Table 6 Error analysis of HDLID-ECSOA technique with existing methods under Edge-IIoT dataset.
Edge-IIoT Dataset | ||||
|---|---|---|---|---|
Approach | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\) | \(\:{\varvec{F}}_{\varvec{M}\varvec{e}\varvec{a}\varvec{s}\varvec{u}\varvec{r}\varvec{e}}\) |
LSTM | 5.17 | 10.97 | 10.07 | 6.01 |
RF | 6.62 | 7.49 | 5.37 | 7.91 |
FL | 3.42 | 5.57 | 5.49 | 10.70 |
FedMLDL-Bayesian HPO | 3.85 | 8.30 | 10.98 | 10.83 |
LDA Model | 1.22 | 7.49 | 8.04 | 8.80 |
GB | 10.96 | 5.45 | 10.98 | 6.77 |
J48 Method | 5.14 | 4.09 | 5.07 | 6.71 |
CNN-LSTM | 5.88 | 6.72 | 4.64 | 7.32 |
DBN | 2.76 | 5.04 | 4.71 | 10.06 |
NeuroSpatialIOT | 3.27 | 7.76 | 10.45 | 10.16 |
HDLID-ECSOA | 0.65 | 3.89 | 3.89 | 3.89 |
