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Hybrid Intelligent Techniques for Pattern Analysis and
Understanding outlines the latest research on the development and
application of synergistic approaches to pattern analysis in
real-world scenarios. An invaluable resource for lecturers,
researchers, and graduates students in computer science and
engineering, this book covers a diverse range of hybrid intelligent
techniques, including image segmentation, character recognition,
human behavioral analysis, hyperspectral data processing, and
medical image analysis.
This book discusses the recent research trends and upcoming
applications based on artificial intelligence. It includes best
selected research papers presented at the International Conference
on Research and Applications in Artificial Intelligence (RAAI
2020), organized by Department of Information Technology, RCC
Institute of Information technology, Kolkata, West Bengal, India
during 19 - 20, December, 2020. Many versatile fields of artificial
intelligence are categorically addressed through different chapters
of this book. The book is a valuable resource and reference for
researchers, instructors, students, scientists, engineers, managers
and industry practitioners in these important areas.
This book is a useful guide for the teaching fraternity,
administrators and education technology professionals to make good
use of AI across outcome-based technical education (OBTE) ecosystem
and infuse innovations and affordable digital technologies to
traditional pedagogic processes to make teaching-learning more
independent of human factor (teacher/student quality), time and
place and at the same time more impactful and enjoyable for the
learners. Providing access to the digital media and learning tools
(even to the extent of mobile apps) to the students would allow
them to keep pace with innovations in learning technologies, learn
according to their own pace and improve their understanding level
and have instantaneous feedback and evaluation. The book explores
these new challenges and scope of using computational intelligence
in educational technology. The book also addresses how based on the
data collected from the outcome of conventional educational system,
intelligent diagnostic and feedback system is developed which will
change the teaching strategies and corresponding teaching-learning
process. The book covers a wider framework of digital pedagogy and
its intelligent applications on various sectors of education
system.
This book contains interesting findings of some state-of-the-art
research in the field of signal and image processing. It contains
twenty one chapters covering a wide range of signal processing
applications involving filtering, encoding, classification,
segmentation, clustering, feature extraction, denoising,
watermarking, object recognition, reconstruction and fractal
analysis. Various types of signals including image, video, speech,
non-speech audio, handwritten text, geometric diagram, ECG and EMG
signals, MRI, PET and CT scan images, THz signals, solar wind speed
signals (SWS) and photoplethysmogram (PPG) signals have been dealt
with. It demonstrates how new paradigms of intelligent computing
like quantum computing can be applied to process and analyze
signals in a most precise and effective manner. Processing of high
precision signals for real time target recognition by radar and
processing of brain images, ECG and EMG signals that feature in
this book have significant implications in defense mechanism and
medical diagnosis. There are also applications of hybrid methods,
algorithms and image filters which are proving to be better than
the individual techniques or algorithms. Thus the present volume,
enriched in depth and variety of techniques and algorithms
concerning processing of various types of signals, is likely to be
used as a compact yet handy reference for the young researchers,
academicians and scientists working in the domain of signal and
image processing and also to the post graduate students of computer
science and information technology.
This volume comprises six well-versed contributed chapters devoted
to report the latest fi ndings on the applications of machine
learning for big data analytics. Big data is a term for data sets
that are so large or complex that traditional data processing
application software is inadequate to deal with them. The possible
challenges in this direction include capture, storage, analysis,
data curation, search, sharing, transfer, visualization, querying,
updating and information privacy. Big data analytics is the process
of examining large and varied data sets - i.e., big data - to
uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful information that can help
organizations make more-informed business decisions. This volume is
intended to be used as a reference by undergraduate and post
graduate students of the disciplines of computer science,
electronics and telecommunication, information science and
electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL
INTELLIGENCE The series Frontiers In Computational Intelligence is
envisioned to provide comprehensive coverage and understanding of
cutting edge research in computational intelligence. It intends to
augment the scholarly discourse on all topics relating to the
advances in artifi cial life and machine learning in the form of
metaheuristics, approximate reasoning, and robotics. Latest
research fi ndings are coupled with applications to varied domains
of engineering and computer sciences. This field is steadily
growing especially with the advent of novel machine learning
algorithms being applied to different domains of engineering and
technology. The series brings together leading researchers that
intend to continue to advance the fi eld and create a broad
knowledge about the most recent research.
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.
The communication field is evolving rapidly in order to keep up
with society's demands. As such, it becomes imperative to research
and report recent advancements in computational intelligence as it
applies to communication networks. The Handbook of Research on
Recent Developments in Intelligent Communication Application is a
pivotal reference source for the latest developments on emerging
data communication applications. Featuring extensive coverage
across a range of relevant perspectives and topics, such as
satellite communication, cognitive radio networks, and wireless
sensor networks, this book is ideally designed for engineers,
professionals, practitioners, upper-level students, and academics
seeking current information on emerging communication networking
trends.
The field of computational intelligence has grown tremendously over
that past five years, thanks to evolving soft computing and
artificial intelligent methodologies, tools and techniques for
envisaging the essence of intelligence embedded in real life
observations. Consequently, scientists have been able to explain
and understand real life processes and practices which previously
often remain unexplored by virtue of their underlying imprecision,
uncertainties and redundancies, and the unavailability of
appropriate methods for describing the incompleteness and vagueness
of information represented. With the advent of the field of
computational intelligence, researchers are now able to explore and
unearth the intelligence, otherwise insurmountable, embedded in the
systems under consideration. Computational Intelligence is now not
limited to only specific computational fields, it has made inroads
in signal processing, smart manufacturing, predictive control,
robot navigation, smart cities, and sensor design to name a few.
Recent Trends in Computational Intelligence Enabled Research:
Theoretical Foundations and Applications explores the use of this
computational paradigm across a wide range of applied domains which
handle meaningful information. Chapters investigate a broad
spectrum of the applications of computational intelligence across
different platforms and disciplines, expanding our knowledge base
of various research initiatives in this direction. This volume aims
to bring together researchers, engineers, developers and
practitioners from academia and industry working in all major areas
and interdisciplinary areas of computational intelligence,
communication systems, computer networks, and soft computing.
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