# Visual Data and Pattern Analysis for Smart Education:
## 4 Proposed Methods  
To enhance the e-learning experience, the proposed E-FedCloud system adopts a comprehensive approach, as depicted in Figure 1, which outlines the system's overall architecture. The process begins by ensuring robust authentication for both students andinstructors, utilising diverse credentials, such as ID, password, voice, facial data, and keystroke dynamics. This is achieved through federated learning combined with the Cycle GAN algorithm. Following authentication, the system shifts focus to student engagement detection, employing deep learning and threshold-based entropy classification methods. This in-depth evaluation includes analyses of student interactions, academic behaviours, instructor behaviours, and academic emotions. Leveraging deep learning and the Att-CapsNet algorithm, E-FedCloud then generates personalised suggestions for students, tailored according to their assessed levels of participation.  
Each student's performance is taken into account when generating these recommendations, which may include initiatives like scholarships for students at risk of dropping out and additional support for those underperforming, encompassing options like retesting and adjustments to their learning styles. The system employs the ID2 QN algorithm, which considers a broad array of factors, including academic aca  
Figure 1: Overall system architecture.  
the teaching methods of course instructors, and students' prior academic backgrounds, thereby enhancing the accuracy of performance prediction. E-FedCloud also uses majority voting-based clustering algorithms to conduct weekly engagement status assessments, categorising students based on their interactivity, academic behaviour, facial emotions, and instructor actions. Furthermore, the system routinely validates weekly statuses and offers opportunities for retesting to monitor students' progress. This comprehensive approach ensures a holistic and adaptable e-learning environment, meeting the varied needs of students and consistently fostering their academic development.  
The primary focus of this research is to develop an accurate, effective, and secure performance prediction and recommendation model to assess and enhance student academics. To achieve this objective, we have integrated Artificial Intelligence (AI), Federated Learning (FL), and Cloud Computing (CC) technologies.  
AI is employed to facilitate automation with high reliability; FL technology is utilised to ensure the privacy of entities; and CC technology is harnessed to provide and store computational resources on-demand without compromising quality.  
The entities involved in this study include students, Course Instructors (CIs), Intelligent Software Agents (ISAs), and Cloud Servers (CS).  
The detailed process, along with the functioning of each entity, is provided in the following Section. This can be understood by reviewing the sequential research processes outlined below:  
* Federated learning-based authentication
* Majority voting-based multi-objective student engagement clustering
* Deep reinforcement learning (DRL)-based early warning system and multi-disciplinary ontology graph construction
* Automated personalised recommendation generation and tracking