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Within the past decade, technology has grown exponentially, and
governments have promoted smart cities. Emerging smart cities have
become both crucibles and showrooms for the practical application
of the internet of things (IoT), cloud computing, and the
integration of big data into everyday life. This complex concoction
requires new thinking of the synergistic utilization of deep
learning and blockchain methods and data-driven decision making
with automation infrastructure, autonomous transportation, and
more. Advances in Deep Learning Applications for Smart Cities
provides a global perspective on current and future trends
concerning the integration of deep learning and blockchain for
smart cities. It provides valuable insights on the best practices
and success factors for smart cities. Covering topics such as
digital healthcare, object detection methods, and power
consumption, this book is an excellent reference for researchers,
scientists, libraries, industry experts, government organizations,
students and educators of higher education, business professionals,
communication and marketing agencies, entrepreneurs, and
academicians.
Smart cities of the future must aspire to accommodate increasing
technology and expectations of modern living, integrated societies,
knowledge-based workforce, factory automation, and virtual-real
social behaviors. This complex concoction of challenges requires
new thinking of the synergistic utilization of reinforcement
learning methods and data-driven decision making with automation
infrastructure, autonomous transportation, connected buildings,
smart homes, and embedded communities. Industrial informatics lies
at this strategic intersection of multiple disciplines that can
comprehensively realize a learning vision of smart cities. This
book is ideal for academicians, researchers, authors, industry
experts, software engineers, and students.
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