Los resultados indican que la mayoría de jóvenes utilizan de manera cotidiana las tecnologías de Internet y dispositivos móviles para actividades curriculares y extracurriculares. En el trabajo se demuestra que el uso inadecuado de estas tecnologías tiene consecuencias negativas de índole académica, familiar y social.
This paper analyzes the application of pixel segmentation techniques, the recognition and selection of image regions, as well as the performing of operations on the regions found within the digital images in order to detect nudity. The research aims to develop a software tool capable of nudity detection on digital images. The segmentation in the HSV color model (Hue, Saturation, and Value) to locate and remove the pixels corresponding to human skin is used. The algorithm in Recognition, Selection and Operations in Regions (RSOR), to recognize and separate the region with the highest number of skin pixels within the segmented image (largest region), is proposed. Once selected the largest region, the RSOR algorithm calculates the percentage on the segmented image taken from the original one, and then it calculates the percentage on the largest region, in order to identify whether there is a nude in the image. The criteria for appraising if an image depicts a nude is the following: If the percentage of skin pixels in the segmented image, in comparison to the original image, is less than 25% it is not considered a nude, but if it exceeds this percentage, then, the image is a nude. However, when the percentage of the largest region has been estimated and it amounts to less than 35%, the image is definitely not a nude. The final result is a message that informs the user whether or not the image is a nude. The RSOR algorithm obtains a 4.7% false positive, compared to other systems, and it has shown to possess optimum performance for nudity detection.
According to the World Health Organization, approximately 70% of adults in Mexico are overweight or obese, determining factors in the development of diabetes mellitus type 2. In addition, according to the National Institute of Public Health, 10.3% of those over 20 years old suffer from diabetes. To facilitate decision or classification tasks when treating a patient, experts develop systems based on fuzzy logic, however, this design is not usually infallible, so it is common to optimize them to improve their performance. The present work shows the results of a comparison between the efficiency in predicting the risk of suffering from type 2 diabetes established by the FINDRISC test and an own design fuzzy system optimized by the Simulated Annealing heuristic for 295 patients from Acapulco, Mexico. The comparison shows that the fuzzy system obtains the same sensitivity, but higher specificity values and positive and negative predictive values with general improvement in the confidence intervals, concluding that using the proposed system as an aid in the prevention of type 2 diabetes is viable and yields results attached to the reality of the patients.
La estimación automática de edad tiene considerables aplicaciones en áreas como el marketing, como por ejemplo, al generar y mostrar contenido específico para cualquier grupo etario, y en la seguridad informática donde se permitiría proteger a menores de edad de contenidos no aptos para su edad. El objetivo general de este trabajo es mostrar la metodología desarrollada para generar un sistema clasificador basado en apariencia utilizando los modelos de representación: análisis de componentes principales y análisis discriminante lineal; y exponer los resultados obtenidos sobre las bases de imágenes FG-Net y IMDB-Wiki con las cuales se clasificaron en dos grupos, mayores (+18) y menores (-18) de edad donde se obtuvo hasta un 89% de efectividad.Palabras clave: FisherFaces, estimación de edad, análisis de componentes principales, análisis discriminante lineal, imágenes faciales, diferenciador de minoría de edad.
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