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Healthcare Data Analytics and Management help readers disseminate
cutting-edge research that delivers insights into the analytic
tools, opportunities, novel strategies, techniques and challenges
for handling big data, data analytics and management in healthcare.
As the rapidly expanding and heterogeneous nature of healthcare
data poses challenges for big data analytics, this book targets
researchers and bioengineers from areas of machine learning, data
mining, data management, and healthcare providers, along with
clinical researchers and physicians who are interested in the
management and analysis of healthcare data.
This comprehensive book focuses on better big-data security for
healthcare organizations. Following an extensive introduction to
the Internet of Things (IoT) in healthcare including challenging
topics and scenarios, it offers an in-depth analysis of medical
body area networks with the 5th generation of IoT communication
technology along with its nanotechnology. It also describes a novel
strategic framework and computationally intelligent model to
measure possible security vulnerabilities in the context of
e-health. Moreover, the book addresses healthcare systems that
handle large volumes of data driven by patients' records and
health/personal information, including big-data-based knowledge
management systems to support clinical decisions. Several of the
issues faced in storing/processing big data are presented along
with the available tools, technologies and algorithms to deal with
those problems as well as a case study in healthcare analytics.
Addressing trust, privacy, and security issues as well as the IoT
and big-data challenges, the book highlights the advances in the
field to guide engineers developing different IoT devices and
evaluating the performance of different IoT techniques.
Additionally, it explores the impact of such technologies on
public, private, community, and hybrid scenarios in healthcare.
This book offers professionals, scientists and engineers the latest
technologies, techniques, and strategies for IoT and big data.
This book thoroughly covers the remote sensing visualization and
analysis techniques based on computational imaging and vision in
Earth science. Remote sensing is considered a significant
information source for monitoring and mapping natural and man-made
land through the development of sensor resolutions that committed
different Earth observation platforms. The book includes related
topics for the different systems, models, and approaches used in
the visualization of remote sensing images. It offers flexible and
sophisticated solutions for removing uncertainty from the satellite
data. It introduces real time big data analytics to derive
intelligence systems in enterprise earth science applications.
Furthermore, the book integrates statistical concepts with
computer-based geographic information systems (GIS). It focuses on
image processing techniques for observing data together with
uncertainty information raised by spectral, spatial, and positional
accuracy of GPS data. The book addresses several advanced
improvement models to guide the engineers in developing different
remote sensing visualization and analysis schemes. Highlights on
the advanced improvement models of the supervised/unsupervised
classification algorithms, support vector machines, artificial
neural networks, fuzzy logic, decision-making algorithms, and Time
Series Model and Forecasting are addressed. This book guides
engineers, designers, and researchers to exploit the intrinsic
design remote sensing systems. The book gathers remarkable material
from an international experts' panel to guide the readers during
the development of earth big data analytics and their challenges.
This book highlights state-of-the-art research on big data and the
Internet of Things (IoT), along with related areas to ensure
efficient and Internet-compatible IoT systems. It not only
discusses big data security and privacy challenges, but also
energy-efficient approaches to improving virtual machine placement
in cloud computing environments. Big data and the Internet of
Things (IoT) are ultimately two sides of the same coin, yet
extracting, analyzing and managing IoT data poses a serious
challenge. Accordingly, proper analytics infrastructures/platforms
should be used to analyze IoT data. Information technology (IT)
allows people to upload, retrieve, store and collect information,
which ultimately forms big data. The use of big data analytics has
grown tremendously in just the past few years. At the same time,
the IoT has entered the public consciousness, sparking people's
imaginations as to what a fully connected world can offer. Further,
the book discusses the analysis of real-time big data to derive
actionable intelligence in enterprise applications in several
domains, such as in industry and agriculture. It explores possible
automated solutions in daily life, including structures for smart
cities and automated home systems based on IoT technology, as well
as health care systems that manage large amounts of data (big data)
to improve clinical decisions. The book addresses the security and
privacy of the IoT and big data technologies, while also revealing
the impact of IoT technologies on several scenarios in smart cities
design. Intended as a comprehensive introduction, it offers
in-depth analysis and provides scientists, engineers and
professionals the latest techniques, frameworks and strategies used
in IoT and big data technologies.
This comprehensive book focuses on better big-data security for
healthcare organizations. Following an extensive introduction to
the Internet of Things (IoT) in healthcare including challenging
topics and scenarios, it offers an in-depth analysis of medical
body area networks with the 5th generation of IoT communication
technology along with its nanotechnology. It also describes a novel
strategic framework and computationally intelligent model to
measure possible security vulnerabilities in the context of
e-health. Moreover, the book addresses healthcare systems that
handle large volumes of data driven by patients' records and
health/personal information, including big-data-based knowledge
management systems to support clinical decisions. Several of the
issues faced in storing/processing big data are presented along
with the available tools, technologies and algorithms to deal with
those problems as well as a case study in healthcare analytics.
Addressing trust, privacy, and security issues as well as the IoT
and big-data challenges, the book highlights the advances in the
field to guide engineers developing different IoT devices and
evaluating the performance of different IoT techniques.
Additionally, it explores the impact of such technologies on
public, private, community, and hybrid scenarios in healthcare.
This book offers professionals, scientists and engineers the latest
technologies, techniques, and strategies for IoT and big data.
The book provides an examination of how fog security is changing
the information technology industry and will continue to in the
next decade. The authors first discuss how fog enables key
applications in wireless 5G, the Internet of Things, and big data.
The book then presents an overview of fog/edge computing, focusing
on its relationship with cloud technology, Internet of Things and
the future with the use of secure 5G/6G communication. The book
also presents a comprehensive overview of liabilities in fog/edge
computing within multi-level architectures and the intelligent
management. The last part of the book reviews applications of
fog/edge computing in smart cities, including in Industrial IoT,
edge-based augmented reality, data streaming, and blockchain-based.
This book thoroughly covers the remote sensing visualization and
analysis techniques based on computational imaging and vision in
Earth science. Remote sensing is considered a significant
information source for monitoring and mapping natural and man-made
land through the development of sensor resolutions that committed
different Earth observation platforms. The book includes related
topics for the different systems, models, and approaches used in
the visualization of remote sensing images. It offers flexible and
sophisticated solutions for removing uncertainty from the satellite
data. It introduces real time big data analytics to derive
intelligence systems in enterprise earth science applications.
Furthermore, the book integrates statistical concepts with
computer-based geographic information systems (GIS). It focuses on
image processing techniques for observing data together with
uncertainty information raised by spectral, spatial, and positional
accuracy of GPS data. The book addresses several advanced
improvement models to guide the engineers in developing different
remote sensing visualization and analysis schemes. Highlights on
the advanced improvement models of the supervised/unsupervised
classification algorithms, support vector machines, artificial
neural networks, fuzzy logic, decision-making algorithms, and Time
Series Model and Forecasting are addressed. This book guides
engineers, designers, and researchers to exploit the intrinsic
design remote sensing systems. The book gathers remarkable material
from an international experts' panel to guide the readers during
the development of earth big data analytics and their challenges.
Wearable and Implantable Medical Devices: Applications and
Challenges, Fourth Edition highlights the new aspects of wearable
and implanted sensors technology in the healthcare sector and
monitoring systems. The book's contributions include several
interdisciplinary domains, such as wearable sensors, implanted
sensors devices, Internet-of-Things (IoT), security, real-time
medical healthcare monitoring, WIBSN design and data management,
encryption, and decision-support systems. Contributions emphasize
several topics, including real-world applications and the design
and implementation of wearable devices. This book demonstrates that
this new field has a brilliant future in applied healthcare
research and in healthcare monitoring systems.
Wireless communication is continuously evolving to improve and be a
part of our daily communication. This leads to improved quality of
services and applications supported by networking technologies. We
are now able to use LTE, LTE-Advanced, and other emerging
technologies due to the enormous efforts that are made to improve
the quality of service in cellular networks. As the future of
networking is uncertain, the use of deep learning and big data
analytics is a point of focus as it can work in many capacities at
a variety of levels for wireless communications. Implementing Data
Analytics and Architectures for Next Generation Wireless
Communications addresses the existing and emerging theoretical and
practical challenges in the design, development, and implementation
of big data algorithms, protocols, architectures, and applications
for next generation wireless communications and their applications
in smart cities. The chapters of this book bring together academics
and industrial practitioners to exchange, discuss, and implement
the latest innovations and applications of data analytics in
advanced networks. Specific topics covered include key encryption
techniques, smart home appliances, fog communication networks, and
security in the internet of things. This book is valuable for
technologists, data analysts, networking experts, practitioners,
researchers, academicians, and students.
Wireless communication is continuously evolving to improve and be a
part of our daily communication. This leads to improved quality of
services and applications supported by networking technologies. We
are now able to use LTE, LTE-Advanced, and other emerging
technologies due to the enormous efforts that are made to improve
the quality of service in cellular networks. As the future of
networking is uncertain, the use of deep learning and big data
analytics is a point of focus as it can work in many capacities at
a variety of levels for wireless communications. Implementing Data
Analytics and Architectures for Next Generation Wireless
Communications addresses the existing and emerging theoretical and
practical challenges in the design, development, and implementation
of big data algorithms, protocols, architectures, and applications
for next generation wireless communications and their applications
in smart cities. The chapters of this book bring together academics
and industrial practitioners to exchange, discuss, and implement
the latest innovations and applications of data analytics in
advanced networks. Specific topics covered include key encryption
techniques, smart home appliances, fog communication networks, and
security in the internet of things. This book is valuable for
technologists, data analysts, networking experts, practitioners,
researchers, academicians, and students.
Modern education has increased its reach through ICT tools and
techniques. To manage educational data with the help of modern
artificial intelligence, data and web mining techniques on
dedicated cloud or grid platforms for educational institutes can be
used. By utilizing data science techniques to manage educational
data, the safekeeping, delivery, and use of knowledge can be
increased for better quality education. Utilizing Educational Data
Mining Techniques for Improved Learning: Emerging Research and
Opportunities is a critical scholarly resource that explores data
mining and management techniques that promote the improvement and
optimization of educational data systems. The book intends to
provide new models, platforms, tools, and protocols in data science
for educational data analysis and introduces innovative hybrid
system models dedicated to data science. Including topics such as
automatic assessment, educational analytics, and machine learning,
this book is essential for IT specialists, data analysts, computer
engineers, education professionals, administrators, policymakers,
researchers, academicians, and technology experts.
Modern education has increased its reach through ICT tools and
techniques. To manage educational data with the help of modern
artificial intelligence, data and web mining techniques on
dedicated cloud or grid platforms for educational institutes can be
used. By utilizing data science techniques to manage educational
data, the safekeeping, delivery, and use of knowledge can be
increased for better quality education. Utilizing Educational Data
Mining Techniques for Improved Learning: Emerging Research and
Opportunities is a critical scholarly resource that explores data
mining and management techniques that promote the improvement and
optimization of educational data systems. The book intends to
provide new models, platforms, tools, and protocols in data science
for educational data analysis and introduces innovative hybrid
system models dedicated to data science. Including topics such as
automatic assessment, educational analytics, and machine learning,
this book is essential for IT specialists, data analysts, computer
engineers, education professionals, administrators, policymakers,
researchers, academicians, and technology experts.
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