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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.
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.
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.
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