August 21, 2024Applied SciencesOpen Access

PRISMA on Machine Learning Techniques in Smart City Development

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ȘIȘtefan Alexandru IonescuUniversity of Bucharest NJNicolae-Marius JulaUniversity of Bucharest GHGheorghe HurduzeuVictor (Japan)

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Implication

Systematic review reveals four core operational themes for machine learning in smart cities, highlighting transformative opportunities to enhance efficiency and sustainability.

Key Points

Machine learning applications in smart cities cluster into four primary domains, including traffic optimization, flow tracking, sustainability, and security.
A systematic review following PRISMA guidelines analyzed 42 relevant studies, demonstrating how urban environments benefit from intelligent computational models.
These findings establish machine learning as a vital driver for smart city development, while highlighting critical research gaps in modern urban environments.

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Ionescu et al. (2024) studied this question.

synapsesocial.com/papers/68e5b746b6db64358754fbce https://doi.org/10.3390/app14167378

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Implication

Systematic review reveals four core operational themes for machine learning in smart cities, highlighting transformative opportunities to enhance efficiency and sustainability.

PRISMA on Machine Learning Techniques in Smart City Development

Discussion

Member takes

Implication

Key Points

Cite This Study

PRISMA on Machine Learning Techniques in Smart City Development

Discussion

Member takes

Implication

Key Points

Cite This Study

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