Smart City Gnosys

Smart city article details

Title	When Smart Cities Get Smarter Via Machine Learning: An In-Depth Literature Review
ID_Doc	61761
Authors	Band S.S.; Ardabili S.; Sookhak M.; Chronopoulos A.T.; Elnaffar S.; Moslehpour M.; Csaba M.; Torok B.; Pai H.-T.; Mosavi A.
Year	2022
Published	IEEE Access, 10
DOI	http://dx.doi.org/10.1109/ACCESS.2022.3181718
Abstract	The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate. © 2013 IEEE.
Author Keywords	artificial intelligence; big data; data science; deep learning; ensemble; machine learning; Smart city; smart grid

Similar Articles

Id	Similarity	Authors	Title	Published
35883	0.949	Oladipo I.D.; AbdulRaheem M.; Awotunde J.B.; Bhoi A.K.; Adeniyi E.A.; Abiodun M.K.	Machine Learning And Deep Learning Algorithms For Smart Cities: A Start-Of-The-Art Review	EAI/Springer Innovations in Communication and Computing (2022)
52939	0.933	Nosratabadi S.; Mosavi A.; Keivani R.; Ardabili S.; Aram F.	State Of The Art Survey Of Deep Learning And Machine Learning Models For Smart Cities And Urban Sustainability	Lecture Notes in Networks and Systems, 101 (2020)
35978	0.926	Dou X.; Chen W.; Zhu L.; Bai Y.; Li Y.; Wu X.	Machine Learning For Smart Cities: A Comprehensive Review Of Applications And Opportunities	International Journal of Advanced Computer Science and Applications, 14, 9 (2023)
10108	0.921	Heidari A.; Navimipour N.J.; Unal M.	Applications Of Ml/Dl In The Management Of Smart Cities And Societies Based On New Trends In Information Technologies: A Systematic Literature Review	Sustainable Cities and Society, 85 (2022)
41666	0.92	Bedi P.; Goyal S.B.; Islam S.M.N.; Liu J.; Budati A.K.	Performance Analysis Of Machine Learning And Deep Learning Algorithms For Smart Cities: The Present State And Future Directions	Cognitive Computing Models in Communication Systems (2022)
40125	0.919	Hurbean L.; Danaiata D.; Militaru F.; Dodea A.-M.; Negovan A.-M.	Open Data Based Machine Learning Applications In Smart Cities: A Systematic Literature Review	Electronics (Switzerland), 10, 23 (2021)
5160	0.917	Saranya M.; Amutha B.	A Survey Of Innovative Machine Learning Approaches In Smart City Applications	Innovative Machine Learning Applications for Cryptography (2024)
35869	0.917	Hammoumi L.; Rhinane H.	Machine Learning (Ai) For Identifying Smart Cities	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, 4/W9-2024 (2024)
8953	0.916	França R.P.; Monteiro A.C.B.; Arthur R.; Iano Y.	An Overview Of The Machine Learning Applied In Smart Cities	Lecture Notes in Intelligent Transportation and Infrastructure, Part F1386 (2021)
35979	0.915	Mahamuni C.V.; Sayyed Z.; Mishra A.	Machine Learning For Smart Cities: A Survey	2022 IEEE International Power and Renewable Energy Conference, IPRECON 2022 (2022)