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This new book discusses the concepts while also highlighting the challenges in the field of quantum cryptography and also covering cryptographic techniques and cyber security techniques, in a single volume. It comprehensively covers important topics in the field of quantum cryptography with applications, including quantum key distribution, position-based quantum cryptography, quantum teleportation, quantum e-commerce, quantum cloning, cyber security techniques’ architectures and design, cyber security techniques management, software-defined networks, and cyber security techniques for 5G communication. The text also discusses the security of practical quantum key distribution systems, applications and algorithms developed for quantum cryptography, as well as cyber security through quantum computing and quantum cryptography. The text will be beneficial for graduate students, academic researchers, and professionals working in the fields of electrical engineering, electronics and communications engineering, computer science, and information technology.
The recent advancements in the field of the internet of things (IoT), AI, big data, blockchain, augmented reality (AR)/virtual reality (VR), cloud platforms, quantum computing, cybersecurity, and telecommunication technology enabled the promotion of conventional computer-aided industry to the metaverse ecosystem that is powered by AR/VR-driven technologies. In this paradigm shift, the integrated technologies of IoT and AI play a vital role to connect the cyberspace of computing systems and virtual environments. AR/VR supports a huge range of industrial applications such as logistics, the food industry, and manufacturing utilities. AI-Based Technologies and Applications in the Era of the Metaverse discusses essential components of the metaverse ecosystem such as concepts, methodologies, technologies, modeling, designs, statistics, implementation, and maintenance. Covering key topics such as machine learning, deep learning, quantum computing, and blockchain, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.
As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.
Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments. The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency. A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.
Advancements in computational intelligence, which encompasses artificial intelligence, machine learning, and data analytics, have revolutionized the way we process and analyze biomedical and health data. These techniques offer novel approaches to understanding complex biological systems, improving disease diagnosis, optimizing treatment plans, and enhancing patient outcomes. Computational Intelligence and Blockchain in Biomedical and Health Informatics introduces the role of computational intelligence and blockchain in the biomedical and health informatics fields and provides a framework and summary of the various methods. The book emphasizes the role of advanced computational techniques and offers demonstrative examples throughout. Techniques to analyze the impacts on the biomedical and health Informatics domains are discussed along with major challenges in deployment. Rounding out the book are highlights of the transformative potential of computational intelligence and blockchain in addressing critical issues in healthcare from disease diagnosis and personalized medicine to health data management and interoperability along with two case studies. This book is highly beneficial to educators, researchers, and anyone involved with health data. Features: • Introduces the role of computational intelligence and blockchain in the biomedical and health informatics fields. • Provides a framework and a summary of various computational intelligence and blockchain methods. • Emphasizes the role of advanced computational techniques and offers demonstrative examples throughout. • Techniques to analyze the impact on biomedical and health informatics are discussed along with major challenges in deployment. • Highlights the transformative potential of computational intelligence and blockchain in addressing critical issues in healthcare from disease diagnosis and personalized medicine to health data management and interoperability.
Blockchain and distributed-ledger technologies enable new modes of communication, synchronization, and transfer of value with a broad impact on Internet of Things (IoT), Data Science, society, industry, commerce, and government. This book studies the potential impact of blockchain and distributed-ledger technologies on IoT. It highlights the application of possible solutions in the domain of blockchain and IoT system security, including cryptology, distributed systems, law, formal methods, code verification and validation, software, and systems metrics. As the field is growing fast, the book adapts to the changing research landscape, integrates and cross-links studies and citations in related subfields, and provides an overview of these fields and how they complement each other. • Highlights how the security aspect of the integration of blockchain with IoT will help to design secure data solutions for various domains • Offers fundamental knowledge of the blockchain concept and its usage in real-life applications • Presents current and future trends on the IoT and blockchain with an efficient, scalable, and sustainable approach • Reviews future developments in blockchain and IoT in future job opportunities • Discusses how blockchain for IoT systems can help a varied range of end-users to access computational and storage resources This book is intended for postgraduate students and researchers in the departments of computer science, working in the areas of IoT, blockchain, deep learning, machine learning, image processing, and big data.
Computational Intelligence in Urban Infrastructure consolidates experiences and research results in computational intelligence and its applications in urban infrastructure. It discusses various techniques and application areas of smart urban infrastructure including topics related to smart city management. Major topics covered include smart home automation, intelligent lighting, smart human care services, intelligent transportation systems, ontologies in urban development domain, and intelligent monitoring, control, and security of critical infrastructure systems supported by case studies. Features: Covers application of AI and computational intelligence techniques in urban infrastructure planning Discusses characteristics and features of smart urban management Explores relationship between smart home and smart city management Deliberates various smart home techniques Includes different case studies for supporting and analyzing various aspects of smart urban infrastructure management This book is aimed at researchers, graduate students, libraries in communication networks, urban and town planning, and civil engineering.
Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments. AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances. This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.
Cloud computing, the Internet of Things (IoT), and big data are three significant technological trends affecting the world's largest corporations. This book discusses big data, cloud computing, and the IoT, with a focus on the benefits and implementation problems. In addition, it examines the many structures and applications pertinent to these disciplines. Also, big data, cloud computing, and the IoT are proposed as possible study avenues. Features: Informs about cloud computing, IoT and big data, including theoretical foundations and the most recent empirical findings Provides essential research on the relationship between various technologies and the aggregate influence they have on solving real-world problems Ideal for academicians, developers, researchers, computer scientists, practitioners, information technology professionals, students, scholars, and engineers exploring research on the incorporation of technological innovations to address contemporary societal challenges