Quantum Inspired Intelligent Systems For Multimedia Data Analysis

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.

Research on applying principles of quantum computing to improve the engineering of intelligent systems has been launched since late 1990s. This emergent research field concentrates on studying on quantum computing that is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and concept of interference, combined with computational intelligence or soft computing approaches, such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence and hybrid soft computing methods. This volume offers a wide spectrum of research work developed using soft computing combined with quantum computing 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.

This volume comprises eight well-versed contributed chapters devoted to report the latest findings on the intelligent approaches to multimedia data analysis. Multimedia data is a combination of different discrete and continuous content forms like text, audio, images, videos, animations and interactional data. At least a single continuous media in the transmitted information generates multimedia information. Due to these different types of varieties, multimedia data present varied degrees of uncertainties and imprecision, which cannot be easy to deal by the conventional computing paradigm. Soft computing technologies are quite efficient to handle the imprecision and uncertainty of the multimedia data and they are flexible enough to process the real-world information. Proper analysis of multimedia data finds wide applications in medical diagnosis, video surveillance, text annotation etc. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent state of the art.

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.

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.

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.