# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods
### Federated Learning-Based Authentication
Number of Epochs

A statistic called the specificity metric is used in classification tasks to assess a model's accuracy in identifying negative situations. It is expressed as the ratio of true negatives (\(\mathfrak{N}\)) to the total of false positives (\(\mathfrak{F}\mathfrak{N}\)) and true negatives (\(\mathfrak{N}\)). The specificity formula is as follows in mathematics:

\[\mathfrak{S}=\frac{\mathfrak{F}\mathfrak{N}}{\mathfrak{F}\mathfrak{N}+ \mathfrak{F}\mathfrak{N}} \tag{24}\]

\(\mathfrak{F}\mathfrak{N}\) stands for True Negatives, or the quantity of negative situations that are accurately detected. \(\mathfrak{F}\mathfrak{N}\) stands for False Positives, or the quantity of positive instances that were mistakenly recognised. Figure 7 displays the specificity, while Table 7 shows the specificity's numerical results. When compared to other current techniques, like PCT, MST-FaDe, fuzzy SVM, and AISAR, the recommended approach provides exceptional specificity. That is the overall utility as the number of epochs increases. The greatest specificity of the suggested methods is 98% in terms of epochs; by comparison, the maximum specificities of PCT, MST-FaDe, fuzzy SVM, and AISAR are 70, 80, 85, and 89 bits per second, respectively. Our suggested strategy outperforms the current ones in terms of specificity. ### Research Summary

Students and Course Instructors (CIs) are initially enrolled in matching sub-servers using credentials such as ID, password, face data, voice data, and keystroke dynamics. The credentials of students and CIs are treated as local models. Once these local models are established, the sub-servers employ CGAN to construct the global model and perform authentication based on this model. The TSE approach is employed to assess students' \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Epochs**} & \multicolumn{5}{c}{**Specificity**} \\ \cline{2-6}  & **PCT** & **MST-FaDe** & **Fuzzy SVM** & **AISAR** & **Proposed** \\ \hline
20 & 10 & 20 & 25 & 28 & 35 \\ \hline
40 & 20 & 30 & 35 & 40 & 47 \\ \hline
60 & 40 & 50 & 55 & 58 & 65 \\ \hline
80 & 50 & 60 & 65 & 67 & 79 \\ \hline
100 & 70 & 80 & 85 & 89 & 98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Numeric results of specificity. Figure 7: Number of epochs vs. specificity. interactiveness, academic emotions, and behaviour, as well as the behaviours and teaching styles of CIs. The weekly engagement status of the students is then determined using the MV-MOC approach. The performance of non-engaged students is subsequently predicted early using the ID2 QN. Personalised recommendations for students are automatically generated using a deep learning technique called Att-CapsNet and are stored on the blockchain using the LWEA. Table 8 presents the numerical evaluation of the current and proposed strategies. ## 6 Conclusions

In conclusion, this research establishes a detailed framework for the management of both students and course instructors, enhancing engagement assessment within the educational sphere. It lays down a solid groundwork for user authentication, incorporating multi-modal biometric data to ensure security. The application of the Threshold-based Shannon Entropy (TSE) method allows for a nuanced analysis of student behaviour, emotional responses, and instructional strategies, shedding light on diverse aspects of the educational process. Utilising the MV-MOC technique, the study enables educators to closely monitor and stimulate student participation on a weekly basis, fostering a more engaged learning environment. Furthermore, the integration of predictive modelling, exemplified by the ID2 QN algorithm, aids in the timely identification of students in need of additional academic support. Additionally, this study innovates by offering personalised recommendations through the Att-CapsNet system, further secured by blockchain technology via the Lightweight Encryption Algorithm (LWEA), marking a significant step towards a more integrated and individualised educational approach.