AI-Based Fake Media Detection Using Machine Learning and Natural Language Processing

Abstract

The rapid advancement of digital technologies has significantly transformed the hospitality and restaurant industry. Traditional food ordering systems often involve manual processes, long waiting times, and inefficiencies in managing customer requests, which can negatively impact customer satisfaction. To address these challenges, this research proposes a QR Code-Based Smart Food Ordering System developed using web technologies and the Django framework.The proposed system enables customers to access restaurant menus by scanning a unique Quick Response (QR) code assigned to each restaurant. Upon scanning, users are redirected to a web interface where they can browse menu items, view prices, and place orders seamlessly without requiring direct interaction with restaurant staff. This contactless approach not only enhances user convenience but also aligns with modern hygiene and safety standards.The system architecture is designed with three primary modules: customer, restaurant owner, and administrator. The customer module allows users to register, log in, scan QR codes, browse menus, and place orders. The restaurant owner module provides functionalities to manage menu items, upload images, add dining capacity details, and monitor daily orders. The backend is implemented using Django, ensuring scalability and robust request handling, while MySQL is used for efficient database management.Additionally, QR code generation is integrated into the system, allowing restaurant owners to generate and download unique QR codes for their establishments. These QR codes are linked to dynamic menu pages, ensuring real-time updates. The system also maintains transaction records, enabling customers to view their order history and restaurant owners to analyze order trends.The implementation leverages Python libraries for QR code generation and image handling, ensuring seamless integration with the web application. The system is designed to be user-friendly, efficient, and adaptable to different restaurant environments. Experimental evaluation demonstrates that the proposed system reduces order processing time, minimizes human errors, and improves overall service efficiency. Furthermore, it supports digital transformation in the food service industry by providing a cost-effective and scalable solution.In conclusion, the QR Code-Based Smart Food Ordering System offers a modern, contactless, and efficient alternative to traditional ordering methods, enhancing both customer experience and operational efficiency in restaurants.