Table 7 Complexity analysis of different DL Methods (Normalized values).
From: Leveraging federated learning and edge computing for pandemic-resilient healthcare
Sl. No | Specific purpose | Training data set (80%) | Testing data set (20%) | Technique | Training time (s) | Testing time (s) | Response time (ms) |
---|---|---|---|---|---|---|---|
1 | Face Mask Wearing | 8400 | 2100 | VGG-16 | 1 | 0.945 | 1 |
RESNET-50 | 0.940 | 0.939 | 0.938 | ||||
MOBILENET | 0.970 | 0.945 | 0.970 | ||||
YOLOV4-SENet | 0.163 | 0.15 | 0.246 | ||||
2 | Improper wearing of Face Mask | 2496 | 623 | RESNET-50 | 0.322 | 0.975 | 0.258 |
MOBILENET | 0.354 | 1 | 0.291 | ||||
YOLOV4-SENet | 0.099 | 0.102 | 0.232 | ||||
3 | Classification of Face Mask | 3897 | 972 | CNN | 0.209 | 0.579 | 0.175 |
RESNET-50 | 0.322 | 0.786 | 0.276 | ||||
MOBILENET | 0.344 | 0.817 | 0.297 | ||||
YOLOV4-SENet | 0.098 | 0.098 | 0.218 | ||||
4 | Social Distance Detection | 6147 | 1455 | RESNET-50 | 0.451 | 0.695 | 0.426 |
5765 | 1057 | MOBILENET | 0.483 | 0.719 | 0.459 | ||
3897 | 972 | SOCIAL DISTANCINGNET-19 | 0.440 | 0.695 | 0.415 | ||
6147 | 1455 | YOLOV4-SENet | 0.091 | 0.095 | 0.218 | ||
5 | Contact Tracing | 12675 | 3075 | DT | 0.774 | 0.524 | 0.795 |
12675 | 3075 | RF | 0.774 | 0.524 | 0.795 | ||
12675 | 3075 | LR | 0.774 | 0.524 | 0.795 | ||
12675 | 3075 | SVM | 0.774 | 0.524 | 0.795 | ||
3897 | 972 | VGG-16 | 0.215 | 0.560 | 0.188 | ||
5765 | 1057 | MOBILENET | 0.284 | 0.573 | 0.256 | ||
6147 | 1455 | RESNET-50 | 0.301 | 0.597 | 0.271 | ||
12675 | 3075 | YOLOV4-SENet | 0.096 | 0.101 | 0.225 |