Authors :
Teja Chalikanti; Bobbili Sreeja Reddy
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4xfnfftf
DOI :
https://doi.org/10.5281/zenodo.8354765
Abstract :
This manuscript offers an extensive and
methodical literature review concerning the application
of machine learning (ML) methods in the burgeoning
field of smart cities. The review spans across vital smart
city domains such as energy management, healthcare,
transportation optimization, security enhancement, and
pollution control. In this research, we present a cutting-
edge research methodology that introduces a state-of-
the-art taxonomy, evaluation framework, and model
performance analysis, categorizing ML algorithms into
four principal classes: decision trees, support vector
machines, artificial neural networks, and advanced
machine learning techniques, encompassing hybrid
models, ensembles, and Deep Learning. Our study
reveals that hybrid models and ensembles consistently
outperform other ML approaches, exhibiting a
compelling combination of high accuracy and cost-
effectiveness. In contrast, deep learning (DL) techniques
showcase superior accuracy but demand substantial
computational resources. Furthermore, all advanced ML
methods exhibit relatively slower processing speeds
compared to single methods. Notably, support vector
machines (SVM) and decision trees (DT) consistently
outperform artificial neural networks (ANN) across
various metrics. However, the margin of superiority is
negligible, suggesting that either SVM or DT may be
employed effectively depending on specific application
requirements.
Keywords :
Smart cities, Machine Learning, Support vector machine, Decision Trees, Artificial Neural Network, urban sustainbility, Deep Learning, Single and Hybrid Models.
This manuscript offers an extensive and
methodical literature review concerning the application
of machine learning (ML) methods in the burgeoning
field of smart cities. The review spans across vital smart
city domains such as energy management, healthcare,
transportation optimization, security enhancement, and
pollution control. In this research, we present a cutting-
edge research methodology that introduces a state-of-
the-art taxonomy, evaluation framework, and model
performance analysis, categorizing ML algorithms into
four principal classes: decision trees, support vector
machines, artificial neural networks, and advanced
machine learning techniques, encompassing hybrid
models, ensembles, and Deep Learning. Our study
reveals that hybrid models and ensembles consistently
outperform other ML approaches, exhibiting a
compelling combination of high accuracy and cost-
effectiveness. In contrast, deep learning (DL) techniques
showcase superior accuracy but demand substantial
computational resources. Furthermore, all advanced ML
methods exhibit relatively slower processing speeds
compared to single methods. Notably, support vector
machines (SVM) and decision trees (DT) consistently
outperform artificial neural networks (ANN) across
various metrics. However, the margin of superiority is
negligible, suggesting that either SVM or DT may be
employed effectively depending on specific application
requirements.
Keywords :
Smart cities, Machine Learning, Support vector machine, Decision Trees, Artificial Neural Network, urban sustainbility, Deep Learning, Single and Hybrid Models.
