Table 3 Comparative results of SFDAB-ARNNSHO technique with existing models19,46,47,48.
Technique | \(Acc{u_y}\) | \(Pre{c_n}\) | \(Rec{a_l}\) | \(F{1_{score}}\) |
|---|---|---|---|---|
ConvNeXtTiny | 90.08 | 82.46 | 81.65 | 81.77 |
ResNet152-V2 | 95.56 | 90.84 | 91.23 | 91.18 |
VGG19 algorithm | 97.46 | 91.82 | 91.51 | 91.38 |
NASNet-large | 96.29 | 92.34 | 92.52 | 92.27 |
DL-MFDSED | 98.17 | 95.47 | 95.36 | 95.45 |
Bi-LSTM model | 99.08 | 93.79 | 93.99 | 94.12 |
Inception time | 98.25 | 96.05 | 95.37 | 96.11 |
Transformer model | 98.97 | 94.53 | 94.21 | 95.65 |
ADLSTM | 96.36 | 91.54 | 91.85 | 91.89 |
ANN | 98.05 | 92.58 | 92.08 | 92.12 |
GNN | 96.89 | 93.06 | 93.28 | 92.97 |
GAN | 98.81 | 96.02 | 96.00 | 96.20 |
SFDAB-ARNNSHO | 99.30 | 96.73 | 99.3 | 97.96 |
