Search Results (30)
Characterization of constrained continuous multiobjective optimization problems
Despite the large volume of recently published papers in the field of constrained multiobjective optimization, the understanding and characterization of constrained multiobjective optimization problems (CMOPs) for benchmarking multiobjective evolutionary algorithms (MOEAs) and the related constraint handling techniques (CHTs) remain unsatisfactory. Therefore, selecting appropriate CMOPs for benchmarking is challenging and lacks …
An intelligent cognitive system for computational psychotherapy with a conversational agent for attitude and behavior change in stress, anxiety and depression
The increasing prevalence of mental health issues worldwide has amplified the significance of computational psychotherapy, which includes creating computational tools for the mental healthcare and tools to support existing mental health professionals. This work presents a computational psychotherapy system that predicts and forecasts mental health issues in users, and utilizes …
Towards understanding the impact of problem landscapes in numerical black-box optimization
In optimization, it is well known that algorithm performance is dependent on the problem being solved. As a consequence of this, achieving good optimization results requires correctly matching an optimization problem to a specific optimization algorithm that performs well on that problem. For this to be possible, knowledge of both …
Optimization and application of MeV time-of-flight secondary ion mass spectrometry in the standard and low primary ion beam energy mode
Time-of-flight secondary ion mass spectrometry using MeV ions (MeV ToF SIMS) has been around for a few decades as a reliable, surface-sensitive method for the detection of molecular ions having masses up to 1000 Da. It provides excellent lateral resolution of a few μm for imaging of organic samples, making …
Electron microscopy of titanium oxynitride supported iridium based electrocatalysts
We are speeding full throttle towards a climate catastrophe and we have only until 2030 to take action to avoid the most devastating and unimaginable consequences. The lack of political and corporate will to change the status quo of “business as usual” is causing mass protests and civil disobedience globally. …
Time-efficient method for monitoring electrochemical energy conversion systems
According to the EU’s Green Deal plan, one of the main objectives for the EU is to become a net-zero greenhouse emission society by 2050. Achieving this goal requires advancements across many areas ranging from energy production, conversion, distribution and energy storage where the key role is taken by the …
Classification of wireless links using machine learning techniques
Due to the nature of the wireless transmission medium, wireless communications are characterised by notably larger losses of data packets than wired communications. The quality of wireless links is highly dependent on channel variations, interference and even transceiver imperfections. Such link uncertainty instigated the development of numerous techniques that can …
Predicting the dynamics of spatio-temporal systems based on heterogeneous data sources
As urbanisation continues to be a trend, in which centralisation of the population into cities is still growing, the importance of intelligent solutions in mobility is in high demand. Likewise, with the integration of renewable energies into all levels of the electrical grid system and the increasing amount of heavy …
Considering autocorrelation in predictive models
Most machine learning, data mining and statistical methods rely on the assumption that the analyzed data are independent and identically distributed (i.i.d.). More specifically, the individual examples included in the training data are assumed to be drawn independently from each other from the same probability distribution. However, cases where this …
A Machine Learning Approach to Polynomial Regression
In the thesis, we address the task of polynomial regression, i.e., inducing regression models based on polynomial equations, from data. We aim at improving and extending the existing approaches to learning polynomial regression models in several directions. First, we improve the existing methods for addressing the issue of over-fitting and …