document

Federated learning: A comparison of methods: How do different ML models compare to each other

Sīpols, Emīls (author)

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and...

bachelor thesis 2023

document

Annotation Practices in Societally Impactful Machine Learning Applications: What are the recommender systems models actually trained on?

Sav, Andra (author)

Machine Learning models are nowadays infused into all aspects of our lives. Perhaps one of its most common applications regards recommender systems, as they facilitate users' decision-making processes in various scenarios (e.g., e-commerce, social media, news, online learning, etc.). Training performed on large volumes of data is what ultimately...

bachelor thesis 2023

document

Finding Biomarkers for Schizophrenia: Can Machine Learning algorithms identify schizophrenia-related biomarkers within metagenomic data derived from the human gut microbiome?

Bastow, Timothy (author)

There is mounting evidence indicating a relation- ship between the gut microbiome composition and the development of mental diseases but the mech- anisms remain unclear. Shotgun sequenced data from 90 schizophrenic patients and 81 sex, age, weight, and location matched controls was used for three machine learning models: Logistic Re- gression,...

bachelor thesis 2023

document

Does text matter?: Extending CLIP with OCR and NLP for image classification and retrieval

Sassoon, Jordan (author)

Contrastive Language-Image Pretraining (CLIP) has gained vast interest due to its impressive performance on a variety of computer vision tasks: image classification, image retrieval, action recognition, feature extraction, and more. The model learns to associate images with their descriptions, a powerful method which allows it to perform well on...

bachelor thesis 2023

document

A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

Vilhjálmsson, Thor (author)

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...

master thesis 2023

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Solving ML with ML: Effectiveness of the Metropolis-Hastings algorithm for synthesizing Machine Learning Pipelines

Sheremet, Denys (author)

In AutoML, the search space of possible pipelines is often large and multidimensional. This makes it very important to use an efficient search algorithm. We measure the effectiveness of the Metropolis-Hastings algorithm (M-H) in a pipeline synthesis framework, when the search space is described by a context-free grammar. We also compare the...

bachelor thesis 2023

document

Feature extraction and classification on heart rate time series for cardiovascular diseases

Beekhuizen, Michael (author)

Cardiovascular diseases are one of the primary causes of mortality worldwide. Paroxysmal atrial fibrillation is a specific type that is difficult to detect and diagnose in a short time frame. To overcome this, we investigated if long-term wearable data can be used for the detection of heart diseases. The BigIdeasLab_STEP dataset and long-term...

master thesis 2023

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The road to intelligent asphalt concrete mixture design: A Data driven analysis of common asphalt concrete property prediction methods and a solution to the inverse problem

Hopman, Luuk (author)

Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the...

master thesis 2023

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Creation and validation of a simulation environment for machine learning with drones

Bojtár, Veronika (author)

Simulation environments are useful for a wide range of applications and their functionalities continue to improve every year. The aim of this thesis project was to create a simulation environment with high levels of realism and assess its capabilities through the use case of generating distributed drone traffic rules.<br/><br/>This thesis...

master thesis 2023

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Machine learning and randomness in mechanical metamaterials

Pahlavani, H. (author)

doctoral thesis 2023

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Spiking Neural Networks Based Data Driven Control: An Illustration Using Cart-Pole Balancing Example

Liu, Yuxiang (author)

Machine learning can be effectively applied in control loops to robustly make optimal control decisions. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering, because SNNs can potentially offer high energy efficiency and new SNN-enabling neuromorphic hardwares are being...

master thesis 2023

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Towards a Linear-Data Monotone Wrapper Algorithm For Machine Learning Algorithms

Kam, Berend (author)

<br/>Machine learning algorithms (learners) are typically expected to produce monotone learning curves, meaning that their performance improves as the size of the training dataset increases. However, it is important to note that this behavior is not universally observed. Recently monotonicity of learning curves has gained renewed attention, as...

master thesis 2023

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Investigating different models that can be used to define the characteristics that influence customer behaviour in the online grocery sector

Middelweerd, Marloes (author)

In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control...

master thesis 2023

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On Safety in Machine Learning

Viering, T.J. (author)

This dissertation focuses on safety in machine learning. Our adopted safety notion is related to robustness of learning algorithms. Related to this concept, we touch upon three topics: explainability, active learning and learning curves.<br/><br/>Complex models can often achieve better performance compared to simpler ones. Such larger models are...

doctoral thesis 2023

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Neural network partitioning for resource-limited environments

Geel, Patrick (author)

The demand for implementing neural networks on edge devices has rapidly increased as they allow designers to move away from expensive server-grade hardware. However, due to the limited resources available on edge devices, it is challenging to implement complex neural networks. This study selected the Kria SoM KV260 hardware platform due to its...

master thesis 2023

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An AI algorithm to predict intubations in ICU patients: Predicting intubation to aid medical personnel in the decision to switch from High Flow Nasal Oxygen therapy to intubation

Hendriks, Manon (author)

Maasstad Hospital is a member of the Santeon hospital group. The ambition of Santeon is to improve healthcare for patients. The project in this internship also aims to improve patients’ health, specifically patients in the Intensive Care Unit (ICU).<br/>The treatment of respiratory insufficient patients in the ICU consists of High Flow Nasal...

master thesis 2023

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Non-invasive measurement of intracardiac pressures in heart failure patients using machine learning techniques

van Ravensberg, Annemiek (author)

Background: Solutions targeting early recognition of congestion in heart failure (HF) patients have the potential to prevent readmissions and can thus significantly reduce the burden on HF care. The gold standard measure of congestion is invasively measured pulmonary capillary wedge pressure (PCWP). However, the invasive nature and accessibility...

master thesis 2023

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Classification of primary liver tumors with radiomics and deep learning based on multiphasic MRI

Goedhart, Aisha (author)

Primary liver cancer is a commonly diagnosed cancer and accurate diagnosis is crucial for treatment planning. To differentiate between malignant and benign liver tumors, contrast-enhanced MRI is typically used as it provides information over multiple contrast phases. However, diagnosis based on MRI is challenging. In this study, automatic...

master thesis 2023

document

On leveraging physical knowledge to augment neural network-based surrogate models for simulations

Kaniewski, Tadeusz (author)

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

master thesis 2023

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Improving satellite remote sensing methodologies for analyzing landscape dynamics in arid environments with focus on Egypt

Delgado Blasco, José Manuel (author)

doctoral thesis 2023