Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering

Knowing student learning styles represents an effective way to design the most suitable methodology for our students so that performance can improve with less effort for both students and teachers. However, a methodology is usually set in teaching guides according to the previous academic year's information without any knowledge of our current audience. In this work, a new software for learning styles and grade analysis based on the Honey-Alonso Learning Styles Questionnaire has been proposed. This tool proposes the average learning style profiles of a given course by clustering student learning styles and analyzes the possible relation between grades and learning style profiles. By using that program, three different courses from Computer Sciences Engineering degrees during an academic year have been analyzed. The obtained results in our specific context exhibit that possible relation. This information could be useful to understand how students approach learning materials

Enhanced transfer learning model by image shifting on a square lattice for skin lesion malignancy assessment

Skin cancer is one of the most prevalent diseases among people. Physicians have a challenge every time they have to determine whether a diseased skin is benign or malign. There exist clinical diagnosis methods (such as the ABCDE rule), but they depend mainly on the physician’s experience and might be imprecise. Deep learning models are very extended in medical image analysis, and several deep models have been proposed for moles classification. In this work, a convolutional neural network is proposed to support the diagnosis procedure. The proposed MobileNetV2-based model is improved by a shifting technique, providing better performance than raw transfer learning models for moles classification. Experiments show that this technique could be applied to the state-of-the-art deep models to improve their results and outperform the training phase.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Segmentación y detección de objetos en imágenes y vídeo mediante inteligencia computacional

Finalmente, se exponen las conclusiones obtenidas tras la realización de esta tesis y unas posibles líneas futuras de investigación. Fecha de lectura de Tesis: 17 diciembre 2018.La presente tesis trata sobre el procesamiento y análisis de imágenes y video mediante sistemas informáticos. Primeramente se hace una introducción, especificando contexto, objetivos y metodología. Luego se muestran los antecedentes, los fundamentos de la videovigilancia, las dificultades existentes y diversos algoritmos del estado del arte, seguido de las principales características del aprendizaje profundo, transporte inteligente y sistemas con cámara PTZ, finalizando con la evaluación de métodos y distintos conjuntos de datos. Después se muestran tres partes. La primera comenta los estudios desarrollados que tratan sobre segmentación. Aquí se explican diferentes modelos desarrollados cuyo objetivo es la detección de objetos, tanto usando hardware genérico o especifico como en ámbitos específicos, o un estudio de cómo influye la reducción del tamaño de las imágenes al rendimiento de los algoritmos. La segunda parte describe los trabajos que utilizan una cámara PTZ. El primero trabajo hace un seguimiento del objeto más anómalo del escenario, siendo el propio sistema el que decide cuáles son anómalos y cuáles no; el segundo muestra un sistema que indica a la cámara los movimientos a realizar en función de la salida producida por un modelo de fondo no panorámico y mejorada con un gas neuronal creciente. La tercera parte trata sobre los estudios desarrollados con relación con el transporte inteligente, como es la clasificación de los vehículos que aparecen en secuencias de tráfico. El primer trabajo aplica técnicas tradicionales como segmentación y extracción de rasgos; el segundo utiliza segmentación y redes convolucionales, complementado con un estudio del redimensionado de imágenes para proveerlas en el formato necesario a cada red; y el tercero emplea un modelo que detecta y clasifica objetos, estimando posteriormente la contaminación generada por los vehículos

A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Vehicle Type Detection by Convolutional Neural Networks

In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Super-resolution of 3D Magnetic Resonance Images by Random Shifting and Convolutional Neural Networks

Enhancing resolution is a permanent goal in magnetic resonance (MR) imaging, in order to keep improving diagnostic capability and registration methods. Super-resolution (SR) techniques are applied at the postprocessing stage, and their use and development have progressively increased during the last years. In particular, example-based methods have been mostly proposed in recent state-of-the-art works. In this paper, a combination of a deep-learning SR system and a random shifting technique to improve the quality of MR images is proposed, implemented and tested. The model was compared to four competitors: cubic spline interpolation, non-local means upsampling, low-rank total variation and a three-dimensional convolutional neural network trained with patches of HR brain images (SRCNN3D). The newly proposed method showed better results in Peak Signal-to-Noise Ratio, Structural Similarity index, and Bhattacharyya coefficient. Computation times were at the same level as those of these up-to-date methods. When applied to downsampled MR structural T1 images, the new method also yielded better qualitative results, both in the restored images and in the images of residuals.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the probabilistic model. Our approach has been compared to some competing methods on a test set of benchmark videos, with favorable results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Covering point sets with two disjoint disks or squares

Open archive-ElsevierWe study the following problem: Given a set of red points and a set of blue points on the plane, find two unit disks CR and CB with disjoint interiors such that the number of red points covered by CR plus the number of blue points covered by CB is maximized. We give an algorithm to solve this problem in O(n8/3 log2 n) time, where n denotes the total number of points. We also show that the analogous problem of finding two axis-aligned unit squares SR and SB instead of unit disks can be solved in O(nlog n) time, which is optimal. If we do not restrict ourselves to axis-aligned squares, but require that both squares have a common orientation, we give a solution using O(n3 log n) time

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech