Karim Kamel

Graduation Project: Pothole Detection and Management System . degree : Excellent

Developed an AI-powered system to detect and classify potholes on roads, leveraging advanced technologies to enhance road safety and maintenance.

The Vezeeta Website project is a backend solution designed to streamline healthcare services, focusing on efficient management for administrators, doctors, and patients. The system includes the following key components:

Features

• Dashboard: Offers comprehensive statistics, including numbers of doctors, patients, and request statuses. Displays top-performing specializations and doctors with filtering options (e.g., last 24 hours, week, month, year).
• Doctor and Patient Management: Enables viewing, adding, editing, and deleting doctors and patients with validations.
• Discount Code Management: Admins can add, update, delete, or deactivate discount codes and track their application to requests.
• Login: Secure access with validated credentials.
• Booking Management: View and manage appointments with search and filtering by date.
• Settings: Define availability and pricing while managing booked slots to prevent conflicts.
• Registration and Login: Enables account creation and secure login
• Doctor Search and Booking: Search for doctors based on specialization and availability, book appointments, and apply discount codes.
• Booking Management: View and cancel existing appointments.

Technical Implementation

• Framework: Built using .NET Core with Onion Architecture for maintainability and scalability.
• API Testing: Uses Swagger for endpoint testing.
• Authentication: Implements Identity for secure login and JWT Tokens for session management.
• Database Management: Adheres to clean coding practices, avoids stored procedures, and builds proper relationships between tables.

Developed an AI-powered system to detect and classify potholes on roads, leveraging advanced technologies to enhance road safety and maintenance. Key features include:

• Pothole Detection: Utilized the YOLOv8 object detection algorithm to identify and classify potholes as "Bad" or "Dangerous" based on severity.
• Mobile Application: Built a Flutter-based Android application to capture images, display pothole locations on Google Maps, and provide real-time updates.
• Backend Development: Implemented a FastAPI-based backend to process images and store data in Firebase Firestore, ensuring efficient communication between app and server.
• Google Maps Integration: Enabled location-based visualization of potholes, allowing road authorities to prioritize repairs effectively.

The system achieved 85% accuracy in detecting potholes and demonstrated scalability and reliability during testing. It contributes to safer road conditions and streamlined maintenance processes.