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Green Information and Communication Systems for a Sustainable
Future covers the fundamental concepts, applications, algorithms,
protocols, new trends, challenges, and research results in the area
of Green Information and Communication Systems. This book provides
the reader with up-to-date information on core and specialized
issues, making it highly suitable for both the novice and the
experienced researcher in the field. The book covers theoretical
and practical perspectives on network design. It includes how green
ICT initiatives and applications can play a major role in reducing
CO2 emissions, and focuses on industry and how it can promote
awareness and implementation of Green ICT. The book discusses
scholarship and research in green and sustainable IT for business
and organizations and uses the power of IT to usher sustainability
into other parts of an organization. Business and management
educators, management researchers, doctoral scholars, university
teaching personnel and policy makers as well as members of higher
academic research organizations will all discover this book to be
an indispensable guide to Green Information and Communication
Systems. It will also serve as a key resource for Industrial and
Management training organizations all over the world.
This book covers all the emerging trends in artificial intelligence
(AI) and the Internet of Things (IoT). The Internet of Things is a
term that has been introduced in recent years to define devices
that are able to connect and transfer data to other devices via the
Internet. While IoT and sensors have the ability to harness large
volumes of data, AI can learn patterns in the data and quickly
extract insights in order to automate tasks for a variety of
business benefits. Machine learning, an AI technology, brings the
ability to automatically identify patterns and detect anomalies in
the data that smart sensors and devices generate, and it can have
significant advantages over traditional business intelligence tools
for analyzing IoT data, including being able to make operational
predictions up to 20 times earlier and with greater accuracy than
threshold-based monitoring systems. Further, other AI technologies,
such as speech recognition and computer vision can help extract
insights from data that used to require human review. The powerful
combination of AI and IoT technology is helping to avoid unplanned
downtime, increase operating efficiency, enable new products and
services, and enhance risk management.
This book covers all the emerging trends in artificial intelligence
(AI) and the Internet of Things (IoT). The Internet of Things is a
term that has been introduced in recent years to define devices
that are able to connect and transfer data to other devices via the
Internet. While IoT and sensors have the ability to harness large
volumes of data, AI can learn patterns in the data and quickly
extract insights in order to automate tasks for a variety of
business benefits. Machine learning, an AI technology, brings the
ability to automatically identify patterns and detect anomalies in
the data that smart sensors and devices generate, and it can have
significant advantages over traditional business intelligence tools
for analyzing IoT data, including being able to make operational
predictions up to 20 times earlier and with greater accuracy than
threshold-based monitoring systems. Further, other AI technologies,
such as speech recognition and computer vision can help extract
insights from data that used to require human review. The powerful
combination of AI and IoT technology is helping to avoid unplanned
downtime, increase operating efficiency, enable new products and
services, and enhance risk management.
Trends in Deep Learning Methodologies: Algorithms, Applications,
and Systems covers deep learning approaches such as neural
networks, deep belief networks, recurrent neural networks,
convolutional neural networks, deep auto-encoder, and deep
generative networks, which have emerged as powerful computational
models. Chapters elaborate on these models which have shown
significant success in dealing with massive data for a large number
of applications, given their capacity to extract complex hidden
features and learn efficient representation in unsupervised
settings. Chapters investigate deep learning-based algorithms in a
variety of application, including biomedical and health
informatics, computer vision, image processing, and more. In recent
years, many powerful algorithms have been developed for matching
patterns in data and making predictions about future events. The
major advantage of deep learning is to process big data analytics
for better analysis and self-adaptive algorithms to handle more
data. Deep learning methods can deal with multiple levels of
representation in which the system learns to abstract higher level
representations of raw data. Earlier, it was a common requirement
to have a domain expert to develop a specific model for each
specific application, however, recent advancements in
representation learning algorithms allow researchers across various
subject domains to automatically learn the patterns and
representation of the given data for the development of specific
models.
Predictive Intelligence in Biomedical and Health Informatics
focuses on imaging, computer-aided diagnosis and therapy as well as
intelligent biomedical image processing and analysis. It develops
computational models, methods and tools for biomedical engineering
related to computer-aided diagnostics (CAD), computer-aided surgery
(CAS), computational anatomy and bioinformatics. Large volumes of
complex data are often a key feature of biomedical and engineering
problems and computational intelligence helps to address such
problems. Practical and validated solutions to hard biomedical and
engineering problems can be developed by the applications of neural
networks, support vector machines, reservoir computing,
evolutionary optimization, biosignal processing, pattern
recognition methods and other techniques to address complex
problems of the real world.
This book presents a variety of techniques designed to enhance and
empower multi-disciplinary and multi-institutional machine learning
research in healthcare informatics. It is intended to provide a
unique compendium of current and emerging machine learning
paradigms for healthcare informatics, reflecting the diversity,
complexity, and depth and breadth of this multi-disciplinary area.
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