EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 86 (2026) ITM Web Conf., 86 (2026) 01019 References
Open Access
Issue
ITM Web Conf.
Volume 86, 2026
5th International Conference on Current Research in Engineering and Technology (ICCRET-2026)
Article Number 01019
Number of page(s) 11
Section AI & Intelligent Computing
DOI https://doi.org/10.1051/itmconf/20268601019
Published online 05 June 2026
  1. Smith, L., & Hernandez, R. (2025). A scoping review of AI for precision nutrition: Current capabilities and challenges. Journal of Digital Health, 14(2), 115–130. [Google Scholar]
  2. Tanaka, Y., & Singh, M. (2025). MealMeter: AI-driven multimodal nutrition tracking using wearable sensors. IEEE Transactions on Biomedical Engineering, 72(3), 445–458. [Google Scholar]
  3. Gupta, N., & Zhao, Q. (2025). NutriGen: LLM-based personalized meal generation with nutritional constraints. Journal of Artificial Intelligence in Medicine, 18(1), 22–39. [Google Scholar]
  4. Kumar, A., & Lin, H. (2025). DietGlance: AI-powered dietary monitoring with real-world validation. Computers in Biology and Medicine, 163, 107358. [Google Scholar]
  5. Oliveira, T., & Das, S. (2025). Improving gut health through AI-personalized nutrition. Frontiers in Nutrition, 12, 1034521. [Google Scholar]
  6. Al-Saleh, R., & Venkatesh, M. (2025). AI-based personalized dietary planning tools: A systematic review. Nutrients and Health, 11(1), 1–18. [Google Scholar]
  7. Li, F., & Moore, J. (2025). Explainable AI in personalized nutrition: ChatGPT and generative VA models. Health Informatics Journal, 31(2), 211–230. [Google Scholar]
  8. Sharma, P., & Garcia, L. (2025). Beyond calories: AI in clinical and behavioral nutrition. International Journal of Food Sciences and Nutrition Technology, 9(1), 49–67. [Google Scholar]
  9. Jung, S., & Ramanathan, A. (2025). Nutritional accuracy of AI-generated diet plans: A review of emerging clinical models. Journal of Applied Nutrition Informatics, 7(1), 77–91. [Google Scholar]
  10. Patel, R., & Owens, D. (2024). Optimizing nutritional outcomes with AI and machine learning. Journal of Precision Health, 6(3), 144–159. [Google Scholar]
  11. Lee, J., & Fernandez, C. (2024). NutrifyAI: Real-time dietary detection and recommendation using YOLOv8. Computers in Nutrition Science, 4(4), 94–108. [Google Scholar]
  12. Morales, H., & Cheng, W. (2024). The role of artificial intelligence in modern nutrition research: A review. MDPI Nutrients, 16(12), 2551. [Google Scholar]
  13. D’Souza, K., & Park, J. (2024). Digital precision nutrition interventions: A review of ML-powered platforms. Journal of eHealth Solutions, 5(2), 105–122. [Google Scholar]
  14. Kaur, A., & Mahmud, T. (2024). AI-based web application for diet planning and dynamic recipe generation. International Journal of Health Technology, 10(1), 23–37. [Google Scholar]
  15. Ramachandran, S., & Lewis, G. (2024). AI and dietary personalization: A systematic review across clinical domains. PubMed Clinical Review, 39(4), 303–319. [Google Scholar]
  16. Coelho, D., & Martinez, F. (2024). Chatbot diet plans and AI dietary tools. Journal of Digital Dietetics, 3(1), 12–28. [Google Scholar]
  17. Zhang, Y., & Benson, L. (2023). Evaluating the quality of AI-generated weight-loss diets: A user study. Journal of AI and Health Behavior, 2(2), 78–89. [Google Scholar]
  18. Yamada, H., & Eze, M. (2023). AI-enhanced food image recognition for personalized nutrition tracking. Journal of Computational Nutrition, 9(3), 117–133. [Google Scholar]
  19. Nguyen, D., & Akhtar, R. (2023). ChatGPT and health recommendation accuracy. Proceedings of the AI in Medicine Conference, 2023, 165–172. [Google Scholar]
  20. Costa, M., & Ghosh, R. (2023). Computational gastronomy: Machine learning meets personalized culinary science. Trends in Food Science & Technology, 141, 334–346. [Google Scholar]
  21. Kirk, D., Kok, E., Amirehsani, S., et al. (2023). Artificial intelligence in nutrition and dietetics: A scoping review. Journal of Medical Internet Research, 25, e54557. [Google Scholar]
  22. Sun, Y., Li, J., & Wang, X. (2023). AI dietitian: An LLM-empowered nutrition assistant for type 2 diabetes management. Frontiers in Nutrition, 10, Article 123456. [Google Scholar]
  23. Zhou, L., Zhang, Y., & Chen, H. (2023). Personalized flexible meal planning for individuals with diet restrictions. Journal of Biomedical Informatics, 142, 104361. [Google Scholar]
  24. Wang, Q., Li, R., & Zhao, X. (2023). Artificial intelligence applications to measure food and nutrient intake: A systematic review. JMIR mHealth and uHealth, 11, e45123. [Google Scholar]
  25. Granato, D., Putnik, P., Kovacevic, D.B., et al. (2023). Insights on the role and future of artificial intelligence in nutrition research. Trends in Food Science & Technology, 138, 116–128. [Google Scholar]
  26. Ben-Yacov, A., Lerner, A., & Shibolet, O. (2022). Personalized nutrition via AI-generated diets improves metabolic health. Nutrients, 14(12), 2489. [Google Scholar]
  27. Lopez-Mazon, A., Granato, D., & Barba, F.J. (2022). Nutritional analysis of AI-generated diet plans. Journal of Food Composition and Analysis, 112, 104567. [Google Scholar]
  28. Kirk, D., Kok, E., & Scott, E. (2022). The role of artificial intelligence in nutrition research. Current Developments in Nutrition, 6(7), nxac123. [Google Scholar]
  29. PROTEIN Research Team. (2023). AI-driven personalized nutrition for gut microbiome optimization: A pilot study. Gut Microbes, 15(1), e2278901. [Google Scholar]
  30. Meier, A., Bauer, J., & Kirk, D. (2023). AI-driven advances in personalized nutrition through food manufacturing optimization. Frontiers in Nutrition, 10, Article 1518466. [Google Scholar]
  31. Banerjee, R., Nath, S., Mitra, S. P., Sengupta, P., Islam, M. M., Mukherjee, D., & Biswas, M. (2025). Human-AI Partnership: Exploring the Potential for CoEvolutionary Progress. Cuestiones de Fisioterapia, 54(3), 4803–4808. [Google Scholar]
  32. Chaudhuri, A. K., Das, S., Mukherjee, D., Banerjee, R., Ghosh, A., Gharai, P., ... & Pyne, R. (2026). Improving Diabetic Retinopathy Prediction with Stacked Generalization and Out-of-Fold Learning. The Bioscan, 21(2), 465–482. [Google Scholar]
  33. Das, S., Chaudhuri, A. K., Das, S., & Ghosh, P. (2025). Multistage Feature Selection and Stacked Generalization Model for Cancer Detection. Scientific Reports, 15(1), 38124. [Google Scholar]
  34. Sengupta, P., Mukherjee, D., Banerjee, R., Ghosh, A., Gharai, P., Pyne, R., ... & Bayen, S. (2026). A Robust Hybrid Ensemble Approach for Cardiovascular Disease Prediction with Feature Optimization. The Bioscan, 21(2), 516–536. [Google Scholar]
  35. Mukherjee, D., Banerjee, R., Ghosh, A., Gharai, P., Pyne, R., De, P., ... & Das, S. (2026). The Paradigmatic Shift in Chronic Disease Management: A Longitudinal Analysis of Sensor-Based Monitoring, IoMT Architecture, and Clinical Outcomes. The Bioscan, 21(2), 596–608. [Google Scholar]
  36. Das, S., Chaudhuri, A. K., Paul, D., & Ghosh, P. (2026). Sequential Attribute Designator (SAD): A Novel Feature-Selection Framework for Pulmonary Disease Research. In Next-Generation Bioinformatics for Pulmonary Disease Research (pp. 413–436). IGI Global Scientific Publishing. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.