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This book is a detailed reference on biomedical applications using
Deep Learning. Because Deep Learning is an important actor shaping
the future of Artificial Intelligence, its specific and innovative
solutions for both medical and biomedical are very critical. This
book provides a recent view of research works on essential, and
advanced topics. The book offers detailed information on the
application of Deep Learning for solving biomedical problems. It
focuses on different types of data (i.e. raw data, signal-time
series, medical images) to enable readers to understand the
effectiveness and the potential. It includes topics such as disease
diagnosis, image processing perspectives, and even genomics. It
takes the reader through different sides of Deep Learning oriented
solutions. The specific and innovative solutions covered in this
book for both medical and biomedical applications are critical to
scientists, researchers, practitioners, professionals, and
educations who are working in the context of the topics.
This book explores various applications of deep learning-oriented
diagnosis leading to decision support, while also outlining the
future face of medical decision support systems. Artificial
intelligence has now become a ubiquitous aspect of modern life, and
especially machine learning enjoysgreat popularity, since it offers
techniques that are capable of learning from samples to solve newly
encountered cases. Today, a recent form of machine learning, deep
learning, is being widely used with large, complex quantities of
data, because today's problems require detailed analyses of more
data. This is critical, especially in fields such as medicine.
Accordingly, the objective of this book is to provide the
essentials of and highlight recent applications of deep learning
architectures for medical decision support systems. The target
audience includes scientists, experts, MSc and PhD students,
postdocs, and any readers interested in the subjectsdiscussed. The
book canbe used as a reference work to support courses on
artificial intelligence, machine/deep learning, medical and
biomedicaleducation.
This book explores various applications of deep learning-oriented
diagnosis leading to decision support, while also outlining the
future face of medical decision support systems. Artificial
intelligence has now become a ubiquitous aspect of modern life, and
especially machine learning enjoysgreat popularity, since it offers
techniques that are capable of learning from samples to solve newly
encountered cases. Today, a recent form of machine learning, deep
learning, is being widely used with large, complex quantities of
data, because today's problems require detailed analyses of more
data. This is critical, especially in fields such as medicine.
Accordingly, the objective of this book is to provide the
essentials of and highlight recent applications of deep learning
architectures for medical decision support systems. The target
audience includes scientists, experts, MSc and PhD students,
postdocs, and any readers interested in the subjectsdiscussed. The
book canbe used as a reference work to support courses on
artificial intelligence, machine/deep learning, medical and
biomedicaleducation.
The book covers a wide topic collection starting from essentials of
Computational Intelligence to advance, and possible application
types against COVID-19 as well as its effects on the field of
medical, social, and different data-oriented research scopes. Among
these topics, the book also covers very recently, vital topics in
terms of fighting against COVID-19 and solutions for future
pandemics. The book includes the use of computational intelligence
for especially medical diagnosis and treatment, and also
data-oriented tracking-predictive solutions, which are key
components currently for fighting against COVID-19. In this way,
the book will be a key reference work for understanding how
computational intelligence and the most recent technologies (i.e.
Internet of Healthcare Thing, big data, and data science
techniques) can be employed in solution phases and how they change
the way of future solutions. The book also covers research works
with negative results so that possible disadvantages of using
computational intelligence solutions and/or experienced
side-effects can be known widely for better future of medical
solutions and use of intelligent systems against COVID-19 and
pandemics. The book is considering both theoretical and applied
views to enable readers to be informed about not only research
works but also theoretical views about essentials/components of
intelligent systems against COVID-19/pandemics, possible modeling
scenarios with current and future perspective as well as solution
strategies thought by researchers all over the world.
The book covers a wide topic collection starting from essentials of
Computational Intelligence to advance, and possible application
types against COVID-19 as well as its effects on the field of
medical, social, and different data-oriented research scopes. Among
these topics, the book also covers very recently, vital topics in
terms of fighting against COVID-19 and solutions for future
pandemics. The book includes the use of computational intelligence
for especially medical diagnosis and treatment, and also
data-oriented tracking-predictive solutions, which are key
components currently for fighting against COVID-19. In this way,
the book will be a key reference work for understanding how
computational intelligence and the most recent technologies (i.e.
Internet of Healthcare Thing, big data, and data science
techniques) can be employed in solution phases and how they change
the way of future solutions. The book also covers research works
with negative results so that possible disadvantages of using
computational intelligence solutions and/or experienced
side-effects can be known widely for better future of medical
solutions and use of intelligent systems against COVID-19 and
pandemics. The book is considering both theoretical and applied
views to enable readers to be informed about not only research
works but also theoretical views about essentials/components of
intelligent systems against COVID-19/pandemics, possible modeling
scenarios with current and future perspective as well as solution
strategies thought by researchers all over the world.
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.
Technological tools and computational techniques have enhanced the
healthcare industry. These advancements have led to significant
progress and novel opportunities for biomedical engineering.
Nature-Inspired Intelligent Techniques for Solving Biomedical
Engineering Problems is a pivotal reference source for emerging
scholarly research on trends and techniques in the utilization of
nature-inspired approaches in biomedical engineering. Featuring
extensive coverage on relevant areas such as artificial
intelligence, clinical decision support systems, and swarm
intelligence, this publication is an ideal resource for medical
practitioners, professionals, students, engineers, and researchers
interested in the latest developments in biomedical technologies.
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