Smart City Gnosys
Smart city article details
| Title | Public Health Perspectives On Green Efficiency Through Smart Cities, Artificial Intelligence For Healthcare And Low Carbon Building Materials |
|---|---|
| ID_Doc | 43670 |
| Authors | Sun J.; Guan X.; Yuan S.; Guo Y.; Tan Y.; Gao Y. |
| Year | 2024 |
| Published | Frontiers in Public Health, 12 |
| DOI | http://dx.doi.org/10.3389/fpubh.2024.1440049 |
| Abstract | Introduction: Smart cities, artificial intelligence (AI) in healthcare, and low-carbon building materials are pivotal to public health, environmental sustainability, and green efficiency. Despite their critical importance, understanding public perceptions and attitudes toward these domains remains underexplored. Additionally, the effective use of advanced technologies like convolutional neural networks (CNN) in predicting and promoting low-carbon solutions in construction is gaining attention. Methods: This study employs a dual approach: (1) A survey of 200 respondents was conducted to gauge public perceptions and attitudes toward smart cities, AI in medicine, and low-carbon building materials. (2) A CNN model was developed and implemented to predict the performance of low-carbon building materials. The model utilized convolutional and pooling layers to capture local features and spatial information from image datasets, with tasks including image classification and segmentation. Results: The survey results indicate high awareness of smart cities (80%), with 60% associating them with environmental protection and green living. For AI in medicine, 70% of respondents are aware of its applications, but only 45% perceive it as environmentally beneficial. Regarding low-carbon building materials, 60% expressed willingness to pay premium prices, and 65% recognized their positive environmental impact. The CNN model demonstrated high prediction accuracy on both training and validation datasets, effectively aiding in the identification of low-carbon materials and reducing building energy consumption and carbon emissions. Discussion: The findings highlight significant public awareness and diverse attitudes toward these critical domains, suggesting the need for improved communication and advocacy for AI’s environmental benefits. The application of CNN models in the construction industry showcases a promising pathway to enhance material selection efficiency and foster sustainable practices. These insights are essential for aligning public understanding with technological advancements to achieve environmental and public health goals. Copyright © 2024 Sun, Guan, Yuan, Guo, Tan and Gao. |
| Author Keywords | artificial intelligence medical care; convolutional neural network; green benefits; low-carbon building materials; smart city |
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