Classifying Human Control Behaviour by Artificial Intelligence

Traditional analysis of human manual control behaviour is currently constrained by the linear time-invariant assumption of the state-of-the-art “cybernetic” approach. This implies that time-varying and nonlinear aspects of human behaviour are generally overlooked, while these could be critical characteristic factors in, for example, the...

Thermosphere Modelling using Machine Learning: Artificial Neural Networks to forecast thermospheric densities and generalise beyond the properties of an acceleration data set using the Swarm satellites as study case

The Low Earth Orbit (LEO) region has been attractive to many space agencies and organisations because of its ease of access and the ideal opportunity for remote sensing. Due to the low altitudes, a satellite's orbital state is highly affected by the atmospheric drag force acting on the satellite's body. The largest variation in this drag force...

Sequential Monte Carlo method for training Neural Networks on non-stationary time series

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can be particularly useful in problems in which the data is sequential, noisy and non-stationary. We compare this algorithm against a gradient-based method known as stochastic gradient descent ...

Generation of multivariate time series using neural networks

Automated Pricing Suggestions

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...