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.

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.