Classification methods to identify lesions in skin starting from spectra of diffuse reflectance

Con el objetivo de discriminar lesiones benignas y malignas en la piel humana a partir de espectros de reflexión difusa, se han analizado diferentes algoritmos de clasificación usando el software de aprendizaje automático y reconocimiento de patrones WEKA. Además, dada la alta dimensionalidad de la señal espectral, fue empleada una técnica selección de atributos para determinar las variables que aporten la mayor cantidad de información. Se probó la clasificación de la señal usando los algoritmos de máquinas de vectores de soporte, redes neuronales y bosques aleatorios, el desempeño fue evaluado usando el promedio de la k-fold cross-validation tomando en cuenta los porcentajes de instancias clasificadas correctamente, el índice kappa, el área bajo la curva ROC, la sensibilidad, y la especifidad. Finalmente se demuestra que el algoritmo de redes neuronales con los parámetros momentum y learning rate en 0,6 y 0,3 respectivamente, es el que mejor se adapta al problema de reconocimiento de patrones ya que clasifica correctamente al 89,89% de los casos.In order to differentiate between benign and malignant lesions in the human skin using diffuse reflection spectra, different classification algorithms were tested using the WEKA data mining software. In addition, due to the high dimensionality of the spectral signal, an attribute selection technique was applied to determine the variables that contribute with more information. The spectral signal classification was tested using support vector machines, neural networks and random forests, their performance was measured using the k-fold cross-validation percentages of the Kappa statistic, area under the ROC curve, specificity and sensitivity. Finally it is shown that the one layer neural network with 6 neurons and the parameters momentum and learning rate in 0.6 and 0.3 respectively, is best suited to the problem of pattern recognition, achieving correctly classify 89.89% of the cases

Bienestar y familia, una mirada desde la psicología positiva

Este libro está dirigido a estudiantes y profesionales en psicología y áreas afines, como enfermería, trabajo social o educación, y a los interesados en personalidad positiva. Cada capítulo se presenta de manera sencilla y con una estructura didáctica, pero sin perder rigor científico y calidad de la revisión, con el fin de facilitar el acceso a la información sobre bienestar individual, familiar y social de una forma accesible para adentrarnos al estudio de temas de psicología positiva. Dadas las temáticas, se consideró pertinente dividir este libro en dos secciones: I. Psicología positiva y bienestar. II. Bienestar individual y familiar.Universidad Autónoma del Estado de México y Ediciones y Gráficos Eón, S.A. de C.V

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation