Research Article
Mood Detection from Physical and Neurophysical Data Using Deep Learning Models
Table 5
Accuracy percentages of deep learning algorithms in terms of each user.
| User | FFNN | CNN | RNN | LSTM |
| 1 | 72.34 | 83.56 | 78.49 | 82.35 | 2 | 65.42 | 74.45 | 67.30 | 75.58 | 3 | 66.90 | 74.52 | 68.24 | 76.21 | 4 | 69.38 | 81.24 | 77.05 | 77.34 | 5 | 65.17 | 79.90 | 75.42 | 74.80 | 6 | 65.88 | 78.17 | 76.10 | 75.39 | 7 | 73.81 | 76.35 | 73.86 | 73.96 | 8 | 70.56 | 75.28 | 74.55 | 72.47 | 9 | 66.34 | 76.34 | 75.29 | 73.56 | 10 | 71.13 | 82.50 | 76.92 | 75.30 | 11 | 72.38 | 83.09 | 76.66 | 77.12 | 12 | 70.15 | 81.24 | 77.81 | 79.36 | 13 | 69.30 | 80.40 | 75.20 | 77.08 | 14 | 69.44 | 80.51 | 74.70 | 75.22 | 15 | 67.27 | 78.36 | 75.30 | 76.13 | Average | 69.03 | 79.06 | 74.86 | 76.12 |
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