Table 4 Comparative study of the PASLR-DDPFEM technique with existing models.
Methodology | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{S}\varvec{e}\varvec{n}{\varvec{s}}_{\varvec{y}}\) | \(\:\varvec{S}\varvec{p}\varvec{e}{\varvec{c}}_{\varvec{y}}\) | \(\:\varvec{F}{1}_{\varvec{S}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\) |
|---|---|---|---|---|---|
CNN Classifier | 95.54 | 81.69 | 76.33 | 93.95 | 75.21 |
VGG16 Method | 89.00 | 81.59 | 77.28 | 90.94 | 81.33 |
EfficientNet V2 | 86.92 | 75.28 | 81.09 | 95.38 | 76.67 |
MobileNetV2 | 88.55 | 82.73 | 84.00 | 99.12 | 77.20 |
SignLan-Net | 84.72 | 77.94 | 81.80 | 96.63 | 75.86 |
Faster R-CNN | 95.87 | 78.55 | 76.58 | 97.60 | 77.49 |
Inception V3 | 91.66 | 81.84 | 83.95 | 92.09 | 78.24 |
PASLR-DDPFEM | 98.80 | 84.44 | 84.42 | 99.38 | 84.42 |
