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Artificial Intelligence for Neurological Disorders provides a
comprehensive resource of state-of-the-art approaches for AI, big
data analytics and machine learning-based neurological research.
The book discusses many machine learning techniques to detect
neurological diseases at the cellular level, as well as other
applications such as image segmentation, classification and image
indexing, neural networks and image processing methods. Chapters
include AI techniques for the early detection of neurological
disease and deep learning applications using brain imaging methods
like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or
neuromuscular rehabilitation. The goal of this book is to provide
readers with broad coverage of these methods to encourage an even
wider adoption of AI, Machine Learning and Big Data Analytics for
problem-solving and stimulating neurological research and therapy
advances.
Wireless Sensor Networks and the Internet of Things: Future
Directions and Applications explores a wide range of important and
real-time issues and applications in this ever-advancing field.
Different types of WSN and IoT technologies are discussed in order
to provide a strong framework of reference, and the volume places
an emphasis on solutions to the challenges of protection,
conservation, evaluation, and implementation of WSN and IoT that
lead to low-cost products, energy savings, low carbon usage, higher
quality, and global competitiveness. The volume is divided into
four sections that cover: Wireless sensor networks and their
relevant applications Smart monitoring and control systems with the
Internet of Things Attacks, threats, vulnerabilities, and defensive
measures for smart systems Research challenges and opportunities
This collection of chapters on an important and diverse range of
issues presents case studies and applications of cutting-edge
technologies of WSN and IoT that will be valuable for academic
communities in computer science, information technology, and
electronics, including cyber security, monitoring, and data
collection. The informative material presented here can be applied
to many sectors, including agriculture, energy and power, resource
management, biomedical and health care, business management, and
others.
Discusses deep learning, IOT, machine learning, and biomedical data
analysis with broad coverage of basic scientific applications
Presents deep learning and the tremendous improvement in accuracy,
robustness, and cross-language generalizability it has over
conventional approaches Discusses various techniques of IOT systems
for healthcare data analytics Provides state-of-the-art methods of
deep learning, machine learning and IoT in biomedical and health
informatics Focuses more on the application of algorithms in
various real life biomedical and engineering problems
Cloud Computing and Big Data technologies have become the new
descriptors of the digital age. The global amount of digital data
has increased more than nine times in volume in just five years and
by 2030 its volume may reach a staggering 65 trillion gigabytes.
This explosion of data has led to opportunities and transformation
in various areas such as healthcare, enterprises, industrial
manufacturing and transportation. New Cloud Computing and Big Data
tools endow researchers and analysts with novel techniques and
opportunities to collect, manage and analyze the vast quantities of
data. In Cloud and Big Data Analytics, the two areas of Swarm
Intelligence and Deep Learning are a developing type of Machine
Learning techniques that show enormous potential for solving
complex business problems. Deep Learning enables computers to
analyze large quantities of unstructured and binary data and to
deduce relationships without requiring specific models or
programming instructions. This book introduces the state-of-the-art
trends and advances in the use of Machine Learning in Cloud and Big
Data Analytics. The book will serve as a reference for Data
Scientists, systems architects, developers, new researchers and
graduate level students in Computer and Data science. The book will
describe the concepts necessary to understand current Machine
Learning issues, challenges and possible solutions as well as
upcoming trends in Big Data Analytics.
Wireless Sensor Networks and the Internet of Things: Future
Directions and Applications explores a wide range of important and
real-time issues and applications in this ever-advancing field.
Different types of WSN and IoT technologies are discussed in order
to provide a strong framework of reference, and the volume places
an emphasis on solutions to the challenges of protection,
conservation, evaluation, and implementation of WSN and IoT that
lead to low-cost products, energy savings, low carbon usage, higher
quality, and global competitiveness. The volume is divided into
four sections that cover: Wireless sensor networks and their
relevant applications Smart monitoring and control systems with the
Internet of Things Attacks, threats, vulnerabilities, and defensive
measures for smart systems Research challenges and opportunities
This collection of chapters on an important and diverse range of
issues presents case studies and applications of cutting-edge
technologies of WSN and IoT that will be valuable for academic
communities in computer science, information technology, and
electronics, including cyber security, monitoring, and data
collection. The informative material presented here can be applied
to many sectors, including agriculture, energy and power, resource
management, biomedical and health care, business management, and
others.
This book plays a significant role in improvising human life to a
great extent. The new applications of soft computing can be
regarded as an emerging field in computer science, automatic
control engineering, medicine, biology application, natural
environmental engineering, and pattern recognition. Now, the
exemplar model for soft computing is human brain. The use of
various techniques of soft computing is nowadays successfully
implemented in many domestic, commercial, and industrial
applications due to the low-cost and very high-performance digital
processors and also the decline price of the memory chips. This is
the main reason behind the wider expansion of soft computing
techniques and its application areas. These computing methods also
play a significant role in the design and optimization in diverse
engineering disciplines. With the influence and the development of
the Internet of things (IoT) concept, the need for using soft
computing techniques has become more significant than ever. In
general, soft computing methods are closely similar to biological
processes than traditional techniques, which are mostly based on
formal logical systems, such as sentential logic and predicate
logic, or rely heavily on computer-aided numerical analysis. Soft
computing techniques are anticipated to complement each other. The
aim of these techniques is to accept imprecision, uncertainties,
and approximations to get a rapid solution. However, recent
advancements in representation soft computing algorithms (fuzzy
logic,evolutionary computation, machine learning, and probabilistic
reasoning) generate a more intelligent and robust system providing
a human interpretable, low-cost, approximate solution. Soft
computing-based algorithms have demonstrated great performance to a
variety of areas including multimedia retrieval, fault tolerance,
system modelling, network architecture, Web semantics, big data
analytics, time series, biomedical and health informatics, etc.
Soft computing approaches such as genetic programming (GP), support
vector machine-firefly algorithm (SVM-FFA), artificial neural
network (ANN), and support vector machine-wavelet (SVM-Wavelet)
have emerged as powerful computational models. These have also
shown significant success in dealing with massive data analysis for
large number of applications. All the researchers and practitioners
will be highly benefited those who are working in field of computer
engineering, medicine, biology application, signal processing, and
mechanical engineering. This book is a good collection of
state-of-the-art approaches for soft computing-based applications
to various engineering fields. It is very beneficial for the new
researchers and practitioners working in the field to quickly know
the best performing methods. They would be able to compare
different approaches and can carry forward their research in the
most important area of research which has direct impact on
betterment of the human life and health. This book is very useful
because there is no book in the market which provides a good
collection of state-of-the-art methods of soft computing-based
models for multimedia retrieval, fault tolerance, system modelling,
network architecture, Web semantics, big data analytics, time
series, and biomedical and health informatics.
Implementation of Smart Healthcare Systems using AI, IoT, and
Blockchain provides imperative research on the development of data
fusion and analytics for healthcare and their implementation into
current issues in a real-time environment. While highlighting IoT,
bio-inspired computing, big data, and evolutionary programming, the
book explores various concepts and theories of data fusion, IoT,
and Big Data Analytics. It also investigates the challenges and
methodologies required to integrate data from multiple
heterogeneous sources, analytical platforms in healthcare sectors.
This book is unique in the way that it provides useful insights
into the implementation of a smart and intelligent healthcare
system in a post-Covid-19 world using enabling technologies like
Artificial Intelligence, Internet of Things, and blockchain in
providing transparent, faster, secure and privacy preserved
healthcare ecosystem for the masses.
This book comprehensively covers the topic of COVID-19 and other
pandemics and epidemics data analytics using computational
modelling. Biomedical and Health Informatics is an emerging field
of research at the intersection of information science, computer
science, and health care. The new era of pandemics and epidemics
bring tremendous opportunities and challenges due to the plentiful
and easily available medical data allowing for further analysis.
The aim of pandemics and epidemics research is to ensure
high-quality, efficient healthcare, better treatment and quality of
life by efficiently analyzing the abundant medical, and healthcare
data including patient's data, electronic health records (EHRs) and
lifestyle. In the past, it was a common requirement to have domain
experts for developing models for biomedical or healthcare.
However, recent advances in representation learning algorithms
allow us to automatically learn the pattern and representation of
the given data for the development of such models. Medical Image
Mining, a novel research area (due to its large amount of medical
images) are increasingly generated and stored digitally. These
images are mainly in the form of: computed tomography (CT), X-ray,
nuclear medicine imaging (PET, SPECT), magnetic resonance imaging
(MRI) and ultrasound. Patients' biomedical images can be digitized
using data mining techniques and may help in answering several
important and critical questions related to health care. Image
mining in medicine can help to uncover new relationships between
data and reveal new and useful information that can be helpful for
scientists and biomedical practitioners. Assessing COVID-19 and
Other Pandemics and Epidemics using Computational Modelling and
Data Analysis will play a vital role in improving human life in
response to pandemics and epidemics. The state-of-the-art
approaches for data mining-based medical and health related
applications will be of great value to researchers and
practitioners working in biomedical, health informatics, and
artificial intelligence..
This book plays a significant role in improvising human life to a
great extent. The new applications of soft computing can be
regarded as an emerging field in computer science, automatic
control engineering, medicine, biology application, natural
environmental engineering, and pattern recognition. Now, the
exemplar model for soft computing is human brain. The use of
various techniques of soft computing is nowadays successfully
implemented in many domestic, commercial, and industrial
applications due to the low-cost and very high-performance digital
processors and also the decline price of the memory chips. This is
the main reason behind the wider expansion of soft computing
techniques and its application areas. These computing methods also
play a significant role in the design and optimization in diverse
engineering disciplines. With the influence and the development of
the Internet of things (IoT) concept, the need for using soft
computing techniques has become more significant than ever. In
general, soft computing methods are closely similar to biological
processes than traditional techniques, which are mostly based on
formal logical systems, such as sentential logic and predicate
logic, or rely heavily on computer-aided numerical analysis. Soft
computing techniques are anticipated to complement each other. The
aim of these techniques is to accept imprecision, uncertainties,
and approximations to get a rapid solution. However, recent
advancements in representation soft computing algorithms (fuzzy
logic,evolutionary computation, machine learning, and probabilistic
reasoning) generate a more intelligent and robust system providing
a human interpretable, low-cost, approximate solution. Soft
computing-based algorithms have demonstrated great performance to a
variety of areas including multimedia retrieval, fault tolerance,
system modelling, network architecture, Web semantics, big data
analytics, time series, biomedical and health informatics, etc.
Soft computing approaches such as genetic programming (GP), support
vector machine-firefly algorithm (SVM-FFA), artificial neural
network (ANN), and support vector machine-wavelet (SVM-Wavelet)
have emerged as powerful computational models. These have also
shown significant success in dealing with massive data analysis for
large number of applications. All the researchers and practitioners
will be highly benefited those who are working in field of computer
engineering, medicine, biology application, signal processing, and
mechanical engineering. This book is a good collection of
state-of-the-art approaches for soft computing-based applications
to various engineering fields. It is very beneficial for the new
researchers and practitioners working in the field to quickly know
the best performing methods. They would be able to compare
different approaches and can carry forward their research in the
most important area of research which has direct impact on
betterment of the human life and health. This book is very useful
because there is no book in the market which provides a good
collection of state-of-the-art methods of soft computing-based
models for multimedia retrieval, fault tolerance, system modelling,
network architecture, Web semantics, big data analytics, time
series, and biomedical and health informatics.
This book comprehensively covers the topic of COVID-19 and other
pandemics and epidemics data analytics using computational
modelling. Biomedical and Health Informatics is an emerging field
of research at the intersection of information science, computer
science, and health care. The new era of pandemics and epidemics
bring tremendous opportunities and challenges due to the plentiful
and easily available medical data allowing for further analysis.
The aim of pandemics and epidemics research is to ensure
high-quality, efficient healthcare, better treatment and quality of
life by efficiently analyzing the abundant medical, and healthcare
data including patient's data, electronic health records (EHRs) and
lifestyle. In the past, it was a common requirement to have domain
experts for developing models for biomedical or healthcare.
However, recent advances in representation learning algorithms
allow us to automatically learn the pattern and representation of
the given data for the development of such models. Medical Image
Mining, a novel research area (due to its large amount of medical
images) are increasingly generated and stored digitally. These
images are mainly in the form of: computed tomography (CT), X-ray,
nuclear medicine imaging (PET, SPECT), magnetic resonance imaging
(MRI) and ultrasound. Patients' biomedical images can be digitized
using data mining techniques and may help in answering several
important and critical questions related to health care. Image
mining in medicine can help to uncover new relationships between
data and reveal new and useful information that can be helpful for
scientists and biomedical practitioners. Assessing COVID-19 and
Other Pandemics and Epidemics using Computational Modelling and
Data Analysis will play a vital role in improving human life in
response to pandemics and epidemics. The state-of-the-art
approaches for data mining-based medical and health related
applications will be of great value to researchers and
practitioners working in biomedical, health informatics, and
artificial intelligence..
AI, Edge, and IoT Smart Agriculture integrates applications of IoT,
edge computing, and data analytics for sustainable agricultural
development and introduces Edge of Thing-based data analytics and
IoT for predictability of crop, soil, and plant disease occurrence
for improved sustainability and increased profitability. The book
also addresses precision irrigation, precision horticulture,
greenhouse IoT, livestock monitoring, IoT ecosystem for
agriculture, mobile robot for precision agriculture, energy
monitoring, storage management, and smart farming. The book
provides an overarching focus on sustainable environment and
sustainable economic development through smart and e-agriculture.
Providing a medium for the exchange of expertise and inspiration,
contributions from both smart agriculture and data mining
researchers around the world provide foundational insights. The
book provides practical application opportunities for the
resolution of real-world problems, including contributions from the
data mining, data analytics, Edge of Things, and cloud research
communities working in the farming production sector. The book
offers broad coverage of the concepts, themes, and instruments of
this important and evolving area of IOT-based agriculture, Edge of
Things and cloud-based farming, Greenhouse IOT, mobile agriculture,
sustainable agriculture, and big data analytics in agriculture
toward smart farming.
The Internet of Medical Things (IoMT) allows clinicians to monitor
patients remotely via a network of wearable or implantable devices.
The devices are embedded with software or sensors to enable them to
send and receive data via the internet so that healthcare
professionals can monitor health data such as vital statistics,
metabolic rates or drug delivery regimens, and can provide advice
or treatment plans based on this real-world, real-time data. This
edited book discusses key IoT technologies that facilitate and
enhance this process, such as computer algorithms, network
architecture, wireless communications, and network security.
Providing a systemic review of trends, challenges and future
directions of IoMT technologies, the book examines applications
such as breast cancer monitoring systems, patient-centric systems
for handling, tracking and monitoring virus variants, and
video-based solutions for monitoring babies. The book discusses
machine learning techniques for the management of clinical data and
includes security issues such as the use of blockchain technology.
Written by a range of international researchers, this book is a
great resource for computer engineering researchers and
practitioners in the fields of data mining, machine learning,
artificial intelligence and the IoT in the healthcare sector.
Blockchain and artificial intelligence (AI) in industrial internet
of things is an emerging field of research at the intersection of
information science, computer science, and electronics engineering.
The radical digitization of industry coupled with the explosion of
the internet of things (IoT) has set up a paradigm shift for
industrial and manufacturing companies. There exists a need for a
comprehensive collection of original research of the best
performing methods and state-of-the-art approaches in this area of
blockchain, AI, and the industrial internet of things in this new
era for industrial and manufacturing companies. Blockchain and AI
Technology in the Industrial Internet of Things compares different
approaches to the industrial internet of things and explores the
direct impact blockchain and AI technology have on the betterment
of the human life. The chapters provide the latest advances in the
field and provide insights and concerns on the concept and growth
of the industrial internet of things. While including research on
security and privacy, supply chain management systems, performance
analysis, and a variety of industries, this book is ideal for
professionals, researchers, managers, technologists, security
analysts, executives, practitioners, researchers, academicians, and
students looking for advanced research and information on the
newest technologies, advances, and approaches for blockchain and AI
in the industrial internet of things.
Blockchain and artificial intelligence (AI) in industrial internet
of things is an emerging field of research at the intersection of
information science, computer science, and electronics engineering.
The radical digitization of industry coupled with the explosion of
the internet of things (IoT) has set up a paradigm shift for
industrial and manufacturing companies. There exists a need for a
comprehensive collection of original research of the best
performing methods and state-of-the-art approaches in this area of
blockchain, AI, and the industrial internet of things in this new
era for industrial and manufacturing companies. Blockchain and AI
Technology in the Industrial Internet of Things compares different
approaches to the industrial internet of things and explores the
direct impact blockchain and AI technology have on the betterment
of the human life. The chapters provide the latest advances in the
field and provide insights and concerns on the concept and growth
of the industrial internet of things. While including research on
security and privacy, supply chain management systems, performance
analysis, and a variety of industries, this book is ideal for
professionals, researchers, managers, technologists, security
analysts, executives, practitioners, researchers, academicians, and
students looking for advanced research and information on the
newest technologies, advances, and approaches for blockchain and AI
in the industrial internet of things.
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