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Digital Twin for Smart Manufacturing: Emerging Approaches and
Applications provides detailed descriptions on how to integrate and
optimize novel digital technologies for smart manufacturing. The
book discusses digital twins, which combine the industrial internet
of things, artificial intelligence, machine learning and software
analytics with spatial network graphs to create living digital
simulation models that update and change as their physical
counterparts change. In addition, they provide an effective way to
integrate technologies like cyber-physical systems into a smart
manufacturing system, potentially optimizing the entire business
process and operating procedure of the manufacturing firm. Drawing
on the latest research, the book addresses the topics and
technologies key to successful implementation of a smart
manufacturing system, including augmented and virtual reality, big
data and energy management. Broader subjects such as additive
manufacturing and robotics are also covered in this context,
covering every aspect of production.
This book promotes and facilitates exchanges of research knowledge
and findings across different disciplines on the design and
investigation of deep learning (DL)-based data analytics of IoT
(Internet of Things) infrastructures. Deep Learning for Internet of
Things Infrastructure addresses emerging trends and issues on IoT
systems and services across various application domains. The book
investigates the challenges posed by the implementation of deep
learning on IoT networking models and services. It provides
fundamental theory, model, and methodology in interpreting,
aggregating, processing, and analyzing data for intelligent
DL-enabled IoT. The book also explores new functions and
technologies to provide adaptive services and intelligent
applications for different end users. FEATURES Promotes and
facilitates exchanges of research knowledge and findings across
different disciplines on the design and investigation of DL-based
data analytics of IoT infrastructures Addresses emerging trends and
issues on IoT systems and services across various application
domains Investigates the challenges posed by the implementation of
deep learning on IoT networking models and services Provides
fundamental theory, model, and methodology in interpreting,
aggregating, processing, and analyzing data for intelligent
DL-enabled IoT Explores new functions and technologies to provide
adaptive services and intelligent applications for different end
users Uttam Ghosh is an Assistant Professor in the Department of
Electrical Engineering and Computer Science, Vanderbilt University,
Nashville, Tennessee, USA. Mamoun Alazab is an Associate Professor
in the College of Engineering, IT and Environment at Charles Darwin
University, Australia. Ali Kashif Bashir is a Senior
Lecturer/Associate Professor and Program Leader of BSc (H) Computer
Forensics and Security at the Department of Computing and
Mathematics, Manchester Metropolitan University, United Kingdom.
Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science
and Engineering at the Independent University, Bangladesh.
This book includes selected peer-reviewed papers presented at the
International Conference on Computing and Communication Networks
(ICCCN 2021), held at Manchester Metropolitan University, United
Kingdom, during 19-20 November 2021. The book covers topics of
network and computing technologies, artificial intelligence and
machine learning, security and privacy, communication systems,
cyber physical systems, data analytics, cyber security for Industry
4.0, and smart and sustainable environmental systems.
Medical imaging informatics play an important role in the
effectiveness of present-day healthcare systems. Advancement of
artificial intelligence, big data analytics, and internet of things
technologies contribute greatly to various healthcare applications.
Artificial intelligence techniques are contributing to improvements
with traditionally human-based systems and ensuring that the
accuracy of prediction and diagnosis is being continually enhanced.
The development of reliable and accurate healthcare models is
becoming ever more possible with the help of machine learning and
deep learning technologies. Artificial intelligence has the power
to solve many complex problems in medical imaging and is a
technology that will help to design the future of many healthcare
systems. This edited book highlights and addresses various issues
in medical imaging and provides viable solutions utilising
artificial intelligence and big data tools. This book discusses
techniques, algorithms, and tools which help build and develop
research practices, platforms, and applications in medical image
informatics. Medical image enhancement, big data analytics and
artificial intelligence models are discussed with relation to
applications in the detection of cancer, autism, allergies and
diabetes. The design and development of internet of medical things
and virtual reality tools for mental health disorders are also
explored. This book is suitable reading for researchers and
scientists, in both academia and industry, working in computer
science and engineering, machine learning, image processing, and
healthcare technologies. Those in aligned professions, such as
healthcare practitioners, administrators, designers and developers
may also find the subject matter of interest.
Wireless communication is continuously evolving to improve and be a
part of our daily communication. This leads to improved quality of
services and applications supported by networking technologies. We
are now able to use LTE, LTE-Advanced, and other emerging
technologies due to the enormous efforts that are made to improve
the quality of service in cellular networks. As the future of
networking is uncertain, the use of deep learning and big data
analytics is a point of focus as it can work in many capacities at
a variety of levels for wireless communications. Implementing Data
Analytics and Architectures for Next Generation Wireless
Communications addresses the existing and emerging theoretical and
practical challenges in the design, development, and implementation
of big data algorithms, protocols, architectures, and applications
for next generation wireless communications and their applications
in smart cities. The chapters of this book bring together academics
and industrial practitioners to exchange, discuss, and implement
the latest innovations and applications of data analytics in
advanced networks. Specific topics covered include key encryption
techniques, smart home appliances, fog communication networks, and
security in the internet of things. This book is valuable for
technologists, data analysts, networking experts, practitioners,
researchers, academicians, and students.
Wireless communication is continuously evolving to improve and be a
part of our daily communication. This leads to improved quality of
services and applications supported by networking technologies. We
are now able to use LTE, LTE-Advanced, and other emerging
technologies due to the enormous efforts that are made to improve
the quality of service in cellular networks. As the future of
networking is uncertain, the use of deep learning and big data
analytics is a point of focus as it can work in many capacities at
a variety of levels for wireless communications. Implementing Data
Analytics and Architectures for Next Generation Wireless
Communications addresses the existing and emerging theoretical and
practical challenges in the design, development, and implementation
of big data algorithms, protocols, architectures, and applications
for next generation wireless communications and their applications
in smart cities. The chapters of this book bring together academics
and industrial practitioners to exchange, discuss, and implement
the latest innovations and applications of data analytics in
advanced networks. Specific topics covered include key encryption
techniques, smart home appliances, fog communication networks, and
security in the internet of things. This book is valuable for
technologists, data analysts, networking experts, practitioners,
researchers, academicians, and students.
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