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@article{180409,
        author = {Huzaifa Sheikh and Shiftain Alam and Mohd. Zayed Ansari and Farhan Shaikh and Monali Arun Bansode},
        title = {Al-Integrated Personalized Diet Planning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1737-1742},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180409},
        abstract = {This research presents the development and implementation of an AI-powered diet planning system capable of generating highly personalized and nutritionally optimized meal plans. The core system employs a content-based filtering algorithm to recommend meals based on detailed user profiles, including demographic data, fitness goals, dietary preferences, seasonal food availability, and user feedback. The platform integrates a dynamic back-end built with Node.js and Express.js and uses MongoDB for database operations. Python-based machine learning models process user data to deliver intelligent, context-aware recommendations. Unlike traditional diet planning applications, our system introduces unique features such as season-specific food suggestions and a continuous feedback loop to improve recommendation accuracy. The system architecture, algorithms used, and performance evaluations are discussed in depth.},
        keywords = {AI in Healthcare, Content-Based Filtering, Meal Recommendation System, Personalized Diet, Seasonal Nutrition},
        month = {June},
        }