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This book emphasizes the applications of advances in data
processing methods for Artificial Intelligence in today's
fast-changing world, as well as to serve society through research,
innovation, and development in this field. This book is applicable
to a wide range of data that contribute to data science concerns
and can be used to promote research in this high-potential new
field. People's perceptions of the world and how they conduct their
lives have changed dramatically as a result of technological
advancements. The world has been gripped by technology, and the
advances that are being made every day are undeniably transforming
the planet. In the domains of Big Data, engineering, and data
science, this cutting-edge technology is ready to support us.
Artificial intelligence (AI) is a current research topic because it
can be applied to a wide range of applications and disciplines to
solve complicated problems and find optimal solutions. In research,
medicine, technology, and the social sciences, the benefits of AI
have already been proven. Data science, also known as pattern
analytics and mining, is a technique for extracting useful and
relevant information from databases, enabling better
decision-making and strategy formulation in a range of fields. As a
result of the exponential growth of data in recent years, the
combined notions of big data and AI have given rise to many study
areas, such as scale-up behaviour from classical algorithms.
Furthermore, combining numerous AI technologies from other areas
(such as vision, security, control, and biology) in order to build
efficient and durable systems that interact in the real world is a
new problem. Despite recent improvements in fundamental AI
technologies, the integration of these skills into larger,
trustworthy, transparent, and maintainable systems is still in its
development. Both conceptually and practically, there are a number
of unanswered issues.
Convolutional neural networks (CNNs), a type of deep neural network
that has become dominant in a variety of computer vision tasks, in
recent few years has attracted interest across a variety of domains
due to their high efficiency at extracting meaningful information
from visual imagery. Convolutional neural networks (CNNs) excel at
a wide range of machine learning and deep learning tasks. As
sensor-enabled internet of things (IoT) devices pervade every
aspect of modern life, it is becoming increasingly critical to run
CNN inference, a computationally intensive application, on
resource-constrained devices. Through this edited volume we aim to
provide a structured presentation of CNN enabled IoT applications
in vision, speech, and natural language processing. This book
discusses a variety of CNN techniques and applications, including
but not limited to, IoT enabled CNN for speech de-noising, a smart
app for visually impaired people, disease detection, ECG signal
analysis, weather monitoring, texture analysis, etc. Unlike other
books on the market, this book covers the tools, techniques, and
challenges associated with the implementation of CNN algorithms,
computation time, and the complexity associated with reasoning and
modelling various types of data. We have included CNN's current
research trends and future directions.
The book presents recent trends and solutions to help healthcare
sectors and medical staff protect themselves and others and limit
the spread of the COVID-19. The book also presents the problems and
challenges researchers and academics face in tackling this
monumental task. Topics include: Unmanned Aerial Vehicle (UAV) or
drones that can be used to detect infected people in different
areas; robots used in fighting the COVID-19 by protecting workers
and staff dealing with infected people; blockchain technology that
secures sensitive transactions in strict confidentiality. With
contributions from experts from around the world, this book aims to
help those creating and honing technology to help with this global
threat.
The book presents recent trends and solutions to help healthcare
sectors and medical staff protect themselves and others and limit
the spread of the COVID-19. The book also presents the problems and
challenges researchers and academics face in tackling this
monumental task. Topics include: Unmanned Aerial Vehicle (UAV) or
drones that can be used to detect infected people in different
areas; robots used in fighting the COVID-19 by protecting workers
and staff dealing with infected people; blockchain technology that
secures sensitive transactions in strict confidentiality. With
contributions from experts from around the world, this book aims to
help those creating and honing technology to help with this global
threat.
With the increase in urban population, it became necessary to keep
track of the object of interest. In favor of SDGs for sustainable
smart city, with the advancement in technology visual tracking
extends to track multi-target present in the scene rather
estimating location for single target only. In contrast to single
object tracking, multi-target introduces one extra step of
detection. Tracking multi-target includes detecting and
categorizing the target into multiple classes in the first frame
and provides each individual target an ID to keep its track in the
subsequent frames of a video stream. One category of multi-target
algorithms exploits global information to track the target of the
detected target. On the other hand, some algorithms consider
present and past information of the target to provide efficient
tracking solutions. Apart from these, deep leaning-based algorithms
provide reliable and accurate solutions. But, these algorithms are
computationally slow when applied in real-time. This book presents
and summarizes the various visual tracking algorithms and
challenges in the domain. The various feature that can be extracted
from the target and target saliency prediction is also covered. It
explores a comprehensive analysis of the evolution from traditional
methods to deep learning methods, from single object tracking to
multi-target tracking. In addition, the application of visual
tracking and the future of visual tracking can also be introduced
to provide the future aspects in the domain to the reader. This
book also discusses the advancement in the area with critical
performance analysis of each proposed algorithm. This book will be
formulated with intent to uncover the challenges and possibilities
of efficient and effective tracking of single or multi-object,
addressing the various environmental and hardware
challenges. The intended audience includes academicians,
engineers, postgraduate students, developers, professionals,
military personals, scientists, data analysts, practitioners, and
people who are interested in exploring more about tracking.·
Another projected audience are the researchers and academicians who
identify and develop methodologies, frameworks, tools, and
applications through reference citations, literature reviews,
quantitative/qualitative results, and discussions.
This book presents recent technologies that explore artificial
intelligence (AI) and its scope in Internet of Things (IoT) enabled
areas for productivity and the betterment of society. The book aims
at targeting audiences of several disciplines to share research,
suggest solutions, and future trends in the field of AI using IoT.
Rather than looking at the field from only a theoretical or only a
practical perspective, this book unifies both aspects to give a
holistic understanding of the AI paradigm for IoT. The book focuses
on timely topics related to the field of AI enabled IoT
applications at large. The book consists of four major parts:
fundamentals, theoretical discussion, critical applications, and
the learning algorithms. These contents shall include the basics,
types, tools, and techniques of AI. Finally, applications of AI
enabled IoT in several areas are presented including health,
security, climate change, agricultural engineering, bioinformatics,
biomedicine, smart applications, natural language processing,
social and economic implications of AI enabled IoT, as well as
robotics, sustainability, risk management, seismic data processing,
smart grid management, text analysis, security, privacy, and
ethics.
This book presents recent technologies that explore artificial
intelligence (AI) and its scope in Internet of Things (IoT) enabled
areas for productivity and the betterment of society. The book aims
at targeting audiences of several disciplines to share research,
suggest solutions, and future trends in the field of AI using IoT.
Rather than looking at the field from only a theoretical or only a
practical perspective, this book unifies both aspects to give a
holistic understanding of the AI paradigm for IoT. The book focuses
on timely topics related to the field of AI enabled IoT
applications at large. The book consists of four major parts:
fundamentals, theoretical discussion, critical applications, and
the learning algorithms. These contents shall include the basics,
types, tools, and techniques of AI. Finally, applications of AI
enabled IoT in several areas are presented including health,
security, climate change, agricultural engineering, bioinformatics,
biomedicine, smart applications, natural language processing,
social and economic implications of AI enabled IoT, as well as
robotics, sustainability, risk management, seismic data processing,
smart grid management, text analysis, security, privacy, and
ethics.
In today's modern world, it is essential for businesses to remain
competitive and up to date on the latest technology that can
support their processes. The use of the internet of things (IoT) in
marketing, particularly in digital marketing, is an evolving field
that requires further study to better understand its potential.
Global Applications of the Internet of Things in Digital Marketing
focuses on the applications of IoT in customizing content and
developing a data-based marketing framework that helps marketers
create different experiences in bridging the digital and physical
world, develop a closer connection with the consumers, and provide
highly contextual and tailored messages to consumers. Covering key
topics such as brand image, social media, and website development,
this premier reference source is ideal for business owners,
managers, marketers, researchers, scholars, academicians,
practitioners, instructors, and students.
In today's modern world, it is essential for businesses to remain
competitive and up to date on the latest technology that can
support their processes. The use of the internet of things (IoT) in
marketing, particularly in digital marketing, is an evolving field
that requires further study to better understand its potential.
Global Applications of the Internet of Things in Digital Marketing
focuses on the applications of IoT in customizing content and
developing a data-based marketing framework that helps marketers
create different experiences in bridging the digital and physical
world, develop a closer connection with the consumers, and provide
highly contextual and tailored messages to consumers. Covering key
topics such as brand image, social media, and website development,
this premier reference source is ideal for business owners,
managers, marketers, researchers, scholars, academicians,
practitioners, instructors, and students.
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