As social robots and the artificial intelligence (AI) that powers them become more advanced, they will likely take on more social and work roles. There is a variety of ways social robots can be engaged in human life, and they can leave an impact in terms of ease of use, productivity, and human support. The interactivity and receptivity of social robots can encourage humans to form social relationships with them. But now robots are intended to perform socially intelligent and interactive services like reception, guidance, emotional companionship, and more, which makes social human-robot interaction essential to help improve aspects of quality of life as well as to improve the efficiency of human care services. AI-Enabled Social Robotics in Human Care Services addresses recent advances in the latest technologies, new research results, and developments in the area of social robotics and AI and the latest developments in the field and future directions that can be beneficial to human society and human care services. Covering topics such as agriculture waste management systems, elder care, and facial emotion recognition, this premier reference source is an essential resource for AI professionals, computer scientists, robotics engineers, human care professionals, students and educators of higher education, librarians, researchers, and academicians.

As the spectrum of the internet of things (IoT) expands, artificial intelligence (AI)-assisted agile IoT is the way forward for sustainable finance. The depth of agile IoT has changed the financial market, and it may quickly evolve as a powerful tool in the future. The convergence of AI and IoT techniques will significantly extract valuable financial information and offer better services to customers. Some of the potential benefits of AI-assisted agile IoT for FinTech include prompt customer support, in-door client navigation, on-site queue management, improved customer experience, security and authenticity, wireless payments, increased business efficiency, self-checkout services, and business automation. There is no doubt that leveraging the complete potential of AI-assisted agile IoT will result in the creation of a new and innovative financial system. AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems presents the advances in AI-assisted agile IoT for financial technologies (FinTech). It further explains the new applications, current issues, challenges, and future directions of the field of AI-assisted agile IoT for FinTech applications and ecosystems. Covering topics such as consensus algorithms, IoT-based banking, and secure authentication, this premier reference source is an excellent resource for business executives and managers, IT managers, librarians, students and faculty of higher education, researchers, and academicians.

Autism spectrum disorder (ASD) is known as a neuro-disorder in which a person may face problems in interaction and communication with people, amongst other challenges. As per medical experts, ASD can be diagnosed at any stage or age but is often noticeable within the first two years of life. If caught early enough, therapies and services can be provided at this early stage instead of waiting until it is too late. ASD occurrences appear to have increased over the last couple of years leading to the need for more research in the field. It is crucial to provide researchers and clinicians with the most up-to-date information on the clinical features, etiopathogenesis, and therapeutic strategies for patients as well as to shed light on the other psychiatric conditions often associated with ASD. In addition, it is equally important to understand how to detect ASD in individuals for accurate diagnosing and early detection. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder discusses the early detection and diagnosis of autism spectrum disorder enabled by artificial intelligence technologies, applications, and therapies. This book will focus on the early diagnosis of ASD through artificial intelligence, such as deep learning and machine learning algorithms, for confirming diagnosis or suggesting the need for further evaluation of individuals. The chapters will also discuss the use of artificial intelligence technologies, such as medical robots, for enhancing the communication skills and the social and emotional skills of children who have been diagnosed with ASD. This book is ideally intended for IT specialists, data scientists, academicians, scholars, researchers, policymakers, medical practitioners, and students interested in how artificial intelligence is impacting the diagnosis and treatment of autism spectrum disorder.

Sudden Cardiac Death (SCD) is a sudden, unexpected death caused by loss of heart function (sudden cardiac arrest) and Sudden Cardiac Arrest (SCA) occurs when the electrical system to the heart malfunctions and suddenly becomes very irregular. Death can often be a result if not handled quick enough or effectively. New technologies seek to help with this issue. Data processing is a crucial step to developing prognostic models. Some of the challenges in data processing are non-linear prediction models, a large number of patients and numerous predictors with complicated correlations. In traditional hypothesis-driven statistical analysis it is difficult to overcome these challenges. Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. So, there is an emergent need of an adaptation of AI technologies such as Machine Learning and Deep Learning Techniques to overcome the challenges. The Machine Learning (ML) approaches have great potential in increasing the accuracy of cardiovascular risk prediction and to avoid unnecessary treatment. The application of ML techniques may have the potential to improve Heart Failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Moreover, ML algorithms can also be applied to predict SCD. Also, Machine Learning offers an opportunity to improve accuracy by exploiting complex interactions between risk factors. The book addresses the impact and power of technology driven approaches for prevention and detection of SCA and SCD. It will provide insights on causes and symptoms of SCA and SCD and evaluate whether AI Technologies can improve the accuracy of cardiovascular risk prediction. It will explore the current issues and future technology driven solutions for SCA and SCD prevention and detection.

In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning's contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments. Demystifying Federated Learning for Blockchain and Industrial Internet of Things rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. It provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication. Covering topics such as smart agriculture, object identification, and educational big data, this premier reference source is an essential resource for computer scientists, programmers, government officials, business leaders and managers, students and faculty of higher education, researchers, and academicians.

This book presents the latest cutting edge research, theoretical methods, and novel applications in the field of computational intelligence and computational biological approaches that are aiming to combat COVID-19. The book gives the technological key drivers behind using AI to find drugs that target the virus, shedding light on the structure of COVID-19, detecting the outbreak and spread of new diseases, spotting signs of a COVID-19 infection in medical images, monitoring how the virus and lockdown is affecting mental health, and forecasting how COVID-19 cases and deaths will spread across cities and why. Further, the book helps readers understand computational intelligence techniques combating COVID-19 in a simple and systematic way.

Strategic analytics is a relatively new field in conjunction with strategic management and business intelligence. Generally, the strategic management field deals with the enhancement of the decision-making capabilities of managers. Typically, such decision-making processes are heavily dependent upon various internal and external reports. Managers need to develop their strategies using clear strategy processes supported by the increasing availability of data. This situation calls for a different approach to strategy, including integration with analytics, as the science of extracting value from data and structuring complex problems. Using Strategy Analytics to Measure Corporate Performance and Business Value Creation discusses how to tackle complex business dynamics using optimization techniques and modern business analytics tools. It covers not only introductory concepts of strategic analytics but also provides strategic analytics applications in each area of management such as market dynamics, customer analysis, operations, and people management. It unveils the best industry practices and how managers can become expert strategists and analysts to better measure and enhance corporate performance and their businesses. This book is ideal for analysts, executives, managers, entrepreneurs, researchers, students, industry professionals, stakeholders, practitioners, academicians, and others interested in the strategic analytics domain and how it can be applied to complex business dynamics.

The success of healthcare decision-making lies in whether healthcare staff, patients, and healthcare organization managers can comprehensively understand the choices and consider future implications to make the best decision possible. Multiple-criteria decision making (MCDM), including multiple rule-based decision making (MRDM), multiple-objective decision making (MODM), and multiple-attribute decision making (MADM), is used by clinical decision-makers to analyze healthcare issues from various perspectives. In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems. MCDM methods mainly include the three parts: data process, evaluation and selection, and planning and design. Data process focuses on analyzing and identifying healthcare management issues and data features for solving practical cases. Evaluation and selection focus on evaluating the performance of each solution for healthcare management, and these methods can be used to support decision-making and help organizations choose the best solution for practical healthcare management cases. Finally, planning and design focus on analyzing and designing the goals of healthcare management applications, which can be modelled as a minimizing or maximizing problem for finding the optimal solutions. Furthermore, these methods can explore the relationship structure construction among criteria between various related issues arising from healthcare.

Green Information and Communication Systems for a Sustainable Future covers the fundamental concepts, applications, algorithms, protocols, new trends, challenges, and research results in the area of Green Information and Communication Systems. This book provides the reader with up-to-date information on core and specialized issues, making it highly suitable for both the novice and the experienced researcher in the field. The book covers theoretical and practical perspectives on network design. It includes how green ICT initiatives and applications can play a major role in reducing CO2 emissions, and focuses on industry and how it can promote awareness and implementation of Green ICT. The book discusses scholarship and research in green and sustainable IT for business and organizations and uses the power of IT to usher sustainability into other parts of an organization. Business and management educators, management researchers, doctoral scholars, university teaching personnel and policy makers as well as members of higher academic research organizations will all discover this book to be an indispensable guide to Green Information and Communication Systems. It will also serve as a key resource for Industrial and Management training organizations all over the world.

This book presents the latest cutting edge research, theoretical methods, and novel applications in the field of computational intelligence and computational biological approaches that are aiming to combat COVID-19. The book gives the technological key drivers behind using AI to find drugs that target the virus, shedding light on the structure of COVID-19, detecting the outbreak and spread of new diseases, spotting signs of a COVID-19 infection in medical images, monitoring how the virus and lockdown is affecting mental health, and forecasting how COVID-19 cases and deaths will spread across cities and why. Further, the book helps readers understand computational intelligence techniques combating COVID-19 in a simple and systematic way.

Knowledge Engineering (KE) is a fi eld within artifi cial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specifi c domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent fi ndings and innovative research in the information system and KE domain. Highlighting the challenges and diffi culties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will fi nd this book a valuable source of ideas and guidance.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.

Knowledge Management makes the management of information and resources within a commercial organization more effective. The contributions of this book investigate the applications of Knowledge Management in the upcoming era of Semantic Web, or Web 3.0, and the opportunities for reshaping and redesigning business strategies for more effective outcomes.

As the spectrum of the internet of things (IoT) expands, artificial intelligence (AI)-assisted agile IoT is the way forward for sustainable finance. The depth of agile IoT has changed the financial market, and it may quickly evolve as a powerful tool in the future. The convergence of AI and IoT techniques will significantly extract valuable financial information and offer better services to customers. Some of the potential benefits of AI-assisted agile IoT for FinTech include prompt customer support, in-door client navigation, on-site queue management, improved customer experience, security and authenticity, wireless payments, increased business efficiency, self-checkout services, and business automation. There is no doubt that leveraging the complete potential of AI-assisted agile IoT will result in the creation of a new and innovative financial system. AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems presents the advances in AI-assisted agile IoT for financial technologies (FinTech). It further explains the new applications, current issues, challenges, and future directions of the field of AI-assisted agile IoT for FinTech applications and ecosystems. Covering topics such as consensus algorithms, IoT-based banking, and secure authentication, this premier reference source is an excellent resource for business executives and managers, IT managers, librarians, students and faculty of higher education, researchers, and academicians.

In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning's contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments. Demystifying Federated Learning for Blockchain and Industrial Internet of Things rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. It provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication. Covering topics such as smart agriculture, object identification, and educational big data, this premier reference source is an essential resource for computer scientists, programmers, government officials, business leaders and managers, students and faculty of higher education, researchers, and academicians.

Strategic analytics is a relatively new field in conjunction with strategic management and business intelligence. Generally, the strategic management field deals with the enhancement of the decision-making capabilities of managers. Typically, such decision-making processes are heavily dependent upon various internal and external reports. Managers need to develop their strategies using clear strategy processes supported by the increasing availability of data. This situation calls for a different approach to strategy, including integration with analytics, as the science of extracting value from data and structuring complex problems. Using Strategy Analytics to Measure Corporate Performance and Business Value Creation discusses how to tackle complex business dynamics using optimization techniques and modern business analytics tools. It covers not only introductory concepts of strategic analytics but also provides strategic analytics applications in each area of management such as market dynamics, customer analysis, operations, and people management. It unveils the best industry practices and how managers can become expert strategists and analysts to better measure and enhance corporate performance and their businesses. This book is ideal for analysts, executives, managers, entrepreneurs, researchers, students, industry professionals, stakeholders, practitioners, academicians, and others interested in the strategic analytics domain and how it can be applied to complex business dynamics.

Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.

Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.