Research Article
Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response
Table 5
Training information and accuracy comparison between DL models.
| Models | CNN | LSTM | RBMs |
| Number of trainable parameters | 2,138,056 | 8,853,576 | 2,096,384 | Training time per epoch (s) | 3 | 5 | 6 | Training time (s) | 300 | 400 | 625 | Prediction time (µs/sample) | 4 | 1 | 9 | Prediction rate | 0.9960 | 1 | 0.8665 | Prediction loss | 0.0188 | 0.00005 | 1.9056 |
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