.,Pattabiraman, V. Pdf
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
Deep Learning for Internet of Things Infrastructure by Uttam Ghosh,Mamoun Alazab,Ali Kashif Bashir,Al-Sakib Khan Pathan Pdf
This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users. FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end users Uttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA. Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.
Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference by José Manuel Machado,Pablo Chamoso,Guillermo Hernández,Grzegorz Bocewicz,Roussanka Loukanova,Esteban Jove,Angel Martin del Rey,Michela Ricca Pdf
DCAI 2022 is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This year’s technical program will present both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 46 papers were submitted, by authors from 28 different countries representing a truly “wide area network” of research activity. The DCAI’22 Special Sessions technical program has selected 22 papers (12 full papers) and, as in past editions, it will be special issues in ranked journals. This symposium is organized by the University of L'Aquila (Italy). We would like to thank all the contributing authors, the members of the Program Committee and the sponsors (IBM, Indra, Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica dell'Università degli Studi dell'Aquila, Armundia Group, Whitehall Reply, T.C. Technologies And Comunication S.R.L., LCL Industria Grafica, AIR Institute, AEPIA, APPIA).
The Effect of Information Technology on Business and Marketing Intelligence Systems by Muhammad Alshurideh,Barween Hikmat Al Kurdi,Ra’ed Masa’deh,Haitham M. Alzoubi,Said Salloum Pdf
Business shapes have been changed these days. Change is the main dominant fact that change the way of business operations running. Topics such as innovation, entrepreneurship, leadership, blockchain, mobile business, social media, e-learning, machine learning, and artificial intelligence become essential to be considered by each institution within the technology era. This book tries to give additional views on how technologies influence business and marketing operations for insuring successful institutions survival. The world needs to develop management and intelligent business scenario plans that suite a variety of crisis appears these days. Also, business and marketing intelligence should meet government priorities in individual countries and minimise the risk of business disruptions. Business intelligence - the strategies and technology companies that use it to collect, interpret, and benefit from data - play a key role in informing company strategies, functions, and efficiency. However, being essential to the success, many companies are not taking advantage of tools that can improve their business intelligence efforts. Information technology become a core stone in business. For example, the combination of machine learning and business intelligence can have a far-reaching impact on the insights the company gets from its available data to improve productivity, quality, customer service and more. This book is important because it introduces a large number of chapters that discussed the implications of different Information technology applications in business. This book contains a set of volumes which are: 1- Social Marketing and Social Media Applications, 2- Social Marketing and Social Media Applications, 3- Business and Data Analytics, 4- Corporate governance and performance, 5- Innovation, Entrepreneurship and leadership, 6- Knowledge management, 7- Machine learning, IOT, BIG DATA, Block Chain and AI, 8- Marketing Mix, Services and Branding.
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.
IoT-based Intelligent Modelling for Environmental and Ecological Engineering by Paul Krause,Fatos Xhafa Pdf
This book brings to readers thirteen chapters with contributions to the benefits of using IoT and Cloud Computing to agro-ecosystems from a multi-disciplinary perspective. IoT and Cloud systems have prompted the development of a Cloud digital ecosystem referred to as Cloud-to-thing continuum computing. The key success of IoT computing and the Cloud digital ecosystem is that IoT can be integrated seamlessly with the physical environment and therefore has the potential to leverage innovative services in agro-ecosystems. Areas such as ecological monitoring, agriculture, and biodiversity constitute a large area of potential application of IoT and Cloud technologies. In contrast to traditional agriculture systems that have employed aggressive policies to increase productivity, new agro-ecosystems aim to increase productivity but also achieve efficiency and competitiveness in modern sustainable agriculture and contribute, more broadly, to the green economy and sustainable food-chain industry. Fundamental research as well as concrete applications from various real-life scenarios, such as smart farming, precision agriculture, green agriculture, sustainable livestock and sow farming, climate threat, and societal and environmental impacts, is presented. Research issues and challenges are also discussed towards envisioning efficient and scalable solutions to agro-ecosystems based on IoT and Cloud technologies. Our fundamental belief is that we can collectively trigger a new revolution that will transition agriculture into an equable system that not only feeds the world, but also contributes to mitigating the climate change and biodiversity crises that our historical actions have triggered.
Generative Adversarial Networks Projects by Kailash Ahirwar Pdf
Explore various Generative Adversarial Network architectures using the Python ecosystem Key FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is for If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
Deep Learning with R for Beginners by Mark Hodnett,Joshua F. Wiley,Yuxi (Hayden) Liu,Pablo Maldonado Pdf
Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processingImplement effective deep learning systems in R with the help of end-to-end projectsBook Description Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. This Learning Path includes content from the following Packt products: R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark HodnettR Deep Learning Projects by Yuxi (Hayden) Liu and Pablo MaldonadoWhat you will learnImplement credit card fraud detection with autoencodersTrain neural networks to perform handwritten digit recognition using MXNetReconstruct images using variational autoencodersExplore the applications of autoencoder neural networks in clustering and dimensionality reductionCreate natural language processing (NLP) models using Keras and TensorFlow in RPrevent models from overfitting the data to improve generalizabilityBuild shallow neural network prediction modelsWho this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.
Making CO2 a Resource by Øyvind Stokke,Elin M. Oftedal Pdf
This interdisciplinary book explores how CO2 can become a resource instead of a waste and, as such, be a tool to meet one of the grandest challenges humanity is facing: climate change. Drawing on a Norwegian narrative that has significance for a global audience, Øyvind Stokke and Elin Oftedal introduce in-depth, multi-perspective analyses of a sustainable innovation research experiment in industrial carbon capture and utilisation technologies. Building on extensive literature within marine sciences, sustainability research, and environmental philosophy and ethics, this book documents how a misplaced resource like CO2 can become valuable within a circular economy in its own right, while at the same time meeting the challenge of food security in a world where food production is increasingly under pressure. The book is diverse in scope and includes chapters on how to reduce the environmental footprint of aquaculture by replacing wild fish and soy from the Amazon, how to optimise the monitoring of aquatic environments via smart technologies, and how to replace materials otherwise sourced from natural environments. The authors also analyse the pivotal role of the university in driving innovation and entrepreneurship, the pitfalls of different carbon technologies, and explore how the link between petroleum dependence and CO2 emissions has been addressed in Norway specifically. Making CO2 a Resource will be of great interest to students and scholars of climate change, environmental ethics, environmental philosophy, sustainable business and innovation, and sustainable development more broadly.
Deep Learning Networks by Jayakumar Singaram,S. S. Iyengar,Azad M. Madni Pdf
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.
