The perception of smart cities encompasses a strategy that uses
different types of technologies, artificial intelligence (AI), and
machine learning and in which, through the internet of things (IoT)
and sensor-based data collection, the strategy extrapolates
information using insights gained from that data to manage or
monitor or track assets, resources, and services efficiently in an
urban area. Both these models deeply affect the localities where
they are applied and can create together immense possibilities for
urban recovery, better quality of life, physical and mental health
protection, and economic and social redevelopment. Smart Cities and
Machine Learning in Urban Health promotes interdisciplinary work
that develops and illustrates the concept of resilience in relation
to smart city and machine learning. The book examines the ability
of an area and its communities to recover quickly from
difficulties; the rigidness and resistance of an area and its
communities to possible crisis; the ability of an area, its
communities, infrastructure, and business to spring back into
shape; and the responsiveness and mitigation towards the crisis
with a special look at the impact of the COVID-19 pandemic. The
research's theoretical foundation rests on a wide range of
non-architectural sources, primarily AI, sociology, urban studies,
and technological development, but it explores everything on cases
taken from real cities, thus transforming them into pieces of
architectural interest. Covering topics such as carbon emissions,
digital healthcare systems, and urban transformation, this book is
an essential resource for graduate and post-graduate students,
policymakers, researchers, university faculty, engineers, public
management, hospital administration, professors, and academicians.
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