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This book proposes soft computing techniques for segmenting
real-life images in applications such as image processing, image
mining, video surveillance, and intelligent transportation systems.
The book suggests hybrids deriving from three main approaches:
fuzzy systems, primarily used for handling real-life problems that
involve uncertainty; artificial neural networks, usually applied
for machine cognition, learning, and recognition; and evolutionary
computation, mainly used for search, exploration, efficient
exploitation of contextual information, and optimization. The
contributed chapters discuss both the strengths and the weaknesses
of the approaches, and the book will be valuable for researchers
and graduate students in the domains of image processing and
computational intelligence.
This book proposes soft computing techniques for segmenting
real-life images in applications such as image processing, image
mining, video surveillance, and intelligent transportation systems.
The book suggests hybrids deriving from three main approaches:
fuzzy systems, primarily used for handling real-life problems that
involve uncertainty; artificial neural networks, usually applied
for machine cognition, learning, and recognition; and evolutionary
computation, mainly used for search, exploration, efficient
exploitation of contextual information, and optimization. The
contributed chapters discuss both the strengths and the weaknesses
of the approaches, and the book will be valuable for researchers
and graduate students in the domains of image processing and
computational intelligence.
Control of an impartial balance between risks and returns has
become important for investors, and having a combination of
financial instruments within a portfolio is an advantage. Portfolio
management has thus become very important for reaching a resolution
in high-risk investment opportunities and addressing the
risk-reward tradeoff by maximizing returns and minimizing risks
within a given investment period for a variety of assets.
Metaheuristic Approaches to Portfolio Optimization is an essential
reference source that examines the proper selection of financial
instruments in a financial portfolio management scenario in terms
of metaheuristic approaches. It also explores common measures used
for the evaluation of risks/returns of portfolios in real-life
situations. Featuring research on topics such as closed-end funds,
asset allocation, and risk-return paradigm, this book is ideally
designed for investors, financial professionals, money managers,
accountants, students, professionals, and researchers.
Control of an impartial balance between risks and returns has
become important for investors, and having a combination of
financial instruments within a portfolio is an advantage. Portfolio
management has thus become very important for reaching a resolution
in high-risk investment opportunities and addressing the
risk-reward tradeoff by maximizing returns and minimizing risks
within a given investment period for a variety of assets.
Metaheuristic Approaches to Portfolio Optimization is an essential
reference source that examines the proper selection of financial
instruments in a financial portfolio management scenario in terms
of metaheuristic approaches. It also explores common measures used
for the evaluation of risks/returns of portfolios in real-life
situations. Featuring research on topics such as closed-end funds,
asset allocation, and risk-return paradigm, this book is ideally
designed for investors, financial professionals, money managers,
accountants, students, professionals, and researchers.
Multimedia represents information in novel and varied formats. One
of the most prevalent examples of continuous media is video.
Extracting underlying data from these videos can be an arduous
task. From video indexing, surveillance, and mining, complex
computational applications are required to process this data.
Intelligent Analysis of Multimedia Information is a pivotal
reference source for the latest scholarly research on the
implementation of innovative techniques to a broad spectrum of
multimedia applications by presenting emerging methods in
continuous media processing and manipulation. This book offers a
fresh perspective for students and researchers of information
technology, media professionals, and programmers.
Churn prediction, recognition, and mitigation have become essential
topics in various industries. As a means for forecasting and
manageing risk, further research in this field can greatly assist
companies in making informed decisions based on future possible
scenarios. Developing Churn Models Using Data Mining Techniques and
Social Network Analysis provides an in-depth analysis of attrition
modeling relevant to business planning and management. Through its
insightful and detailed explanation of best practices, tools, and
theory surrounding churn prediction and the integration of
analytics tools, this publication is especially relevant to
managers, data specialists, business analysts, academicians, and
upper-level students.
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