Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction.Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback.

IoT has been transforming the world of information significantly. It hasallowed administration services without human interventions for a range ofapplications including healthcare with Remote Patient Monitoring (RPM). Blockchainis broadly utilized in IoT applications like smart health monitoring that providesprivacy and security. Blockchain innovation presents freedom for the medical servicesindustry, for example, reduced transaction costs, transparency for reporting regulatoryentities, productive medical care information, privacy, confidentiality and universalityof healthcare records. This chapter deals with the collaboration of blockchaininnovation in IoT security in terms of the RPM framework. A Patient Centric Agent isused for end-to-end design for nonstop tolerant checking. The patient agent deals witha segment of blockchain to provide protection when streaming information from bodyarea sensors that need to be securely stored and analyzed critical parts of blockchain toa wellbeing application network where patient's information can be utilized to makealarms that are essential to validate medical services suppliers in a protected way. Thismethodology is valuable as they assemble information for longer time frames. Theaccess control manager stores and safely accesses information needed by the classifierduring retraining progressively in realtime from an external data storage.

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