document

Dynamically Forecasting Airline Departure Delay Probability Distributions for Individual Flights using Supervised Learning

Beltman, Maarten (author)

Punctuality is a key performance indicator for any airline. Hub-and-spoke airlines are particularly committed to on-time arrivals to guarantee passenger connections. Flights that are delayed at departure need to compensate for the lost time whilst airborne. Because fueling takes place well before scheduled departure, predicted departure delays...

master thesis 2023

document

Uncertainty quantification for tensor network constrained kernel machines: A frequentist and Bayesian approach

Smeenk, Rutger (author)

This research aims at quantifying the uncertainty in the predictions of tensor network constrained kernel machines, focusing on the Canonical Polyadic Decomposition (CPD) constrained kernel machine. Constraining the parameters in the kernel machine optimization problem to be a CPD results in a linear computational complexity in the number of...

master thesis 2023

document

Identification of Bacteria with Artificial Intelligence

Mendoza Silva, Santiago (author)

Bacterial identification is crucial for addressing infectious diseases and enabling effective treatment strategies. Conventional bacteria identification methods like MALDI-TOF, while efficient, lack the capability for screening the effectiveness of antibiotics. On the other hand, existing antimicrobial resistance (AMR) tests, despite being...

master thesis 2023

document

Memory Mechanisms in Spiking Neural Networks

Lammers, Laurens (author)

Neuromorphic sensors, like for example event cameras, detect incremental changes in the sensed quantity and communicate these via a stream of events. Desired properties of these signals such as high temporal resolution and asynchrony are not always fully exploited by algorithms that process these signals. Spiking neural networks (SNNs) have...

master thesis 2023

document

Inferring Segments of Speaking Intention Using a Body-worn Accelerometer: Enhancing social interaction with AI-powered systems

Achy, Nils (author)

This research paper proposes a deep learning model to infer segments of speaking intentions using body language captured by a body-worn accelerometer. The objective of the study is to detect instances where individuals exhibit a desire to speak based on their body language cues. The labeling scheme employed is a binary string, with “0”...

bachelor thesis 2023

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Learning to Make Routing Decisions Based on Expert Examples

Schouten, Job (author)

Logistics and mobility services play a major role in our society, and efficient routing is a crucial part of this. However, even though routing problems have been widely researched, the solutions provided by algorithms do not always match drivers' expectations. Routing costs used by these algorithms are often based on one or a few parameters,...

master thesis 2022

document

Individual Prediction Modelling for Air Traffic Control using Supervised Learning

Kloosterman, Luc (author)

In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated...

master thesis 2022

document

One Step Ahead: A weakly-supervised approach to training robust machine learning models for transaction monitoring

van der Werf, Daan (author)

In recent years financial fraud has seen substantial growth due to the advent of electronic financial services opening many doors for fraudsters. Consequently, the industry of fraud detection has seen a significant growth in scale, but moves slowly in comparison to the ever-changing nature of fraudulent behavior. As the monetary losses...

master thesis 2021

document

Data efficient pick location learning from images for sequential tasks

Arunmoli, Karthik Arvind (author)

Learning from demonstration is a technique where the robot learns directly from humans. It can be beneficial to learn from humans directly because humans can easily demonstrate complex behaviors without being experts in demonstrating required tasks. However, it can be challenging to gather large amounts of data from humans because humans often...

master thesis 2021

document

Vessel Performance Evaluation Using A Digital Twin: A Black-box Model Approach

De Vocht, Roel (author)

An important step in determining the performance of a commercial ship is to determine the vessel fuel consumption in offdesign conditions. In previous studies, the vessel fuel consumption is obtained using machine learning algorithms which use navigational data and meteorological data to train the models. Due to the inaccuracy of the weather...

master thesis 2021

document

Automatic Identification of Fault Types in the Distribution Network using Supervised Learning

Ranganathan, Archana (author)

Electrical faults in the distribution system can lead to interruptions in customer power supply resulting in penalties that are borne by the distribution system operator. Accurate fault classification is an important step in locating the fault to achieve faster network restoration times. This reduces the operational costs of the system operator...

master thesis 2021

document

Classification Algorithm for Early Detection of Atrial Fibrillation: The Development of a Supervised Learning Method Using Photoplethysmography Signals for an ARM Processor

van Es, Tim (author), Helfferich, Florens (author)

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia occurring in around 0.5% of the world population. AF is characterized by the rapid and irregular beating of the atrial chambers of the heart, which can cause lead to strokes and other heart-failures. To prevent these consequences the early detection of AF is paramount. Using...

bachelor thesis 2021

document

Data-Driven Soft Discriminant Maps: Class-aware Linear Feature Extraction in Imaging Mass Spectrometry

Booij, Thomas (author)

Retrieving actionable information from large datasets is increasingly computationally expensive due to the current trend of ever-increasing dataset sizes. Reducing dataset sizes with dimensionality reduction techniques is often necessary for statistical analysis techniques, such as classification, to be computationally feasible. Most...

master thesis 2021

document

Predicting True Vulnerabilities from Static Analyzer Warnings in Industry: An Attempt to Faster Releasing Software in Industry

Bisesser, Dinesh (author)

An increasing digital world, comes with many benefits but unfortunately also many drawbacks. The increase of the digital world means an increase in data and software. Developing more software unfortunately also means a higher probability of vulnerabilities, which can be exploited by adversaries. Adversaries taking advantage of users and software...

master thesis 2020

document

Indoor 3D Reconstruction from a Single Image

Garg, Chirag (author)

3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has...

master thesis 2020

document

Supervised Learning in Spiking Neural Networks

Büller, Bas (author)

Spiking neural networks are notoriously hard to train because of their complex dynamics and sparse spiking signals. However, in part due to these properties, spiking neurons possess high computa- tional power and high theoretical energy efficiency. This thesis introduces an online, supervised, and gradient-based learning algorithm for spiking...

master thesis 2020

document

Detecting Rumors in Twitter for Humanitarian Activities

Vaporidis, Dimitrios-Marios (author)

In this Master Thesis project, the objective is to study how can Supervised Machine Learning be used to detect text-based rumours for humanitarian activities in Twitter. A model was developed in this project in order to classify a tweet at question whether is a rumour or not and whether is relevant to humanitarian activities or not. The findings...

master thesis 2019

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An automated ECG signal quality assessment method with supervised learning algorithm

Wang, Yuyang (author)

Wearable health has become a striking area in our daily life.<br/>Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable devices, which is widely used in heart rate monitoring and cardiac diagnosis. However, automatic ECG signal analysis is difficult in real application because the signals are easy to be...

master thesis 2018

document

Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search

Zhong, Shijian (author)

The Train Maintenance Scheduling Problem (TMSP) is a real-world problem that aims at complete maintenance tasks of trains by scheduling their activities on a service site. Common methods of constructing optimal solutions to this problem are difficult as the problem consists of several highly-related sub-problems. Currently, NS is using a lo- cal...

master thesis 2018

document

Comparison of Optimal Control Techniques for Learning-based RRT

Paramkusam, Deepak (author)

Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications....

master thesis 2018