AI & GPS-Based Attendance Monitoring System

I'll create a comprehensive attendance monitoring system that uses GPS location to verify attendance and AI to detect potential fraud. This system will have a user-friendly interface built with Bootstrap and a robust backend using Python and MySQL.

Backend Implementation with Python and MySQL

To complete the system, we need to implement the Python backend that will handle the database operations and AI-based fraud detection. Here's how you can set up the Python backend:

MySQL Database Setup

Here's the SQL script to set up the MySQL database for the attendance system:

System Architecture Diagram

Here's a diagram showing how all the components of the system work together:

How the System Works

1. User Authentication:

   • Users register and log in through the web interface
   • Authentication is handled securely with password hashing (in a production environment)

2. GPS Location Tracking:

   • When a user checks in, their GPS location is captured
   • The system verifies if the user is within a designated geofence area
   • If outside the allowed area, check-in is rejected

3. AI Fraud Detection:

   • The system uses an Isolation Forest algorithm to detect anomalies in attendance patterns
   • It checks for unusual movement speeds between check-ins
   • It verifies device consistency to prevent buddy punching
   • Suspicious activities are logged for admin review

4. Attendance Management:

   • Admins can view real-time attendance data on the dashboard
   • The system automatically marks users as present, late, or absent
   • Detailed reports can be generated for specific dates or time periods

5. Reporting and Analytics:

   • The system provides daily, weekly, and monthly attendance reports
   • Department-wise attendance statistics are available
   • Fraud detection logs help identify potential attendance abuse

Deployment Instructions

1. Database Setup:

   mysql -u root -p < setup_database.sql

2. Python Backend Setup:

   pip install flask flask-cors mysql-connector-python numpy scikit-learn python app.py

3. Frontend Setup:

   npm install npm run dev

4. Production Deployment:

   • Deploy the Python backend on a server with WSGI (Gunicorn/uWSGI)
   • Deploy the Next.js frontend using Vercel or similar platforms
   • Set up a production MySQL database with proper security measures

This comprehensive system provides a secure and efficient way to track attendance using GPS location verification and AI-based fraud detection, making it suitable for educational institutions and businesses alike.

I'll fix the import error by updating the code to use the correct package for Lucide icons.

I've updated all the imports from "react-icons/lucide" to "lucide-react" and removed the "Lucide" prefix from all the icon component names. This should fix the error you were encountering.

The issue was that we were using the wrong import path for the Lucide icons. In Next.js with the App Router, we should use the "lucide-react" package directly, which provides all the icon components without the "Lucide" prefix.

For example, instead of:

We now use:

And then in the JSX:

This change has been applied to all components that were using Lucide icons. The application should now render correctly without the import error.