# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
Performance-wise, the proposed model outperforms existing systems, such as PCT, MST-FaDe, Fuzzy SVM, and AISAR, exhibiting significant improvements in accuracy (97%), F1-score (95%), precision-recall balance (0.99), and true positive rates, while simultaneously minimising false negatives and positives. This system has been refined to enhance agility, personalisation, and security, operating effectively without added complexity. These qualities underscore its robustness and efficiency across a range of classification and prediction scenarios, markedly enhancing the quality and responsiveness of e-learning platforms. However, the proposed work has its limitations, which should be addressed. These may include the dependency on high-quality, multi-modal biometric data, which may not be readily available or may raise privacy concerns among users. Additionally, the complexity of the ID2 QN and Att-CapsNet algorithms may require substantial computational resources, potentially limiting their applicability in resource-constrained environments. Furthermore, the effectiveness of the proposed model in diverse educational settings, with varying curricula and student populations, needs to be thoroughly evaluated. Future work could focus on addressing these limitations, exploring the scalability of the system, and extending its applicability to broader educational contexts. W.B. : developed the system; wrote the literature review; tested and analysed the data; wrote, edited, and formatted the paper. N.C.: provided overall leadership and direction for the research; contributed to and edited the literature review; reviewed the paper. A.S.S. : co \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S. No** & **Success Metrics** & **PCT** & **MST-FaDe** & **Fuzzy SVM** & **AISAR** & **Proposed** \\ \hline
1 & Accuracy (\%) & 60 & 65 & 75 & 79 & 97 \\ \hline
2 & F1-Score (\%) & 76 & 79 & 80 & 81 & 95 \\ \hline
3 & Recall Rate & 0.85 & 0.90 & 0.95 & 0.96 & 0.99 \\ \hline
4 & True Negative Rate & 0.80 & 0.85 & 0.945 & 0.96 & 0.99 \\ \hline
5 & True Positive Rate & 0.77 & 0.84 & 0.90 & 0.91 & 0.98 \\ \hline
6 & Specificity & 70 & 80 & 85 & 89 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Numerical evaluation of proposed vs. existing methods. supervised the research; and contributed to reviewing and editing the paper. S.B. : worked on the authentication parts. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was tested using the OULAD dataset. It is available online at [[https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset)]([https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset)) (accessed on 26 September 2022). The authors declare no conflicts of interest. ## References

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