Now showing items 9-19 of 19

    • Modelling and analysis of health care services using regression and Markov models 

      Hindenes, Lars Bakke (Master thesis; Mastergradsoppgave, 2017-05-12)
      Using data from electronic health records this thesis aims to model and analyse health care services provided to adult patients with chronic conditions. Two aspects of health care services, with unique aims, have been examined. The first aspect is related to the aim of investigating factors affecting the patients' self experienced quality of the health care encounters with regards to satisfaction, ...
    • Modelling of Viral Disease Risk 

      Hahn, Nico (Mastergradsoppgave; Master thesis, 2021-06-19)
      Covid-19 has had a significant impact on daily life since the initial outbreak of the global pandemic in late 2019. Countries have been affected to varying degrees, depending on government actions and country characteristics such as infrastructure and demographics. Using Norway and Germany as a case study, this thesis aims to determine which factors influence the risk of infection in each country, ...
    • Personalized optimization of blood glucose regulation: A Diabetes Mellitus case study 

      Drangsholt, Preben (Mastergradsoppgave; Master thesis, 2022-07-13)
      Type 1 diabetes (T1D) is a chronic autoimmune disease that leads to insulin deficiency. Consequently, the disease will lead to a poor blood glucose (BG) regulation, and in situations of high energy expenditure like exercise, a dysfunctional regulation can cause severe damage or death [5] [14]. Diabetes is responsible for one death every five seconds and financially drains approximately 11% of the ...
    • Power Flow Optimization with Graph Neural Networks 

      Hansen, Jonas Berg (Mastergradsoppgave; Master thesis, 2021-06-01)
      Power flow analysis is an important tool in power engineering for planning and operating power systems. The standard power flow problem consists of a set of non-linear equations, which are traditionally solved using numerical optimization techniques, such as the Newton-Raphson method. However, these methods can become computationally expensive for larger systems, and convergence to the global optimum ...
    • Probabilistic Wind Power and Electricity Load Forecasting with Echo State Networks 

      Støtvig, Petter (Mastergradsoppgave; Master thesis, 2022-05-15)
      With the introduction of distributed generation and the establishment of smart grids, several new challenges in energy analytics arose. These challenges can be solved with a specific type of recurrent neural networks called echo state networks, which can handle the combination of both weather and power consumption or production depending on the dataset to make predictions. Echo state networks ...
    • Separation of fish stocks by otoliths: Image representation, Fourier approximation and discrimination 

      Hagen, Reidar Strand (Master thesis; Mastergradsoppgave, 2015-05-18)
      In this thesis the entire process of Fourier Contour Analysis have been investigated for potential sources of bias. No potential biases has been found when creating the contour, however it has been shown there is a slight bias in which otoliths get invalidated by breakage. A probablity based sampling strategy has been suggested instead of the previously used stratification based. It has been shown ...
    • Statistical analysis of CGPS time series 

      Ouassou, Mohammed (Master thesis; Mastergradsoppgave, 2007)
      All points on the surface of the Earth are moving. To define the velocity of a given point, we can place a GPS receiver there and measure the coordinates every day. After collecting enough data, we can generate a time series of three coordinates, North, East and Height directions. The most used technique to determine such displacements, is the linear model. The main objective of this thesis ...
    • A Step Towards Deep Learning-based CADs for Cancer Analysis in Medical Imaging 

      Pedersen, André (Master thesis; Mastergradsoppgave, 2019-06-01)
      In 2018, cancer was the second leading cause of death worldwide. Early detection can reduce mortality. Screening programs intended for early detection increases the workload for clinicians. To improve efficiency CAD systems would be highly beneficial. We have developed CAD systems using deep learning, for automatic tissue segmentation and prediction of diagnosis in lung and breast cancer. The ...
    • A threshold cointegration analysis of Norwegian interest rates 

      Larsen, Berner (Master thesis; Mastergradsoppgave, 2012-04)
      In this thesis we generalize the Hansen and Seo test in the R package tsDyn, which tests a linear cointegration model against a two-regime threshold cointegration model, to the case of three regimes in the alternative hypothesis. As the Lagrange Multiplier test statistic used in the Hansen and Seo test in tsDyn is different from the LM statistic described in Hansen and Seo (2002), we generalize ...
    • Trans-dimensional inference over Bayesian neural networks 

      Berezowski, Jonathan (Master thesis; Mastergradsoppgave, 2021-06-03)
      Trans-dimensional Bayesian inference for multi-layer perceptron architectures of varying size by reversible jump Markov chain Monte Carlo is developed and examined for its theoretical and practical merits and considerations. The algorithm features the No-U-Turn Sampler and Hamiltonian Monte Carlo for within-dimension moves, and makes use of a delayed-rejection sampler while exploring a variety of ...
    • Unsupervised segmentation of skin lesions 

      Møllersen, Kajsa (Master thesis; Mastergradsoppgave, 2008-11-15)
      During the last decades, the incidence rate of cutaneous malignant melanoma, a type of skin cancer developing from melanocytic skin lesions, has risen to alarmingly high levels. As there is no effective treatment for advanced melanoma, recognizing the lesion at an early stage is crucial for successful treatment. A trained expert dermatologist has an accuracy of around 75 % when diagnosing melanoma, ...