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This book covers virtually all aspects of image formation in
medical imaging, including systems based on ionizing radiation
(x-rays, gamma rays) and non-ionizing techniques (ultrasound,
optical, thermal, magnetic resonance, and magnetic particle
imaging) alike. In addition, it discusses the development and
application of computer-aided detection and diagnosis (CAD) systems
in medical imaging. Given its coverage, the book provides both a
forum and valuable resource for researchers involved in image
formation, experimental methods, image performance, segmentation,
pattern recognition, feature extraction, classifier design, machine
learning / deep learning, radiomics, CAD workstation design,
human-computer interaction, databases, and performance evaluation.
This book covers virtually all aspects of image formation in
medical imaging, including systems based on ionizing radiation
(x-rays, gamma rays) and non-ionizing techniques (ultrasound,
optical, thermal, magnetic resonance, and magnetic particle
imaging) alike. In addition, it discusses the development and
application of computer-aided detection and diagnosis (CAD) systems
in medical imaging. Given its coverage, the book provides both a
forum and valuable resource for researchers involved in image
formation, experimental methods, image performance, segmentation,
pattern recognition, feature extraction, classifier design, machine
learning / deep learning, radiomics, CAD workstation design,
human-computer interaction, databases, and performance evaluation.
This book explores the significant role of granular computing in
advancing machine learning towards in-depth processing of big data.
It begins by introducing the main characteristics of big data,
i.e., the five Vs-Volume, Velocity, Variety, Veracity and
Variability. The book explores granular computing as a response to
the fact that learning tasks have become increasingly more complex
due to the vast and rapid increase in the size of data, and that
traditional machine learning has proven too shallow to adequately
deal with big data. Some popular types of traditional machine
learning are presented in terms of their key features and
limitations in the context of big data. Further, the book discusses
why granular-computing-based machine learning is called for, and
demonstrates how granular computing concepts can be used in
different ways to advance machine learning for big data processing.
Several case studies involving big data are presented by using
biomedical data and sentiment data, in order to show the advances
in big data processing through the shift from traditional machine
learning to granular-computing-based machine learning. Finally, the
book stresses the theoretical significance, practical importance,
methodological impact and philosophical aspects of
granular-computing-based machine learning, and suggests several
further directions for advancing machine learning to fit the needs
of modern industries. This book is aimed at PhD students,
postdoctoral researchers and academics who are actively involved in
fundamental research on machine learning or applied research on
data mining and knowledge discovery, sentiment analysis, pattern
recognition, image processing, computer vision and big data
analytics. It will also benefit a broader audience of researchers
and practitioners who are actively engaged in the research and
development of intelligent systems.
The ideas introduced in this book explore the relationships among
rule based systems, machine learning and big data. Rule based
systems are seen as a special type of expert systems, which can be
built by using expert knowledge or learning from real data. The
book focuses on the development and evaluation of rule based
systems in terms of accuracy, efficiency and interpretability. In
particular, a unified framework for building rule based systems,
which consists of the operations of rule generation, rule
simplification and rule representation, is presented. Each of these
operations is detailed using specific methods or techniques. In
addition, this book also presents some ensemble learning frameworks
for building ensemble rule based systems.
The ideas introduced in this book explore the relationships among
rule based systems, machine learning and big data. Rule based
systems are seen as a special type of expert systems, which can be
built by using expert knowledge or learning from real data. The
book focuses on the development and evaluation of rule based
systems in terms of accuracy, efficiency and interpretability. In
particular, a unified framework for building rule based systems,
which consists of the operations of rule generation, rule
simplification and rule representation, is presented. Each of these
operations is detailed using specific methods or techniques. In
addition, this book also presents some ensemble learning frameworks
for building ensemble rule based systems.
This book covers virtually all aspects of image formation in
medical imaging, including systems based on ionizing radiation
(x-rays, gamma rays) and non-ionizing techniques (ultrasound,
optical, thermal, magnetic resonance, and magnetic particle
imaging) alike. In addition, it discusses the development and
application of computer-aided detection and diagnosis (CAD) systems
in medical imaging. Also there will be a special track on
computer-aided diagnosis on COVID-19 by CT and X-rays images. Given
its coverage, the book provides both a forum and valuable resource
for researchers involved in image formation, experimental methods,
image performance, segmentation, pattern recognition, feature
extraction, classifier design, machine learning / deep learning,
radiomics, CAD workstation design, human-computer interaction,
databases, and performance evaluation.
This book covers virtually all aspects of image formation in
medical imaging, including systems based on ionizing radiation
(x-rays, gamma rays) and non-ionizing techniques (ultrasound,
optical, thermal, magnetic resonance, and magnetic particle
imaging) alike. In addition, it discusses the development and
application of computer-aided detection and diagnosis (CAD) systems
in medical imaging. Also there will be a special track on
computer-aided diagnosis on COVID-19 by CT and X-rays images. Given
its coverage, the book provides both a forum and valuable resource
for researchers involved in image formation, experimental methods,
image performance, segmentation, pattern recognition, feature
extraction, classifier design, machine learning / deep learning,
radiomics, CAD workstation design, human-computer interaction,
databases, and performance evaluation.
As the open education movement has been blooming for more than a
decade in PK-12 education, digital media for teaching and learning
manifested in OERs (Open Educational Resources) have increased
exponentially in quantity, variety, and more pedagogically sound
designs along with emerging cutting-edge technology. Teachers in
early childhood education integrate digital media on a daily basis
for their classroom instruction and assigned student learning
activities at home or in other digital environments. While
celebrating the boundless immensity and easy accessibility of
digital media, teachers are increasingly facing constant challenges
in identifying high quality OERs for lesson planning and
instruction. There is an imperative need to integrate digital media
in the classroom following the developmentally appropriate
principles for the vast majority of teachers in early childhood
settings. Evaluation & Integration of Digital Media in the
Early Childhood Classroom introduces the strategies and techniques
in logical steps of OERs' searching, curation, evaluation, and
integration. At the instructional level, the book presents an
evaluation-based ETACS model to demonstrate how to use digital
media to engage learners, teach content, assess learning, create
digital projects, and share learning results. Evaluation &
Integration also covers popular topics and related online tools in
digital learning environment management, digital curriculum
management, STEM education, computational thinking, and coding
instruction for young children.
Carl Schmitt and Leo Strauss in the Chinese-Speaking World:
Reorienting the Political examines the reception of Carl Schmitt
and Leo Strauss in China and Taiwan. The legacies of both Schmitt,
the German legal theorist and thinker who joined the Nazi party,
and Strauss, the German-Jewish classicist and political philosopher
who became famous after his emigration to the United States, are
highly controversial. Since the 1990s, however, these thinkers have
had a powerful resonance for Chinese scholars. Today, when Chinese
intellectuals debate the Chinese state, the future role of China in
the world, the liberal international order, and even the meaning of
Confucian civilization, they often employ Schmittian and Straussian
concepts like "the political," "friend-enemy," "state of
exception," "liberal education," and "natural right." The very
possibility of a genuine Chinese political theory is often thought
to be tied to the legacy of these two thinkers. This volume
explores this complex phenomenon with a cross-cultural and
interdisciplinary approach. The twelve essays in this volume are
written from a range of perspectives by philosophers, political
theorists, historians, and legal scholars from China, Germany,
Taiwan, and the United States.
Excellence in Teaching and Learning is a collaborative effort among
education scholars that addresses the theory, practice, and policy
gaps that have plagued classrooms for a long time. Divided into
three parts, it focuses on practical strategies for teaching and
learning in different subject areas and at all levels; provides
research-based models for improving teacher quality; and addresses
diversity within classrooms with regard to the requirements for
achieving excellence. This book will interest teachers, teacher
educators, administrators, and policy makers.
Excellence in Teaching and Learning is a collaborative effort among
education scholars that addresses the theory, practice, and policy
gaps that have plagued classrooms for a long time. Divided into
three parts, it focuses on practical strategies for teaching and
learning in different subject areas and at all levels; provides
research-based models for improving teacher quality; and addresses
diversity within classrooms with regard to the requirements for
achieving excellence. This book will interest teachers, teacher
educators, administrators, and policy makers.
Carl Schmitt and Leo Strauss in the Chinese-Speaking World:
Reorienting the Political examines the reception of Carl Schmitt
and Leo Strauss in China and Taiwan. The legacies of both Schmitt,
the German legal theorist and thinker who joined the Nazi party,
and Strauss, the German-Jewish classicist and political philosopher
who became famous after his emigration to the United States, are
highly controversial. Since the 1990s, however, these thinkers have
had a powerful resonance for Chinese scholars. Today, when Chinese
intellectuals debate the Chinese state, the future role of China in
the world, the liberal international order, and even the meaning of
Confucian civilization, they often employ Schmittian and Straussian
concepts like "the political," "friend-enemy," "state of
exception," "liberal education," and "natural right." The very
possibility of a genuine Chinese political theory is often thought
to be tied to the legacy of these two thinkers. This volume
explores this complex phenomenon with a cross-cultural and
interdisciplinary approach. The twelve essays in this volume are
written from a range of perspectives by philosophers, political
theorists, historians, and legal scholars from China, Germany,
Taiwan, and the United States.
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