NutriPredictAI: intelligent planning of budget-friendly recipes

Rosales Rodriguez, Ivan Camilo

NutriPredictAI: intelligent planning of budget-friendly recipes

dc.contributor.advisor	Galpin, Ixent
dc.contributor.advisor	Garcia Bedoya, Olmer
dc.creator	Rosales Rodriguez, Ivan Camilo
dc.date.accessioned	2025-06-16T22:23:59Z
dc.date.available	2025-06-16T22:23:59Z
dc.date.created	2025-06-11
dc.description.abstract	La planificación de dietas personalizadas requiere considerar las necesidades nutricionales del usuario y la variabilidad en los precios de los alimentos. Este trabajo presenta un robot conversacional basado en inteligencia artificial (IA) que usa técnicas de aprendizaje automático (ML) para predecir precios de productos agrícolas y generar planes alimenticios personalizados. Implementado en una plataforma de mensajería instantánea, el sistema integra un modelo LLM a través de Groq para mejorar la interacción en lenguaje natural, permitiendo a los usuarios recibir recomendaciones dietéticas adaptadas a sus preferencias y restricciones presupuestarias. Se usan modelos de predicción de precios, basados en datos históricos de productos agrícolas, para ofrecer sugerencias con costos estimados. La arquitectura del asistente combina IA generativa y técnicas de ML, permitiendo mejorar la precisión en la recomendación de alimentos y optimizar la experiencia del usuario. Los resultados preliminares muestran que el sistema es capaz de generar planes nutricionales personalizados de manera eficaz, considerando de forma conjunta, factores económicos y nutricionales.	spa
dc.description.abstractenglish	Personalized diet planning requires considering the user's nutritional needs and the variability in food prices. This work presents an artificial intelligence (AI)-based conversational agent that uses machine learning (ML) techniques to predict price for agricultural products and generate personalized meal plans. Implemented on an instant messaging platform, the system integrates a large language model (LLM) through Groq to enhance natural language interaction, allowing users to receive dietary recommendations tailored to their preferences and budget constraints. Price prediction models, based on historical agricultural product data, are used to provide suggestions with estimated costs. The assistant's architecture combines generative AI and ML techniques, enabling improved accuracy in food recommendations and optimizing the user experience. Preliminary results show that the system can effectively generate personalized nutrition plans by jointly considering both economic and nutritional factors.	spa
dc.format.extent	16 páginas	spa
dc.format.mimetype	application/pdf	spa
dc.identifier.uri	https://hdl.handle.net/20.500.12010/36883
dc.language.iso	eng	spa
dc.relation.references	Calvaresi, D., Eggenschwiler, S., Calbimonte, J.P., Manzo, G., Schumacher, M.: A personalized agent-based chatbot for nutritional coaching. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. pp. 682–687 (2021)
dc.relation.references	fao: Organización de las naciones unidas para la alimentación y la agricultura: las cifras del hambre se mantienen persistentemente altas por tres años consecutivos, mientras se agravan las crisis mundiales: informe de la onu \| fao en colombia \| organización de las naciones unidas para la alimentación y la agricultura (7 2024), https://www.fao.org/colombia/noticias/detail-events/es/c/1708009/
dc.relation.references	Fathalla, A., Salah, A., Li, K., Li, K., Francesco, P.: Deep end-to-end learning for price prediction of second-hand items. Knowledge and Information Systems 62, 4541–4568 (2020). https://doi.org/10.1007/s10115-020-01495-8
dc.relation.references	Karakan, T., Gundogdu, A., Alagözlü, H., Ekmen, N., Ozgul, S., Tunali, V., Hora, M., Beyazgul, D., Nalbantoglu, O.U.: Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome. Gut Microbes 14(1), 2138672 (2022)
dc.relation.references	Karunasingha, D.S.K.: Root mean square error or mean absolute error? use their ratio as well. Information Sciences 585, 609–629 (2022)
dc.relation.references	Kim, D., Park, J., Sharma, K., Velazquez, A., Li, L., Ostrominski, J., Tran, T., Peréz, R., Shin, J.: Qualitative evaluation of artificial intelligence-generated weight management diet plans. Frontiers in Nutrition 11 (2024). https://doi.org/10.3389/ fnut.2024.1374834
dc.relation.references	Krzeslak, S., Kowalski, O.: Evaluation of the compliance of diet plans and nutritional advice generated by artificial intelligence with guidelines for cardiac patients. European Journal of Cardiovascular Nursing (2024). https://doi.org/10. 1093/eurjcn/zvae098.120
dc.relation.references	Lee, C., Kim, S., Kim, J., Lim, C., Jung, M.: Challenges of diet planning for children using artificial intelligence. Nutrition Research and Practice 16, 801–812 (2022). https://doi.org/10.4162/nrp.2022.16.6.801
dc.relation.references	Mienye, I.D., Swart, T.G., Obaido, G.: Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information 2024, Vol. 15, Page 517 15, 517 (8 2024). https://doi.org/10.3390/INFO15090517, https://www. mdpi.com/2078-2489/15/9/517/htmhttps://www.mdpi.com/2078-2489/15/9/517
dc.relation.references	Mohamed, M., El-Henawy, I., Salah, A.: Price prediction of seasonal items using machine learning and statistical methods. Computers, Materials Continua (2022). https://doi.org/10.32604/cmc.2022.020782
dc.relation.references	Murumkar, A., Singh, A., Chachar, B., Bagade, P., Zaware, G.: Artificial intelligence (ai) based nutrition advisor using an app. In: 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS). pp. 586–590 (2023). https://doi.org/10.1109/ICSCSS57650.2023.10169703
dc.relation.references	Okadome, Y., Nakamura, Y.: Feature extraction method using lag operation for sub-grouped multidimensional time series data. IEEE Access 12, 98945–98959 (2024). https://doi.org/10.1109/ACCESS.2024.3429529
dc.relation.references	Prakash, V.S., Rayi, H., Reddy, P., Maridu, B.: Product price prediction using ensemble learning techniques. In: 2024 8th International Conference on Inventive Systems and Control (ICISC). pp. 73–78 (2024). https://doi.org/10.1109/ICISC62624. 2024.00019
dc.relation.references	Raghuwanshi, G., Zamare, G., Apar, R., Dabhade, R., Wankhade, V.: Advanced forecasting of demandable products prices using machine learning algorithm. International Journal of Advanced Research in Science, Communication and Technology (2023). https://doi.org/10.48175/ijarsct-9511
dc.relation.references	Rahmanti, A.R., Yang, H.C., Bintoro, B.S., Nursetyo, A., Muhtar, M.S., SyedAbdul, S., Li, Y.: Slimme, a chatbot with artificial empathy for personal weight management: System design, preliminary findings and lessons (preprint) (2021). https://doi.org/10.2196/preprints.27393
dc.relation.references	Ritul: Lstm-msnet: Leveraging forecasts on sets of related time series with multiple seasonal patterns (paper summary and implementation) (2020), disponible en Medium: ritul-patidar.medium.com
dc.relation.references	Ruiz-Rincón, S., Galpin, I.: Snapchef: Ai-powered recipe suggestions (2024)
dc.relation.references	Schröer, C., Kruse, F., Gómez, J.M.: A systematic literature review on applying crisp-dm process model. Procedia Computer Science 181, 526–534 (2021)
dc.relation.references	Shrivastava, S.: Cross validation in time series (2020), https://medium.com/ @soumyachess1496/cross-validation-in-time-series-566ae4981ce4, consultado el 4 de marzo de 2025
dc.relation.references	Stefanidis, K.e.a.: Protein ai advisor: A knowledge-based recommendation framework using expert-validated meals for healthy diets. Nutrients 14 (2022). https: //doi.org/10.3390/nu14204435
dc.relation.references	Sullivan, G.M., Artino, J.A.R.: Analyzing and interpreting data from likert-type scales. Journal of Graduate Medical Education 5, 541 (12 2013). https://doi.org/ 10.4300/JGME-5-4-18, https://pmc.ncbi.nlm.nih.gov/articles/PMC3886444/
dc.relation.references	Vegesna, D.: Ai-driven personalized nutrition: A system for tailored dietary recommendations. International Research Journal of Computer Science (2024). https: //doi.org/10.26562/irjcs.2024.v1107.02
dc.relation.references	Wong, L.T., Chui, K.T., Pang, S.S.H., Liu, J., Fong, E.K.S., Lee, L.K.: A preliminary analysis of ai chatbots for individualized nutrition advice. 2024 International Symposium on Educational Technology (ISET) pp. 70–74 (2024). https: //doi.org/10.1109/ISET61814.2024.00023
dc.relation.references	Zhang, J., Oh, Y., Lange, P., Yu, Z., Fukuoka, Y.: Artificial intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet: Viewpoint. Journal of Medical Internet Research 22 (2020). https://doi.org/10.2196/22845
dc.relation.references	Zhao, H.: Futures price prediction of agricultural products based on machine learning. Neural Computing and Applications 33, 837–850 (2020). https://doi.org/10. 1007/s00521-020-05250-6
dc.subject	Large Language Models (LLMs)	spa
dc.subject	Robot Conversacional	spa
dc.subject	Aprendizaje Supervisado	spa
dc.subject	Planificación Nutricional	spa
dc.subject.keyword	Large Language Models (LLMs)	spa
dc.subject.keyword	Robot Conversacional	spa
dc.subject.keyword	Aprendizaje Supervisado	spa
dc.subject.keyword	Supervised Learning	spa
dc.subject.keyword	Nutritional Planning	spa
dc.subject.lemb	Inteligencia artificial - Aplicaciones en nutrición
dc.subject.lemb	Dietas personalizadas - Planificación - Aspectos económicos
dc.subject.lemb	Aprendizaje automático - Aplicaciones en precios agrícolas
dc.title	NutriPredictAI: intelligent planning of budget-friendly recipes	spa
dc.type.coar	http://purl.org/coar/resource_type/c_6501	spa

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