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Smart city article details
| Title | Cloud-Enabled Neural Networks For Intelligent Vehicle Emissions Tracking And Analysis |
|---|---|
| ID_Doc | 14482 |
| Authors | Anusha N.; Jeslin J.G.; Srividhya V.; Gupta N.S.; Meenakshi R.; Srinivasan C. |
| Year | 2024 |
| Published | 2024 International Conference on Automation and Computation, AUTOCOM 2024 |
| DOI | http://dx.doi.org/10.1109/AUTOCOM60220.2024.10486105 |
| Abstract | A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is crucial. Scalability and real-time data processing are issues for traditional approaches. Thus, cloud-enabled neural network architecture for scalable and efficient vehicle emissions monitoring using distributed computing is developed. A neural network model trained on various vehicle emission datasets, allowing for precise pattern analysis and prediction of emissions. The scalability of the system is guaranteed by using cloud resources, which can accommodate the rising amount of data created by an increasing number of vehicles. By incorporating cloud-based data processing seamlessly, real-time monitoring is accomplished, enabling immediate analysis and prompt emission alerts. The suggested emissions tracking platform is robust and scalable to intelligent transportation systems by revealing vehicle emissions' environmental effect. Future smart city and sustainable urban transportation developments are built on cloud computing and neural networks. The proposed system shows how cloud-enabled neural networks may solve complicated vehicle emissions problems. © 2024 IEEE. |
| Author Keywords | Data Analytics; Emissions Tracking; Environmental Sustainability; Performance Evaluation; Pollution Control; Predictive Analysis; Real-time Monitoring; Sensor Integration |
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