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Ihaddouchen, Imane (author)Introduction: In intensive care units (ICU), the most significant life support technology for patients with acute respiratory failure is mechanical ventilation. A mismatch between ventilatory support and patient demand is referred to as patient-ventilator asynchrony (PVA), and it is associated with a series of adverse...master thesis 2023
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Meester, Anne (author)Introduction: Critically ill children admitted to the Paediatric Intensive Care Unit (PICU) have a high risk of disruption of their normal sleep rhythm, which is associated with disturbances in physiology and negative effects on psychological and cognitive functioning. There is a need for real-time, automatic sleep monitoring to minimise...master thesis 2023
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van der Wal, Robin (author)Multiple Instance Learning (MIL) is a type of semi-supervised machine learning used recently in medical and multi-media fields. In MIL, instead of a single feature vector, a set of feature vectors has to be classified. Standard MIL algorithms assume that only some of these vectors are useful for building a classifier. This paper extends the...master thesis 2022
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Freyer, Caroline (author)Outlier detection in time series has important applications in a wide variety of fields, such as patient health, weather forecasting, and cyber security. Unfortunately, outlier detection in time series data poses many challenges, making it difficult to establish an accurate and efficient detection method. In this thesis, we propose the Random...master thesis 2022
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van 't Wout, Maarten (author)Handling missing values is crucial for accurately forecasting time series with different sampling rates. In stock price prediction, for example, the daily stock prices and quarterly valuation figures are sampled at a different rate, and both are useful in estimating the daily stock price’s future. This research proposes combining imputation...master thesis 2021
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Lange, Berend-Jan (author)Artificial neural networks are the key driver of progress in various semantic computer vision tasks such as age prediction and digit classification. For the successful application of neural network algorithms, the representation of the data is an important factor. A good representation can significantly simplify a regression or prediction task....master thesis 2021
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Hiemstra, Floor (author)Introduction: Sleep deprivation is commonly encountered in critically ill children admitted to the pediatric intensive care unit (PICU) and is associated with poor clinical outcome. Automated electroencephalography (EEG)-based depth of sleep monitoring enables real-time continuous study of sleep in PICU patients without the...master thesis 2021
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Biesheuvel, Julian (author)Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...bachelor thesis 2021
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Lamon, Julien (author)With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust...bachelor thesis 2021
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Thakoersingh, Ratish (author)This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...bachelor thesis 2021
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den Heijer, Remco (author)Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...bachelor thesis 2021
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Verschuuren, Rolf (author)Methods for learning vector space representations of words have yielded spaces which contain semantic and syntactic regularities. These regularities mean that vector arithmetic operations in the latent space represent meaningful and interpretable relations between words. These word vectors have been so successful in capturing such relations, as...master thesis 2021
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Zhao, Xunyi (author)Dropout is one of the most popular regularization methods used in deep learning. The general form of dropout is to add random noise to the training process, limiting the complexity of the models and preventing overfitting. Evidence has shown that dropout can effectively reduce overfitting. This thesis project will show some results where dropout...master thesis 2021
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Wendel, K. (author)Despite widespread adoption of machine learning models in automatic decision making, many of them remain black boxes of which their inner workings are unknown. To be able to reason about given predictions, a local surrogate model can be used to approximate the local decision boundary of the black box. One particular local explanation method,...master thesis 2021
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Werner, Oliver (author)In clinical practice, as a first approximation, the severity of an abnormality on an image is often determined by measuring its volume. Researchers often first segment this abnormality with a neural network trained by voxel-wise labels and thereafter extract the volume. Instead of this indirect two steps approach, we propose to train neural...master thesis 2020
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Sudharsan, S. (author)Deep learning has enabled technologies that have been perceived complex or impossible a few years ago. Deep learning models can be used to solve several complex problem statements thereby making it a prominent field of research. With the advancements of Deep learning models, their application in domains have diversified. One prominent use-case...master thesis 2020
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van Tussenbroek, Thomas (author)Authorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature...bachelor thesis 2020
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Keukeleire, Pia (author)In recent years many new text generation models have been developed while evaluation of text generation remains a considerable challenge. Currently, the only metric that is able to fully capture the quality of a generated text is human evaluation, which is expensive and time consuming. One of the most used intrinsic...bachelor thesis 2020
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Happel, David (author)Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF...bachelor thesis 2020
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Schmahl, Katja (author)Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain a stereotypical gender bias. This unwanted bias is then also present in the word embeddings and in downstream applications in the field of natural language processing. To prevent and reduce this, more knowledge about the gender bias is...bachelor thesis 2020