Название: Data-Driven Mining, Learning and Analytics for Secured Smart Cities: Trends and AdvancesАвтор: Chinmay Chakraborty, Jerry Chun-Wei Lin
Издательство: Springer
Год: 2021
Страниц: 390
Язык: английский
Формат: pdf (true), epub
Размер: 39.1 MB
In recent years, Artificial Intelligence/Machine Learning (AI/ML) methods have become an emerge research topic as its powerful computational models and have shown significant success to deal with a massive amount of data in unsupervised settings. AI/ML influences various technologies because it offers an effective way of learning representation and allows the system to learn features automatically from data without the need of explicitly designation. With the emerging technologies, the Internet of Things (IoT), wearable devices, cloud computing, and data analytics offer the potential of acquiring and processing a tremendous amount of data from the physical world.
AI/ML-based algorithms help efficiently to leverage IoT and big data aspects in the development of personalized services in smart cities. The Cyber-Physical Systems (CPS) can be thought as an integral part of the smart city ecosystem. The automation of objects of the smart city is facilitated by different types of CPSs. A CPS is a collection of physical devices, networking, and communication protocols which makes the devices being connected and communicated with each other under minimum human interventions.
This book aims will provide the data-driven designation of infrastructure, analytical approaches, and technological solutions with case studies for smart cities. This book can also attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real-world challenges for building smart cities.
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