Earticle

초록

영어

Recently, cloud computing technology is rapidly growing at a faster rate offering cloud-based driver profiling applications with lower latency. In this study, we have proposed a computational efficient cloud-based architecture for the deployment of driver profiling deep learning algorithms. In order to validate the efficacy of the proposed architecture, we have evaluated the performance of the proposed deep learning architecture for the recent driver behavior identification using time series sensor data. We have utilized an Amazon web service-based cloud computing solution for the deployment of the proposed architecture. The experimental results show that the proposed architecture improves end-to-end latency by 3.1 times compared to the traditional method.

목차

Abstract
1. Introduction
2. Related Works
3. Proposed Method
4. Preliminary Experimental Result
5. Conclusion
Acknowledgement
References

저자

• 메디 피라한데 [ Mehdi Pirahandeh | Department of Electrical and Computer Engineering, Inha University, South Korea ]
• 김덕환 [ Deok-Hwan Kim | Department of Electrical and Computer Engineering, Inha University, South Korea ]

참고문헌