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RNA molecules dynamically interact with RNA-binding proteins (RBPs), which control various aspects of RNA fate, such as its processing, localisation, and stability. Intricate networks of protein-RNA interactions thereby regulate gene expression and have a profound effect on downstream cellular processes. Most RBPs recognise specific motifs on their bound RNAs, characterised …

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 …

This thesis examines how machine learning can be applied in demand forecasting. In particular, it describes a novel approach toward lumpy and intermittent demand forecasting. It advocates using a two-fold model for forecasting lumpy (irregular demand occurrence, strong demand size variability) and intermittent (irregular demand occurrence, little demand size variability) …

This thesis focuses on the information dynamics of children’s melody and, more specifically, on modeling children’s perception of melodic surface. The topic of children’s melody has not received much attention in the past, and there are currently no clear definitions of what exactly a “children’s melody” is. As neither children’s …

Characterization of the indoor radio environment (RE) is a prerequisite for advances in the design and optimization of next-generation indoor wireless networks and for the construction of a digital twin of the building. The need for comprehensive and accurate indoor characterization will be evident in the future hyper-connected mixed real-virtual …

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 …

Human knowledge about food and nutrition has evolved drastically with time. With food and nutrition-related data being mass produced and easily accessible, the next step is to use Artificial Intelligence (AI) to translate data into knowledge. The majority of AI research is model-driven, and classical Machine Learning (ML) pipelines concentrate …

The thesis addresses a novel representation learning framework, combining neural and symbolic text representations, and demonstrates its utility for tackling diverse natural language processing problems. The proposed approach, avoiding the deficiencies of purely symbolic and purely neural methods, can be applied for the generation of efficient text representations. Its usefulness …

The use of low-cost sensing technologies increasingly used in participatory environmental health studies brings both opportunities and challenges. While previous research mostly focused on technical aspects, this thesis brings participants to the foreground and articulates their experiences. It aims to evaluate if low-cost sensing technologies are fitfor- purpose in environmental …

With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …