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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.
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 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.
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.
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.
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.
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.
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.
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.
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