## 4 Discussion
Healthcare application settings with human supervision may need to prioritize recall so that aggressive instances will not be overlooked, whereas in automated surveillance systems, it might be more crucial to emphasize precision in order to minimize unnecessary calls for interventions. The main reason Late Fusion is better at recall is in its ability to decouple and process each modality separately before making a final decision. This decoupling allows the model to establish modality-specific decision boundaries that are less susceptible to cross-modal interference. For instance, even when the visual cues include motion blur, occlusion, or uneven lighting, the audio classifier can still provide a strong and assured input to the final prediction. The redundancy naturally resolves the modality-specific noise risk. Further, Late Fusion circumvents temporal asynchrony by disconnecting the learning processes. As compared to relying on ideal temporal coincidence, each modality is free to detect patterns of aggression from within its native temporal context, and the fusion system is tasked with combining these asynchronous predictions. Unlike Early Fusion, this technique does not suffer from the necessity of temporally aligned and cleaned features for best performance and thus is less sensitive to performance degradation when confronting real-world aberrations. Thus, the power of Late Fusion not only involves its decision-level structure but also its noise insensitivity, modality imbalance insensitivity, and temporal lag insensitivity. These characteristics are the most critical in the context of identifying aggressive behavior in erratic clinical environments. **Key Points of Comparison**  
* **Strengths of Late Fusion:**
* **Higher accuracy, recall, and F1-score:** Late Fusion outperforms Early Fusion in overall classification performance, making it better suited for detecting aggression with high reliability;
* **More effective handling of uncertainty**: By aggregating the probabilities from the independent audio and visual models, Late Fusion significantly reduces false negatives. This is particularly useful in applications where missing an aggressive instance could have serious consequences;
* **Independent feature extraction for each modality**: The separation of visual and audio processing prevents interference between modalities, ensuring that each model is optimized for its respective feature space before fusion (**S1.7**);  
**Faster inference times**: Since predictions are fused at the decision level rather than requiring a joint feature space, Late Fusion achieves better computational efficiency, making it more viable for real-time applications like clinical monitoring and security surveillance. * **Limitations of Late Fusion:** \(\odot\) **Slightly lower precision**: Although Late Fusion has superior recall, it suffers from a higher false positive rate. This could lead to unnecessary interventions in scenarios where precision is critical; \(\odot\) **Increased computational cost**: The need to train separate models for audio and visual data before fusion adds to the overall processing time and computational resources required; \(\odot\) **Dependence on model confidence scores**: Since the final decision is influenced by the confidence levels of each model, any inaccuracies in confidence estimation could affect performance.