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Machine Learning and Knowledge Discovery for Engineering Systems Health Management

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Release : 2016
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Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Machine Learning and Knowledge Discovery for Engineering Systems Health Management by : Ashok Srivastava

Download or read book Machine Learning and Knowledge Discovery for Engineering Systems Health Management written by Ashok Srivastava. This book was released on 2016. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.

Machine Learning and Knowledge Discovery for Engineering Systems Health Management

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Author :
Release : 2016-04-19
Genre : Computers
Kind : eBook
Book Rating : 711/5 ( reviews)

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Book Synopsis Machine Learning and Knowledge Discovery for Engineering Systems Health Management by : Ashok N. Srivastava

Download or read book Machine Learning and Knowledge Discovery for Engineering Systems Health Management written by Ashok N. Srivastava. This book was released on 2016-04-19. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.

Machine Learning for Healthcare Applications

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Release : 2021-04-13
Genre : Computers
Kind : eBook
Book Rating : 812/5 ( reviews)

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Book Synopsis Machine Learning for Healthcare Applications by : Sachi Nandan Mohanty

Download or read book Machine Learning for Healthcare Applications written by Sachi Nandan Mohanty. This book was released on 2021-04-13. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Health Informatics

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Release : 2016-12-09
Genre : Computers
Kind : eBook
Book Rating : 789/5 ( reviews)

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Book Synopsis Machine Learning for Health Informatics by : Andreas Holzinger

Download or read book Machine Learning for Health Informatics written by Andreas Holzinger. This book was released on 2016-12-09. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

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Release : 2021-06-10
Genre : Science
Kind : eBook
Book Rating : 449/5 ( reviews)

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Book Synopsis Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics by : Pradeep N

Download or read book Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics written by Pradeep N. This book was released on 2021-06-10. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics Unique case study approach provides readers with insights for practical clinical implementation

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