Research Article
Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass
Table 5
Recall analysis for the RBFN-RAISR technique with existing systems.
| No. of data from dataset | CNN | R-CNN | SVM | ANN | DT | BNN | RBFN-RAISR |
| 100 | 71.65 | 75.12 | 78.13 | 80.23 | 84.98 | 87.11 | 92.98 | 200 | 71.45 | 76.45 | 79.56 | 81.56 | 83.12 | 87.34 | 93.45 | 300 | 72.33 | 76.34 | 78.12 | 82.91 | 84.22 | 88.97 | 94.12 | 400 | 72.19 | 75.19 | 78.87 | 81.22 | 84.77 | 88.13 | 94.89 | 500 | 71.67 | 75.44 | 79.33 | 82.98 | 85.12 | 89.34 | 95.55 | 600 | 72.19 | 76.12 | 79.12 | 84.88 | 86.67 | 90.45 | 95.78 | 700 | 74.55 | 77.33 | 79.67 | 82.77 | 86.34 | 91.55 | 96.44 |
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