Table 3 Comparative outcome of APOFTLM-EGR model with existing approaches18,19,20,21,22,23,54,55,56.
Model | \({\varvec{A}}{\varvec{c}}{\varvec{c}}{{\varvec{u}}}_{{\varvec{y}}}\) | \({\varvec{P}}{\varvec{r}}{\varvec{e}}{{\varvec{c}}}_{{\varvec{n}}}\) | \({\varvec{R}}{\varvec{e}}{\varvec{c}}{{\varvec{a}}}_{{\varvec{l}}}\) | \({{\varvec{F}}}_{{\varvec{S}}{\varvec{c}}{\varvec{o}}{\varvec{r}}{\varvec{e}}}\) |
|---|---|---|---|---|
APOFTLM-EGR | 99.46 | 98.72 | 98.52 | 98.58 |
BiLSTM | 94.05 | 96.52 | 96.65 | 94.43 |
XLNet-BIGRU-Attention | 95.08 | 97.20 | 95.64 | 96.57 |
YOLOv5s | 97.18 | 96.93 | 94.97 | 94.67 |
IoT-FAR | 98.88 | 94.67 | 95.13 | 92.34 |
DSMS-TF | 93.42 | 95.96 | 96.21 | 94.04 |
MSAD | 94.51 | 96.69 | 95.00 | 96.03 |
ResNeXt | 98.81 | 94.62 | 95.07 | 92.29 |
DenseNet121 | 93.34 | 95.90 | 96.16 | 93.97 |
ResNet50 | 94.46 | 96.63 | 94.92 | 95.97 |
SLDC-RSAHDL | 99.23 | 95.74 | 96.18 | 94.18 |
kNN | 97.55 | 92.34 | 96.59 | 95.64 |
ANN Algorithm | 98.76 | 94.55 | 95.01 | 92.22 |
3D-CNN Model | 93.27 | 95.84 | 96.08 | 93.90 |
LSTM Method | 94.40 | 96.58 | 94.86 | 95.90 |
KRLS Method | 96.58 | 96.42 | 94.34 | 94.01 |
AiFusion | 92.66 | 96.71 | 93.67 | 93.82 |
