document

The Hierarchical Subspace Iteration Method for Computing Vibration Modes of Elastic Objects

van Dijk, Julian (author)

The Hierarchical Subspace Iteration Method is a novel method used to compute eigenpairs of the Laplace-Beltrami problem. It reduces the number of iterations required for convergence by restricting the problem to a smaller space and prolonging the solution as a starting point. This method has shown great performance improvements for Laplace...

master thesis 2023

document

A System for Model Diagnosis centered around Human Computation

Ziad Ahmad Saad Soliman Nawar, Ziad (author)

Machine learning (ML) systems for computer vision applications are widely deployed in decision-making contexts, including high-stakes domains such as autonomous driving and medical diagnosis. While largely accelerating the decision-making process, those systems have been found to suffer from a severe issue of reliability, i.e., they can easily...

master thesis 2023

document

Automatic feature discovery: A comparative study between filter and wrapper feature selection techniques

Mânăstireanu, Andrei (author)

The curse of dimensionality is a common challenge in machine learning, and feature selection techniques are commonly employed to address this issue by selecting a subset of relevant features. However, there is no consistently superior approach for choosing the most significant subset of features. We conducted a comprehensive analysis comparing...

bachelor thesis 2023

document

Encoding methods for categorical data: A comparative analysis for linear models, decision trees, and support vector machines

Udilă, Andrei (author)

This paper presents a comprehensive evaluation and comparison of encoding methods for categorical data in the context of machine learning. The study focuses on five popular encoding techniques: one-hot, ordinal, target, catboost, and count encoders. These methods are evaluated using linear models, decision trees, and support vector machines ...

bachelor thesis 2023

document

Filtering Knowledge: A Comparative Analysis of Information-Theoretical-Based Feature Selection Methods

Vasilev, Kiril (author)

The data used in machine learning algorithms strongly influences the algorithms' capabilities. Feature selection techniques can choose a set of columns that meet a certain learning goal. There is a wide variety of feature selection methods, however, the ones we cover in this comparative analysis are part of the information-theoretical-based...

bachelor thesis 2023

document

Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques

Buşe, Florena (author)

Thus far the democratization of machine learning, which resulted in the field of AutoML, has focused on the automation of model selection and hyperparameter optimization. Nevertheless, the need for high-quality databases to increase performance has sparked interest in correlation-based feature selection, a simple and fast, yet effective approach...

bachelor thesis 2023

document

Graph Regularized Tensor Decomposition for Recommender Systems

Chandrashekar, Rohan (author)

Humans make decisions when presented with choices based on influences. The Internet today presents people with abundant choices to choose from. Recommending choices with an emphasis on people's preferences has become increasingly sought. Grundy (1979), the first computer librarian Recommender System (RS), provided users with book recommendations...

master thesis 2022

document

Self-Supervised Few Shot Learning: Prototypical Contrastive Learning with Graphs

Shirekar, Ojas (author)

A primary trait of humans is the ability to learn rich representations and relationships between entities from just a handful of examples without much guidance. Unsupervised few-shot learning is an undertaking aimed at reducing this fundamental gap between smart human adaptability and machines. We present a contrastive learning scheme for...

master thesis 2022

document

Deep Statistical Solver for Distribution System State Estimation

Habib, Benjamin (author)

Whereas in the past, Distribution Systems played a passive role in connecting customers to electricity, Distribution System Operators (DSOs) will have to take in the future a more active role in monitoring and regulating the network to deal with the new behaviors and dynamics of the system brought by the energy transition. State Estimation, a...

master thesis 2022

document

Hardware-based implementations in Side-Channel Analysis: A comparison study of DL SCA attacks against HW and SW AES and a novel methodology

Bubberman, Wolf (author)

Side-Channel Attacks (SCA) attempt to recover the secret cryptographic key from an electronic device by exploiting the unintended physical leakages of said device. With the devices that are being attacked becoming more sophisticated, so is SCA. In the past few years, the focus of the research in the field of SCA shifted towards the application...

master thesis 2022

document

Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions

van den Belt, Glenn (author)

Earthquakes can have tremendous effects. They can result in casualties, massive damage, and hurt the economy. Therefore, one would like to predict earthquakes as early as possible and with the highest accuracy possible. This paper contains the proposal for the optimal prediction-time, which is the time between the execution of a prediction and...

bachelor thesis 2022

document

Improving cell type matching across species in scRNA-seq data using protein embeddings and transfer learning

Biharie, Kirti (author)

Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species, is discarded by most of the...

master thesis 2022

document

Development of an LSTM-based methodology for burst detection in water distribution systems

Glynis, Konstantinos (author)

Water utilities face many challenges, including pipe bursts that cause significant non-revenue water losses. Detecting those bursts early is important for the water sector in its path to achieve sustainable water resource management. This study presents a scalable data-driven methodology for burst detection in water distribution systems that is...

master thesis 2022

document

How long before strike can we predict earthquakes with an LSTM neural network?

Charlot, Amaury (author)

Different methods have been studied to predict earthquakes, but the results are still far from optimal. Due to their seemingly dynamic and unpredictable nature, it has been very hard to find data correlating with earthquakes happening. But recently, various research has been done using neural networks, and some has suggested that it could...

bachelor thesis 2022

document

Assessing the Capability of Multimodal Variational Auto-Encoders in Combining Information From Biological Layers in Cancer Cells

Pronk, Bram (author)

Personalized treatment methods for a complex disease such as cancer benefit from using multiple data modalities from a patient's cancer cells. Multiple modalities allow for analysis of dependencies between complex biological processes and downstream tasks, such as drug response and/or expected survival rate. To this end, it is important to gain...

bachelor thesis 2021

document

Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment

Korkić, Armin (author)

Cancer has been known as a deadly and complex disease to tackle. By applying machine learning algorithms we hope to improve personalized treatment for cancer patients. These machine learning algorithms are trying to learn a (latent) representation of the input. The problem is that this representation is hard to interpret and to observe the...

bachelor thesis 2021

document

Benchmarking VAE latent features in downstream tasks for cancer related predictions

van Groeningen, Boris (author)

Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Variational Auto-Encoders (VAEs) are used to assist in this process. VAEs are a type of neural network seeking to encode data into a smaller dimension called latent space. These latent features are then used to do downstream task analysis such as...

bachelor thesis 2021

document

Finding disentangled representations using VAE

d'Anjou, Raymond (author)

This study presents a comparison of different VariationalAutoencoder(VAE) models to see which VAE models arebetter at finding disentangled representations. Specificallytheir ability to encode biological processes into distinct la-tent dimensions. The biological processes that will be lookedat are the cell cycle and differentiation state. The...

bachelor thesis 2021

document

The Effect of Different Initialization Methods on VAEs for Modeling Cancer using RNA Genome Expressions

Kroskinski, Ivo (author)

Variational Auto-Encoders are a class of machine learning models that have been used in varying context, such as cancer research. Earlier research has shown that initialization plays a crucial part in training these models, since it can increase performance. Therefore, this paper studies the effect initialization methods on VAEs. This research...

bachelor thesis 2021

document

Improving the Performance of Object Counting Using Training Images in the Frequency Domain

Rogmans, Dani (author)

Convolutional Neural Networks (CNNs) have made significant strides in the field of image processing over the last decade. Different approaches have been taken and improvements have been suggested. This paper looks at a newer novelty to neural networks for image counting, which is based on single-pixel center localization instead of the...

bachelor thesis 2021