3,272 research outputs found
FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS
Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen
The probability of default in internal ratings based (IRB) models in Basel II: an application of the rough sets methodology
El nuevo Acuerdo de Capital de junio de 2004 (Basilea II) da cabida e incentiva la
implantación de modelos propios para la medición de los riesgos financieros en las
entidades de crédito. En el trabajo que presentamos nos centramos en los modelos internos
para la valoración del riesgo de crédito (IRB) y concretamente en la aproximación a uno de
sus componentes: la probabilidad de impago (PD).
Los métodos tradicionales usados para la modelización del riesgo de crédito, como son el
análisis discriminante y los modelos logit y probit, parten de una serie de restricciones
estadísticas. La metodología rough sets se presenta como una alternativa a los métodos
estadísticos clásicos, salvando las limitaciones de estos.
En nuestro trabajo aplicamos la metodología rought sets a una base de datos, compuesta
por 106 empresas, solicitantes de créditos, con el objeto de obtener aquellos ratios que
mejor discriminan entre empresas sanas y fallidas, así como una serie de reglas de decisión
que ayudarán a detectar las operaciones potencialmente fallidas, como primer paso en la
modelización de la probabilidad de impago. Por último, enfrentamos los resultados obtenidos
con los alcanzados con el análisis discriminante clásico, para concluir que la metodología de
los rough sets presenta mejores resultados de clasificación, en nuestro caso.The new Capital Accord of June 2004 (Basel II) opens the way for and encourages credit entities to implement
their own models for measuring financial risks. In the paper presented, we focus on the use of internal rating
based (IRB) models for the assessment of credit risk and specifically on the approach to one of their
components: probability of default (PD).
In our study we apply the rough sets methodology to a database composed of 106 companies, applicants for
credit, with the object of obtaining those ratios that discriminate best between healthy and bankrupt companies,
together with a series of decision rules that will help to detect the operations potentially in default, as a first step
in modelling the probability of default. Lastly, we compare the results obtained against those obtained using
classic discriminant análisis. We conclude that the rough sets methodology presents better risk classification
results.Junta de Andalucía P06-SEJ-0153
Effect of nano black rice husk ash on the chemical and physical properties of porous concrete pavement
Black rice husk is a waste from this agriculture industry. It has been found that majority inorganic element in rice husk is silica. In this study, the effect of Nano from black rice husk ash (BRHA) on the chemical and physical properties of concrete pavement was investigated. The BRHA produced from uncontrolled burning at rice factory was taken. It was then been ground using laboratory mill with steel balls and steel rods. Four different grinding grades of BRHA were examined. A rice husk ash dosage of 10% by weight of binder was used throughout the experiments. The chemical and physical properties of the Nano BRHA mixtures were evaluated using fineness test, X-ray Fluorescence spectrometer (XRF) and X-ray diffraction (XRD). In addition, the compressive strength test was used to evaluate the performance of porous concrete pavement. Generally, the results show that the optimum grinding time was 63 hours. The result also indicated that the use of Nano black rice husk ash ground for 63hours produced concrete with good strengt
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