Quantum computing is radically different from the conventional approach of transforming bits strings from one set of 0's and 1's to another. With quantum computing, everything changes. The physics that we use to understand bits of information and the devices that manipulate them are totally different. The way in which we build such devices is different, requiring new materials, new design rules and new processor architectures. Finally, the way we program these systems is entirely different. Quantum engineering is a revolutionary approach to quantum technology. It encompasses both fundamental physics and the broad engineering skill-set necessary to meet the practical challenges of the future. The proposed book will cover the high-quality reviewed book chapters on original research & innovations and compelling insights in Quantum Computing and Engineering. Data scientists, Engineers, Industry, researchers and students working in the field of quantum computing and its allied research will benefit greatly from this publication.

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. Applications of Artificial Intelligence, 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 reference work is ideal for computer scientists, industry professionals, researchers, scholars, academicians, librarians, policymakers, practitioners, educators, and students.

Because of the increased access to high-speed Internet and smart phones, many patients have started to use mobile applications to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (IoT). The Handbook of Research on Big Data Management and the Internet of Things for Improved Health Systems is a critical scholarly resource that examines the digital transformation of healthcare. Featuring coverage on a broad range of topics, such as brain computer interface, data reduction techniques, and risk factors, this book is geared towards academicians, practitioners, researchers, and students seeking research on health and well-being data.

The Internet of Things has revolutionized many industries and sectors by connecting devices to the Internet with the use of smart sensors and actuators, resulting in many advantages to businesses and organizations, such as better information and resource sharing, better supply chain efficiency, among other benefits, resulting in better overall efficiency and cost savings. Internet of Things: Technological Advances and New Applications investigates the potential for initiating data-enabled and IoT-intensive applications to provide control and optimization of industrial operations and services. It presents an informative selection of quantitative research, case studies, conceptual chapters, model articles, theoretical backing, and more on many important technological advances, applications, and challenges in the current status of Internet of Things (IIoT). The book features examples of IoT applications in such areas as food processing, automotive engineering, mental health, health tracking, security, and more. It discusses applying IoT in reverse logistics processes, developments in the Internet of Vehicles, the use of smart antennas, and machine learning in IoT. One chapter discusses a ground-breaking new device that uses IoT to convert audio recordings to Braille. Also discussed is the growing use of IoT in biometric technology (the use of technology to identify a person based on some aspect of their biology, such as fingerprint and eye unique pattern recognition). The enlightening information shared here offers state-of-the-art IoT solutions to many of today’s challenges of improving efficiency and bringing important information to the surface more quickly than systems depending on human intervention. The volume will be of value for computer science engineers and researchers, instructors and students in the field, and professionals that are interested in exploring the areas of next-generations IoT.

This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics. Topics Covered: The many academic areas covered in this publication include, but are not limited to: Data visualization Distributed Computing Systems Opinion Mining Privacy and security Risk analysis Social Network Analysis Text Data Analytics Web Data Analytics

Network infrastructures are growing rapidly to meet the needs of business, but the required repolicing and reconfiguration provide challenges that need to be addressed. The software-defined network (SDN) is the future generation of Internet technology that can help meet these challenges of network management. This book includes quantitative research, case studies, conceptual papers, model papers, review papers, and theoretical backing on SDN. This book investigates areas where SDN can help other emerging technologies deliver more efficient services, such as IoT, industrial IoT, NFV, big data, blockchain, cloud computing, and edge computing. The book demonstrates the many benefits of SDNs, such as reduced costs, ease of deployment and management, better scalability, availability, flexibility and fine-grained control of traffic, and security. The book demonstrates the many benefits of SDN, such as reduced costs, ease of deployment and management, better scalability, availability, flexibility and fine-grained control of traffic, and security. Chapters in the volume address: Design considerations for security issues and detection methods State-of-the-art approaches for mitigating DDos attacks using SDN Big data using Apache Hadoop for processing and analyzing large amounts of data Different tools used for attack simulation Network policies and policy management approaches that are widely used in the context of SDN Dynamic flow tables, or static flow table management A new four-tiered architecture that includes cloud, SDN-controller, and fog computing Architecture for keeping computing resources available near the industrial IoT network through edge computing The impact of SDN as an innovative approach for smart city development More. The book will be a valuable resource for SDN researchers as well as academicians, research scholars, and students in the related areas.

Network infrastructures are growing rapidly to meet the needs of business, but the required repolicing and reconfiguration provide challenges that need to be addressed. The software-defined network (SDN) is the future generation of Internet technology that can help meet these challenges of network management. This book includes quantitative research, case studies, conceptual papers, model papers, review papers, and theoretical backing on SDN. This book investigates areas where SDN can help other emerging technologies deliver more efficient services, such as IoT, industrial IoT, NFV, big data, blockchain, cloud computing, and edge computing. The book demonstrates the many benefits of SDNs, such as reduced costs, ease of deployment and management, better scalability, availability, flexibility and fine-grained control of traffic, and security. The book demonstrates the many benefits of SDN, such as reduced costs, ease of deployment and management, better scalability, availability, flexibility and fine-grained control of traffic, and security. Chapters in the volume address: Design considerations for security issues and detection methods State-of-the-art approaches for mitigating DDos attacks using SDN Big data using Apache Hadoop for processing and analyzing large amounts of data Different tools used for attack simulation Network policies and policy management approaches that are widely used in the context of SDN Dynamic flow tables, or static flow table management A new four-tiered architecture that includes cloud, SDN-controller, and fog computing Architecture for keeping computing resources available near the industrial IoT network through edge computing The impact of SDN as an innovative approach for smart city development More. The book will be a valuable resource for SDN researchers as well as academicians, research scholars, and students in the related areas.

This volume provides informative chapters on the emerging issues, challenges, and new methods and state-of-the-art technologies on the Internet of Things and blockchain technology. It presents case studies and solutions that can be applied in the current business scenario, resolving challenges and providing solutions by integrating IoT with blockchain technology. The chapters discuss how the Internet of Things (IoT) represents a revolution of the Internet that can connect nearly all environment devices over the Internet to share data to create novel services and applications for improving quality of life. Although the centralized IoT system provides countless benefits, it raises several challenges. The volume presents IoT techniques and methodologies, blockchain techniques and methodologies, and case studies and applications for data mining algorithms, heart rate monitoring, climate prediction, disease prediction, security issues, automotive supply chains, voting prediction, forecasting particulate matter pollution, customer relationship management, and more.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

This book includes selected papers from the International Conference on Next Generation of Internet of Things (ICNGIoT 2021), organized by the Department of Computer Science and Engineering, School of Engineering, GIET University, Gunupur, Odisha, India, during 5-6 February 2021. The book covers topics such as IoT network design and architecture, IoT network virtualization, IoT sensors, privacy and security for IoT, SMART environment, social networks, data science and data analytics, cognitive intelligence and augmented intelligence, and case studies and applications.

Handbook of IoT and Blockchain: Methods, solutions, and Recent Advancements includes contributions from around the globe on recent advances and findings in the domain of Internet of Things (IoT) and Blockchain. Chapters include theoretical analysis, practical implications, and extensive surveys with analysis on methods, algorithms, and processes for new product development. IoT and Blockchain are the emerging topics in the current manufacturing scenario.This handbook includes recent advances; showcases the work of research around the globe; offers theoretical analysis and practical implications; presents extensive surveys with analysis, new contributions, and proposals on methods, algorithms, and processes; and also covers recent advances from quantitative and qualitative articles, case studies, conceptual works, and theoretical backing. This handbook will be of interest to graduate students, researchers, academicians, institutions, and professionals that are interested in exploring the areas of IoT and Blockchain.

There are a lot of e-business security concerns. Knowing about e-business security issues will likely help overcome them. Keep in mind, companies that have control over their e-business are likely to prosper most. In other words, setting up and maintaining a secure e-business is essential and important to business growth. This book covers state-of-the art practices in e-business security, including privacy, trust, security of transactions, big data, cloud computing, social network, and distributed systems.

There are a lot of e-business security concerns. Knowing about e-business security issues will likely help overcome them. Keep in mind, companies that have control over their e-business are likely to prosper most. In other words, setting up and maintaining a secure e-business is essential and important to business growth. This book covers state-of-the art practices in e-business security, including privacy, trust, security of transactions, big data, cloud computing, social network, and distributed systems.

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.

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.