University dropout rates are a problem that presents many negative consequences. It is an academic issue and carries an unfavorable economic impact. In recent years, significant efforts have been devoted to the early detection of students likely to drop out. This paper uses data corresponding to dropout candidates after their first year in the third largest face-to-face university in Europe, with the goal of predicting likely dropout either at the beginning of the course of study or at the end of the first semester. In this prediction, we considered the five major program areas. Different techniques have been used: first, a Feature Selection Process in order to identify the variables more correlated with dropout; then, some Machine Learning Models (Support Vector Machines, Decision Trees and Artificial Neural Networks) as well as a Logistic Regression. The results show that dropout detection does not work only with enrollment variables, but it improves after the first semester results. Academic performance is always a relevant variable, but there are others, such as the level of preference that the student had over the course that he or she was finally able to study. The success of the techniques depends on the program areas. Machine Learning obtains the best results, but a simple Logistic Regression model can be used as a reasonable baseline.

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012–2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.

El propósito de este trabajo es explicar el rendimiento académico en Matemáticas a partir de características individuales de los estudiantes. Se toma el caso particular de Paraguay, país ubicado por debajo del promedio regional en recientes evaluaciones internacionales sobre logros de aprendizaje en el área de Matemáticas por estudiantes de Educación Escolar Básica. Se aplicó un cuestionario a 899 estudiantes del tercer ciclo de Educación Escolar Básica de instituciones educativas de gestión pública y privada del distrito de Concepción. Se utilizaron dos técnicas de modelación estadística: Árbol de decisión y Regresión lineal múltiple sobre variables observables. Se concluye que el rendimiento académico en Matemáticas se explica en gran medida por el aprendizaje que logra el estudiante en el contexto del aula, así como por la percepción que tiene de su propia capacidad.

Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.