As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students.

The optimization of traffic management operations has become a considerable challenge in today's global scope due to the significant increase in the number of vehicles, traffic congestions, and automobile accidents. Fortunately, there has been substantial progress in the application of intelligent computing devices to transportation processes. Vehicular ad-hoc networks (VANETs) are a specific practice that merges the connectivity of wireless technologies with smart vehicles. Despite its relevance, empirical research is lacking on the developments being made in VANETs and how certain intelligent technologies are being applied within transportation systems. IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks provides emerging research exploring the theoretical and practical aspects of intelligent transportation systems and analyzing the modern techniques that are being applied to smart vehicles through cloud technology. Featuring coverage on a broad range of topics such as health monitoring, node localization, and fault tolerance, this book is ideally designed for network designers, developers, analysists, IT specialists, computing professionals, researchers, academics, and post-graduate students seeking current research on emerging computing concepts and developments in vehicular ad-hoc networks.

With near-universal internet access and ever-advancing electronic devices, the ability to facilitate interactions between various hardware and software provides endless possibilities. Though internet of things (IoT) technology is becoming more popular among individual users and companies, more potential applications of this technology are being sought every day. There is a need for studies and reviews that discuss the methodologies, concepts, and possible problems of a technology that requires little or no human interaction between systems. The Handbook of Research on the Internet of Things Applications in Robotics and Automation is a pivotal reference source on the methods and uses of advancing IoT technology. While highlighting topics including traffic information systems, home security, and automatic parking, this book is ideally designed for network analysts, telecommunication system designers, engineers, academicians, technology specialists, practitioners, researchers, students, and software developers seeking current research on the trends and functions of this life-changing technology.

Sustainable development helps undo the havoc that has been created by human beings in the last few years in the name of development and growth. It helps to promote a more social, environmental, and economical way of living. There are many ways in which we all can practice sustainable development in our daily lives and further study is required. Multidisciplinary Approaches to Sustainable Human Development focuses on all agendas of sustainable development goals and offers approaches to develop a transdisciplinary perspective that encompasses the natural, social, and human sciences in the search for a sustainable society. Covering topics such as green economy, social innovation, and climate change, this premier reference work is ideal for environmentalists, government officials, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

The process of globalization based on major forms of entertainment consumption has promoted the interest of enlarged social actors toward cultural experiencing. Disseminated by social media, new forms of information and knowledge about exotic tourism destinations have endorsed an increasing interest in forms of cultural tourism. This cultural tourism turnout results from a significant change in the traveler's demands and behaviors and has led to a new and renovated interest in cultural heritage that must be studied further. The Handbook of Research on Cultural Tourism and Sustainability explores theoretical concepts related to cultural tourism and cultural routes and provides original viewpoints and empirical research with case studies and best practices for the future of cultural tourism. Covering a range of topics such as creative tourism and sustainable tourism, this major reference work is ideal for academicians, practitioners, professionals, policymakers, government officials, instructors, and students.

This new volume explores a plethora of blockchain-based solutions for big data and IoT applications, looking at advances in real-world applications in several sectors, including higher education, cybersecurity, agriculture, business and management, healthcare and biomedical science, construction and project management, smart city development, and others. Chapters explore emerging technology to combat the ever-increasing threat of security to computer systems and offer new architectural solutions for problems encountered in data management and security. The chapters help to provide a high level of understanding of various blockchain algorithms along with the necessary tools and techniques. The novel architectural solutions in the deployment of blockchain presented here are the core of the book.

This edited book presents an insight for modelling, procuring, and building the smart city plan using IoT and a security framework using blockchain technology. The applications of Li-Fi and 5G in smart cities are included along with their implementation, challenges, and advantages. This book focusses on use of IoT and blockchain in day-to-day transparent and recorded activities of citizens of smart city like smart citizen management. The future for upgrading the system as per technological advancements is also discussed. Integrates IoT, blockchain, Li-Fi and 5G in smart city implementation Covers smart supply chain management using IoT Outlines the state-of-the-art and sustainable implementation of smart cities and practical challenges Includes sustainable development of smart cities Presents detailed explanation of case studies of smart cities of developed countries and developing countries and their comparisons This book is aimed at researchers and graduate students in Artificial Intelligence, Urban Planning, and Information Technology Systems and Management.

This text provides novel smart network systems, wireless telecommunications infrastructures, and computing capabilities to help healthcare systems using computing techniques like IoT, cloud computing, machine and deep learning Big Data along with smart wireless networks. It discusses important topics, including robotics manipulation and analysis in smart healthcare industries, smart telemedicine framework using machine learning and deep learning, role of UAV and drones in smart hospitals, virtual reality based on 5G/6G and augmented reality in healthcare systems, data privacy and security, nanomedicine, and cloud-based artificial intelligence in healthcare systems. The book: * Discusses intelligent computing through IoT and Big Data in secure and smart healthcare systems. * Covers algorithms, including deterministic algorithms, randomized algorithms, iterative algorithms, and recursive algorithms. * Discusses remote sensing devices in hospitals and local health facilities for patient evaluation and care. * Covers wearable technology applications such as weight control and physical activity tracking for disease prevention and smart healthcare. This book will be useful for senior undergraduate, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and information technology. Discussing concepts of smart networks, advanced wireless communication, and technologies in setting up smart healthcare services, this text will be useful for senior undergraduate, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and information technology. It covers internet of things (IoT) implementation and challenges in healthcare industries, wireless network, and communication-based optimization algorithms for smart healthcare devices.

Integration of IoT with Cloud Computing for Smart Applications provides an integrative overview of the Internet of Things (IoT) and cloud computing to be used for the various futuristic and intelligent applications. The aim of this book is to integrate IoT and cloud computing to translate ordinary resources into smart things. Discussions in this book include a broad and integrated perspective on the collaboration, security, growth of cloud infrastructure, and real-time data monitoring. Features: Presents an integrated approach to solve the problems related to security, reliability, and energy consumption. Explains a unique approach to discuss the research challenges and opportunities in the field of IoT and cloud computing. Discusses a novel approach for smart agriculture, smart healthcare systems, smart cities and many other modern systems based on machine learning, artificial intelligence, and big data, etc. Information presented in a simplified way for students, researchers, academicians and scientists, business innovators and entrepreneurs, management professionals and practitioners. This book can be great reference for graduate and postgraduate students, researchers, and academicians working in the field of computer science, cloud computing, artificial intelligence, etc.

The book acts as a guide; taking the reader into the smart system domain and providing theoretical and practical knowledge along with case studies in smart healthcare. The book uses a blend of interdisciplinary approaches such as IoT, blockchain, augmented reality, and virtual reality for the implementation of cost-effective, real-time, and user-friendly solutions for healthcare problems. Immersive Virtual and Augmented Reality in Healthcare: An IoT and Blockchain Perspective presents the trends, best practices, techniques, developments, sensors, materials, and case studies that are using augmented and virtual reality environments with the state-of-the-art latest technologies like IoT, blockchain, and machine learning in the implementation of healthcare systems. The book focuses on the design and implementation of smart healthcare systems with major challenges to further explore more robust and efficient healthcare solutions in terms of low cost, faster algorithms, more sensitive IoT sensors, faster data communication, and real-time solutions for treatment. It discusses the use of virtual and augmented reality and how it can provide user-friendly and interactive communication within healthcare systems. Illustrated through case studies, the book conveys how different hospitals and healthcare equipment providers can adopt good practices found in the book to improve the performance/productivity of their staff and system. The content is rounded out by providing how IoT, blockchain, and artificial intelligence can provide the framework for designing and/or upgrading traditional healthcare systems by increasing security and data privacy. A valuable resource for engineers working with systems, the healthcare professionals involved in the design and development of healthcare devices and systems, researcher scholars, multidisciplinary scientists, students, and academics who are wishing to explore the use of virtual and augmented reality in new and existing healthcare systems.

This powerful new volume explores the diverse and sometimes unexpected roles that IoT and AI technologies played during the recent COVID-19 global pandemic. The book discusses the how existing and new state-of-the art technology has been and can be applied for global health crises in a multitude of ways. The chapters in Pandemic Detection and Analysis through Smart Computing Technologies look at exciting technological solutions for virus detection, prediction, classification, prevention, and communication outreach. The book considers the various modes of transmission of the virus as well as how technology has been implemented for personalized healthcare systems and how it can be used for future pandemics. The huge importance of social and mobile communication and networks during the pandemic is addressed such as in business, education, and healthcare; in research and development; for health information and outreach; in social life; and more. A chapter also addresses using smart computing for forecasting the damage caused by COVID-19 using time series analyses. This up-to-the-minute volume illuminates on the many ways AI, IoT, machine learning, and other technologies have important roles in the diverse challenges faced during COVID-19 and how they can be enhanced for future pandemic situations. The volume will be of high interest to those in different fields of computer science and other domains as well as to data scientists, government agencies and policymakers, doctors and healthcare professionals, engineers, economists, and many other professionals. This book will also be very helpful to faculty, students, and research scholars in understanding the pre- and post-effect of this pandemic.

This new volume, Cognitive Computing Systems: Applications and Technological Advancements, explores the emerging area of artificial intelligence that encompasses machine self-learning, human-computer interaction, natural language processing, data mining and more. It introduces cognitive computing systems, highlights their key applications, discusses the technologies used in cognitive systems, and explains underlying models and architectures. Focusing on scientific work for real-world applications, each chapter presents the use of cognitive computing and machine learning in specific application areas. These include the use of speech recognition technology, application of neural networks in construction management, elevating competency in education, comprehensive health monitoring systems, predicting type 2 diabetes, applications for smart agricultural technology, human resource management, and more. With chapters from knowledgeable researchers in the area of artificial intelligence, cognitive computing, and allied areas, this book will be an asset for researchers, faculty, advances students, and industry professionals in many fields.

Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.

Covers evolutionary approaches to solve optimization problems in biomedical engineering. Discusses IoT, Cloud computing, data analytics in healthcare informatics. Provides computational intelligence-based solution for diagnosis of diseases. Reviews modelling and simulations in designing of biomedical equipment. Promotes machine learning based approaches to improvements in biomedical engineering problems.

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies

This new volume, Cognitive Computing Systems: Applications and Technological Advancements, explores the emerging area of artificial intelligence that encompasses machine self-learning, human-computer interaction, natural language processing, data mining and more. It introduces cognitive computing systems, highlights their key applications, discusses the technologies used in cognitive systems, and explains underlying models and architectures. Focusing on scientific work for real-world applications, each chapter presents the use of cognitive computing and machine learning in specific application areas. These include the use of speech recognition technology, application of neural networks in construction management, elevating competency in education, comprehensive health monitoring systems, predicting type 2 diabetes, applications for smart agricultural technology, human resource management, and more. With chapters from knowledgeable researchers in the area of artificial intelligence, cognitive computing, and allied areas, this book will be an asset for researchers, faculty, advances students, and industry professionals in many fields.

Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

IoT-enabled healthcare technologies can be used for remote health monitoring, rehabilitation assessment and assisted ambient living. Healthcare analytics can be applied to the data gathered from these different areas to improve healthcare outcomes by providing clinicians with real-world, real-time data so they can more easily support and advise their patients. The book explores the application of AI systems to analyse patient data and guide interventions. IoT-based monitoring systems and their security challenges are also discussed. The book is designed to be a reference for healthcare informatics researchers, developers, practitioners, and people who are interested in the personalised healthcare sector. The book will be a valuable reference tool for those who identify and develop methodologies, frameworks, tools, and applications for working with medical big data and researchers in computer engineering, healthcare electronics, device design and related fields.

This new volume explores the computational intelligence techniques necessary to carry out different software engineering tasks. Software undergoes various stages before deployment, such as requirements elicitation, software designing, software project planning, software coding, and software testing and maintenance. Every stage is bundled with a number of tasks or activities to be performed. Due to the large and complex nature of software, these tasks can become costly and error prone. This volume aims to help meet these challenges by presenting new research and practical applications in intelligent techniques in the field of software engineering. Computational Intelligence Applications for Software Engineering Problems discusses techniques and presents case studies to solve engineering challenges using machine learning, deep learning, fuzzy-logic-based computation, statistical modeling, invasive weed meta-heuristic algorithms, artificial intelligence, the DevOps model, time series forecasting models, and more.

* Focuses on the computational intelligence techniques of security system design * Covers applications and algorithms of discussed computational intelligence techniques * Includes convergence-based and enterprise integrated security systems with their applications * Explains emerging laws, policies, and tools affecting the landscape of cyber security * Discusses application of sensors towards the design of security systems

Covers applications of Internet of Things (IoT) in Vehicular ad-hoc network (VANETs). Discusses use of machine learning and other computing techniques for enhancing performance of networks. Covers game theory-based vertical handoffs in Heterogeneous Wireless Networks. Examines monitoring and surveillance of vehicles through the vehicular sensor network. Discusses theoretical approaches on software-defined vehicular Ad-hoc network.

THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.