Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

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

Modelos log-lineares em tabelas de contingência

Os modelos log-lineares permitem enriquecer bastante a análise e interpretação das tabelas de contingência. Embora a nível teórico a sua importância já tenha sido reconhecida há bastante tempo, a nível da sua aplicação prática só há relativamente pouco tempo é que foi reconhecida, devido, sobretudo, às dificuldades de cálculo que lhe são inerentes e que só se resolveram completamente com o desenvolvimento dos computadores e do software adequado. Neste trabalho apresentam-se os métodos básicos da análise log-linear de tabelas de contingência bidimensionais e tridimensionais.

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Safety function analysis in an industrial production process

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Machine learning Gaussian short rate

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.