| Title |
Sensor Sequential Data-Stream Classification Using Deep Gated Hybrid Architecture |
| ID_Doc |
39334 |
| Authors |
Elsayed, N; Maida, AS; Bayoumi, M |
| Title |
Sensor Sequential Data-Stream Classification Using Deep Gated Hybrid Architecture |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/greentech.2019.8767136 |
| Abstract |
Sensors are the main components to supply information for resource management in a smart city. This paper studies the sensor data-stream classification problem using different time series state-of-the-art classification models. In this study, we found that the hybrid architecture of gated recurrent units and temporal fully convolutional neural network (GRU-FCN) model outperforms the existing state-of-the-art classification techniques in most of the benchmark sensor-obtained datasets. Moreover, the GRU-FCN model is simpler than the other existing gate-based recurrent classification architectures. Thus, it is an appropriate model to be implemented on small or portable hardware devices. |
| Author Keywords |
Deep learning; GRU; GRU-FCN; convolution neural network; sensors; time series; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000492303200018 |
| WoS Category |
Green & Sustainable Science & Technology; Energy & Fuels; Engineering, Electrical & Electronic |
| Research Area |
Science & Technology - Other Topics; Energy & Fuels; Engineering |
| PDF |
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