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This new volume looks at the electrifying world of blockchain
technology and how it has been revolutionizing the Internet of
Things and cyber-physical systems. Aimed primarily at business
users and developers who are considering blockchain-based projects,
the volume provides a comprehensive introduction to the theoretical
and practical aspects of blockchain technology. It presents a
selection of chapters on topics that cover new information on
blockchain and bitcoin security, IoT security threats and attacks,
privacy issues, fault-tolerance mechanisms, and more. Some major
software packages are discussed, and it also addresses the legal
issues currently affecting the field. The information presented
here is relevant to current and future problems relating to
blockchain technology and will provide the tools to build efficient
decentralized applications. Blockchain technology and the IoT can
profoundly change how the world-and businesses-work, and this book
provides a window into the current world of blockchain. No longer
limited to just Bitcoin, blockchain technology has spread into many
sectors and into a significant number of different technologies.
This new volume looks at the electrifying world of blockchain
technology and how it has been revolutionizing the Internet of
Things and cyber-physical systems. Aimed primarily at business
users and developers who are considering blockchain-based projects,
the volume provides a comprehensive introduction to the theoretical
and practical aspects of blockchain technology. It presents a
selection of chapters on topics that cover new information on
blockchain and bitcoin security, IoT security threats and attacks,
privacy issues, fault-tolerance mechanisms, and more. Some major
software packages are discussed, and it also addresses the legal
issues currently affecting the field. The information presented
here is relevant to current and future problems relating to
blockchain technology and will provide the tools to build efficient
decentralized applications. Blockchain technology and the IoT can
profoundly change how the world-and businesses-work, and this book
provides a window into the current world of blockchain. No longer
limited to just Bitcoin, blockchain technology has spread into many
sectors and into a significant number of different technologies.
Machine Learning for Healthcare: Handling and Managing Data
provides in-depth information about handling and managing
healthcare data through machine learning methods. This book
expresses the long-standing challenges in healthcare informatics
and provides rational explanations of how to deal with them.
Machine Learning for Healthcare: Handling and Managing Data
provides techniques on how to apply machine learning within your
organization and evaluate the efficacy, suitability, and efficiency
of machine learning applications. These are illustrated in a case
study which examines how chronic disease is being redefined through
patient-led data learning and the Internet of Things. This text
offers a guided tour of machine learning algorithms, architecture
design, and applications of learning in healthcare. Readers will
discover the ethical implications of machine learning in healthcare
and the future of machine learning in population and patient health
optimization. This book can also help assist in the creation of a
machine learning model, performance evaluation, and the
operationalization of its outcomes within organizations. It may
appeal to computer science/information technology professionals and
researchers working in the area of machine learning, and is
especially applicable to the healthcare sector. The features of
this book include: A unique and complete focus on applications of
machine learning in the healthcare sector. An examination of how
data analysis can be done using healthcare data and bioinformatics.
An investigation of how healthcare companies can leverage the
tapestry of big data to discover new business values. An
exploration of the concepts of machine learning, along with recent
research developments in healthcare sectors.
Artificial Intelligence and Industry 4.0 explores recent
advancements in blockchain technology and artificial intelligence
(AI) as well as their crucial impacts on realizing Industry 4.0
goals. The book explores AI applications in industry including
Internet of Things (IoT) and Industrial Internet of Things (IIoT)
technology. Chapters explore how AI (machine learning, smart
cities, healthcare, Society 5.0, etc.) have numerous potential
applications in the Industry 4.0 era. This book is a useful
resource for researchers and graduate students in computer science
researching and developing AI and the IIoT.
Deep Learning in Personalized Healthcare and Decision Support
discusses the potential of deep learning technologies in the
healthcare sector. The book covers the application of deep learning
tools and techniques in diverse areas of healthcare, such as
medical image classification, telemedicine, clinical decision
support system, clinical trials, electronic health records,
precision medication, Parkinson disease detection, genomics, and
drug discovery. In addition, it discusses the use of DL for fraud
detection and internet of things. This is a valuable resource for
researchers, graduate students and healthcare professionals who are
interested in learning more about deep learning applied to the
healthcare sector. Although there is an increasing interest by
clinicians and healthcare workers, they still lack enough knowledge
to efficiently choose and make use of technologies currently
available. This book fills that knowledge gap by bringing together
experts from technology and clinical fields to cover the topics in
depth.
Researchers, academicians and professionals expone in this book
their research in the application of intelligent computing
techniques to software engineering. As software systems are
becoming larger and complex, software engineering tasks become
increasingly costly and prone to errors. Evolutionary algorithms,
machine learning approaches, meta-heuristic algorithms, and others
techniques can help the effi ciency of software engineering.
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.
The book examines the role of artificial intelligence during the
COVID-19 pandemic, including its application in i) early warnings
and alerts, ii) tracking and prediction, iii) data dashboards, iv)
diagnosis and prognosis, v) treatments, and cures, and vi) social
control. It explores the use of artificial intelligence in the
context of population screening and assessing infection risks, and
presents mathematical models for epidemic prediction of COVID-19.
Furthermore, the book discusses artificial intelligence-mediated
diagnosis, and how machine learning can help in the development of
drugs to treat the disease. Lastly, it analyzes various artificial
intelligence-based models to improve the critical care of COVID-19
patients.
Agriculture is one of the most fundamental human activities. As the
farming capacity has expanded, the usage of resources such as land,
fertilizer, and water has grown exponentially, and environmental
pressures from modern farming techniques have stressed natural
landscapes. Still, by some estimates, worldwide food production
needs to increase to keep up with global food demand. Machine
Learning and the Internet of Things can play a promising role in
the Agricultural industry, and help to increase food production
while respecting the environment. This book explains how these
technologies can be applied, offering many case studies developed
in the research world.
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.
This book explores potentially disruptive and transformative
healthcare-specific use cases made possible by the latest
developments in Internet of Things (IoT) technology and
Cyber-Physical Systems (CPS). Healthcare data can be subjected to a
range of different investigations in order to extract highly useful
and usable intelligence for the automation of traditionally manual
tasks. In addition, next-generation healthcare applications can be
enhanced by integrating the latest knowledge discovery and
dissemination tools. These sophisticated, smart healthcare
applications are possible thanks to a growing ecosystem of
healthcare sensors and actuators, new ad hoc and
application-specific sensor and actuator networks, and advances in
data capture, processing, storage, and mining. Such applications
also take advantage of state-of-the-art machine and deep learning
algorithms, major strides in artificial and ambient intelligence,
and rapid improvements in the stability and maturity of mobile,
social, and edge computing models.
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.
This book explores potentially disruptive and transformative
healthcare-specific use cases made possible by the latest
developments in Internet of Things (IoT) technology and
Cyber-Physical Systems (CPS). Healthcare data can be subjected to a
range of different investigations in order to extract highly useful
and usable intelligence for the automation of traditionally manual
tasks. In addition, next-generation healthcare applications can be
enhanced by integrating the latest knowledge discovery and
dissemination tools. These sophisticated, smart healthcare
applications are possible thanks to a growing ecosystem of
healthcare sensors and actuators, new ad hoc and
application-specific sensor and actuator networks, and advances in
data capture, processing, storage, and mining. Such applications
also take advantage of state-of-the-art machine and deep learning
algorithms, major strides in artificial and ambient intelligence,
and rapid improvements in the stability and maturity of mobile,
social, and edge computing models.
Feature selection as an area of interest within pattern
recognition, deals with selection of a subset of attributes used in
construction of a model describing observations. The purpose of
this stage includes reducing data dimensionality by removing
irrelevant and redundant features, reducing the amount of learning
data, improving predictive accuracy and comprehensibility of a
classification hypothesis. This book introduces Feature Usability
Index (FUI) as a measure for evaluating classification efficacy of
features and its application in feature selection. Experimental
applications presents optimal feature subset selection through
ordering based on FUI, use of FUI for ranking and selection of
feature extraction techniques for a specific linguistic feature,
and Color Usability Index as a measure for predicting performance
of image segmentation algorithms.
This book comprehensively reviews the potential of Artificial
Intelligence (AI) in biomedical research and healthcare, with a
major emphasis on virology. The initial chapter presents the
applications of machine learning methods for structured data, such
as the classical support vector machine and neural network, modern
deep learning, and natural language processing for unstructured
data in biomedical research and healthcare. The subsequent chapters
explore the applications of AI in tackling COVID-19, analysis of
the pandemic, viral infection, disease spread, and control. The
book further identifies the potential applications of machine
learning in the field of virology with a focus on the key aspects
of infection: diagnosis, transmission, response to treatment, and
resistance. The book also discusses progress and challenges in
developing viral vaccines and examines the application of viruses
in translational research and human healthcare. Furthermore, the
book covers the applications of artificial intelligence-mediated
diagnosis and the development of drugs to treat the disease.
Towards the end, the book summarizes the ethical and legal
challenges posed by AI in healthcare and biomedical research. This
book is an invaluable source for researchers, medical and industry
practitioners, academicians, and students exploring the
applications of AI in biomedical research and healthcare.
This book offers a holistic approach to the Internet of Things
(IoT) model, covering both the technologies and their applications,
focusing on uniquely identifiable objects and their virtual
representations in an Internet-like structure. The authors add to
the rapid growth in research on IoT communications and networks,
confirming the scalability and broad reach of the core concepts.
The book is filled with examples of innovative applications and
real-world case studies. The authors also address the business,
social, and legal aspects of the Internet of Things and explore the
critical topics of security and privacy and their challenges for
both individuals and organizations. The contributions are from
international experts in academia, industry, and research.
The book examines the role of artificial intelligence during the
COVID-19 pandemic, including its application in i) early warnings
and alerts, ii) tracking and prediction, iii) data dashboards, iv)
diagnosis and prognosis, v) treatments, and cures, and vi) social
control. It explores the use of artificial intelligence in the
context of population screening and assessing infection risks, and
presents mathematical models for epidemic prediction of COVID-19.
Furthermore, the book discusses artificial intelligence-mediated
diagnosis, and how machine learning can help in the development of
drugs to treat the disease. Lastly, it analyzes various artificial
intelligence-based models to improve the critical care of COVID-19
patients.
This book offers a holistic approach to the Internet of Things
(IoT) model, covering both the technologies and their applications,
focusing on uniquely identifiable objects and their virtual
representations in an Internet-like structure. The authors add to
the rapid growth in research on IoT communications and networks,
confirming the scalability and broad reach of the core concepts.
The book is filled with examples of innovative applications and
real-world case studies. The authors also address the business,
social, and legal aspects of the Internet of Things and explore the
critical topics of security and privacy and their challenges for
both individuals and organizations. The contributions are from
international experts in academia, industry, and research.
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.
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