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Real-Time Queen Bee Tracking Using Advanced Computer Vision
SE-C-07
Maxim Teslenko; maximte@ac.sce.ac.il Rom Ihia; romihia01@gmail.com
Advisors: Dr. Hadassa Daltrophe1, Dr. Tammar Shrot1 1SCE – Shamoon College of Engineering, Ashdod
Worldwide declining hive health threatens pollination and ecological balance. This project, in collaboration with Hebrew University’s Bee Research Center, is developing a low-cost computer vision system to monitor queen bee movements. Using advanced “YOLOv8” algorithms, one camera detects the queen’s exit from the hive, triggering a second camera to track her nuptial flight.. These synchronized recordings are saved as events in our web app, providing researchers with a detailed log of this rare occurrence—currently neglected due to limited data. Future findings will reveal movement patterns and behavioral insights about the queen and her hive. Drone integration is planned to further track her—ultimately enhancing our understanding of hive dynamics.
Keywords: computer vision, hive health, insect tracking, queen bee, “YOLOv8”
Anti-Cheat System for Online Exams
SE-C-08
Sagi Lidani; sagilidani@gmail.com Matan Ofri; matano2099@example.com
Advisor: Dr. Hadassa Daltrophe
SCE - Shamoon College of Engineering, Ashdod
This project presents an anti-cheat system for online exams using AI to monitor student behavior in real-time. Our system includes eye movement tracking, blocking access to external websites and applications, and locking browsers. In case of cheating, it sends immediate alerts to the instructor. The system is designed with security layers to ensure safe data management, including encrypted communication (TLS/SSL), strong authentication mechanisms, and encrypted data storage. The objective is to provide a reliable solution for maintaining the integrity of online exams.
Keywords: AI, anti-cheat system, data security, eye tracking, online exams, real-time monitoring