Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today's digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

The Definitive Guide to Arm (R) Cortex (R)-M23 and Cortex-M33 Processors focuses on the Armv8-M architecture and the features that are available in the Cortex-M23 and Cortex- M33 processors. This book covers a range of topics, including the instruction set, the programmer's model, interrupt handling, OS support, and debug features. It demonstrates how to create software for the Cortex-M23 and Cortex-M33 processors by way of a range of examples, which will enable embedded software developers to understand the Armv8-M architecture. This book also covers the TrustZone (R) technology in detail, including how it benefits security in IoT applications, its operations, how the technology affects the processor's hardware (e.g., memory architecture, interrupt handling, etc.), and various other considerations in creating secure software.

Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods.

With the far-reaching global impact of the COVID-19 pandemic, the demand and the necessity for digital enterprise transformation have accelerated exponentially. Management and strategies for the adoption and wider usage of newer digital technologies for the transformation of an enterprise through digital tools such as real-time video communications have shown that people no longer need to be required to be physically present in the same place; rather, they can be geographically dispersed. Technologies such as artificial intelligence, cloud computing, digital banking, and cloud data have taken over tasks that were initially done by human hands and have increased both the automation and efficiency of tasks and the accessibility of information and services. Inclusion of all these newer technologies has shown the fast pace at which the digital enterprise transformation is rapidly evolving and how new ecosystems are reshaping the digital enterprise model. Disruptive Technology and Digital Transformation for Business and Government presents interesting research on digital enterprise transformation at different stages and across different settings within government and industry, along with key issues and deeper insights on the core problems and developing solutions and recommendations for digital enterprise transformation. The chapters examine the three core leaders of transformation: the people such as managers, employees, and customers; the digital technology such as artificial intelligence and robotics; and the digital enterprise, including the products and services being transformed. They unravel the underlying process for management and strategies to fully incorporate new digital tools and technologies across all aspects of an enterprise undergoing transformation. This book is ideally intended for managers, executives, IT consultants, business professionals, government officials, researchers, students, practitioners, stakeholders, academicians, and anyone else looking to learn about new developments in digital enterprise transformation of business systems from a global perspective.

This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.

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.

This book serves as a guide to help the reader develop an awareness of security vulnerabilities and attacks, and encourages them to be circumspect when using the various computer resources and tools available today. For experienced users, Computer Science Security presents a wide range of tools to secure legacy software and hardware. Computing has infiltrated all fields nowadays. No one can escape this wave and be immune to security attacks, which continue to evolve, gradually reducing the level of expertise needed by hackers. It is high time for each and every user to acquire basic knowledge of computer security, which would enable them to mitigate the threats they may face both personally and professionally. It is this combined expertise of individuals and organizations that will guarantee a minimum level of security for families, schools, the workplace and society in general.

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