Meta-Exploration of Machine Learning in Smart Cities

Machine Learning (ML) significantly drives the advancement of smart cities. This survey, using databases like IEEE Explorer, Web of Sciences, and Google Scholar, thoroughly investigated 22 papers published between 2021 and 2023. The focus was on identifying the prevalent ML models in smart cities and the specific sub-areas capturing the most attention. The study says that out of 22 research papers, about 63% used supervised learning techniques for smart city applications. The most common models were Naive Bayes and Support Vector Machines, especially in the areas of transportation, energy, environment, and healthcare. The industry has significance, due to its potential for conversion, especially with the urbanization of rural areas. This highlights the necessity for extensive future advancements. The results of this survey about the significance of machine learning in smart cities give us a path that will demand ongoing innovation to ensure the sustainable growth of both urban and rural areas going forward. Using machine learning, we can not only enhance the productivity of the city system but also increase the efficiency in diverse aspects of urban life.