Skin cancer is one of the most important challenges in modern medicine, especially skin melanoma, being the main causer of deaths for this disease. Images analysis is one of the most transcendental techniques for Melanoma early detection as a prevention method. Artificial neural networks are one of the many developed techniques for images digital processing and characteristic similarities detection. In this work a graphic processing unit (GPU) is developed for clinical skin images analysis getting through an artificial neural networks system for similar patterns detection through processing in a collection of modules tasked of silhouette detection of the object to analyze into the image, and tasked to study borders or contour to determinate a final diagnostic, the dataset used for the training of the artificial neural network designed is gotten from the MED-NODE project and project of international skin images collaboration (ISIC) with 730 images of positive and negative cases as full, the proposed system presents finally an accuracy level of 76.67%, with a level of success of 78.79% in melanoma specific cases, and 74.07% in benign lesions cases.

Fake news has been spreading in greater numbers and has generated more and more misinformation, one of the clearest examples being the United States presidential elections of 2016, for which a lot of false information was circulated before the votes that improved the image of Donald Trump overs Hilary's Clinton (Singh, Dasgupta, Sonagra, Raman, & Ghosh, n.d.). Because fake news is too much, it becomes necessary to use computational tools to detect them; this is why the use of algorithms of Machine Learning like "CountVectorizer", "TfidfVectorizer", a Naive Bayes Model and natural language processing for the identification of false news in public data sets is proposed.

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La inadecuada visibilidad de documentos de grado en modalidad monografía impide la consulta y acceso por parte de nuevos integrantes de la comunidad académica, limitando significativamente la posibilidad de dar continuidad y complementar versiones de trabajos ya finalizados, así como la aplicación de resultados tanto a nivel social como industrial. Este artículo propone un modelo de base de conocimiento soportado en una ontología para mejorar la pertinencia de documentos presentados a nivel digital como resultado de una búsqueda sobre aspectos de interés específico. Para la obtención de nuevo conocimiento se hace uso de la lógica de predicados aplicada sobre el modelo ontológico de representación de monografías, con el fin de establecer de manera dinámica y confiable inferencias o llegar a realizar modificaciones en las situaciones donde sean requeridas.

<span>This paper presents the results of a humidity and temperature prediction model in the environment for agriculture, using diffuse sets and optimizing their parameters by heuristic methods, such as genetic algorithms, and exact methods such as Quasi-Newton. It has been identified that non-specialized users could have difficulties in understanding the system dynamics and the behavior of variables over time. The aim of this research is obtain models with a high level of interpretability and accuracy that allows predicting the temperature and humidity values for the environment. The use of fuzzy logic to present a solution has great advantages as this system is highly rated for interpretability. Furthermore, by relating the obtained values for environment humidity and temperature to qualitative categories as high, medium or low, it allows non-specialized users to have a better understanding of the system dynamics. Two optimization techniques are applied to two different diffuse sets that allow the prediction of the humidity and temperature. It is found that the best implementation involves a Mamdani fuzzy inference system optimized with Quasi-Newton algorithm that uses a set of initial values attained through a previous optimization process with a genetic algorithm.</span>