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@article{201799,
        author = {Ryan Tom Vinoy and Dr. Reshma J and Shreesha Shetty and Vishwanath S and Uday Kumar H P},
        title = {ReNosh: AI-Driven Food Management For Waste Management And Sustainability},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10166-10172},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201799},
        abstract = {ReNosh is a cross-platform mobile and web application developed using Flutter to address the growing issue of food waste in restaurants and food establishments. The system leverages AI-driven predictions and real-time data tracking to optimize food production, minimize surplus, and promote sustainable practices. By integrating Firebase for authentication and cloud storage, along with machine learning models such as XGBoost and external APIs like the Gemini API, the platform provides intelligent demand forecasting and actionable insights. 
The application enables users to monitor surplus food, donate excess meals, and visualize sustainability metrics such as food saved, meals donated, and waste reduction impact through interactive dashboards. Additionally, ReNosh supports offline functionality using local caching, ensuring uninterrupted access to essential features. The system is designed with scalability in mind, using modular architecture and cloud-based services. This solution aims to assist food businesses in reducing operational inefficiencies while contributing to environmental sustainability and social good.},
        keywords = {Food Waste Management, AI Predictions, Sustainability, Flutter, Firebase, Surplus Food, Smart Inventory, Gemini API},
        month = {May},
        }