As multimedia data advances in technology and becomes more complex, the hybridization of soft computing tools allows for more robust and safe solutions in data processing and analysis. Quantum-Inspired Intelligent Systems for Multimedia Data Analysis provides emerging research on techniques used in multimedia information processing using intelligent paradigms including swarm intelligence, neural networks, and deep learning. While highlighting topics such as clustering techniques, neural network architecture, and text data processing, this publication explores the methods and applications of computational intelligent tools. This book is an important resource for academics, computer engineers, IT professionals, students, and researchers seeking current research in the field of multimedia data processing and quantum intelligent systems.

The rapid growth and capability of artificial intelligence, digital twin, and the internet of things are unlocking incredible opportunities to overcome some of the greatest environmental and social impact challenges currently facing the global community, such as feeding a growing population, safety, affordable housing, and environmental sustainability. The Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development provides an interdisciplinary platform encompassing research on the potential opportunities and risks of reaching sustainable development using artificial intelligence, digital twin, and the internet of things. Covering key topics such as big data, environmental protection, and smart cities, this major reference work is ideal for computer scientists, industry professionals, researchers, scholars, academicians, librarians, policymakers, practitioners, educators, and students.

As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.

A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.Related Link(s)

In traditional educational settings, teachers often need help engaging students, adapting to diverse learning styles, and keeping pace with technological advancements, which can lead to disengagement, limited learning outcomes, and a lack of preparation for the demands of the modern world. Navigating Innovative Technologies and Intelligent Systems in Modern Education offers a transformative solution to these challenges. By exploring innovative strategies such as flipped learning, gamification, and personalized instruction, this book equips educators with the tools to create dynamic, student-centered learning environments. It emphasizes the importance of leveraging digital tools and resources to enhance teaching, assessment, and feedback, ensuring educators stay at the forefront of modern education. By implementing the strategies outlined in this book, educators can create inclusive, interactive classrooms that inspire, motivate, and empower students to reach their full potential.

The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial intelligence, machine learning, robotics, and other specialties, which inhibits the potential for the expansion of this technology and its many benefits. A resource that encourages interdisciplinary collaboration is needed to bridge the gap between these respected leaders of their own fields. Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems represents an exploration of the forefront of artificial intelligence, navigating the complexities of this field and its many applications. This guide expertly navigates through the intricate domains of deep learning and reinforcement learning, offering an in-depth journey through foundational principles, advanced methodologies, and cutting-edge algorithms shaping the trajectory of intelligent systems. The book covers an introduction to artificial intelligence and its subfields, foundational aspects of deep learning, a demystification of the architecture of neural networks, the mechanics of backpropagation, and the intricacies of critical elements such as activation and loss functions. The book serves as a valuable educational resource for professionals. Its structured approach makes it an ideal reference for students, researchers, and industry professionals.

The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems. The text covers advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics and their applications to engineering problems. The book will be accompanied by additional resources including video demonstration for each chapter. It will be a useful text for graduate students and professional in the field of electrical engineering, electronics and communications engineering, and computer science engineering, this text: Discusses quantum mechanical principles in detail. Emphasizes the recent and upcoming hybrid quantum metaheuristics in a comprehensive manner. Provides comparative statistical test analysis with conventional hybrid metaheuristics. Highlights real-life case studies, applications, and video demonstrations.

Quantum technology has arrived as one of the most important new topics of research, as it is the newest way to create computing power, harness secure communications, and use sensitive measurement methods that surpass the capabilities of modern supercomputers. If successfully developed, quantum computers and technology will be able to perform algorithms at impressively quick rates and solve problems that were previously deemed impossible. This technology will disrupt what is already known about computing and will be able to reach new heights, speeds, and problem-solving capabilities not yet seen. Beyond its inherent benefits comes the fact that quantum technology will create improvements in many everyday gadgets as well, spanning many industries. The Research Anthology on Advancements in Quantum Technology presents the latest discoveries in quantum technology itself along with providing its essential uses, applications, and technologies that will impact computing in modern times and far into the future. Along with this overview comes a look at quantum technology in many different fields such as healthcare, communications, aviation, automotive, forecasting, and more. These industries will be looked at from the perspective of data analytics, pattern matching, cryptography, algorithms, and more. This book is essential for computer scientists, engineers, professionals, researchers, students, and practitioners interested in the latest information on quantum technology.

In order to survive an increasingly competitive market, corporations must adopt and employ optimization techniques and big data analytics for more efficient product development and value creation. Understanding the strengths, weaknesses, opportunities, and threats of new techniques and manufacturing processes allows companies to succeed during the rise of Industry 4.0. Optimizing Big Data Management and Industrial Systems With Intelligent Techniques explores optimization techniques, recommendation systems, and manufacturing processes that support the evaluation of cyber-physical systems, end-to-end engineering, and digitalized control systems. Featuring coverage on a broad range of topics such as digital economy, fuzzy logic, and data linkage methods, this book is ideally designed for manufacturers, engineers, professionals, managers, academicians, and students.

With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers.