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There are a number of books on computational intelligence (CI), but
they tend to cover a broad range of CI paradigms and algorithms
rather than provide an in-depth exploration in learning and
adaptive mechanisms. This book sets its focus on CI based
architectures, modeling, case studies and applications in big data
analytics, and business intelligence. The intended audiences of
this book are scientists, professionals, researchers, and
academicians who deal with the new challenges and advances in the
specific areas mentioned above. Designers and developers of
applications in these areas can learn from other experts and
colleagues through this book.
This unique book dicusses the latest research, innovative ideas,
challenges and computational intelligence (CI) solutions in
sustainable computing. It presents novel, in-depth fundamental
research on achieving a sustainable lifestyle for society, either
from a methodological or from an application perspective.
Sustainable computing has expanded to become a significant research
area covering the fields of computer science and engineering,
electrical engineering and other engineering disciplines, and there
has been an increase in the amount of literature on aspects
sustainable computing such as energy efficiency and natural
resources conservation that emphasizes the role of ICT (information
and communications technology) in achieving system design and
operation objectives. The energy impact/design of more efficient IT
infrastructures is a key challenge in realizing new computing
paradigms. The book explores the uses of computational intelligence
(CI) techniques for intelligent decision support that can be
exploited to create effectual computing systems, and addresses
sustainability problems in computing and information processing
environments and technologies at the different levels of CI
paradigms. An excellent guide to surveying the state of the art in
computational intelligence applied to challenging real-world
problems in sustainable computing, it is intended for scientists,
practitioners, researchers and academicians dealing with the new
challenges and advances in area.
This book brings a high level of fluidity to analytics and
addresses recent trends, innovative ideas, challenges and cognitive
computing solutions in big data and the Internet of Things (IoT).
It explores domain knowledge, data science reasoning and cognitive
methods in the context of the IoT, extending current data science
approaches by incorporating insights from experts as well as a
notion of artificial intelligence, and performing inferences on the
knowledge The book provides a comprehensive overview of the
constituent paradigms underlying cognitive computing methods, which
illustrate the increased focus on big data in IoT problems as they
evolve. It includes novel, in-depth fundamental research
contributions from a methodological/application in data science
accomplishing sustainable solution for the future perspective.
Mainly focusing on the design of the best cognitive embedded data
science technologies to process and analyze the large amount of
data collected through the IoT, and aid better decision making, the
book discusses adapting decision-making approaches under cognitive
computing paradigms to demonstrate how the proposed procedures as
well as big data and IoT problems can be handled in practice. This
book is a valuable resource for scientists, professionals,
researchers, and academicians dealing with the new challenges and
advances in the specific areas of cognitive computing and data
science approaches.
Cognitive Systems and Signal Processing in Image Processing
presents different frameworks and applications of cognitive signal
processing methods in image processing. This book provides an
overview of recent applications in image processing by cognitive
signal processing methods in the context of Big Data and Cognitive
AI. It presents the amalgamation of cognitive systems and signal
processing in the context of image processing approaches in solving
various real-word application domains. This book reports the latest
progress in cognitive big data and sustainable computing. Various
real-time case studies and implemented works are discussed for
better understanding and more clarity to readers. The combined
model of cognitive data intelligence with learning methods can be
used to analyze emerging patterns, spot business opportunities, and
take care of critical process-centric issues for computer vision in
real-time.
This unique book dicusses the latest research, innovative ideas,
challenges and computational intelligence (CI) solutions in
sustainable computing. It presents novel, in-depth fundamental
research on achieving a sustainable lifestyle for society, either
from a methodological or from an application perspective.
Sustainable computing has expanded to become a significant research
area covering the fields of computer science and engineering,
electrical engineering and other engineering disciplines, and there
has been an increase in the amount of literature on aspects
sustainable computing such as energy efficiency and natural
resources conservation that emphasizes the role of ICT (information
and communications technology) in achieving system design and
operation objectives. The energy impact/design of more efficient IT
infrastructures is a key challenge in realizing new computing
paradigms. The book explores the uses of computational intelligence
(CI) techniques for intelligent decision support that can be
exploited to create effectual computing systems, and addresses
sustainability problems in computing and information processing
environments and technologies at the different levels of CI
paradigms. An excellent guide to surveying the state of the art in
computational intelligence applied to challenging real-world
problems in sustainable computing, it is intended for scientists,
practitioners, researchers and academicians dealing with the new
challenges and advances in area.
The goal of this book is to explore various security paradigms such
as Machine Learning, Big data, Cyber Physical Systems, and
Blockchain to address both intelligence and reconfigurability in
various IoT devices. The book further aims to address and analyze
the state of the art of blockchain-based intelligent networks in
IoT systems and related technologies including healthcare sector.
AI can ease, optimize, and automate the blockchain-based
decision-making process for better governance and higher
performance in IoT systems. Considering the incredible progress
made by AI models, a blockchain system powered by intelligent AI
algorithms can detect the existence of any kind of attack and
automatically invoke the required defense mechanisms. In case of
unavoidable damage, AI models can help to isolate the compromised
component from the blockchain platform and safeguard the overall
system from crashing. Furthermore, AI models can also contribute
toward the robustness and scalability of blockchain-based
intelligent IoT networks. The book is designed to be the
first-choice reference at university libraries, academic
institutions, research and development centers, information
technology centers, and any institutions interested in integration
of AI and IoT. The intended audience of this book include UG/PG
students, Ph.D. scholars of this fields, industry technologists,
young entrepreneurs, professionals, network designers, data
scientists, technology specialists, practitioners, and people who
are interested in exploring the role of AI and blockchain
technology in IoT systems.
There are a number of books on computational intelligence (CI), but
they tend to cover a broad range of CI paradigms and algorithms
rather than provide an in-depth exploration in learning and
adaptive mechanisms. This book sets its focus on CI based
architectures, modeling, case studies and applications in big data
analytics, and business intelligence. The intended audiences of
this book are scientists, professionals, researchers, and
academicians who deal with the new challenges and advances in the
specific areas mentioned above. Designers and developers of
applications in these areas can learn from other experts and
colleagues through this book.
Cognitive Big Data Intelligence with a Metaheuristic Approach
presents an exact and compact organization of content relating to
the latest metaheuristics methodologies based on new challenging
big data application domains and cognitive computing. The combined
model of cognitive big data intelligence with metaheuristics
methods can be used to analyze emerging patterns, spot business
opportunities, and take care of critical process-centric issues in
real-time. Various real-time case studies and implemented works are
discussed in this book for better understanding and additional
clarity. This book presents an essential platform for the use of
cognitive technology in the field of Data Science. It covers
metaheuristic methodologies that can be successful in a wide
variety of problem settings in big data frameworks.
Intelligent IoT Systems in Personalized Health Care delivers a
significant forum for the technical advancement of IoMT learning in
parallel computing environments across biomedical engineering
diversified domains and its applications. Pursuing an
interdisciplinary approach, the book focuses on methods used to
identify and acquire valid, potentially useful knowledge sources.
The book presents novel, in-depth, fundamental research
contributions from a methodological/application perspective to help
readers understand the fusion of AI with IoT and its capabilities
in solving a diverse range of problems for biomedical engineering
and its real-world personalized health care applications. The book
is well suited for researchers exploring the significance of IoT
based architecture to perform predictive analytics of user
activities in sustainable health.
Computational Intelligence for Multimedia Big Data on the Cloud
with Engineering Applications covers timely topics, including the
neural network (NN), particle swarm optimization (PSO),
evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS),
etc. Furthermore, the book highlights recent research on
representative techniques to elaborate how a data-centric system
formed a powerful platform for the processing of cloud hosted
multimedia big data and how it could be analyzed, processed and
characterized by CI. The book also provides a view on how
techniques in CI can offer solutions in modeling, relationship
pattern recognition, clustering and other problems in
bioengineering. It is written for domain experts and developers who
want to understand and explore the application of computational
intelligence aspects (opportunities and challenges) for design and
development of a data-centric system in the context of multimedia
cloud, big data era and its related applications, such as smarter
healthcare, homeland security, traffic control trading analysis and
telecom, etc. Researchers and PhD students exploring the
significance of data centric systems in the next paradigm of
computing will find this book extremely useful.
Deep Learning and Parallel Computing Environment for Bioengineering
Systems delivers a significant forum for the technical advancement
of deep learning in parallel computing environment across
bio-engineering diversified domains and its applications. Pursuing
an interdisciplinary approach, it focuses on methods used to
identify and acquire valid, potentially useful knowledge sources.
Managing the gathered knowledge and applying it to multiple domains
including health care, social networks, mining, recommendation
systems, image processing, pattern recognition and predictions
using deep learning paradigms is the major strength of this book.
This book integrates the core ideas of deep learning and its
applications in bio engineering application domains, to be
accessible to all scholars and academicians. The proposed
techniques and concepts in this book can be extended in future to
accommodate changing business organizations' needs as well as
practitioners' innovative ideas.
Today, the scope of image processing and recognition has broadened
due to the gap in scientific visualization. Thus, new imaging
techniques have developed, and it is imperative to study this
progression for optimal utilization. The Handbook of Research on
Advanced Image Processing Techniques and Applications is an
essential reference publication for the latest research on digital
image processing advancements. Featuring expansive coverage on a
broad range of topics and perspectives, such as image and video
steganography, pattern recognition, and artificial vision, this
publication is ideally designed for scientists, professionals,
researchers, and academicians seeking current research on solutions
for new challenges in image processing.
Soft computing techniques are innovative tools that use
nature-inspired algorithms to run predictive analysis of industries
from business to software measurement. These tools have gained
momentum in recent years for their practicality and flexibility.
The Handbook of Research on Fuzzy and Rough Set Theory in
Organizational Decision Making collects both empirical and applied
research in the field of fuzzy set theory, and bridges the gap
between the application of soft computational approaches and the
organizational decision making process. This publication is a
pivotal reference for business professionals, IT specialists,
software engineers, and advanced students of business and
information technology.
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