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As technology weaves itself more tightly into everyday life,
socio-economic development has become intricately tied to these
ever-evolving innovations. Technology management is now an integral
element of sound business practices, and this revolution has opened
up many opportunities for global communication. However, such swift
change warrants greater research that can foresee and possibly
prevent future complications within and between organizations. The
Handbook of Research on Engineering Innovations and Technology
Management in Organizations is a collection of innovative research
that explores global concerns in the applications of technology to
business and the explosive growth that resulted. Highlighting a
wide range of topics such as cyber security, legal practice, and
artificial intelligence, this book is ideally designed for
engineers, manufacturers, technology managers, technology
developers, IT specialists, productivity consultants, executives,
lawyers, programmers, managers, policymakers, academicians,
researchers, and students.
The healthcare industry is starting to adopt digital twins to
improve personalized medicine, healthcare organization performance,
and new medicine and devices. These digital twins can create useful
models based on information from wearable devices, omics, and
patient records to connect the dots across processes that span
patients, doctors, and healthcare organizations as well as drug and
device manufacturers. Digital twins are digital representations of
human physiology built on computer models. The use of digital twins
in healthcare is revolutionizing clinical processes and hospital
management by enhancing medical care with digital tracking and
advancing modelling of the human body. These tools are of great
help to researchers in studying diseases, new drugs, and medical
devices. Digital Twins and Healthcare: Trends, Techniques, and
Challenges facilitates the advancement and knowledge dissemination
in methodologies and applications of digital twins in the
healthcare and medicine fields. This book raises interest and
awareness of the uses of digital twins in healthcare in the
research community. Covering topics such as deep neural network,
edge computing, and transfer learning method, this premier
reference source is an essential resource for hospital
administrators, pharmacists, medical professionals, IT consultants,
students and educators of higher education, librarians, and
researchers.
With the emergence of smart technology and automated systems in
today's world, big data is being incorporated into many
applications. Trends in data can be detected and objects can be
tracked based on the real-time data that is utilized in everyday
life. These connected sensor devices and objects will provide a
large amount of data that is to be analyzed quickly, as it can
accelerate the transformation of smart technology. The accuracy of
prediction of artificial intelligence (AI) systems is drastically
increasing by using machine learning and other probability and
statistical approaches. Big data and geospatial data help to solve
complex issues and play a vital role in future applications.
Emerging Trends, Techniques, and Applications in Geospatial Data
Science provides an overview of the basic concepts of data science,
related tools and technologies, and algorithms for managing the
relevant challenges in real-time application domains. The book
covers a detailed description for readers with practical ideas
using AI, the internet of things (IoT), and machine learning to
deal with the analysis, modeling, and predictions from big data.
Covering topics such as field spectra, high-resolution sensing
imagery, and spatiotemporal data engineering, this premier
reference source is an excellent resource for data scientists,
computer and IT professionals, managers, mathematicians and
statisticians, health professionals, technology developers,
students and educators of higher education, librarians,
researchers, and academicians.
The recent advancements in the machine learning paradigm have
various applications, however, it has shown significant results in
the field of medical data analysis. The results are highly accurate
and are comparable to human experts. The various research has
proved the high accuracy of deep learning algorithms and has become
a standard choice for analysing medical data, especially medical
images, video, and electronic health records. Deep learning methods
applied to electronic health records are contributing to
understanding the evolution of chronic diseases and predicting the
risk of developing those diseases. Researchers in industry,
hospitals, and academia have published hundreds of scientific
contributions in this area during a pandemic. This book is an ideal
and relevant source of content for data science and healthcare
professionals who want to delve into complex deep learning
algorithms, calibrate models, and improve the predictions of the
trained model on medical imaging. Primary audiences for this book
are professionals and researchers in the fields of data science,
machine learning, deep learning, and AI. Also academicians,
healthcare professionals, or anyone who may have a keen interest in
how the machine and deep learning algorithms are helping in the
identification of solutions to medical sensor/image data analysis,
event detection, segmentation, and abnormality detection,
object/lesion classification, organ/region/landmark localization,
object/lesion detection, organ/substructure segmentation, lesion
segmentation, and medical image registration. The variety of
readers in the fields of government, consulting, healthcare
professionals, as well as the readers from all the social strata,
can also be benefited from this book to improve understanding of
the cutting-edge theory, technologies, methodologies, and
applications of deep Learning algorithms for medical care.
This new volume explores a plethora of blockchain-based solutions
for big data and IoT applications, looking at advances in
real-world applications in several sectors, including higher
education, cybersecurity, agriculture, business and management,
healthcare and biomedical science, construction and project
management, smart city development, and others. Chapters explore
emerging technology to combat the ever-increasing threat of
security to computer systems and offer new architectural solutions
for problems encountered in data management and security. The
chapters help to provide a high level of understanding of various
blockchain algorithms along with the necessary tools and
techniques. The novel architectural solutions in the deployment of
blockchain presented here are the core of the book.
Provides a technical introduction to deepfakes, its benefits, and
the potential harms Presents practical approaches of creation and
detection of deepfakes using Deep Learning (DL) Techniques Draws
attention towards various challenging issues and societal impact of
deepfakes with their existing solutions Includes research analysis
in the domain of DL fakes for assisting the creation and detection
of deepfakes applications Discusses future research directions with
emergence of deepfakes technology
Transportation typically entails crucial "life-death" choices,
delegating crucial decisions to an AI algorithm without any
explanation poses a serious threat. Hence, explainability and
responsible AI is crucial in the context of intelligent
transportation. In Intelligence Transportation System (ITS)
implementations such as traffic management systems and autonomous
driving applications, AI-based control mechanisms are gaining
prominence. Explainable artificial intelligence for intelligent
transportation system tackling certain challenges in the field of
autonomous vehicle, traffic management system, data integration and
analytics and monitor the surrounding environment. The book
discusses and inform researchers on explainable Intelligent
Transportation system. It also discusses prospective methods and
techniques for enabling the interpretability of transportation
systems. The book further focuses on ethical considerations apart
from technical considerations.
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 advanced AI techniques are essential for resolving various
problematic aspects emerging in the field of bioinformatics. This
book covers the recent approaches in artificial intelligence and
machine learning methods and their applications in Genome and Gene
editing, cancer drug discovery classification, and the protein
folding algorithms among others. Deep learning, which is widely
used in image processing, is also applicable in bioinformatics as
one of the most popular artificial intelligence approaches. The
wide range of applications discussed in this book are an
indispensable resource for computer scientists, engineers,
biologists, mathematicians, physicians, and medical informaticists.
Features: Focusses on the cross-disciplinary relation between
computer science and biology and the role of machine learning
methods in resolving complex problems in bioinformatics Provides a
comprehensive and balanced blend of topics and applications using
various advanced algorithms Presents cutting-edge research
methodologies in the area of AI methods when applied to
bioinformatics and innovative solutions Discusses the AI/ML
techniques, their use, and their potential for use in common and
future bioinformatics applications Includes recent achievements in
AI and bioinformatics contributed by a global team of researchers
New technologies such as artificial intelligence, blockchain, the
Internet of Things (IoT), etc. are redefining business processes
around the world at a rapid rate and resulting in both great
opportunities and challenges for businesses. Though these
technologies are extensively being used in developed countries,
emerging economies are also not far behind. Disruptive Technologies
in International Business advances the understanding of
technological applications in business within an international
paradigm. With its in-depth discussions of diverse topics such as
the global value chain (GVC), environmental risk management, IoT,
Surface Mobility, and anime, the book argues that technologies
offer many advantages but there are accompanying risks, challenges,
and disadvantages as well. The need of the hour is to address the
impact of these technologies on the environment, society, and
economy of the world. This book offers a collage of insights on how
these technologies can potentially change the playing field in
businesses and countries and contribute to the betterment of
society. This book will provide business practitioners,
international organizations, government officials, and policy
makers with inspiration and new leads toward more efficient
systems, policies, and operational frameworks in our increasingly
technology-driven society.
Transportation typically entails crucial “life-death” choices,
delegating crucial decisions to an AI algorithm without any
explanation poses a serious threat. Hence, explainability and
responsible AI is crucial in the context of intelligent
transportation. In Intelligence Transportation System (ITS)
implementations such as traffic management systems and autonomous
driving applications, AI-based control mechanisms are gaining
prominence. Explainable artificial intelligence for intelligent
transportation system tackling certain challenges in the field of
autonomous vehicle, traffic management system, data integration and
analytics and monitor the surrounding environment. The book
discusses and inform researchers on explainable Intelligent
Transportation system. It also discusses prospective methods and
techniques for enabling the interpretability of transportation
systems. The book further focuses on ethical considerations apart
from technical considerations.
The concept of Internet of Things has silently existed since the
late nineteenth century but in the current decade expectations and
excitement has peaked. However not many have understood the
profound change that it can usher in. How big this change can be
and how it can transform our working!! This book aims to bring in
this realization with illustrative and practical case studies with
comprehensive concepts. From beginners to practitioners in the
field of academics or industry, it serves as a comprehensive yet
easy to comprehend source of information on the multiple facets of
IoT. Simplistic but comprehensive introduction of the facets of
primarily the industrial IoT Practical adoption cases explaining
the Core technology stack and business applications Comprehensive
view of current technologies which complete the IoT delivery
ecosystem, followed by overview of IoT enabled new business models.
Realistic view of how industrial firms can evolve into the next
stage of maturity along with determinants influencing this
transformation since manufacturing is envisioned to be a key
segment to adopt and benefit from IoT. Detailed analysis of IoT
benefits for the universal triad- energy management, logistics
optimization and distribution channel management. A full-fledged
case study on Adoption of Green manufacturing using IoT. Real world
example of gauging End User perception using different models which
is important for a successful adoption of IoT. A futuristic
visionary view of IoT as comprehended based on evolution of
technology and platforms, and finally analysis of the extremely
crucial concepts of security, privacy and governance.
This book discusses and evaluates AI and machine learning (ML)
algorithms in dealing with challenges that are primarily related to
public health. It also helps find ways in which we can measure
possible consequences and societal impacts by taking the following
factors into account: open public health issues and common AI
solutions (with multiple case studies, such as TB and SARS:
COVID-19), AI in sustainable health care, AI in precision medicine
and data privacy issues. Public health requires special attention
as it drives economy and education system. COVID-19 is an example-a
truly infectious disease outbreak. The vision of WHO is to create
public health services that can deal with abovementioned crucial
challenges by focusing on the following elements: health
protection, disease prevention and health promotion. For these
issues, in the big data analytics era, AI and ML tools/techniques
have potential to improve public health (e.g., existing healthcare
solutions and wellness services). In other words, they have proved
to be valuable tools not only to analyze/diagnose pathology but
also to accelerate decision-making procedure especially when we
consider resource-constrained regions.
With the emergence of smart technology and automated systems in
today's world, big data is being incorporated into many
applications. Trends in data can be detected and objects can be
tracked based on the real-time data that is utilized in everyday
life. These connected sensor devices and objects will provide a
large amount of data that is to be analyzed quickly, as it can
accelerate the transformation of smart technology. The accuracy of
prediction of artificial intelligence (AI) systems is drastically
increasing by using machine learning and other probability and
statistical approaches. Big data and geospatial data help to solve
complex issues and play a vital role in future applications.
Emerging Trends, Techniques, and Applications in Geospatial Data
Science provides an overview of the basic concepts of data science,
related tools and technologies, and algorithms for managing the
relevant challenges in real-time application domains. The book
covers a detailed description for readers with practical ideas
using AI, the internet of things (IoT), and machine learning to
deal with the analysis, modeling, and predictions from big data.
Covering topics such as field spectra, high-resolution sensing
imagery, and spatiotemporal data engineering, this premier
reference source is an excellent resource for data scientists,
computer and IT professionals, managers, mathematicians and
statisticians, health professionals, technology developers,
students and educators of higher education, librarians,
researchers, and academicians.
The book introduces a variety of latest techniques designed to
represent, enhance, and empower multi-disciplinary approaches of
geographic information system (GIS), artificial intelligence (AI),
deep learning (DL), machine learning, and cloud computing research
in healthcare. It provides a unique compendium of the current and
emerging use of geospatial data for healthcare and reflects the
diversity, complexity, and depth and breadth of this
multi-disciplinary area. This book addresses various aspects of how
smart healthcare devices can be used to detect and analyze
diseases. Further, it describes various tools and techniques to
evaluate the efficacy, suitability, and efficiency of geospatial
data for health-related applications. It features illustrative case
studies, including future applications and healthcare challenges.
This book is beneficial for computer science and engineering
students and researchers, medical professionals, and anyone
interested in using geospatial data in healthcare. It is also
intended for experts, offering them a valuable retrospective and a
global vision for the future, as well as for non-experts who are
curious to learn about this important subject. The book presents an
effort to draw how we can build health-related applications using
geospatial big data and their subsequent analysis.
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