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In today's modernized world, the field of healthcare has seen
significant practical innovations with the implementation of
computational intelligence approaches and soft computing methods.
These two concepts present various solutions to complex scientific
problems and imperfect data issues. This has made both very popular
in the medical profession. There are still various areas to be
studied and improved by these two schemes as healthcare practices
continue to develop. Computational Intelligence and Soft Computing
Applications in Healthcare Management Science is an essential
reference source that discusses the implementation of soft
computing techniques and computational methods in the various
components of healthcare, telemedicine, and public health.
Featuring research on topics such as analytical modeling, neural
networks, and fuzzy logic, this book is ideally designed for
software engineers, information scientists, medical professionals,
researchers, developers, educators, academicians, and students.
This book presents a number of approaches to Fine-Kinney-based
multi-criteria occupational risk-assessment. For each proposed
approach, it provides case studies demonstrating their
applicability, as well as Python coding, which will enable readers
to implement them into their own risk assessment process. The book
begins by giving a review of Fine-Kinney occupational
risk-assessment methods and their extension by fuzzy sets. It then
progresses in a logical fashion, dedicating a chapter to each
approach, including the fuzzy best and worst method,
interval-valued Pythagorean fuzzy VIKOR and interval type-2 fuzzy
QUALIFLEX. This book will be of interest to professionals and
researchers working in the field of occupational risk management,
as well as postgraduate and undergraduate students studying
applications of fuzzy systems.
Multi-Criteria Decision-Making (MCDM) includes methods and tools
for modeling and solving complex problems. MCDM has become popular
in the production and service sectors to improve the quality of
service, reduce costs, and make people more prosperous. This book
illustrates applications through case studies focused on disaster
management. With a presentation of both Multi-Attribute
Decision-Making (MADM) and Multi-Objective Decision-Making (MODM)
models, this is the first book to merge these methods and tools
with disaster management. This book raises awareness for society
and decision-makers on how to measure readiness and what necessary
preventive measures need to be taken. It offers models and case
studies that can be easily adapted to solve complex problems and
find solutions in other fields. Multi-Criteria Decision Analysis:
Case Studies in Disaster Management will offer new insights to
researchers working in the areas of industrial engineering, systems
engineering, healthcare systems, operations research, mathematics,
business, computer science, and disaster management, and,
hopefully, the book will also stimulate further work in MCDM.
This book presents a number of approaches to Fine-Kinney-based
multi-criteria occupational risk-assessment. For each proposed
approach, it provides case studies demonstrating their
applicability, as well as Python coding, which will enable readers
to implement them into their own risk assessment process. The book
begins by giving a review of Fine-Kinney occupational
risk-assessment methods and their extension by fuzzy sets. It then
progresses in a logical fashion, dedicating a chapter to each
approach, including the fuzzy best and worst method,
interval-valued Pythagorean fuzzy VIKOR and interval type-2 fuzzy
QUALIFLEX. This book will be of interest to professionals and
researchers working in the field of occupational risk management,
as well as postgraduate and undergraduate students studying
applications of fuzzy systems.
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Paperback
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R391
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Discovery Miles 3 620
Not available
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