The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biol...
As part of the growth in the automotive industry, there is an increase in the number of radar sensors that are deployed today. This growth comes at the cost of a potential increase in interference emanating from neighboring radar sensors that are within range. Traditionally, signal processing techniques have been used to mitigate interference but d...
Single-cell methodologies are recognized for their capacity to elucidate tissue heterogeneity at a detailed level, sequencing each cell's own characteristics. First used for RNA sequencing, it provides critical information to better understand the behavior of cells and the cellular microenvironment. It has rapidly evolved, now offering new opportun...
This thesis presents a comparative analysis of conventional image compression techniques such as JPEG, WebP, and TIFF, against modern autoencoder-based compression methods. The main objective is to evaluate the performance and quality of autoencoders relative to established algorithms in image compression. Theoretical backgrounds of each compressio...
This work introduces a novel methodology for the development of interpretable reduced-order dynamic models specifically tailored for jumping quadruped robots. Leveraging Symbolic Regression combined with autoencoder neural networks, the framework autonomously derives symbolic equations from data and fundamental physics principles capturing the comp...
Published in Computers in biology and medicine
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed ...
In the field of actuarial science, statistical methods have been extensively studied toestimate the risk of insurance. These methods are good at estimating the risk of typicalinsurance policies, as historical data is available. However, their performance can be pooron unique insurance policies, which require the manual assessment of an underwriter....
Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion met...
Neural networks (NN) have demonstrated considerable capabilities in tackling tasks in a diverse set of fields, including natural language processing, image classification, and regression. In recent years, the amount of available data to train Deep Learning (DL) models has increased tremendously, thus requiring larger and larger models to learn the ...
Environmental factors can subtly shape our motor skills from infancy, where infants transition from spontaneous to coordinated movements. This early development, largely unaffected by varied surroundings, offers a unique insight into the natural evolution of motor abilities. This thesis utilises unsupervised machine learning (UML) to analyse sponta...
