Table 4 Comparative analysis of AAIFLF-PPCD method with existing models20,21,22,38,39,40.
Technique | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\) | \(\:{\varvec{F}1}_{\varvec{s}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\) |
|---|---|---|---|---|
SVM Classifier | 81.86 | 66.22 | 69.57 | 70.56 |
kNN Algorithm | 86.88 | 77.90 | 70.04 | 69.77 |
MLP Algorithm | 92.93 | 81.71 | 75.62 | 93.31 |
CNN + GRU Model | 87.44 | 78.34 | 70.13 | 88.31 |
HZDA-5G IIoT | 99.33 | 94.34 | 94.46 | 94.84 |
PSO Ensemble | 98.80 | 96.56 | 95.97 | 95.49 |
IRMOFNN-AD | 99.10 | 95.33 | 95.33 | 95.45 |
XAI | 98.54 | 94.61 | 94.58 | 94.80 |
SHAP | 97.96 | 94.00 | 93.87 | 94.26 |
VAE | 97.28 | 93.46 | 93.16 | 93.71 |
AGRU | 96.67 | 92.66 | 92.36 | 93.12 |
AAIFLF-PPCD | 99.47 | 97.20 | 96.84 | 96.92 |
