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This book, Hybridization of Blockchain and Cloud Computing:
Overcoming Security Issues in IoT, explores many aspects of
blockchain technologies and provides an overview of the latest
cutting-edge developments along with their diversified business
applications. It addresses the challenges, emerging issues, and
problems in classical centralized architecture and how blockchain
platforms provide almost magical solutions and novel services for
improving business processes. Focusing on blockchain
technology-based distributed transactions for industrial use, the
chapters address applications in sectors such as healthcare,
pharmaceutical drug supply, finance and banking, agriculture and
farming, semantic web services, etc. The book explores blockchain
applications associated with security issues, cryptocurrencies,
cloud computing, Internet of Things, estimating intelligence (of
crows, as an example) using artificial intelligence, and more. The
chapters discuss deployment, feasibility studies, and the many
diverse services offered by blockchain technology. This volume will
provide valuable up-to-date information for researchers,
professionals, and anyone involved in developing an interactive
secured environment for their respective domains.
This volume helps to fill the gap between data analytics, image
processing, and soft computing practices. Soft computing methods
are used to focus on data analytics and image processing to develop
good intelligent systems. To this end, readers of this volume will
find quality research that presents the current trends, advanced
methods, and hybridized techniques relating to data analytics and
intelligent systems. The book also features case studies related to
medical diagnosis with the use of image processing and soft
computing algorithms in particular models. Providing extensive
coverage of biometric systems, soft computing, image processing,
artificial intelligence, and data analytics, the chapter authors
discuss the latest research issues, present solutions to research
problems, and look at comparative analysis with earlier results.
Topics include some of the most important challenges and
discoveries in intelligent systems today, such as computer vision
concepts and image identification, data analysis and computational
paradigms, deep learning techniques, face and speaker recognition
systems, and more.
Cognitive Computing is a new topic which aims to simulate human
thought processes using computers that self-learn through data
mining, pattern recognition, and natural language processing. This
book focuses on the applications of Cognitive Computing in areas
like Robotics, Blockchain, Deep Learning, and Wireless
Technologies. This book covers the basics of Green Computing,
discusses Cognitive Science methodologies in Robotics, Computer
Science, Wireless Networks, and Deep Learning. It goes on to
present empirical data and research techniques, modelling
techniques and offers a data-driven approach to decision making and
problem solving. This book is written for researchers,
academicians, undergraduate and graduate students, and industry
persons who are working on current applications of Cognitive
Computing.
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.
This cutting-edge volume focuses on how artificial intelligence can
be used to give computers the ability to imitate human sight. With
contributions from researchers in diverse countries, including
Thailand, Spain, Japan, Turkey, Australia, and India, the book
explains the essential modules that are necessary for comprehending
artificial intelligence experiences to provide machines with the
power of vision. The volume also presents innovative research
developments, applications, and current trends in the field. The
chapters cover such topics as visual quality improvement,
Parkinson's disease diagnosis, hypertensive retinopathy detection
through retinal fundus, big image data processing, N-grams for
image classification, medical brain images, chatbot applications,
credit score improvisation, vision-based vehicle lane detection,
damaged vehicle parts recognition, partial image encryption of
medical images, and image synthesis. The chapter authors show
different approaches to computer vision, image processing, and
frameworks for machine learning to build automated and stable
applications. Deep learning is included for making immersive
application-based systems, pattern recognition, and biometric
systems. The book also considers efficiency and comparison at
various levels of using algorithms for real-time applications,
processes, and 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.
This book focuses on the impact of artificial intelligence (AI) and
machine learning (ML) models on supply chain operations in industry
4.0. The chapters illustrate the AI and ML models for all
functional areas of operations in SCM. The book also includes
examples using ML models like handling supply-to-demand imbalances,
triggering automated responses, and reinforcing customer
relationships. It describes the evolution of blockchain technology
coupled with the ability to automate business logic for the
transparency of goods, infrastructure, products, and licenses in
software. The book also includes case studies that provide a
problem statement and industry overcome by applying ML and AI
technologies. This book is suitable for undergraduates,
postgraduates, industrial professionals, business executives,
entrepreneurs, and freelancers to encourage practical learning on
AI and ML algorithms in SCM 4.0. Additionally, this book will
provide computer science and information system professionals with
the latest technologies embedded in the corporate world.
This book focuses on the impact of artificial intelligence (AI) and
machine learning (ML) models on supply chain operations in industry
4.0. The chapters illustrate the AI and ML models for all
functional areas of operations in SCM. The book also includes
examples using ML models like handling supply-to-demand imbalances,
triggering automated responses, and reinforcing customer
relationships. It describes the evolution of blockchain technology
coupled with the ability to automate business logic for the
transparency of goods, infrastructure, products, and licenses in
software. The book also includes case studies that provide a
problem statement and industry overcome by applying ML and AI
technologies. This book is suitable for undergraduates,
postgraduates, industrial professionals, business executives,
entrepreneurs, and freelancers to encourage practical learning on
AI and ML algorithms in SCM 4.0. Additionally, this book will
provide computer science and information system professionals with
the latest technologies embedded in the corporate world.
The approaches to computer vision have undergone a long journey in
recent years, but still, innovations are continuing with leverage
increases in computing power, new data availability, and new ways
to leverage machine-learning algorithms. As a branch of artificial
intelligence (AI), computer vision brings meaningful information
from images and videos. Such innovations help communicators to run
better campaigns, amplify messages further, and stand out in a
noisy, crowded marketplace. Investigations in Pattern Recognition
and Computer Vision for Industry 4.0 provides a holistic discussion
of the new practical applications and use cases of computer vision
and communications. Covering topics such as social media filters,
mobile computer vision, and AI-powered image editing, this book is
ideal for academicians, researchers, postgraduate students,
professional data analysts, research and development centers,
organizations dealing with healthcare informatics, and IT firms.
Emotional intelligence has emerged as an important area of research
in the artificial intelligence field as it covers a wide range of
real-life domains. Though machines may never need all the emotional
skills that people need, there is evidence to suggest that machines
require at least some of these skills to appear intelligent when
interacting with people. To understand how deep learning-based
emotional intelligence can be applied and utilized across
industries, further study on its opportunities and future
directions is required. Multidisciplinary Applications of Deep
Learning-Based Artificial Emotional Intelligence explores
artificial intelligence applications, such as machine and deep
learning, in emotional intelligence and examines their use towards
attaining emotional intelligence acceleration and augmentation. It
provides research on tools used to simplify and streamline the
formation of deep learning for system architects and designers.
Covering topics such as data analytics, deep learning, knowledge
management, and virtual emotional intelligence, this reference work
is ideal for computer scientists, engineers, industry
professionals, researchers, scholars, practitioners, academicians,
instructors, and students.
Computer vision is an interdisciplinary scientific field that deals
with how computers obtain, store, interpret and understand digital
images or videos using artificial intelligence based on neural
networks, machine learning and deep learning methodologies. They
are used in countless applications such as image retrieval and
classification, driving and transport monitoring, medical
diagnostics and aerial monitoring. Written by a team of
international experts, this edited book covers the state-of-the-art
of advanced research in the fields of computer vision and
recognition systems from fundamental concepts to methodologies and
technologies and real world applications including object
detection, biometrics, Deepfake detection, sentiment and emotion
analysis, traffic enforcement camera monitoring, vehicle control
and aerial remote sensing imagery. The book will be useful for
industry and academic researchers, scientists and engineers in the
fields of computer vision, machine vision, image processing and
recognition, multimedia, AI, machine and deep learning, data
science, biometrics, security, and signal processing. It will also
make a great course reference for advanced students and lecturers
in these fields of research.
In Computer Vision, Image Processing, Artificial Intelligence and
Neural Networks object recognition is one of the most successful
applications of image or object analysis and understanding.The
recognition system typically involves some sort of sensor, the use
of a model database in which all the objects "models"
representations are saved, and a decision-making ability.When a
sensor views an object the digitized image is processed so as to
represent it in the same way as the models are represented in the
databases.Then a recognition algorithm tries to find the model to
which the object best matches.For the view-based recognition, the
representations take into account the appearance of the object. To
achieve 3D Object recognition(3DOR) the pose of objects are also
saved in the database.In general two 3DOR techniques. They are
Geometric feature-based approach and Appearance- based approach.The
geometric feature-based approach uses properties of shape of object
i.e. lines, curves, and vertices for object recognition
descriptions.But appearance-based 3DOR is the combined effects of
objects shape, reflectance properties, pose and the illumination.
Leading technology firms and research institutions are continuously
exploring new techniques in artificial intelligence and machine
learning. As such, deep learning has now been recognized in various
real-world applications such as computer vision, image processing,
biometrics, pattern recognition, and medical imaging. The deep
learning approach has opened new opportunities that can make such
real-life applications and tasks easier and more efficient. The
Handbook of Research on Deep Learning Innovations and Trends is an
essential scholarly resource that presents current trends and the
latest research on deep learning and explores the concepts,
algorithms, and techniques of data mining and analysis.
Highlighting topics such as computer vision, encryption systems,
and biometrics, this book is ideal for researchers, practitioners,
industry professionals, students, and academicians.
This volume helps to fill the gap between data analytics, image
processing, and soft computing practices. Soft computing methods
are used to focus on data analytics and image processing to develop
good intelligent systems. To this end, readers of this volume will
find quality research that presents the current trends, advanced
methods, and hybridized techniques relating to data analytics and
intelligent systems. The book also features case studies related to
medical diagnosis with the use of image processing and soft
computing algorithms in particular models. Providing extensive
coverage of biometric systems, soft computing, image processing,
artificial intelligence, and data analytics, the chapter authors
discuss the latest research issues, present solutions to research
problems, and look at comparative analysis with earlier results.
Topics include some of the most important challenges and
discoveries in intelligent systems today, such as computer vision
concepts and image identification, data analysis and computational
paradigms, deep learning techniques, face and speaker recognition
systems, and more.
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