Tahnoon Alhosani

Recent graduate in Computer Science, majoring in Artificial Intelligence, with hands-on experience building practical AI systems using deep learning, computer vision, and gesture recognition. Proficient in cross-cultural collaboration, information analysis, and project management. Fluent in Arabic, English, and Japanese.

AI-Based Attendance System using Object Detection

Developed an AI-powered attendance system using YOLOv8 for student detection and MediaPipe for real-time tracking. The system mapped detected individuals to pre-defined classroom seating zones and marked attendance based on presence duration. Used OpenCV for video processing and SQLite to log attendance data. Enabled automated, timestamped attendance with high accuracy. Applied bounding box overlap logic with seat mapping. Successfully tested under varied lighting and movement conditions.

Drop Detection and Object Ownership Recognition System

Designed a classroom monitoring system to detect and log dropped objects such as wallets and phones using YOLOv8 and OpenCV. Integrated MediaPipe for motion tracking and built proximity-based logic to identify and associate the object with the nearby student. - Built custom dataset and trained YOLOv8 model. Tracked objects and students using real-time bounding box and MediaPipe pose detection. Developed GUI dashboard with Tkinter and stored alerts using SQLite.