Machine Learning Classification Algorithms-Based Smart Cities Applications: A Review

Abstract

Smart cities leverage advanced technologies and data-driven solutions to enhance the quality of life for residents, improve efficiency, and promote sustainable development. Machine learning (ML) plays a crucial role in enabling smart city applications by providing the capability to autonomously analyze complex, unstructured data and make informed decisions. This review paper provides an overview of the use of ML classification algorithms for various smart city applications. It begins by introducing the concept of smart cities and the role of ML in optimizing urban systems. The background theory section discusses the key features of smart cities, such as mobility, economy, people, living, environment, and governance. The paper then delves into specific applications of ML classification algorithms in smart cities, including traffic management, healthcare models, and energy storage systems. It highlights how ML-based approaches can tackle challenges in these domains, such as traffic congestion, emergency response, dynamic hospital environments, and renewable energy integration. Overall, this review underscores the significance of ML classification techniques in enhancing the functionality and efficiency of smart city infrastructure. It also identifies research gaps and areas for further exploration to fully harness the potential of ML in driving sustainable urban development.