Ingeniería de Sistemas

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Browsing Ingeniería de Sistemas by Author "Abril Ulloa, Sandra Victoria"

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    Desarrollo de un sistema integrado de captura, procesamiento y consulta de información nutricional de productos procesados y ultra procesados: una aplicación móvil con asistente virtual basado en RAG
    (Universidad de Cuenca, 2025-01-24) León Carrión, José Luis; Quinde Pasato, Henry Miguel; Espinoza Mejía, Jorge Mauricio; Abril Ulloa, Sandra Victoria
    The increasing consumption of processed and ultra-processed foods poses significant public health challenges due to its association with non-communicable diseases. This study proposes an integrated system for capturing, processing, and querying nutritional information of these products, specifically tailored for the Ecuadorian context. The system includes a mobile application that utilizes Optical Character Recognition (OCR) and a Named Entity Recognition (NER) model to extract data from product labels, which are then stored in a knowledge graph managed with Neo4j. A web platform enables the validation and management of the captured data, while a virtual assistant based on Retrieval-Augmented Generation (RAG) facilitates natural language queries, generating precise and context-aware responses. The system architecture combines advanced technologies such as Google Vision API, OpenAI, Flutter, and Angular, providing an efficient solution adapted to local needs. Preliminary evaluations demonstrate high levels of usability and accuracy, showing that the system not only automates the collection of nutritional information but also enhances its accessibility and reliability, significantly contributing to data management in this field.
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    Diseño e implementación de un agente conversacional para optimizar el consumo de medicamentos en adultos mayores en la ciudad de Cuenca
    (Universidad de Cuenca, 2023-07-27) Armijos Pulla, Carlos Sebastián; Cambizaca Quinde, Juan Daniel; Espinoza Mejía, Jorge Mauricio; Abril Ulloa, Sandra Victoria
    Centered on the need to prevent drug-drug and drug-food interactions among the elderly population in Cuenca, this project presents the development of a conversational agent implemented in a mobile application. The study incorporates a database on drug-drug and drug-food interactions, together with a content-based recommendation algorithm, specifically designed to suggest dietary alternatives in the event of potential interactions between a drug and a food item. The findings, endorsed by evaluations conducted by both nutrition students and field experts, confirm the efficacy of the chatbot in identifying pharmacological interactions and generating appropriate recommendations. These results underline the importance of precision systems that facilitate user interaction, for instance, through conversational agents and recommendation algorithms that provide personalized information to prevent drug interactions; moreover, the evaluation by experts assures the reliability and effectiveness of such systems.
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    Sistema de información nutricional y recomendación de sustituciones saludables de productos procesados para la prevención de DMT2 en Cuenca
    (Universidad de Cuenca, 2023-08-10) Herrera Galárraga, Víctor Enrique; Solano Córdova, Pablo Antonio; Espinoza Mejía, Jorge Mauricio; Abril Ulloa, Sandra Victoria
    Type 2 Diabetes Mellitus (T2DM) is one of the biggest threats to Ecuador's health. The consumption of processed foods has been linked to a higher risk of acquiring T2DM. Therefore, efforts to discourage the consumption of these products are necessary. This paper proposes FoodSub, a mobile application to recommend food substitutes to replace processed foods using the NOVA Classification. The proposed approach uses nutrient-based food clustering to identify substitute pairs between processed and unprocessed foods. The recommendation of food substitutes is supported and personalized using a knowledge graph that contains foods, dietary guidelines, and user information. In addition, a chatbot is implemented to answer simple questions about food nutritional values, comparisons, ingredients, and general information. This chatbot is developed using a Large Language Model (LLM) to query the implemented knowledge base. The mobile application and the chatbot are evaluated in terms of usability; both perform well according to the System Usability Scale (SUS) and the Chatbot Usability Questionnaire (CUQ), respectively, but there is some scope for improvement. Additionally, the substitute recommendations performance is evaluated through expert verification. The results indicate that the recommendations perform well in cases where the differences between the original food and the substitute are not relevant in terms of food transformation processes, flavor, context, or meal time. Future work lines are provided, considering the enhancement of the chatbot and the improvement of substitute recommendations when the mentioned issues are relevant.