| Title |
PRISMA on Machine Learning Techniques in Smart City Development |
| ID_Doc |
38126 |
| Authors |
Ionescu, SA; Jula, NM; Hurduzeu, G; Pauceanu, AM; Sima, AG |
| Title |
PRISMA on Machine Learning Techniques in Smart City Development |
| Year |
2024 |
| Published |
Applied Sciences-Basel, 14, 16 |
| DOI |
10.3390/app14167378 |
| Abstract |
This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML's ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments. |
| Author Keywords |
sustainable development; machine learning; smart cities; smart energy; technological changes; PRISMA; optimization and control techniques |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001305299600001 |
| WoS Category |
Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied |
| Research Area |
Chemistry; Engineering; Materials Science; Physics |
| PDF |
https://doi.org/10.3390/app14167378
|