Artificial Intelligence And Smart Cities: A Bibliometric Analysis

Gupta, Ashulekha; Memoria, Minakshi; Kumar, Rajiv; Kumar, Sanjeev; Singh, Devendrá; Tyagi, Shagun; Ansari, Nabila

doi:10.1109/com-it-con54601.2022.9850656

Cited by 18 publications

(2 citation statements)

References 23 publications

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“…Smart city development is, however, equally addressed on social, economic and environmental fronts by the literature on technology. Gupta et al (2022) did a bibliometric analysis of AI and smart cities literature, which outlines the research trends and knowledge gaps in the field. (Teredesai,2023) investigates social public health infrastructure for smart city citizens, emphasizing the need for inclusive and equitable urban development.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Healthcare Management Model for Proactive Patient Monitoring

Hussain,

Azam,

Bano

et al. 2024

ABBDM

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The rapid development of Artificial Intelligence (AI) is leading urban centers to employ AI technologies to improve efficiency and solve urban problems. This research proposes a Centralized System Model for Smart Cities (CSMSC) that centralizes AI-oriented data acquisition, processing, and decision-making. CSMSC uses real-time sensor networks for data collection, sophisticated AI algorithms for nuanced data interpretation, and unified storage for streamlined information management. Additionally, CSMSC integrates AI-based analyzers to autonomously produce alerts, evaluate their urgency, and decide upon suitable responses, enabling quick and targeted city interventions. The paper combines field evidence with theoretical frameworks to highlight the transformative potential of cognitive sensing and machine learning in smart city development. Recent studies have shown that AI on edge is revolutionizing the infrastructure of smart cities by bringing advanced intelligence and real-time analytics closer to the data source. AI on edge enables real-time decision-making, reduces latency, optimizes bandwidth usage, and enhances privacy and security. The potential benefits of using data analytics in smart cities are significant, and future research should focus on developing new algorithms and tools to analyze data and explore new IoT and machine-learning applications.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Healthcare Management Model for Proactive Patient Monitoring

Hussain,

Azam,

Bano

et al. 2024

ABBDM

View full text Add to dashboard Cite

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“…The role of AI in the domain of smart cities [1][2][3][4], especially in garbage collection, has recently emerged in the landscape of urban development and sustainable practices. In the contemporary landscape, the exploration of intelligent technologies-encompassing smart bins, robotic systems, predictive modeling, and optimized routing algorithms-and their pivotal role in optimizing waste collection processes has become essential.…”

Section: Introductionmentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

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The Vision of Artificial Intelligence. The Text-to-Image Algorithm for Contemporary Architecture

Zerlenga,

Iaderosa

2024

Springer Tracts in Civil Engineering

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Artificial Intelligence And Smart Cities: A Bibliometric Analysis

Cited by 18 publications

References 23 publications

Smart Healthcare Management Model for Proactive Patient Monitoring

Smart Healthcare Management Model for Proactive Patient Monitoring

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

The Vision of Artificial Intelligence. The Text-to-Image Algorithm for Contemporary Architecture

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