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Demonstrates how unsupervised learning approaches can be used for
dimensionality reduction Neatly explains algorithms with focus on
the fundamentals and underlying mathematical concepts Describes the
comparative study of the algorithms and discusses when and where
each algorithm is best suitable for use Provides use cases,
illustrative examples and visualizations of each algorithm Helps
visualize and create compact representations of high dimensional
and intricate data for various real-world applications and data
analysis
Demonstrates how unsupervised learning approaches can be used for
dimensionality reduction Neatly explains algorithms with focus on
the fundamentals and underlying mathematical concepts Describes the
comparative study of the algorithms and discusses when and where
each algorithm is best suitable for use Provides use cases,
illustrative examples and visualizations of each algorithm Helps
visualize and create compact representations of high dimensional
and intricate data for various real-world applications and data
analysis
This edited book provides information on emerging fields of
next-generation healthcare informatics with a special emphasis on
emerging developments and applications of artificial intelligence,
deep learning techniques, computational intelligence methods,
Internet of medical things (IoMT), optimization techniques,
decision making, nanomedicine, and cloud computing. The book
provides a conceptual framework and roadmap for decision-makers for
this transformation. The chapters involved in this book cover
challenges and opportunities for diabetic retinopathy detection
based on deep learning applications, deep learning accelerators in
IoT and IoMT, health data analysis, deep reinforcement-based
conversational AI agent in healthcare systems, examination of
health data performance, multisource data in intelligent medicine,
application of genetic algorithms in health care, mental disorder,
digital healthcare system with big data analytics, encryption
methods in healthcare data security, computation and cognitive bias
in healthcare intelligence and pharmacogenomics, guided imagery
therapy, cancer detection and prediction techniques, medical image
processing for coronavirus, and imbalance learning in health care.
The term IoT, which was first proposed by Kevin Ashton, a British
technologist, in 1999 has the potential to impact everything from
new product opportunities to shop floor optimization to factory
worker efficiency gains, that will power top-line and bottom-line
gains. As IoT technology is being put to diversified use, the
current technology needs to be improved to enhance privacy and
built secure devices by adopting a security-focused approach,
reducing the amount of data collected, increasing transparency and
providing consumers with a choice to opt out. Therefore, the
current volume has been compiled, in an effort to draw the various
issues in IoT, challenges faced and existing solutions so far. Key
Points: * Provides an overview of basic concepts and technologies
of IoT with communication technologies ranging from 4G to 5G and
its architecture. * Discusses recent security and privacy studies
and social behavior of human beings over IoT. * Covers the issues
related to sensors, business model, principles, paradigms, green
IoT and solutions to handle relevant challenges. * Presents the
readers with practical ideas of using IoT, how it deals with human
dynamics, the ecosystem, the social objects and their relation. *
Deals with the challenges involved in surpassing diversified
architecture, protocol, communications, integrity and security.
This edited book provides information on emerging fields of
next-generation healthcare informatics with a special emphasis on
emerging developments and applications of artificial intelligence,
deep learning techniques, computational intelligence methods,
Internet of medical things (IoMT), optimization techniques,
decision making, nanomedicine, and cloud computing. The book
provides a conceptual framework and roadmap for decision-makers for
this transformation. The chapters involved in this book cover
challenges and opportunities for diabetic retinopathy detection
based on deep learning applications, deep learning accelerators in
IoT and IoMT, health data analysis, deep reinforcement-based
conversational AI agent in healthcare systems, examination of
health data performance, multisource data in intelligent medicine,
application of genetic algorithms in health care, mental disorder,
digital healthcare system with big data analytics, encryption
methods in healthcare data security, computation and cognitive bias
in healthcare intelligence and pharmacogenomics, guided imagery
therapy, cancer detection and prediction techniques, medical image
processing for coronavirus, and imbalance learning in health care.
The term IoT, which was first proposed by Kevin Ashton, a British
technologist, in 1999 has the potential to impact everything from
new product opportunities to shop floor optimization to factory
worker efficiency gains, that will power top-line and bottom-line
gains. As IoT technology is being put to diversified use, the
current technology needs to be improved to enhance privacy and
built secure devices by adopting a security-focused approach,
reducing the amount of data collected, increasing transparency and
providing consumers with a choice to opt out. Therefore, the
current volume has been compiled, in an effort to draw the various
issues in IoT, challenges faced and existing solutions so far. Key
Points: * Provides an overview of basic concepts and technologies
of IoT with communication technologies ranging from 4G to 5G and
its architecture. * Discusses recent security and privacy studies
and social behavior of human beings over IoT. * Covers the issues
related to sensors, business model, principles, paradigms, green
IoT and solutions to handle relevant challenges. * Presents the
readers with practical ideas of using IoT, how it deals with human
dynamics, the ecosystem, the social objects and their relation. *
Deals with the challenges involved in surpassing diversified
architecture, protocol, communications, integrity and security.
This book focuses on the fundamentals of deep learning along with
reporting on the current state-of-art research on deep learning. In
addition, it provides an insight of deep neural networks in action
with illustrative coding examples. Deep learning is a new area of
machine learning research which has been introduced with the
objective of moving ML closer to one of its original goals, i.e.
artificial intelligence. Deep learning was developed as an ML
approach to deal with complex input-output mappings. While
traditional methods successfully solve problems where final value
is a simple function of input data, deep learning techniques are
able to capture composite relations between non-immediately related
fields, for example between air pressure recordings and English
words, millions of pixels and textual description, brand-related
news and future stock prices and almost all real world problems.
Deep learning is a class of nature inspired machine learning
algorithms that uses a cascade of multiple layers of nonlinear
processing units for feature extraction and transformation. Each
successive layer uses the output from the previous layer as input.
The learning may be supervised (e.g. classification) and/or
unsupervised (e.g. pattern analysis) manners. These algorithms
learn multiple levels of representations that correspond to
different levels of abstraction by resorting to some form of
gradient descent for training via backpropagation. Layers that have
been used in deep learning include hidden layers of an artificial
neural network and sets of propositional formulas. They may also
include latent variables organized layer-wise in deep generative
models such as the nodes in deep belief networks and deep boltzmann
machines. Deep learning is part of state-of-the-art systems in
various disciplines, particularly computer vision, automatic speech
recognition (ASR) and human action recognition.
The digital age is ripe with emerging advances and applications in
technological innovations. Mimicking the structure of complex
systems in nature can provide new ideas on how to organize
mechanical and personal systems. The Handbook of Research on
Modeling, Analysis, and Application of Nature-Inspired
Metaheuristic Algorithms is an essential scholarly resource on
current algorithms that have been inspired by the natural world.
Featuring coverage on diverse topics such as cellular automata,
simulated annealing, genetic programming, and differential
evolution, this reference publication is ideal for scientists,
biological engineers, academics, students, and researchers that are
interested in discovering what models from nature influence the
current technology-centric world.
Data has increased due to the growing use of web applications and
communication devices. It is necessary to develop new techniques of
managing data in order to ensure adequate usage. Modern
Technologies for Big Data Classification and Clustering is an
essential reference source for the latest scholarly research on
handling large data sets with conventional data mining and provide
information about the new technologies developed for the management
of large data. Featuring coverage on a broad range of topics such
as text and web data analytics, risk analysis, and opinion mining,
this publication is ideally designed for professionals,
researchers, and students seeking current research on various
concepts of big data analytics. Topics Covered: The many academic
areas covered in this publication include, but are not limited to:
Data visualization Distributed Computing Systems Opinion Mining
Privacy and security Risk analysis Social Network Analysis Text
Data Analytics Web Data Analytics
As the amount of accumulated data across a variety of fields
becomes harder to maintain, it is essential for a new generation of
computational theories and tools to assist humans in extracting
knowledge from this rapidly growing digital data. Global Trends in
Intelligent Computing Research and Development brings together
recent advances and in depth knowledge in the fields of knowledge
representation and computational intelligence. Highlighting the
theoretical advances and their applications to real life problems,
this book is an essential tool for researchers, lecturers,
professors, students, and developers who have seek insight into
knowledge representation and real life applications.
Interest in e-government, both in industry and in academia, has
grown rapidly over the past decade, and continues to grow. ""Global
E-Government: Theory, Applications and Benchmarking"" is written by
experts from academia and industry, examining the practices of
e-government in developing and developed countries, presenting
recent theoretical research in e-government, and providing a
platform to benchmark the best practices in implementing
e-government programs. ""Global E-Government: Theory, Applications
and Benchmarking"" provides helpful examples from practitioners and
managers involving real-life applications, while academics and
researchers in the fields of information systems and e-government
contribute theoretical insights.
Technological advancements have extracted a vast amount of useful
knowledge and information for applications and services. These
developments have evoked intelligent solutions that have been
utilsed in efforts to secure this data and avoid potential complex
problems. Advances in Secure Computing, Internet Services, and
Applications presents current research on the applications of
computational intelligence in order to focus on the challenge
humans face when securing knowledge and data. This book is a vital
reference source for researchers, lecturers, professors, students,
and developers, who have interest in secure computing and recent
advanced in real life applications.
Technology has become profoundly integrated into modern society;
however, this increases the risk of vulnerabilities, such as
hacking and other system errors, along with other online threats.
Security, Privacy, and Anonymization in Social Networks: Emerging
Research and Opportunities is a pivotal reference source for the
most up-to-date research on edge clustering models and weighted
social networks. Presenting widespread coverage across a range of
applicable perspectives and topics, such as neighborhood attacks,
fast k-degree anonymization (FKDA), and vertex-clustering
algorithms, this book is ideally designed for academics,
researchers, post-graduates, and practitioners seeking current
research on undirected networks and greedy algorithms for social
network anonymization.
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