Tendencias en torno a la innovación curricular

Es común la preocupación por una participación activa y comprometida de los académicos para que los cambios que se generen sean compartidos y asumidos por los profesores de tiempo completo y medio tiempo, así como por los de asignatura. Esto habla del reconocimiento institucional de que en las aulas es donde se hace evidente el avance y los logros reales de las propuestas y los proyectos curriculares, con la participación de los alumnos.ITESO, A.C

Classification of Data to Extract Knowledge from Neural Networks

A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant

Simultaneous Control of Chaotic Systems Using RBF Networks

Chaos control is a concept that recently acquiring more attention among the research community, concerning the fields of engineering, physics, chemistry, biology and mathematic. This paper presents a method to simultaneous control of deterministic chaos in several nonlinear dynamical systems. A radial basis function networks (RBFNs) has been used to control chaotic trajectories in the equilibrium points. Such neural network improves results, avoiding those problems that appear in other control methods, being also efficient dealing with a relatively small random dynamical noise

Trained Neural Network Characterizing Variables for Predicting Organic Retention by Nanofiltration Membranes

Many organic compounds cause an irreversible damage to human health and the ecosystem and are present in water resources. Among these hazard substances, phenolic compounds play an important role on the actual contamination. Utilization of membrane technology is increasing exponentially in drinking water production and waste water treatment. The removal of organic compounds by nanofiltration membranes is characterized not only by molecular sieving effects but also by membrane-solute interactions. Influence of the sieving parameters (molecular weight and molecular diameter) and the physicochemical interactions (dissociation constant and molecular hydrophobicity) on the membrane rejection of the organic solutes were studied. The molecular hydrophobicity is expressed as logarithm of octanol-water partition coefficient. This paper proposes a method used that can be used for symbolic knowledge extraction from a trained neural network, once they have been trained with the desired performance and is based on detect the more important variables in problems where exist multicolineality among the input variables

Sobrevida en pacientes con cáncer colorectal estadios I a IV y descripción de factores clínicos y biológicos en pacientes atendidos en el Hospital Militar Central entre 1998 y 2008

El presente es un estudio de análisis de sobrevida basado en una cohorte retrospectiva de pacientes con CCR, el cual nos permite establecer el desenlace primario que es sobrevida global a 3 y 5 cinco años. El análisis se realizará mediante el programa estadístico SPSS. Se realizara un cálculo global de la sobrevida de los pacientes, así como la probabilidad de morir a 3 y 5 años respectivamente. El análisis de sobrevida se realizara con curvas de Kaplan Meyer global y ajustando por estadio clínic

Extraction of temporal information of the DBpedia: Integration proposal in a semi-structured corpus

En este trabajo, se hace una propuesta para la extracción automática de información temporal en la DBpedia, suficientemente general para ser aplicada a diferentes dominios. Se experimenta en un dominio concreto, para el que se identificarán y gestionarán recursos DBpedia relacionados. Con la información temporal extraída de los recursos, se alimentará una línea de tiempo y se intersecará a su vez con la información temporal extraída del dominio, en este caso del corpus DIMH (textos semiestructurados o fichas). A continuación, se enriquecerán las fichas originales con la información temporal y se visualizarán y accederá a los resultados organizados sobre la base de su dimensión léxica y temporal. Ante la ausencia de un gold standard para evaluar intrínsecamente la propuesta, se aplican criterios dependientes del dominio y de los usuarios y se pone a disposición de la comunidad científica (GitHub) el corpus anotado temporalmente.The goal of this work is to make a proposal for the automatic extraction of temporal information in the DBpedia, general enough to be applied to different domains. The experiment is performed using a concrete domain by the identification and management of domain related DBpedia resources. With the relevant temporal information extracted from the resources it will be feed a timeline and intersected with the temporal information of the DIMH corpus (semi-structured texts or cards). Thus, we will enrich these cards with related events of the timeline. In order to visualize the results, we are using a graphical interface to facilitate the lexical and the temporal information access. In the absence of a gold standard to intrinsically evaluate the proposal, it will be applied domain and users dependent criteria and the annotated corpus is made available to the scientific community (GitHub).Este trabajo ha sido financiado parcialmente por los proyectos DIMH (HAR2012-31117) y Musacces (S2015/HUM3494)

Aplications of Neural Networks to Find the Impact of Water in Different Berry Components in Grapes

Grape juice composition during the different stages of berry growth was compared. The analytical data collected were used to investigate the relationships between some of the different components studied in these berries during the ripening period. Our goal is to study, with neural networks, the impact of water availability on Vitis vinifera L. cv. Tempranillo grape yields and juice composition over a three-year period