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Machine Learning and Knowledge Discovery for Engineering Systems Health Management

Author : Ashok Srivastava
Publisher :
Page : 502 pages
File Size : 24,27 MB
Release : 2016
Category :
ISBN :

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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

Author : Ashok N. Srivastava
Publisher : CRC Press
Page : 505 pages
File Size : 28,5 MB
Release : 2016-04-19
Category : Computers
ISBN : 1000755711

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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

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
Page : 418 pages
File Size : 23,35 MB
Release : 2021-04-13
Category : Computers
ISBN : 1119791812

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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 and Analytics in Healthcare Systems

Author : Himani Bansal
Publisher : CRC Press
Page : 275 pages
File Size : 18,52 MB
Release : 2021-06-30
Category : Computers
ISBN : 1000406199

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This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications.

Machine Learning for Health Informatics

Author : Andreas Holzinger
Publisher : Springer
Page : 503 pages
File Size : 35,90 MB
Release : 2016-12-09
Category : Computers
ISBN : 3319504789

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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

Author : Pradeep N
Publisher : Academic Press
Page : 374 pages
File Size : 45,28 MB
Release : 2021-06-10
Category : Science
ISBN : 0128220449

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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

Smart Healthcare Systems

Author : Adwitiya Sinha
Publisher : CRC Press
Page : 249 pages
File Size : 43,12 MB
Release : 2019-07-24
Category : Computers
ISBN : 0429671776

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About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

AI and Machine Learning Paradigms for Health Monitoring System

Author : Hasmat Malik
Publisher : Springer Nature
Page : 513 pages
File Size : 30,57 MB
Release : 2021-02-14
Category : Technology & Engineering
ISBN : 9813344121

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This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.

Data-Driven Technology for Engineering Systems Health Management

Author : Gang Niu
Publisher : Springer
Page : 364 pages
File Size : 36,39 MB
Release : 2016-07-27
Category : Technology & Engineering
ISBN : 9811020329

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This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems

Author : Parvathaneni Naga Srinivasu
Publisher : Bentham Science Publishers
Page : 225 pages
File Size : 15,98 MB
Release : 2022-10-05
Category : Technology & Engineering
ISBN : 1681089572

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Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through deep learning and soft-computing techniques. Readers are given a fresh perspective on the impact on the outcomes for healthcare professionals who are assisted by advanced computing algorithms. Key Features: - Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context - Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms - Introduces readers to the concept of telemedicine and electronic healthcare systems - Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer's disease - Summarizes key information for learners - Includes references for advanced readers The book serves as an essential reference for academic readers, as well as computer science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.