Remediating Sound studies the phenomena of remixing, mashup and recomposition: forms of reuse and sampling that have come to characterise much of YouTube's audiovisual content. Through collaborative composition, collage and cover songs to reaction videos and political activism , users from diverse backgrounds have embraced the democratised space of YouTube to open up new and innovative forms of sonic creativity and push the boundaries of audiovisual possibilities. Observing the reciprocal flow of influence that runs between various online platforms, 12 chapters position YouTube as a central hub for the exploration of digital sound, music and the moving image. With special focus on aspects of networked creativity that remain overlooked in contemporary scholarship, including library music, memetic media, artificial intelligence, the sonic arts and music fandom, this volume offers interdisciplinary insight into contemporary audiovisual culture.

Communication based on the internet of things (IoT) generates huge amounts of data from sensors over time, which opens a wide range of applications and areas for researchers. The application of analytics, machine learning, and deep learning techniques over such a large volume of data is a very challenging task. Therefore, it is essential to find patterns, retrieve novel insights, and predict future behavior using this large amount of sensory data. Artificial intelligence (AI) has an important role in facilitating analytics and learning in the IoT devices. Applying AI-Based IoT Systems to Simulation-Based Information Retrieval provides relevant frameworks and the latest empirical research findings in the area. It is ideal for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society and trust at the levels of the global economy, networks and organizations, teams and work groups, information systems, and individuals as actors in the networked environments. Covering topics such as blockchain visualization, computer-aided drug discovery, and health monitoring, this premier reference source is an excellent resource for business leaders and executives, IT managers, security professionals, data scientists, students and faculty of higher education, librarians, hospital administrators, researchers, and academicians.

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

This book explores 10 unique facets of Internet health and safety, including physical safety, information security, and the responsible use of technology, offering takeaways from interviews with experts in the field and suggestions for proactively improving users' Internet safety. The Internet has become for many people—especially students and young adults—an essential and intrinsic part of their lives. It makes information available to be shared worldwide, at any time; enables learning about any topic; and allows for instantaneous communication. And it provides endless entertainment as well. But the benefits of online access are accompanied by serious potential risks. This book covers the key elements of Internet health and safety, including physical safety, information security, and the responsible use of technology. It begins with an introductory essay that gives readers the necessary conceptual framework, and then explains specific topics such as cyberbullying, file sharing, online predators, Internet fraud, and obscene and offensive content. The book also answers readers' questions in a "Q & A" section with a subject expert and includes a directory of resources that provides additional information and serves as a gateway to further study.

Modelling and simulation (M&S) based systems engineering (MSBSE) is the extension of MBSE, which enhances the value of MBSE and the ability of digitally evaluating and optimizing the whole system through comprehensive applications of M&S technologies. This book puts together the recent research in MSBSE, and hopefully this will provide the researchers and engineers with reference cases in M&S technologies to support the R&D of complex products and systems.

The Internet is now, but the future is the metaverse. The metaverse is a virtual-reality space in which users can interact with a computer-generated environment and others. The prediction is that the next ten years will be the golden era of the metaverse, and everyone's life, entertainment, social interaction, and work will increasingly take place in the metaverse world. This book outlines six important trends in the era of the metaverse, that will see dramatic changes in technology and the bringing together of digital and physical worlds. People will experience a great migration of their social life and economic activities into the metaverse. Furthermore, the authors argue that, in the metaverse, we can get rid of many of the constraints of the physical world, achieve a better self in the new digital space, and truly maximize our own value as human beings. This book sets out how you can seize the opportunity of the metaverse era.

It is crucial that forensic science meets challenges such as identifying hidden patterns in data, validating results for accuracy, and understanding varying criminal activities in order to be authoritative so as to hold up justice and public safety. Artificial intelligence, with its potential subsets of machine learning and deep learning, has the potential to transform the domain of forensic science by handling diverse data, recognizing patterns, and analyzing, interpreting, and presenting results. Machine Learning and deep learning frameworks, with developed mathematical and computational tools, facilitate the investigators to provide reliable results. Further study on the potential uses of these technologies is required to better understand their benefits. Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks provides an outline of deep learning and machine learning frameworks and methods for use in forensic science to produce accurate and reliable results to aid investigation processes. The book also considers the challenges, developments, advancements, and emerging approaches of deep learning and machine learning. Covering key topics such as biometrics, augmented reality, and fraud investigation, this reference work is crucial for forensic scientists, law enforcement, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

Innovation is the key to maintain competitive advantage. Innovation in products, processes, and business models help companies to provide economic value to their customers. Identifying the innovative ideas, implementing those ideas, and absorbing them in the market requires investing many resources that could incur large costs. Technology encourages companies to foster innovation to remain competitive in the marketplace. Emerging Technologies for Innovation Management in the Software Industry serves as a resource for technology absorption in companies supporting innovation. It highlights the role of technology to assist software companies-especially small start-ups-to innovate their products, processes, and business models. This book provides the necessary guidelines of which tools to use and under what situations. Covering topics such as risk management, prioritization approaches, and digitally-enabled innovation processes, this premier reference source is an ideal resource for entrepreneurs, software developers, software managers, business leaders, engineers, students and faculty of higher education, researchers, and academicians.

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.