The application of artificial intelligence technology to 5G wireless communications is now appropriate to address the design of optimized physical layers, complicated decision-making, network management, and resource optimization tasks within networks. In exploring 5G wireless technologies and communication systems, artificial intelligence is a powerful tool and a research topic with numerous potential fields of application that require further study. Applications of Artificial Intelligence in Wireless Communication Systems explores the applications of artificial intelligence for the optimization of wireless communication systems, including channel models, channel state estimation, beamforming, codebook design, signal processing, and more. Covering key topics such as neural networks, deep learning, and wireless systems, this reference work is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.

Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation.

Digital technologies are currently dramatically changing healthcare. Cloud healthcare is an increasingly trending topic in the field, converging skills from computer and health science. This new strategy fosters the management of health data at a large scale and makes it easier for healthcare organizations to improve patient experience and health team productivity while helping the support, security, compliance, and interoperability of health data. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare is a reference in the ongoing digital transformation of the healthcare sector. It presents a comprehensive state-of-the-art approach to cloud internet of things health technologies and practices. It provides insights over strategies, methodologies, techniques, tools, and services based on emerging cloud digital health solutions to overcome digital health challenges. Covering topics such as auxiliary systems, the internet of medical things, and natural language processing, this premier reference source is an essential resource for medical professionals, hospital administrators, medical students, medical professors, libraries, researchers, and academicians.

As the progression of the internet continues, society is finding easier, quicker ways of simplifying their needs with the use of technology. With the growth of lightweight devices, such as smart phones and wearable devices, highly configured hardware is in heightened demand in order to process the large amounts of raw data that are acquired. Connecting these devices to fog computing can reduce bandwidth and latency for data transmission when associated with centralized cloud solutions and uses machine learning algorithms to handle large amounts of raw data. The risks that accompany this advancing technology, however, have yet to be explored. Architecture and Security Issues in Fog Computing Applications is a pivotal reference source that provides vital research on the architectural complications of fog processing and focuses on security and privacy issues in intelligent fog applications. While highlighting topics such as machine learning, cyber-physical systems, and security applications, this publication explores the architecture of intelligent fog applications enabled with machine learning. This book is ideally designed for IT specialists, software developers, security analysts, software engineers, academicians, students, and researchers seeking current research on network security and wireless systems.

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Many professional fields have been affected by the rapid growth of technology and information. Included in this are the business and management markets as the implementation of e-commerce and cloud computing have caused enterprises to make considerable changes to their practices. With the swift advancement of this technology, professionals need proper research that provides solutions to the various issues that come with data integration and shifting to a technology-driven environment. Cloud Computing Applications and Techniques for E-Commerce is an essential reference source that discusses the implementation of data and cloud technology within the fields of business and information management. Featuring research on topics such as content delivery networks, virtualization, and software resources, this book is ideally designed for managers, educators, administrators, researchers, computer scientists, business practitioners, economists, information analysists, sociologists, and students seeking coverage on the recent advancements of e-commerce using cloud computing techniques.

The world of esports in education is booming, and the field needs empirical studies to help ground much of what is going on in the field. Over the last couple years, there appears to be a large amount of anecdotal evidence surrounding esports and its role in education, but researchers, teachers, coaches, and organizations need peer-reviewed, research-based evidence so they can evolve the field at large. As the amount of esports teams and organizations continues to rise, so will the need for the field to provide empirical research about esports and education and the effect it has on students and those who partake in it. Esports Research and Its Integration in Education is an essential reference source for those interested in educational research related to esports topics as they are approached through multiple ages of schooling and infused throughout a variety of content areas and research methodologies. The book covers empirical studies that help practitioners to understand how esports is developing within and around learning institutions and what the impact may be on students and their contemporary educational experiences. Covering topics such as college and career readiness, literacy practices, and urban education, this text is essential for stakeholders involved in the rise of esports, administrators, teachers, coaches, researchers, students, and academicians.

DHM and Posturography explores the body of knowledge and state-of-the-art in digital human modeling, along with its application in ergonomics and posturography. The book provides an industry first introductory and practitioner focused overview of human simulation tools, with detailed chapters describing elements of posture, postural interactions, and fields of application. Thus, DHM tools and a specific scientific/practical problem - the study of posture - are linked in a coherent framework. In addition, sections show how DHM interfaces with the most common physical devices for posture analysis. Case studies provide the applied knowledge necessary for practitioners to make informed decisions. Digital Human Modelling is the science of representing humans with their physical properties, characteristics and behaviors in computerized, virtual models. These models can be used standalone, or integrated with other computerized object design systems, to design or study designs, workplaces or products in their relationship with humans.

Advances in Domain Adaptation Theory gives current, state-of-the-art results on transfer learning, with a particular focus placed on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version. Additional sections present generalization guarantees based on the robustness and stability properties of the learning algorithm.

Computational Modeling in Bioengineering and Bioinformatics promotes complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, and in the environment, public health, drug design, and so on. It provides a common platform by bridging these two very important and complementary disciplines into an interactive and attractive forum. Chapters cover biomechanics and bioimaging, biomedical decision support system, data mining, personalized diagnoses, bio-signal processing, protein structure prediction, tissue and cell engineering, biomedical image processing, analysis and visualization, high performance computing and sports bioengineering. The book's chapters are the result of many international projects in the area of bioengineering and bioinformatics done at the Research and Development Center for Bioengineering BioIRC and by the Faculty of Engineering at the University of Kragujevac, Serbia.

Advances in Imaging and Electron Physics, Volume 212, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains.

Unmanned Aerial Vehicle (UAV) has extended the freedom to operate and monitor the activities from remote locations. It has advantages of flying at low altitude, small size, high resolution, lightweight, and portability. UAV and artificial intelligence have started gaining attentions of academic and industrial research. UAV along with machine learning has immense scope in scientific research and has resulted in fast and reliable outputs. Deep learning-based UAV has helped in real time monitoring, data collection and processing, and prediction in the computer/wireless networks, smart cities, military, agriculture and mining. This book covers artificial techniques, pattern recognition, machine and deep learning - based methods and techniques applied to different real time applications of UAV. The main aim is to synthesize the scope and importance of machine learning and deep learning models in enhancing UAV capabilities, solutions to problems and numerous application areas. This book is ideal for researchers, scientists, engineers and designers in academia and industry working in the fields of computer science, computer vision, pattern recognition, machine learning, imaging, feature engineering, UAV and sensing.