Prognostic Models in Healthcare: AI and Statistical Approaches, Saba
Автор: O`Brien Название: Age-Period-Cohort Models, Approache ISBN: 1466551534 ISBN-13(EAN): 9781466551534 Издательство: Taylor&Francis Рейтинг: Цена: 12707.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Develop a Deep Understanding of the Statistical Issues of APC Analysis
Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach.
See How Common Methods Are Related to Each Other
After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects.
Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables
This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.
Автор: Daniel Cremers; Bodo Rosenhahn; Alan L. Yuille; Fr Название: Statistical and Geometrical Approaches to Visual Motion Analysis ISBN: 3642030602 ISBN-13(EAN): 9783642030604 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: International Dagstuhl Seminar Dagstuhl Castle July 1318 2008 Revised Papers. .
Описание: This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.
Автор: Roy Sudipta, Goyal Lalit Mohan, Mittal Mamta Название: Advanced Prognostic Predictive Modelling in Healthcare Data Analytics ISBN: 9811605408 ISBN-13(EAN): 9789811605406 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis.
Описание: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis.
Описание: This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty.
Автор: Chao Hu; Byeng D. Youn; Pingfeng Wang Название: Engineering Design under Uncertainty and Health Prognostics ISBN: 3030064646 ISBN-13(EAN): 9783030064648 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical description of what probabilistic analysis and design is and how it can be implemented. The text is packed with many practical engineering examples (e.g., electric power transmission systems, aircraft power generating systems, and mechanical transmission systems) and exercise problems. It is an up-to-date, fully illustrated reference suitable for both undergraduate and graduate engineering students, researchers, and professional engineers who are interested in exploring the fundamentals, implementation, and applications of probabilistic analysis and design methods.
Автор: Ekwaro-Osire Stephen, Gonзalves Aparecido Carlos, Alemayehu Fisseha M. Название: Probabilistic Prognostics and Health Management of Energy Systems ISBN: 3319857649 ISBN-13(EAN): 9783319857640 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty.
Автор: Mellal, Mohamed Arezki Название: Nature-Inspired Computing Paradigms In Systems ISBN: 012823749X ISBN-13(EAN): 9780128237496 Издательство: Elsevier Science Рейтинг: Цена: 15487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM) covers several areas that include bioinspired techniques and optimization approaches for system dependability.
The book addresses the issue of integration and interaction of the bioinspired techniques in system dependability computing so that intelligent decisions, design, and architectures can be supported. It brings together these emerging areas under the umbrella of bio- and nature-inspired computational intelligence.
The primary audience of this book includes experts and developers who want to deepen their understanding of bioinspired computing in basic theory, algorithms, and applications. The book is also intended to be used as a textbook for masters and doctoral students who want to enhance their knowledge and understanding of the role of bioinspired techniques in system dependability.
Описание: Current book compares the techniques and models used to estimate the RUL of different assets including review of the relevant literature on prognostic techniques and their use in the industrial field. It describes different approaches and prognosis methods for different assets backed up by appropriate case studies.
Описание: 1. Concept on Landslides and Landslide Susceptibility2. Geomorphic, Geo-tectonic and Hydrologic Attributes and Landslide Probability3. Frequency Ratio Model (FRM) and Information Value Model (IVM) in landslide susceptibility Assessment and Prediction4. Logistic Regression Model (LRM) and Landslide Susceptibility: A RS & GIS based approach5. Artificial Neural Network (ANN) Model and Landslide Susceptibility6. Weighted Index Overlay Model (WIOM), Certainty factor approach (CFA) and Analytical Hierarchy Process (AHP) in Landslide Susceptibility Studies7. Knowledge driven Statistical Approach for Landslide Susceptibility Assessment using GIS and Fuzzy Logic8. Comparison between Statistical Models: A Review and EvaluationIndex
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