Research Details

The paper you co-authored, presented at the International Conference on Emerging Trends in Science and Engineering (ICETSE), focuses on the "Classroom Mood and Attention Monitoring System."

In this paper, you explore the integration of AI, machine learning, and IoT technologies to monitor and improve student engagement and well-being in educational settings. The system leverages IoT devices to gather real-time data from the classroom environment, including factors such as noise levels, temperature, and physiological indicators like heart rate or facial expressions.

Machine learning algorithms are used to analyze this data and assess students' mood and attention levels. The paper discusses how these algorithms process and interpret the collected data to provide actionable insights for educators. For example, the system can detect patterns that suggest when students are losing focus or feeling uncomfortable, allowing teachers to make adjustments to their teaching methods or classroom conditions accordingly.