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- Nutrition Control System Based on Short-term Personal DemandsPublication . Cunha, Carlos; P. Duarte, Rui; Oliveira, RafaelPersonalized nutrition considers an individual’s unique genetic, metabolic, and lifestyle factors to create a customized dietary plan tailored to their needs. People seeking to optimize their health and wellness through diet and lifestyle changes can benefit from technological advances in machine learning and deep learning approaches to create personalized models of nutritional requirements that override traditional food plans. These models will provide users with an unprecedented decision tool for informing them of the impact of specific food intake and exercise on their goals. This article presents the architecture, implementation, and preliminary results of a deep learning-based control system for nutrition. It allows users to understand the impact of their food and exercise immediate choices on their goals while reducing user interaction demands. Preliminary results have shown that it is possible to predict BMI (Body Mass Index) accurately within a time window of three days.
- Meal Suggestions for Caregivers and Indecisive Individuals Without a Set Food PlanPublication . Cunha, Carlos; Cardoso, Tiago R.; P. Duarte, RuiRecommendation systems have played a crucial role in assisting users with decision-making across various domains. In nutrition, these systems can provide valuable assistance by offering alternatives to inflexible food plans that often result in abandonment due to personal food preferences or the temporary unavailability of certain ingredients. Moreover, they can aid caregivers in selecting the most suitable food options for dependent individuals based on their specific daily goals. In this article, we develop a data-driven model using a multilayer perceptron (MLP) network to assist individuals in making informed meal choices that align with their preferences and daily goals. Our study focuses on predicting complete meals rather than solely on predicting individual food items since food choices are often influenced by specific combinations of ingredients that work harmoniously together. Based on our evaluation of a comprehensive dataset, the results of our study demonstrate that the model achieves a prediction accuracy of over 60% for an individual complete meal.