| 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 | |
| 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. |
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