International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Powered Smart Dietary Management and Personalized Recipe Generation System |
|---|---|
| ABSTRACT | This paper presents an AI-Powered Smart Dietary Management and Personalized Recipe Generation System a comprehensive cross-platform application that unifies meal planning, nutrition tracking, grocery management, and AI recipe generation within a single interface. The system integrates Google Gemini 2.5 Flash for real-time personalized recipe generation conditioned on user-supplied ingredients, dietary restrictions, allergies, cuisine preferences, and nutritional goals computed via the Mifflin-St Jeor equation. Core modules include a calendar-based weekly meal planner with AI auto-fill, an atomic grocery list synchronizer, a real-time health dashboard providing BMI tracking and macronutrient analysis, native push notifications, and Free/Pro/Annual subscription tiers. Built on React 19 with TypeScript, Node.js with Express, Supabase PostgreSQL with row-level security, and Capacitor for Android and iOS, the platform delivers a native-quality cross-platform experience. User acceptance testing with 25 participants over two weeks yielded an overall satisfaction rating of 4.6/5.0, with 95 percent of recipe generations completing within the 5-minute target window and average API response times of 180 ms under normal load. |
| AUTHOR | K. MOKSHITH RAO, K. AVINASH, G. ASHISH, E. SATISH BABU UG Students, Dept. of Computer Science & Engineering, Jyothishmathi Institute of Technology & Science (Autonomous), Karimnagar, Telangana, India Assistant Professor (Guide), Dept. of Computer Science & Engineering, Jyothishmathi Institute of Technology & Science (Autonomous), Karimnagar, Telangana, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403055 |
| pdf/55_AI-Powered Smart Dietary Management and Personalized Recipe Generation System.pdf | |
| KEYWORDS | |
| References | 1. H. H. Lee et al., “RecipeGPT: Generative Pre-training Based Cooking Recipe Generation,” in Companion Proc. Web Conference 2020, pp. 181–184. 2. M. Bien et al., “RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation,” in Proc. 13th Int. Conf. Natural Language Generation, 2020, pp. 22–28. 3. D. Choi, J. Hwang, M. Gim, and D. Park, “RecipeBuild: Learning to Build Recipe Ideas Using LM-Based Recipe Contextual Modeling,” IEEE Access, vol. 13, pp. 9788–9802, 2025. 4. J. Marin et al., “Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes,” IEEE TPAMI, vol. 43, no. 1, pp. 187–203, 2021. 5. C. A. Celis-Morales et al., “Effect of Personalized Nutrition on Health-Related Behaviour Change,” Int. J. Epidemiol., vol. 46, no. 2, pp. 578–588, 2017. 6. United Nations Environment Programme, “UNEP Food Waste Index Report 2021,” Nairobi, 2021. 7. R. S. H. Istepanian and J. Lacal, “Emerging Mobile Communication Technologies for Health,” in Proc. IEEE EMBS 2003, pp. 1414–1416. 8. J. Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” in Proc. NeurIPS 2022. 9. D. Li and M. J. Zaki, “Reciptor: An Effective Pretrained Model for Recipe Representation Learning,” in Proc. 26th ACM SIGKDD, 2020, pp. 1719–1727. 10. M. Gim et al., “RecipeBowl: A Cooking Recommender Using Set Transformer,” IEEE Access, vol. 9, pp. 143623–143633, 2021. |
