ESTRUCTURAS DE CONTROL DE FLUJO SELECTIVAS

SÓLO VISIÓN PROYECTABLE

UC 1: INTRODUCCIÓN A LA INVESTIGACIÓN

SÓLO VISIÓN PROYECTABLE

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way. The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method

Sistematización de cuestionarios para egresados universitarios y empleadores de la Región Zumpango, Estado de México

Sistematización de la Encuesta Electrónica ALFA TUNING para medir competencias académicas de egresados a travéz de Sistema Operativo LimeSurvey. Aplicado a la población de 232 egresados del Centro Universitario UAEM Zumpango y 55 empleadores de la zona de ZumpangoLa presente investigación propone la implementación del Sistema Operativo LimeSurvey en la Encuesta Electrónica ALFA TUNING para medir competencias académicas de egresados y su empleabilidad en región de Zumpango. Material: Encuesta Electrónica ALFA TUNING. Objetivo: ayudar a obtener información sobre la empleabilidad de los egresados, indicadores requeridos por los organismos certificadores para medir la calidad educativa. Metodología: diseño no experimental, uso de muestras aleatorias, con cortes trasversales y estadística descriptiva. Dividido en cuatro fases: Fase 0, validez de contenidos del Cuestionario ALFA TUNING con coordinadores académicos; Fase 1, prueba piloto de la Encuesta Electrónica ALFA TUNING; fase 2, aplicación de la encuesta a egresados; fase 3, aplicación de encuentra a empleadores. Resultados: La comparación de seis investigaciones podemos decir que la en encuesta utilizada para el Programa de Movilidad Universitaria Internacional (PMUI) y el sistema LimeSurvey; ambas presenta tiempos estimados menor a dos horas, pero el tiempo utilizado en el sistema LimeSurvey es seis veces menor al sistema utilizado en la plataforma PMUI. Referente al costo en uso, la Encuesta Electrónica ALFA TUNING a través del sistema operativo LimeSurvey presenta un costo menor que todos los sistemas operativos

Classification of mexican paper currency denomination by extracting their discriminative colors

In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote’s color is characterized by summing all the color vectors of the image’s pixels to obtain a resultant vector, the banknote’s denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.Proyecto PROMEP 103.5/13/653

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Color characterization comparison for machine vision-based fruit recognition

In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes

Análisis del Sistema de Gestión del Aprendizaje, Moodle y niveles de confort en usuarios

Conocimiento de los Sistemas de Apoyo a la Gestión de educación virtualThis study analyzes the workflow experiences in the teaching-learning process of teachers and students enrolled in mixed (face to face /distance education) and distance education systems. The main objective is to evaluate Moodle, Learning Management System with the systematized Brief Inventory of Experiences. In this study we measure and compare the flow and comfort experiences of a population of 8140 participants, divided into two groups: 320 teachers and 7820 students. We can conclude that there is a significant difference in the p <0.05 level, in the following two variables: 1) When performing the activity, I forget about the problems and concerns and 2) I do things spontaneously and automatically. Tasks they must face in order to provide the students the appropriate tools in order to learn. In addition to this challenge, the teacher does not achieve an optimal level of comfort throughout the teaching process due the lack of positive feedback. Encouraging the teachers to guide the students to takeoff the learning and the teacher to get positive feedback. While the students receive and assimilate, the teacher gives and experiments. Because the transmission of knowledge is a function of who assimilates the legacy.UAEM, PROYECTO INVESTIGACIÓN SIEA “Comparación de dos vertientes en B-learning para medir competencias metacognitivas, base de la solución de problemas CLAVE 4548/201

Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works

Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means

Se calcula la cantidad de grupos en que los vectores de color son agrupados usando fuzzy c-meansFuzzy C-means (FCM) is one of the most often techniques employed for color image segmentation; the drawback with this technique is the number of clusters the data, pixels’ colors, is grouped must be defined a priori. In this paper we present an approach to compute the number of clusters automatically. A competitive neural network (CNN) and a self-organizing map (SOM) are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram. The number of clusters is set by computing the number of the most activated neurons. The number of clusters is adjusted by comparing the similitude of colors. We show successful segmentation results obtained using images of the Berkeley segmentation database by training only one time the CNN and SOM, using only chromaticity data