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Explores the role of Artificial Intelligence and Smart Computing in
health informatics and healthcare with an emphasis on clinical data
management and analysis for precise prediction and prompt action
Presents cutting edge tracking, monitoring, real time assistance,
and security for IoT in healthcare Discusses broadly on wearable
sensors and IoT devices and their role in smart living assistance
and energy conservation Describes a system mode and architecture
for a clear picture of IoT in healthcare Explains the challenges
and opportunities with IoT based healthcare industries and includes
a study of threats, impacts, and the need of information security
Covers applications of AI & IoT in healthcare Discusses recent
case studies Focuses on how applications can facilitate and
structure the healthcare systems Promotes scholarship and research
Provides how applications can unleash the potential to keep
patients safe and health, and also empower physicians to deliver
better care
This book discusses major technical advancements and research
findings in the field of prognostic modelling in healthcare image
and data analysis. The use of prognostic modelling as predictive
models to solve complex problems of data mining and analysis in
health care is the feature of this book. The book examines the
recent technologies and studies that reached the practical level
and becoming available in preclinical and clinical practices in
computational intelligence. The main areas of interest covered in
this book are highest quality, original work that contributes to
the basic science of processing, analysing and utilizing all
aspects of advanced computational prognostic modelling in
healthcare image and data analysis.
This book explores the inputs with regard to individuals and
companies who have developed technologies and innovative solutions,
bioinformatics, datasets, apps for diagnosis, etc., that can be
leveraged for strengthening the fight against coronavirus. It
focuses on technology solutions to stop Covid-19 outbreak and
mitigate the risk. The book contains innovative ideas from active
researchers who are presently working to find solutions, and they
give insights to other researchers to explore the innovative
methods and predictive modeling techniques. The novel applications
and techniques of established technologies like artificial
intelligence (AI), Internet of things (IoT), big data, computer
vision and machine learning are discussed to fight the spread of
this disease, Covid-19. This pandemic has triggered an
unprecedented demand for digital health technology solutions and
unleashing information technology to win over this pandemic.
This book focuses on sustainability issues post COVID-19 outbreak,
discusses ways to restrict global spread of the pandemic, and also
how to survive holistically in the environment. It also discusses
the economic impacts on the world due to the coronavirus outbreak.
There is a strong need for monitoring and analysis of pandemics for
sustainability like epidemic risk analysis by using pattern
recognition or the mental health challenges during an outbreak.
This book presents ways to find solutions and gives insights to
explore innovative methods and predictive modeling techniques, such
that masses are prevented from pandemics.
This book discusses major technical advancements and research
findings in the field of prognostic modelling in healthcare image
and data analysis. The use of prognostic modelling as predictive
models to solve complex problems of data mining and analysis in
health care is the feature of this book. The book examines the
recent technologies and studies that reached the practical level
and becoming available in preclinical and clinical practices in
computational intelligence. The main areas of interest covered in
this book are highest quality, original work that contributes to
the basic science of processing, analysing and utilizing all
aspects of advanced computational prognostic modelling in
healthcare image and data analysis.
This book explores the inputs with regard to individuals and
companies who have developed technologies and innovative solutions,
bioinformatics, datasets, apps for diagnosis, etc., that can be
leveraged for strengthening the fight against coronavirus. It
focuses on technology solutions to stop Covid-19 outbreak and
mitigate the risk. The book contains innovative ideas from active
researchers who are presently working to find solutions, and they
give insights to other researchers to explore the innovative
methods and predictive modeling techniques. The novel applications
and techniques of established technologies like artificial
intelligence (AI), Internet of things (IoT), big data, computer
vision and machine learning are discussed to fight the spread of
this disease, Covid-19. This pandemic has triggered an
unprecedented demand for digital health technology solutions and
unleashing information technology to win over this pandemic.
The book describes the emergence of big data technologies and the
role of Spark in the entire big data stack. It compares Spark and
Hadoop and identifies the shortcomings of Hadoop that have been
overcome by Spark. The book mainly focuses on the in-depth
architecture of Spark and our understanding of Spark RDDs and how
RDD complements big data's immutable nature, and solves it with
lazy evaluation, cacheable and type inference. It also addresses
advanced topics in Spark, starting with the basics of Scala and the
core Spark framework, and exploring Spark data frames, machine
learning using Mllib, graph analytics using Graph X and real-time
processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It
then goes on to investigate Spark using PySpark and R. Focusing on
the current big data stack, the book examines the interaction with
current big data tools, with Spark being the core processing layer
for all types of data. The book is intended for data engineers and
scientists working on massive datasets and big data technologies in
the cloud. In addition to industry professionals, it is helpful for
aspiring data processing professionals and students working in big
data processing and cloud computing environments.
Predictive Modeling in Biomedical Data Mining and Analysis presents
major technical advancements and research findings in the field of
machine learning in biomedical image and data analysis. The book
examines recent technologies and studies in preclinical and
clinical practice in computational intelligence. The authors
present leading-edge research in the science of processing,
analyzing and utilizing all aspects of advanced computational
machine learning in biomedical image and data analysis. As the
application of machine learning is spreading to a variety of
biomedical problems, including automatic image segmentation, image
classification, disease classification, fundamental biological
processes, and treatments, this is an ideal reference. Machine
Learning techniques are used as predictive models for many types of
applications, including biomedical applications. These techniques
have shown impressive results across a variety of domains in
biomedical engineering research. Biology and medicine are data-rich
disciplines, but the data are complex and often ill-understood,
hence the need for new resources and information.
This book focuses on energy efficiency concerns in fog-edge
computing and the requirements related to Industry 4.0 and
next-generation networks like 5G and 6G. This book guides the
research community about practical approaches, methodological, and
moral questions in any nations' journey to conserve energy in
fog-edge computing environments. It discusses a detailed approach
required to conserve energy and comparative case studies with
respect to various performance evaluation metrics, such as energy
conservation, resource allocation strategies, task allocation
strategies, VM migration, and load-sharing strategies with
state-of-the-art approaches, with fog and edge networks.
This book focuses on sustainability issues post COVID-19 outbreak,
discusses ways to restrict global spread of the pandemic, and also
how to survive holistically in the environment. It also discusses
the economic impacts on the world due to the coronavirus outbreak.
There is a strong need for monitoring and analysis of pandemics for
sustainability like epidemic risk analysis by using pattern
recognition or the mental health challenges during an outbreak.
This book presents ways to find solutions and gives insights to
explore innovative methods and predictive modeling techniques, such
that masses are prevented from pandemics.
This book discusses an interdisciplinary field which combines two
major domains: healthcare and data analytics. It presents research
studies by experts helping to fight discontent, distress, anxiety
and unrealized potential by using mathematical models, machine
learning, artificial intelligence, etc. and take preventive
measures beforehand. Psychological disorders and biological
abnormalities are significantly related with the applications of
cognitive illnesses which has increased significantly in
contemporary years and needs rapid investigation. The research
content of this book is helpful for psychological undergraduates,
health workers and their trainees, therapists, medical
psychologists, and nurses.
This book addresses the Internet of Things (IoT), an essential
topic in the technology industry, policy, and engineering circles,
and one that has become headline news in both the specialty press
and the popular media. The book focuses on energy efficiency
concerns in IoT and the requirements related to Industry 4.0. It is
the first-ever "how-to" guide on frequently overlooked practical,
methodological, and moral questions in any nations' journey to
reducing energy consumption in IoT devices. The book discusses
several examples of energy-efficient IoT, ranging from simple
devices like indoor temperature sensors, to more complex sensors
(e.g. electrical power measuring devices), actuators (e.g. HVAC
room controllers, motors) and devices (e.g. industrial
circuit-breakers, PLC for home, building or industrial automation).
It provides a detailed approach to conserving energy in IoT
devices, and comparative case studies on performance evaluation
metrics, state-of-the-art approaches, and IoT legislation.
This book addresses the Internet of Things (IoT), an essential
topic in the technology industry, policy, and engineering circles,
and one that has become headline news in both the specialty press
and the popular media. The book focuses on energy efficiency
concerns in IoT and the requirements related to Industry 4.0. It is
the first-ever "how-to" guide on frequently overlooked practical,
methodological, and moral questions in any nations' journey to
reducing energy consumption in IoT devices. The book discusses
several examples of energy-efficient IoT, ranging from simple
devices like indoor temperature sensors, to more complex sensors
(e.g. electrical power measuring devices), actuators (e.g. HVAC
room controllers, motors) and devices (e.g. industrial
circuit-breakers, PLC for home, building or industrial automation).
It provides a detailed approach to conserving energy in IoT
devices, and comparative case studies on performance evaluation
metrics, state-of-the-art approaches, and IoT legislation.
The book describes the emergence of big data technologies and the
role of Spark in the entire big data stack. It compares Spark and
Hadoop and identifies the shortcomings of Hadoop that have been
overcome by Spark. The book mainly focuses on the in-depth
architecture of Spark and our understanding of Spark RDDs and how
RDD complements big data's immutable nature, and solves it with
lazy evaluation, cacheable and type inference. It also addresses
advanced topics in Spark, starting with the basics of Scala and the
core Spark framework, and exploring Spark data frames, machine
learning using Mllib, graph analytics using Graph X and real-time
processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It
then goes on to investigate Spark using PySpark and R. Focusing on
the current big data stack, the book examines the interaction with
current big data tools, with Spark being the core processing layer
for all types of data. The book is intended for data engineers and
scientists working on massive datasets and big data technologies in
the cloud. In addition to industry professionals, it is helpful for
aspiring data processing professionals and students working in big
data processing and cloud computing environments.
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