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.