Searched for: subject:"Machine%5C+Learning"
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van de Kamp, Lars (author)
Machine learning techniques receive significant responsibilities, despite growing privacy concerns. Early-stage autonomous vehicles are increasingly appearing on the streets, carrying the burden of transporting human-lives to their destination. Meanwhile, doctors are involving Artificial Intelligence (AI) in their medical diagnoses, basing...
master thesis 2018
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Hes, Robin (author)
This thesis explores the use of machine learning techniques in an effort to increase insurer competitiveness. It asks whether it is possible to accurately estimate the expected financial loss of a given insurance contract and how this information can be used to gain a competitive edge in the business. To answer these questions, some basic...
master thesis 2018
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Plaisant van der Wal, Renzo (author)
Machine learning methods are explored in an attempt to achieve better predictive performance than the legacy rule-based fraud detection systems that are currently used to detect fraudulent car insurance claims. There are two key principles that lead the exploration of machine learning techniques and algorithms in this thesis, namely, the...
master thesis 2018
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Raats, Jason (author)
Forecasting football match outcomes have been investigated previously, with the primary goal of these studies being to accurately predict the outcome for the highest number of matches. This thesis takes a different approach, comparing different methods to investigate which would result in the highest profit, rather than focusing on predictive...
master thesis 2018
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IJzermans, Erik (author)
The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific...
master thesis 2018
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Katzy, Jonathan (author), Rietveld, Tim (author), van der Steeg, Jaap-Jan (author), Wiegel, Erik (author)
As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a...
bachelor thesis 2018
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Andringa, Sytze (author), Zoon, Job (author), van der Werf, Daan (author)
One of the greatest challenges in marketing is measuring the return of investment of a marketing campaign and translating that into a strategy. Companies spend a lot of money on marketing without knowing how eective certain marketing campaigns are. To solve this problem for bunq, we will be using machine learning to create a marketing...
bachelor thesis 2018
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Verburg, Floris (author)
For performing technical maintenance, it is important to keep a detailed schedule of resources and temporal constraints. The Resource Constrained Project Scheduling Problem (RCPSP) is a well de- fined scheduling model with both resources and temporal constraints. Precedence Constraint Posting (PCP) is a technique to solve the NP-hard RCPSP...
master thesis 2018
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Kouw, W.M. (author)
Artificial intelligence, and in particular machine learning, is concerned with teaching computer systems to perform tasks. Tasks such as autonomous driving, recognizing tumors in medical images, or detecting suspicious packages in airports. Such systems learn by observing examples, i.e. data, and forming a mathematical description of what types...
doctoral thesis 2018
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Ruiz Arenas, S. (author)
Typically, emerging system failures have a strong impact on the performance of industrial systems as well as on the efficiency of their operational and servicing processes. Being aware of these, maintenance and repair researchers have developed multiple failure detection and diagnosis techniques that allow early recognition of system or...
doctoral thesis 2018
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Wymenga, Jan (author)
This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle camera, a machine learning and computer vision framework is employed to estimate the current weather condition in realtime and in-vehicle. The use of different...
master thesis 2018
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van Marrewijk, Gijsbert (author)
Launch costs for high-resolution space telescopes for Earth observation can be reduced when the telescope mirrors are made deployable. However, such a system is subject to optical aberrations that decreases image quality. To counter these aberrations, an Aberration Correction System (ACS) is proposed that uses a deformable mirror (DM) which is...
master thesis 2018
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den Hartog, Daniel (author)
Self-driving vehicles are the future of automotive engineering. Systems that take over control from the driver are developed to be able to interact with the conditions of the road and other obstacles. To develop these systems, developers use vehicle models to simulate the behaviour of the moving vehicle. The systems developed using these models...
master thesis 2018
document
Samiotis, Ioannis Petros (author)
Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this...
master thesis 2018
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Betting, Jan-Harm (author)
The movement of whiskers in head-fixed mice is of high interest for neurological research, as it allows scientists to learn more about learning processes during active touch. However, manual tracking of whiskers in thousands of frames is not feasible, and reliable tracking of individual whiskers is not possible with the best current software...
master thesis 2018
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Uijens, Wouter (author)
Convolutional Neural Networks (CNNs) are achieving state of the art performance in computer vision. One downside of CNNs is their computational complexity. One way to make CNNs more computational efficient is by implementing their convolutions in the frequency domain, using Fast Fourier Transforms (FFTs). This has as a consequence that most...
master thesis 2018
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Döpke, Max (author)
In this research a global-coefficient non-linear eddy viscosity model (NLEVM) is studied. This model stems from the inherent inability of the Boussinesq approximation to model anisotropy and therefore flow features such as: swirl, stream-line curvature and secondary motions (Lumley, 1970; Pope, 1975; Craft et al., 1996). The focus lies on the...
master thesis 2018
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van der Arend, Dennis (author)
Traditionally a wind turbine’s power curve is used to model the long-term energy yield of the wind turbine and afterwards assess the performance of the turbine (power curve verification). But the current power curve is typically univariate: only dependent on the wind speed and slightly adjusted for site density, turbulence intensity and wind...
master thesis 2018
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Dai, Lu (author)
Planning and scheduling problem is a hard problem, especially in real life cases. The time and space complexity increase quickly along with the increase of problem size. In transportation systems, such problems exist a lot. The automation of transportation systems depends a lot on the improvements of developing planning and scheduling algorithm....
master thesis 2018
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Kaandorp, Mikael (author)
The application of machine learning algorithms as data-driven turbulence modelling tools for Reynolds Averaged Navier-Stokes (RANS) simulations is presented. A novel machine learning algorithm, called the Tensor Basis Random Forest (TBRF) is introduced, which is able to predict the Reynolds stress anisotropy tensor. The algorithm is trained on...
master thesis 2018
Searched for: subject:"Machine%5C+Learning"
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