There is no doubt that there has been much excitement regarding the pioneering contributions of artificial intelligence (AI), the internet of things (IoT), and blockchain technologies and tools in visualizing and realizing smarter as well as sophisticated systems and services. However, researchers are being bombarded with various machine and deep learning algorithms, which are categorized as a part and parcel of the enigmatic AI discipline. The knowledge discovered gets disseminated to actuators and other concerned systems in order to empower them to intelligently plan and insightfully execute appropriate tasks with clarity and confidence. The IoT processes in conjunction with the AI algorithms and blockchain technology are bound to lay out a stimulating foundation for producing and sustaining smarter systems for society. Advancing Smarter and More Secure Industrial Applications Using AI, IoT, and Blockchain Technology articulates and accentuates various AI algorithms, fresh innovations in the IoT, and blockchain spaces. The domain of transforming raw data to information and to relevant knowledge is gaining prominence with the availability of data ingestion, processing, mining, analytics algorithms, platforms, frameworks, and other accelerators. Covering topics such as blockchain applications, Industry 4.0, and cryptography, this book serves as a comprehensive guide for AI researchers, faculty members, IT professionals, academicians, students, researchers, and industry professionals.

Many industries are affected by the growing advancements and stability of the internet of things (IoT) technologies and tools. These include the agricultural fields. With such advancements, decision-enabling agricultural field data gets gathered and transmitted meticulously through numerous IoT sensors and devices deployed in agricultural fields and their surroundings. Further study on these technologies is required to ensure they are utilized appropriately within the field. Applying Drone Technologies and Robotics for Agricultural Sustainability conveys the latest trends and transitions happening in the digital space in order to fulfill the varying needs and sentiments of the agriculture domain. Covering key topics such as deep learning, robots, sustainability, and smart farming, this premier reference source is ideal for industry professionals, farmers, computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.

Blockchain is the most disruptive technology to emerge in the last decade. The evolution of cryptocurrencies has carried with it a revolution in digital economics that has catapulted the application of blockchain technology to a new level across a variety of industries, including banking, security, networking, and more. Blockchain Technology and Computational Excellence for Society 5.0 closes the gap in existing literature by presenting a selection of chapters that not only shape the research domain, but also present supportive real-life problems and pragmatic solutions. This book presents a variety of highly relevant themes, concepts, and applications in blockchain, discussing topics such as cyber security, digital currencies, and intelligent networks, fueling awareness and interest. With its insight into various platforms, techniques, and tools, this book serves as a valuable resource for academicians, researchers, research scholars, postgraduates, professors, computer scientists, and technology enthusiasts.

In the rapidly evolving digital era, businesses undergo profound transformations requiring strategic adaptation and optimization. Digital Business and Optimizing Operating Strategies delves into the intricate interplay between digital technology and business strategies, exploring various facets such as management, marketing, finance, human resources, and more within the digital market structure. This comprehensive book critically examines enterprises' skills, productivity, and adaptability in response to technological advancements and customer expectations. Covering a wide range of sub-topics, this book addresses the management of social media applications, the technological capabilities of human resources, digital sustainability, and the integration of artificial intelligence technology. By highlighting opportunities and challenges, the book serves as a valuable resource for researchers and practitioners across diverse disciplines, offering novel perspectives and cutting-edge practices in the digital business landscape. This book is designed to resonate with a diverse audience, and will appeal to researchers, marketing managers, metaverse enthusiasts, website designers and developers, e-commerce practitioners, social media users, consumer behavior and marketing researchers, and brand experts. It is an essential resource for both undergraduate and graduate students while also serving as a valuable reference for professionals in the field.

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

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.

Optimized linear algebra (LA) libraries that are able to exploit the underlying hardware are always of interest in the high-performance computing community. The implementation of LA software has evolved along with computer architecture, while the specification remains unaltered almost from the beginning. It is important to differentiate between the specification of LA libraries and their implementation. Because LA libraries pursue high performance, the implementation for a given architecture needs to be optimized for it specifically. However, the type of operations included in the libraries, the input/output parameters, and the data types to be handled are common to all of them. This is why, while the specification remains constant, the implementation evolves with the creation of new architectures. Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities presents the main characteristics of LA libraries, showing the differences between the standards for sparse and dense versions. It further explores relevant linear algebra problems and shows, in a clear and understandable way, how to solve them using different computer architectures. Covering topics such as programming models, batched computing, and distributed memory platforms, this premier reference source is an excellent resource for programmers, computer scientists, engineers, students and faculty of higher education, librarians, researchers, and academicians.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Quantum computing is radically different from the conventional approach of transforming bit-strings from one set of zeros and ones to another. With quantum computing, everything changes. The physics used to understand bits of information and the devices that manipulate them are vastly different. Quantum engineering is a revolutionary approach to quantum technology. Technology Road Mapping for Quantum Computing and Engineering explores all the aspects of quantum computing concepts, engineering, technologies, operations, and applications from the basics to future advancements. Covering topics such as machine learning, quantum software technology, and technology road mapping, this book is an excellent resource for data scientists, engineers, students and professors of higher education, computer scientists, researchers, and academicians.

Data science has been playing a vital role in almost all major fields. Many researchers are interested in the development of IT applications, which are user-driven with a focus on issues. This can be addressed using data science. User-driven research and data science have gained much attention from many private, public, and government organizations and research institutions. Designing User Interfaces With a Data Science Approach promotes the inclusion of more diversified users for user-centered designs of applications across domains and analyzes user data with a data science approach for effective and user-friendly user interface designs. It introduces the foundations of advanced topics of human-computer interaction, particularly with user-centered designs and techniques. Covering topics such as artificial neural networks, natural dialog systems, and machine learning, this book is an essential resource for faculty, research scholars, industry professionals, students of higher education, mathematicians, data scientists, interaction designers, visual designers, software engineers, user experience researchers, accessibility engineers, cognitive system engineers, academicians, and libraries.