Abstract Prostate cancer is the most common malignancy affecting men and thesecond-leading cause of cancer death in the US. To detect and classify the risk of prostate cancer, doctors perform screening of prostate-specific antigen (PSA) levels. In this Thesis we want to improve thisrisk classification with unsupervised machine learning methods, as thelabels of cancer or no cancer may not reflect the truth; given that canceris only considered as such after confirmation by biopsy and thus possi-bly leaving some patients with cancer with the wrong label.
Abstract Cancer detection is one of the leading research topics in medical science. Whetherit is breast, lung, brain, or prostate cancer, progress is being made to improve theaccuracy and timing of detection. Prostate cancer is the second most commoncancer in men and the sixth leading cause of cancer death among men in the world.Many prostate cancers are indolent and do not result in cancer mortality, evenwithout treatment. However, a significant percentage of prostate cancer patientshave aggressive cancers that rapidly progress to metastatic disease and are oftendangerous.
Abstract Modelling the behaviour and performance of cyclists during competition ahead of time has several useful applications to professional cycling teams. Although existing physics-based approaches perform reasonably well in this regard, they generally require several assumed parameters. Performance is also dependant on several variables which may be hard to quantify, such as a rider’s cornering ability and pacing strategy. The work presented in this thesis is a fully data-driven approach to speed pre- diction in time trials performed by elite cyclists.
Abstract In the telecommunications domain, as network infrastructure grows more complex, it is becoming increasingly important to develop systems that are able to automate efficient and accurate anomaly detection. Such systems operate on Key Performance Indicators (KPIs) collected from network base stations, and are vital to alert for possible critical operational incidents in a timely manner. These KPIs are most commonly in the form of multivariate time-series, which are challenging to perform anomaly detection on, as anomalous patterns occurs both as correlations between features, as well as temporal windows.
Abstract In the following pages lies our master’s thesis on how the recent advances in deep learning architectures, namely graph neural networks, can perform unsupervised anomaly detection in the Telecommunications (telco) domain. This work is moti- vated by the need for efficient and accurate anomaly detection in the telco domain, where Key Performance Indicators (KPIs) of base stations are continuously being monitored. Furthermore, network infrastructures are constantly being upgraded, 5G is on its way, and there is an exponential increase of devices and antennas.
Abstract The aim of this thesis was to predict the wind power production volume of a large geographical region given the Numerical Weather Prediction data (NWP) over the region using deep learning. Accurate production volume predictions is important for power grid balancing, production planning, and price estimation. Having an accurate forecast for the upcoming wind power production volume has become more and more important in the past years due to the fast increasing number of installed wind turbines and installed total production capacity.
Abstract With the growing inclusion of renewable energy sources, developing price models for intraday trading has become an essential task for many market participants in order to optimize the decision-making process. Yet the available literature on the topic has not been keeping up with the pace of increased intraday trading activity. We predict prices in the final hour prior to delivery on the German intraday market, utilizing Deep Learning techniques. This thesis looks into the usage of feed-forward neural networks and recurrent neural networks (LSTM and GRU).
