Abstract
In our days, a recurrent problem in the Smart Cities of all Latin America will be the degenerative illnesses linked to food, one of the main reasons for the lack of organization of each citizen, is not being able to adequately determine the caloric intake, a proposal for a solution , is the development of an application that has the capacity to be able by means of the recognition of patterns and a deep learning model, to be able to specify what percentage of the nutritional value of each meal is covered by the associated quantities, one of the advantages of using a food repository is that solutions based on deep learning are not ready-made solutions. A development process is necessary to acquire an adequate set of instances and to customize the intelligent system. The latter includes the customization of the user interface, as well as the way in which the system retrieves and processes the feeding scenarios later. The resulting scenarios can be shown to the user in different ways, and/or retrieved cases can be adapted to be reused later. This research is about an intelligent model for decision making based on deep learning to solve the existing problem in the planning of food distribution in the population of a Smart City, for this first, we mentioned the need for intelligent systems in the processes of decision-making, where they are necessary due to the limitations associated with conventional human decision-making processes, among them: human experience is very scarce with respect to being able to calculate in a correct way the caloric value of food intake and we must to consider that citizens in a smart city are tired of the burden of physical or mental work, in addition to human beings forget the crucial details of a problem, and many times are inconsistent in their daily decisions. Complexity and investment of the time necessary to make food decisions tend to be complex for health as well as the high frequency of decision making found in the distribution to supermarkets, which mostly supply the food of a population with tendency to increase of individuals of the groutier type when they eat their food late at night. We use an image repository from DataWorld to our research (https://data.world/).
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Mejía, J., Ochoa-Zezzatti, A., Contreras-Masse, R., Rivera, G. (2020). Intelligent System for the Visual Support of Caloric Intake of Food in Inhabitants of a Smart City Using a Deep Learning Model. In: Oliva, D., Hinojosa, S. (eds) Applications of Hybrid Metaheuristic Algorithms for Image Processing. Studies in Computational Intelligence, vol 890. Springer, Cham. https://doi.org/10.1007/978-3-030-40977-7_19
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