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As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …

In the scope of this thesis, I focused on SrTiO3 (ST)-based dielectric ceramic composites as a lead-free perovskite material, which holds great potential for use in various electronic components. Such materials are large-scale and fabricated under high energy requirements, indicating an increasing need for a more environmentally friendly processing method. …

Complex systems, composed of numerous interacting subsystems, exhibit intricate dynamics that are challenging to model and understand. This dissertation focuses on advancing methodologies for detecting the direction and quantifying the strength of couplings in such systems and presents their application to real data. The basics of dynamical systems and chaos …

Alzheimer's disease (AD) is the most prevalent form of dementia, ranking as the 7th deadliest world’s disease. Over 99% of cases are triggered by unknown environmental factors. Despite decades of research, the exact cause remains elusive, and effective treatments are yet to emerge. Recent epidemiological evidence strongly links cognitive decline …

One of commonly used analytical technique for direct elemental analysis of solid samples is Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Although widely used for the analysis of various samples, obtaining accurate quantitative results remains a major challenge as matrix-matched standards are required. The aim of this thesis was …

Automatic terminology extraction, also known as automatic term extraction (ATE), is a natural language processing (NLP) task that identifies specialized terminology from domain-specific corpora. ATE is often used for terminographic tasks (e.g., the creation of specialized dictionaries) and contributes to several complex downstream tasks (e.g., machine translation and information retrieval). …

Over the past decade, rapid advancements in natural language processing have opened up new avenues for tackling complex issues such as news bias analysis. This progress has empowered researchers to explore innovative approaches to uncovering the complex biases inherent in news production and coverage processes. News bias, a multifaceted reflection …

This dissertation examines two main topics. The first topic is focused on the potential of strontium (Sr) isotope analysis to characterise milk and truffles as selected food commodities, traditionally important for Slovenia. The 87Sr/86Sr ratio for determining the provenance of bovine milk and truffles originating from different regions of Slovenia …

Omnipresence of sensor-equipped devices spurred rapid development of e-health and mhealth applications in the past decades. Despite their wide-spread adoption in the form of wearables, such devices are ultimately not a universal or ideal solution for regular health monitoring due to their reliance on battery, requiring skin contact and general …

This thesis introduces a novel machine learning methodology for automatically assigning metadata to digitized artifacts in cultural heritage. Cultural heritage is an example of a domain that requires expert labeling, with few pre-existing labeled datasets and where simply getting more labeled data is challenging. The societal importance of cultural heritage …