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This book reviews that narrate the development of current
technologies under the theme of the emerging concept of healthcare,
specifically in terms of what makes healthcare more efficient and
effective with the help of high-precision algorithms. The mechanism
that drives it is machine learning, deep learning, big data, and
Internet of Things (IoT)-the scientific field that gives machines
the ability to learn without being strictly programmed. It has
emerged together with big data technologies and high-performance
computing to create new opportunities to unravel, quantify, and
understand data-intensive processes in healthcare operational
environments. This book offers comprehensive coverage of the most
essential topics, including: Introduction to e-monitoring for
healthcare Case studies based on big data and healthcare
Intelligent learning analytics in healthcare sectors using machine
learning and IoT Identifying diseases and diagnosis using machine
learning and IoT Deep learning architecture and framework for
healthcare using IoT Knowledge discovery from big data of
healthcare-related processing Big data and IoT in healthcare Role
of IoT in sustainable healthcare A heterogeneous IoT-based
application for remote monitoring of physiological and
environmental parameters
This book endeavours to highlight the untapped potential of Smart
Agriculture for the innovation and expansion of the agriculture
sector. The sector shall make incremental progress as it learns
from associations between data over time through Artificial
Intelligence, deep learning and Internet of Things applications.
The farming industry and Smart agriculture develop from the
stringent limits imposed by a farm's location, which in turn has a
series of related effects with respect to supply chain management,
food availability, biodiversity, farmers' decision-making and
insurance, and environmental concerns among others. All of the
above-mentioned aspects will derive substantial benefits from the
implementation of a data-driven approach under the condition that
the systems, tools and techniques to be used have been designed to
handle the volume and variety of the data to be gathered.
Contributions to this book have been solicited with the goal of
uncovering the possibilities of engaging agriculture with equipped
and effective profound learning algorithms. Most agricultural
research centres are already adopting Internet of Things for the
monitoring of a wide range of farm services, and there are
significant opportunities for agriculture administration through
the effective implementation of Machine Learning, Deep Learning,
Big Data and IoT structures.
This book endeavours to highlight the untapped potential of Smart
Agriculture for the innovation and expansion of the agriculture
sector. The sector shall make incremental progress as it learns
from associations between data over time through Artificial
Intelligence, deep learning and Internet of Things applications.
The farming industry and Smart agriculture develop from the
stringent limits imposed by a farm's location, which in turn has a
series of related effects with respect to supply chain management,
food availability, biodiversity, farmers' decision-making and
insurance, and environmental concerns among others. All of the
above-mentioned aspects will derive substantial benefits from the
implementation of a data-driven approach under the condition that
the systems, tools and techniques to be used have been designed to
handle the volume and variety of the data to be gathered.
Contributions to this book have been solicited with the goal of
uncovering the possibilities of engaging agriculture with equipped
and effective profound learning algorithms. Most agricultural
research centres are already adopting Internet of Things for the
monitoring of a wide range of farm services, and there are
significant opportunities for agriculture administration through
the effective implementation of Machine Learning, Deep Learning,
Big Data and IoT structures.
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