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.

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.

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.

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.