… machine learning (ML), and deep reinforcement learning (DRL) in the evolution of smart
cities… used to design optimal policy regarding various smart city-oriented complex problems. In …

… smart cities to address this challenge. We also propose a three-level learning framework for
smart cities that matches the hierarchical nature of big data generated by smart cities with a …

… of Machine Learning (ML) algorithms and explores the role of ML, Deep Reinforcement …
smart cities and enumerates the major driving forces for the adoption of smart city concepts. …

… development of personalised services in Smart Cities. We do this by … Smart City applications
that may benefit from our work Keywords—classification; personalisation; machine learning…

… At present, some scholars predict that machine learning has a good effect on improving
MEC, but there is no corresponding performance test. In this research, machine learning is used …

… IoT, smart healthcare, smart cities, machine learning and their … in IoT-based smart cities and
healthcare is explained. In Section … of machine learning in conjunction with the IoT and smart …

… generated by different smart devices and smart city applications machine learning approach
is … key contribution of this paper is a machine learning application survey towards smart city. …

… Machine learning techniques can detect attacks during runtime and take less processing …
a machine learning-based attack and anomaly detection technique in IoT-based smart city …

… traffic of a smart city via loT … in a smart city, we propose an Anomaly Detection loT (AD-IoT)
system, which is an intelligent anomaly detection based on Random Forest machine learning …

… waste management, smart governance, smart environment monitoring, etc. Now, we will
map each problem of smart cities with the solution provided by machine learning algorithms in …