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The evolution of deep learning models, combined with with advances
in the Internet of Things and sensor technology, has gained more
importance for weather forecasting, plant disease detection,
underground water detection, soil quality, crop condition
monitoring, and many other issues in the field of agriculture.
agriculture. Deep Learning for Sustainable Agriculture discusses
topics such as the impactful role of deep learning during the
analysis of sustainable agriculture data and how deep learning can
help farmers make better decisions. It also considers the latest
deep learning techniques for effective agriculture data management,
as well as the standards established by international organizations
in related fields. The book provides advanced students and
professionals in agricultural science and engineering, geography,
and geospatial technology science with an in-depth explanation of
the relationship between agricultural inference and the
decision-support amenities offered by an advanced mathematical
evolutionary algorithm.
Cyber-Physical Systems: AI and COVID-19 highlights original
research which addresses current data challenges in terms of the
development of mathematical models, cyber-physical systems-based
tools and techniques, and the design and development of algorithmic
solutions, etc. It reviews the technical concepts of gathering,
processing and analyzing data from cyber-physical systems (CPS) and
reviews tools and techniques that can be used. This book will act
as a resource to guide COVID researchers as they move forward with
clinical and epidemiological studies on this outbreak, including
the technical concepts of gathering, processing and analyzing data
from cyber-physical systems (CPS). The major problem in the
identification of COVID-19 is detection and diagnosis due to
non-availability of medicine. In this situation, only one method,
Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been
widely adopted and used for diagnosis. With the evolution of
COVID-19, the global research community has implemented many
machine learning and deep learning-based approaches with
incremental datasets. However, finding more accurate identification
and prediction methods are crucial at this juncture.
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