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Summary of Existing studies_
| References No. | Dataset used | Algorithm used | Result achieved |
|---|---|---|---|
| [26] | AWID dataset | ML algorithm | With a detection rate of 99.75% and an accuracy of 99.45%, the model performed better than the others |
| [27] | CICD DoS2019 dataset | DL algorithm | F1-score and accuracy rate >98% |
| [28] | UCF crime dataset | ML algorithm | The average of 98.0%. In the meantime, our PBVAD-MIM approach yielded an average success rate of 80.7% for the tests |
| [29] | CIC-DDoS 2019 dataset | ML and DL algorithms | Obtained a 99.50% accuracy rate with a delay |
| [30] | Historical daily weather dataset | ML and DL algorithms | Accuracy rate of 96.65% and 84.0% |
Ablation study
| Configuration | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
| Full CMS (H-CapsNet + C3D + CNN) | 93.5 | 92.8 | 91.7 | 92.2 |
| Without H-CapsNet | 88.1 | 87.4 | 85.9 | 86.6 |
| Without C3D | 89.3 | 88.7 | 86.2 | 87.4 |
| Without CNN (face recognition) | 90.2 | 89.8 | 88.1 | 88.9 |
| Only H-CapsNet | 86.7 | 85.3 | 84.5 | 84.9 |
| Only C3D | 84.9 | 83.6 | 82.4 | 83.0 |
| Only CNN | 85.5 | 84.8 | 83.3 | 84.0 |
Weapon detection performance metrics
| Metric | Value (%) |
|---|---|
| Accuracy | 93.5 |
| Precision | 92.8 |
| Recall | 91.7 |
| F1 score | 92.2 |
| FPR | 3.1 |
| FNR | 2.7 |