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This book discusses recent developments in the vast domain of
optimization. Featuring papers presented at the 1st International
Conference on Frontiers in Optimization: Theory and Applications
(FOTA 2016), held at the Heritage Institute of Technology, Kolkata,
on 24-26 December 2016, it opens new avenues of research in all
topics related to optimization, such as linear and nonlinear
optimization; combinatorial-, stochastic-, dynamic-, fuzzy-, and
uncertain optimization; optimal control theory; as well as
multi-objective, evolutionary and convex optimization and their
applications in intelligent information and technology, systems
science, knowledge management, information and communication,
supply chain and inventory control, scheduling, networks,
transportation and logistics and finance. The book is a valuable
resource for researchers, scientists and engineers from both
academia and industry.
There has been rapid growth in biomedical engineering in recent
decades, given advancements in medical imaging and physiological
modelling and sensing systems, coupled with immense growth in
computational and network technology, analytic approaches,
visualization and virtual-reality, man-machine interaction and
automation. Biomedical engineering involves applying engineering
principles to the medical and biological sciences and it comprises
several topics including biomedicine, medical imaging,
physiological modelling and sensing, instrumentation, real-time
systems, automation and control, signal processing, image
reconstruction, processing and analysis, pattern recognition, and
biomechanics. It holds great promise for the diagnosis and
treatment of complex medical conditions, in particular, as we can
now target direct clinical applications, research and development
in biomedical engineering is helping us to develop innovative
implants and prosthetics, create new medical imaging technologies
and improve tools and techniques for the detection, prevention and
treatment of diseases. The contributing authors in this edited book
present representative surveys of advances in their respective
fields, focusing in particular on techniques for the analysis of
complex biomedical data. The book will be a useful reference for
graduate students, researchers and industrial practitioners in
computer science, biomedical engineering, and computational and
molecular biology.
Proper analysis of image and multimedia data requires efficient
extraction and segmentation techniques. Among the many
computational intelligence approaches, the soft computing paradigm
is best equipped with several tools and techniques that incorporate
intelligent concepts and principles. This book is dedicated to
object extraction, image segmentation, and edge detection using
soft computing techniques with extensive real-life application to
image and multimedia data. The authors start with a comprehensive
tutorial on the basics of brain structure and learning, and then
the key soft computing techniques, including evolutionary
computation, neural networks, fuzzy sets and fuzzy logic, and rough
sets. They then present seven chapters that detail the application
of representative techniques to complex image processing tasks such
as image recognition, lighting control, target tracking, object
extraction, and edge detection. These chapters follow a structured
approach with detailed explanations of the problems, solutions,
results, and conclusions. This is both a standalone textbook for
graduates in computer science, electrical engineering, system
science, and information technology, and a reference for
researchers and engineers engaged with pattern recognition, image
processing, and soft computing.
This is the first book primarily dedicated to clustering using
multiobjective genetic algorithms with extensive real-life
applications in data mining and bioinformatics. The authors first
offer detailed introductions to the relevant techniques - genetic
algorithms, multiobjective optimization, soft computing, data
mining and bioinformatics. They then demonstrate systematic
applications of these techniques to real-world problems in the
areas of data mining, bioinformatics and geoscience. The authors
offer detailed theoretical and statistical notes, guides to future
research, and chapter summaries. The book can be used as a textbook
and as a reference book by graduate students and academic and
industrial researchers in the areas of soft computing, data mining,
bioinformatics and geoscience.
Proper analysis of image and multimedia data requires efficient
extraction and segmentation techniques. Among the many
computational intelligence approaches, the soft computing paradigm
is best equipped with several tools and techniques that incorporate
intelligent concepts and principles. This book is dedicated to
object extraction, image segmentation, and edge detection using
soft computing techniques with extensive real-life application to
image and multimedia data. The authors start with a comprehensive
tutorial on the basics of brain structure and learning, and then
the key soft computing techniques, including evolutionary
computation, neural networks, fuzzy sets and fuzzy logic, and rough
sets. They then present seven chapters that detail the application
of representative techniques to complex image processing tasks such
as image recognition, lighting control, target tracking, object
extraction, and edge detection. These chapters follow a structured
approach with detailed explanations of the problems, solutions,
results, and conclusions. This is both a standalone textbook for
graduates in computer science, electrical engineering, system
science, and information technology, and a reference for
researchers and engineers engaged with pattern recognition, image
processing, and soft computing.
Applied Smart Health Care Informatics Explores how intelligent
systems offer new opportunities for optimizing the acquisition,
storage, retrieval, and use of information in healthcare Applied
Smart Health Care Informatics explores how health information
technology and intelligent systems can be integrated and deployed
to enhance healthcare management. Edited and authored by leading
experts in the field, this timely volume introduces modern
approaches for managing existing data in the healthcare sector by
utilizing artificial intelligence (AI), meta-heuristic algorithms,
deep learning, the Internet of Things (IoT), and other smart
technologies. Detailed chapters review advances in areas including
machine learning, computer vision, and soft computing techniques,
and discuss various applications of healthcare management systems
such as medical imaging, electronic medical records (EMR), and drug
development assistance. Throughout the text, the authors propose
new research directions and highlight the smart technologies that
are central to establishing proactive health management, supporting
enhanced coordination of care, and improving the overall quality of
healthcare services. Provides an overview of different deep
learning applications for intelligent healthcare informatics
management Describes novel methodologies and emerging trends in
artificial intelligence and computational intelligence and their
relevance to health information engineering and management Proposes
IoT solutions that disseminate essential medical information for
intelligent healthcare management Discusses mobile-based healthcare
management, content-based image retrieval, and computer-aided
diagnosis using machine and deep learning techniques Examines the
use of exploratory data analysis in intelligent healthcare
informatics systems Applied Smart Health Care Informatics: A
Computational Intelligence Perspective is an invaluable text for
graduate students, postdoctoral researchers, academic lecturers,
and industry professionals working in the area of healthcare and
intelligent soft computing.
This is the first book primarily dedicated to clustering using
multiobjective genetic algorithms with extensive real-life
applications in data mining and bioinformatics. The authors first
offer detailed introductions to the relevant techniques - genetic
algorithms, multiobjective optimization, soft computing, data
mining and bioinformatics. They then demonstrate systematic
applications of these techniques to real-world problems in the
areas of data mining, bioinformatics and geoscience. The authors
offer detailed theoretical and statistical notes, guides to future
research, and chapter summaries. The book can be used as a textbook
and as a reference book by graduate students and academic and
industrial researchers in the areas of soft computing, data mining,
bioinformatics and geoscience.
The growth in the amount of data collected and generated has
exploded in recent times with the widespread automation of various
day-to-day activities, advances in high-level scienti?c and
engineering research and the development of e?cient data collection
tools. This has given rise to the need for automa-
callyanalyzingthedatainordertoextractknowledgefromit, therebymaking
the data potentially more useful. Knowledge discovery and data
mining (KDD) is the process of identifying valid, novel,
potentially useful and ultimately understandable patterns from
massive data repositories. It is a multi-disciplinary topic,
drawing from s- eral ?elds including expert systems, machine
learning, intelligent databases, knowledge acquisition, case-based
reasoning, pattern recognition and stat- tics. Many data mining
systems have typically evolved around well-organized database
systems (e.g., relational databases) containing relevant
information. But, more and more, one ?nds relevant information
hidden in unstructured text and in other complex forms. Mining in
the domains of the world-wide web, bioinformatics, geoscienti?c
data, and spatial and temporal applications comprise some
illustrative examples in this regard. Discovery of knowledge, or
potentially useful patterns, from such complex data often requires
the - plication of advanced techniques that are better able to
exploit the nature and representation of the data. Such advanced
methods include, among o- ers, graph-based and tree-based
approaches to relational learning, sequence mining, link-based
classi?cation, Bayesian networks, hidden Markov models, neural
networks, kernel-based methods, evolutionary algorithms, rough sets
and fuzzy logic, and hybrid systems. Many of these methods are
developed in the following chapters
The growth in the amount of data collected and generated has
exploded in recent times with the widespread automation of various
day-to-day activities, advances in high-level scienti?c and
engineering research and the development of e?cient data collection
tools. This has given rise to the need for automa-
callyanalyzingthedatainordertoextractknowledgefromit, therebymaking
the data potentially more useful. Knowledge discovery and data
mining (KDD) is the process of identifying valid, novel,
potentially useful and ultimately understandable patterns from
massive data repositories. It is a multi-disciplinary topic,
drawing from s- eral ?elds including expert systems, machine
learning, intelligent databases, knowledge acquisition, case-based
reasoning, pattern recognition and stat- tics. Many data mining
systems have typically evolved around well-organized database
systems (e.g., relational databases) containing relevant
information. But, more and more, one ?nds relevant information
hidden in unstructured text and in other complex forms. Mining in
the domains of the world-wide web, bioinformatics, geoscienti?c
data, and spatial and temporal applications comprise some
illustrative examples in this regard. Discovery of knowledge, or
potentially useful patterns, from such complex data often requires
the - plication of advanced techniques that are better able to
exploit the nature and representation of the data. Such advanced
methods include, among o- ers, graph-based and tree-based
approaches to relational learning, sequence mining, link-based
classi?cation, Bayesian networks, hidden Markov models, neural
networks, kernel-based methods, evolutionary algorithms, rough sets
and fuzzy logic, and hybrid systems. Many of these methods are
developed in the following chapters
Quantum Inspired Computational Intelligence: Research and
Applications explores the latest quantum computational intelligence
approaches, initiatives, and applications in computing,
engineering, science, and business. The book explores this emerging
field of research that applies principles of quantum mechanics to
develop more efficient and robust intelligent systems. Conventional
computational intelligence-or soft computing-is conjoined with
quantum computing to achieve this objective. The models covered can
be applied to any endeavor which handles complex and meaningful
information.
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