Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network

[EN] Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a latitude by longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFSMODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6?atm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

Análisis del uso de las redes neuronales artificiales en el diseño de filtros antispam : una propuesta basada en arquitecturas neuronales no supervisadas

[ES]El spam, o correo no deseado enviado masivamente, es una amenaza que afecta al correo electrónico y otros medios de comunicación telemática. Su alto volumen de circulación genera pérdidas temporales y económicas considerables. Se presenta una solución a este problema: un sistema inteligente híbrido de filtrado antispam, basado en redes neuronales artificiales (RNA) no supervisadas. Consta de una etapa de preprocesado y de otra de procesado, basadas en distintos modelos de computación: programada (con 2 fases: manual y computacional) y neuronal (mediante mapas autoorganizados de Kohonen, SOM), respectivamente. Este sistema ha sido optimizado usando, como cuerpo de datos, ham de “Enron Email” y spam de dos fuentes diferentes. Se analiza la calidad y el rendimiento del mismo mediante diferentes métricas.