PartiSee: Anomaly Detection System Tailored for Partition Classrooms

This paper aims to detect anomalies occurring in partition classrooms, where male instructors cannot observe through direct eyesight. One of the biggest problems of partition rooms is monitoring the attendance of students. These problems are addressed by creating a prototype system called PartiSee that can effectively monitor attendance and occupancy through the pressure and the light sensors' readings. The collected data is utilized into two android applications, a tablet application for the male instructor, and a smartphone application for the students. The data is analyzed to provide the male instructor with a glimpse of what is happening behind the partition. The system results in preventing cheating and reducing attendance taking time, effort, and error rate. To showcase the variety of anomalies that can be included in such a system, it also highlights some aspects of energy consumption by monitoring light levels in classrooms. This paper intended to solve the anomalies in the partition rooms of the College of Computer Sciences and Information Technology at Imam Abdulrahman Bin Faisal University.