The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.

Introduces the topic gently and intuitively with ample famous examples and case studies Develops and explains intuitively the information flow models, and thereafter builds the control theory for information management and propagation Includes mathematical treatment of information spread and fake news epidemics and step by step development of modeling framework Discusses Control methods and application examples Borrows from multiple disciplines and sub-disciplines and tries to create a new unified structure for digital information spread and control

This book offers a holistic framework to study behavior and evolutionary dynamics in large-scale, decentralized, and heterogeneous crowd networks. In the emerging crowd cyber-ecosystems, millions of deeply connected individuals, smart devices, government agencies, and enterprises actively interact with each other and influence each other’s decisions. It is crucial to understand such intelligent entities’ behaviors and to study their strategic interactions in order to provide important guidelines on the design of reliable networks capable of predicting and preventing detrimental events with negative impacts on our society and economy. This book reviews the fundamental methodologies to study user interactions and evolutionary dynamics in crowd networks and discusses recent advances in this emerging interdisciplinary research field. Using information diffusion over social networks as an example, it presents a thorough investigation of the impact of user behavior on the network evolution process and demonstrates how this can help improve network performance. Intended for graduate students and researchers from various disciplines, including but not limited to, data science, networking, signal processing, complex systems, and economics, the book encourages researchers in related research fields to explore the many untouched areas in this domain, and ultimately to design crowd networks with efficient, effective, and reliable services.

The book lies at the interface of mathematics, social media analysis, and data science. Its authors aim to introduce a new dynamic modeling approach to the use of partial differential equations for describing information diffusion over online social networks. The eigenvalues and eigenvectors of the Laplacian matrix for the underlying social network are used to find communities (clusters) of online users. Once these clusters are embedded in a Euclidean space, the mathematical models, which are reaction-diffusion equations, are developed based on intuitive social distances between clusters within the Euclidean space. The models are validated with data from major social media such as Twitter. In addition, mathematical analysis of these models is applied, revealing insights into information flow on social media. Two applications with geocoded Twitter data are included in the book: one describing the social movement in Twitter during the Egyptian revolution in 2011 and another predicting influenza prevalence. The new approach advocates a paradigm shift for modeling information diffusion in online social networks and lays the theoretical groundwork for many spatio-temporal modeling problems in the big-data era.

The Routledge Companion to Digital Journalism Studies offers an unprecedented collection of essays addressing the key issues and debates shaping the field of Digital Journalism Studies today. Across the last decade, journalism has undergone many changes, which have driven scholars to reassess its most fundamental questions, and in the face of digital change, to ask again: ‘Who is a journalist?’ and ‘What is journalism?’. This companion explores a developing scholarly agenda committed to understanding digital journalism and brings together the work of key scholars seeking to address key theoretical concerns and solve unique methodological riddles. Compiled of 58 original essays from distinguished academics across the globe, this Companion draws together the work of those making sense of this fundamental reconceptualization of journalism, and assesses its impacts on journalism’s products, its practices, resources, and its relationship with audiences. It also outlines the challenge presented by studying digital journalism and, more importantly, offers a first set of answers. This collection is the very first of its kind to attempt to distinguish this emerging field as a unique area of academic inquiry. Through identifying its core questions and presenting its fundamental debates, this Companion sets the agenda for years to come in defining this new field of study as Digital Journalism Studies, making it an essential point of reference for students and scholars of journalism.

The mathematical sciences are part of nearly all aspects of everyday life-the discipline has underpinned such beneficial modern capabilities as Internet search, medical imaging, computer animation, numerical weather predictions, and all types of digital communications. The Mathematical Sciences in 2025 examines the current state of the mathematical sciences and explores the changes needed for the discipline to be in a strong position and able to maximize its contribution to the nation in 2025. It finds the vitality of the discipline excellent and that it contributes in expanding ways to most areas of science and engineering, as well as to the nation as a whole, and recommends that training for future generations of mathematical scientists should be re-assessed in light of the increasingly cross-disciplinary nature of the mathematical sciences. In addition, because of the valuable interplay between ideas and people from all parts of the mathematical sciences, the report emphasizes that universities and the government need to continue to invest in the full spectrum of the mathematical sciences in order for the whole enterprise to continue to flourish long-term.

The 4th edition of the Handbook of Research on Educational Communications and Technology expands upon the previous 3 versions, providing a comprehensive update on research pertaining to new and emerging educational technologies. Chapters that are no longer pertinent have been eliminated in this edition, with most chapters being completely rewritten, expanded, and updated Additionally, new chapters pertaining to research methodologies in educational technology have been added due to expressed reader interest. Each chapter now contains an extensive literature review, documenting and explaining the most recent, outstanding research, including major findings and methodologies employed. The Handbook authors continue to be international leaders in their respective fields; the list is cross disciplinary by design and great effort was taken to invite authors outside of the traditional instructional design and technology community.