Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era. There is considerable interest in developing methods, tools, architectures and applications to support many-core computing. The primary aim of this edited book is to provide a timely and coherent account of the recent advances in many-core computing research. Starting with programming models, operating systems and their applications; the authors present runtime management techniques, followed by system modelling, verification and testing methods, and architectures and systems. The book ends with some examples of innovative applications.

The technological advancements of today not only affect individual's personal lives. They also affect the way urban communities regard the improvement of their resident's lives. Research involving these autonomic reactions to the growing needs of the people is desperately needed to transform the cities of today into the cities of the future. Driving the Development, Management, and Sustainability of Cognitive Cities is a pivotal reference source that explores and improves the understanding of the strategic role of sustainable cognitive cities in residents' routine life styles. Such benefits to residents and businesses include having access to world-class training while sitting at home, having their wellbeing observed consistently, and having their medical issues identified before occurrence. This book is ideally designed for administrators, policymakers, industrialists, and researchers seeking current research on developing and managing cognitive cities.

As a popular and powerful medium, mobile use has increased significantly across the world. The effects of these communication devices have not only transformed how we communicate but also how we gather and distribute information in a variety of industries including healthcare, business, and education. Impacts of Mobile Use and Experience on Contemporary Society provides cross-disciplinary research that examines mobile use and its impact through 16 different stages of life, ranging from pre-birth through after-death. Featuring research on topics such as academic application, economic value, and mobile learning, scholars from different disciplines identify the crucial implications behind one of the leading communication tools from all over the world. Included amongst the targeted audience are educators, policymakers, healthcare professionals, managers, academicians, researchers, and practitioners.

The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks. The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols.

With the internet of things (IoT), it is proven that enormous networks can be created to interconnect objects and facilitate daily life in a variety of domains. Research is needed to study how these improvements can be applied in different ways, using different technologies, and through the creation of different applications. IoT Protocols and Applications for Improving Industry, Environment, and Society contains the latest research on the most important areas and challenges in the internet of things and its intersection with technologies and tools such as artificial intelligence, blockchain, model-driven engineering, and cloud computing. The book covers subfields that examine smart homes, smart towns, smart earth, and the industrial internet of things in order to improve daily life, protect the environment, and create safer and easier jobs. While covering a range of topics within IoT including Industry 4.0, security, and privacy, this book is ideal for computer scientists, engineers, practitioners, stakeholders, researchers, academicians, and students who are interested in the latest applications of IoT.

is a pocket-sized book that provides a teach on media concerning social media and affective media. The context of media described here can include the media of television in broadcasting. Social media is based on concept of next generation child from 1- 12 years who are involved in media of selfie (picture or video uploads). The next-Gen birth-cast is implemented on Facebook platform with name ?Birthcast?. In this book, affective media is rather on the adult ages that needs affection sourced as love and searches via on-line pictures and on-line video or electronic pictures and electronic video. The media can be accessed on the web at the URL: http: //www.facebook.com/birthcastridiculous http: //www.facebook.com/birthcastwormzoo http: //www.facebook.com/birthcastartlet http: //www.facebook.com/birthcastbabycob http: //www.facebook.com/sociocastridicu lous

The book deals with various tools and applications of bioinformatics in the fields of: o agriculture, corals, structural bioinformatics, data-mining, text-mining; o medicinal plants, antibiotics, protein structure prediction, drug design; o gene expression, micro-arrays, proteomics, molecular phylogenic location of the Indian Liver Fluke, rough sets to predict protein structural class; o artificial neural networks for prediction of amino acids levels, plant systems biology, molecular modeling, homology modeling, bio-efficacy of indigenous bacillus through in-silico approach; o fresh aquaculture and fisheries, pesticides and insecticides, databases and tools development in the relevant area. The book would be of much use to the person working in the field of agricultural biotechnology, bioinformatics, computer science and applied statistics. This can act as a book for M.Sc, M.Tech and Ph.D students of and the faculty of Bioinformatics/Biotechnologists.

Today, network technology is ubiquitous. Whether at home or on the move, at work or at play, the modern data network is a part of our daily lives. Streaming video, social media and web browsing are just a few of the popular applications that rely on the network, and this list will continue to grow with autonomous vehicles, virtual reality and others, each with their own unique needs. To address the challenges of the demand for these services, the network must continually evolve with new technologies. However, determining which technologies are worth focusing on today is difficult, and the issues which they represent, and address are often complex. In Network Horizons Emerging Technologies and Applications 2018 - 2019 Edition, the author highlights key areas of interest for network technology, helping the reader to identify those of the highest importance by explaining the what, why and when of each of these important areas of development to make sure they and their business are prepared for the future.

This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks.The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.

As technology continues to expand and develop, the internet of things (IoT) is playing a progressive role in the infrastructure of electronics. The increasing amount of IoT devices, however, has led to the emergence of significant privacy and security challenges. Security and Privacy Issues in Sensor Networks and IoT is a collection of innovative research on the methods and applications of protection disputes in the internet of things and other computing structures. While highlighting topics that include cyber defense, digital forensics, and intrusion detection, this book is ideally designed for security analysts, IT specialists, software developers, computer engineers, industry professionals, academicians, students, and researchers seeking current research on defense concerns in cyber physical systems.

In recent years, most applications deal with constraint decision-making systems as problems are based on imprecise information and parameters. It is difficult to understand the nature of data based on applications and it requires a specific model for understanding the nature of the system. Further research on constraint decision-making systems in engineering is required. Constraint Decision-Making Systems in Engineering derives and explores several types of constraint decisions in engineering and focuses on new and innovative conclusions based on problems, robust and efficient systems, and linear and non-linear applications. Covering topics such as fault detection, data mining techniques, and knowledge-based management, this premier reference source is an essential resource for engineers, managers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.