Proboscideans and other vertebrates from Anchrif, Morocco

The paleontological richness of Morocco has been scientifically known since at least the early 20th century. The region of the Middle Atlas, more specifically the Boulemane area, has been however only sparsely studied since the 60’s when it supplied various vertebrate fossils from the Middle Jurassic.In the beginning of the 2000’s some fossil bones were discovered in a new fossil-site near the village of Taghrout, in the Boulemane area and in September 2013 a Moroccan-Portuguese expedition made excavations in the site with the help locals from the village of Taghrout. The site is Pleistocene in age and is located on a rare bone bearing small high-altitude sedimentary basin, non-charted in previous geological maps. The excavations yielded new bone material from large mammals. The most common findings are elephants ascribed to the genus Elephas, but artiodactyls, turtles, and in-situ hominid Acheulean tools were also collected. During the excavation campaign the Jurassic sites were revisited and new dinosaur trails and possible crocodilomorph bones were discovered. Surface collection of in a cave near Taghrout with Holocene mammal material, including the genera Canis, Capra, Bos, Panthera and Hystrix was also conducted and its fossils elements identified.

The Cornerstones of Modern Government Maps, Weights and Measures and Census in Liberal Portugal (19th century)

Thesis submitted for assessment with a view to obtaining the degree of Doctor in History and Civilisation from the European University Institute

Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

CARTO-SOM - Cartogram creation using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Exploratory geospatial data analysis using self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Abelian kernels of monoids of order-preserving maps and of some of its extensions

Semigroup Forum vol. 68 (2004), p. 335–356

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Criação de clusters de clientes de um banco através do self-organizing maps hierárquico: comparação entre HSOM e SOM

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Análise do mercado financeiro baseada em análise técnica com self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.