Описание: In practical health care cases, semi-structured and unstructured decision-making issues involve multiple criteria (or goals) that may conflict with each other. Thus, the use of MCDM is a promising source of practical solutions for such problems.
Описание: A combination of several technological, healthcare and financial factors are driving this trend to create a new healthcare model that stresses preventative `health-care` rather than `sick-care`, and a shift from volume to value.
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.
Описание: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis.
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.
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: 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: Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.
Автор: Olga Kosheleva; Karen Villaverde Название: How Interval and Fuzzy Techniques Can Improve Teaching ISBN: 3662572567 ISBN-13(EAN): 9783662572566 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains how to teach better and presents the latest research on processing educational data and presents traditional statistical techniques as well as probabilistic, interval, and fuzzy approaches. Teaching is a very rewarding activity; it is also a very difficult one – because it is largely an art. There is a lot of advice on teaching available, but it is usually informal and is not easy to follow. To remedy this situation, it is reasonable to use techniques specifically designed to handle such imprecise knowledge: the fuzzy logic techniques. Since there are a large number of statistical studies of different teaching techniques, the authors combined statistical and fuzzy approaches to process the educational data in order to provide insights into improving all the stages of the education process: from forming a curriculum to deciding in which order to present the material to grading the assignments and exams. The authors do not claim to have solved all the problems of education. Instead they show, using numerous examples, that an innovative combination of different uncertainty techniques can improve teaching. The book offers teachers and instructors valuable advice and provides researchers in pedagogical and fuzzy areas with techniques to further advance teaching.
Описание: Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques.
Описание: Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques.
Автор: Chengliang, Yang, Chengliang, Название: Data driven approaches for healthcare high utilizers / ISBN: 0367342901 ISBN-13(EAN): 9780367342906 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges posed by this problem.
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