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Machine Learning for Healthcare Applications
When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
Spectrums of Amyotrophic Lateral Sclerosis
SPECTRUMS OF AMYOTROPHIC LATERAL SCLEROSIS Discover state-of-the-art research findings on ALS from leading authors and editors in the field In Spectrums of Amyotrophic Lateral Sclerosis: Heterogeneity, Pathogenesis & Therapeutic Directions , distinguished researchers and editors Dr. Christopher A. Shaw and Jessica R. Morrice deliver a practical and powerful perspective on Amyotrophic Lateral Sclerosis (ALS) as a heterogeneous spectrum of disorders. This increasingly accepted point-of-view allows researchers and medical professionals to develop better targeted interventions and more precise therapies.In the book, readers will find chapters on a wide variety of critical issues facing ALS researchers and healthcare practitioners treating ALS sufferers, including animal models of ALS, neuronal support cells known to have a pivotal role in ALS, and current challenges in ALS clinical trials, among others. The authors describe pathologic features common to all cases of ALS and why animal models, though crucial, should be interpreted with caution. Finally, multiple genetic and environmental etiologies of the disease are discussed.Readers will also benefit from the inclusion of:A thorough introduction to ALS as a spectrum disease and the implications for models, therapeutic development and clinical trial designExplorations of the genetic basis of ALS, prospective sALS etiologies, and the involvement of microbiome in ALSDiscussions of ALS-PDC and environmental risk factors, protein aggregation in ALS, defects in RNA metabolism in ALS, and the non-cell autonomous nature of ALS and the involvement of glial cellsExaminations of animal models of ALS and perspectives on previously failed ALS therapeutics and current therapeutic strategiesPerfect for clinical neurologists, healthcare providers and caretakers, clinicians, and researchers studying motor neuron disease, Spectrums of Amyotrophic Lateral Sclerosis: Heterogeneity, Pathogenesis & Therapeutic Directions is also an indispensable resource for the neurodegenerative research community, neurology residents, and graduate-level neuroscience students.
Digital Cities Roadmap
A good management strategy must be expected to mitigate the dangerous consequences of rapid urbanization that modern society, the economy, and the environment may face. Sustainable smart cities include established structures, infrastructures, communities, institutions, and individuals. On the other hand, this book also deals with energy consumption. Such energy consumption leads to a rapid depletion of energy resources, an increased need for building maintenance, an improvised comfortable lifestyle, and an increase in time spent on building construction. A sustainable building mainly explains the renewable sources used for construction, which helps the structure withstand atmospheric changes. Currently, all countries are looking for ecological materials, that is, renewable plant materials such as straw and clay bricks, wood from forests certified for sustainable management, recycled materials, and other non-toxic, reusable and renewable products. For sustainable and durable construction, energy efficiency is an urgent problem, and researchers are currently actively involved in this area. This book will provide an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. The book also highlights smart automation technologies that help save energy, as well as various performance indicators needed to make construction easier. The book aims to create a strong research community, to have a deep understanding and the latest knowledge in the field of energy and comfort, to offer solid ideas in the nearby future for sustainable and resilient buildings. These buildings will help the city grow as a smart city. The smart city has also focused on low energy consumption, renewable energy, and small carbon footprints. To find the optimal use of energy resources, researchers must study optimization methods.