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Thimmapa, Pradeepthi (author)
Contemporary project-based organisations recognise the importance of leveraging past project knowledge for competitive advantage. By leveraging the lessons learned from previous and ongoing projects, organisations can benefit from managing learning within and across projects effectively. Despite these benefits, challenges persist in effectively...
master thesis 2024
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Leenen, Femke (author)
Introduction <br/>Opioids are vital for pain management but are highly addictive and may lead to opioid-induced respiratory depression (OIRD), which is the primary cause of death related to both prescription and illicit opioid use. This study employed unsupervised machine learning (ML) to examine potential changes in cluster patterns post-opioid...
master thesis 2024
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Nair, Ruben (author)
Mixed-integer optimization problems, incorporating both discrete and continuous variables, present unique challenges across various domains such as computer science, finance, logistics, and healthcare. Evolutionary Algorithms (EAs) have emerged as powerful optimization techniques capable of tackling such complex problems in either the discrete...
master thesis 2024
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Anton, Mihai (author)
In large-scale ML, data size becomes a critical variable, especially in the context of large companies, where models already exist and are hard to change and fine-tune. Time to market and model quality are essential metrics, thus looking for ways to select, prune and augment the input data while treating the model as a black box can speed up the...
master thesis 2024
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Long, Youyuan (author)
In Inverse Optimization (IO), it is hypothesized that experts, when making decisions, implicitly engage in solving an optimization problem. If we can reconstruct this optimization problem using the decision data of the expert, then the behavior of the expert can be emulated. In this thesis, a novel inverse optimization model, Kernel Inverse...
master thesis 2024
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te Marvelde, Pepijn (author)
In the realm of machine learning (ML), the need for efficiency in training processes is paramount. The conventional first step in an ML workflow involves collecting data from various sources and merging them into a single table, a process known as materialization, which can introduce inefficiencies caused by redundant data. Factorized ML strives...
master thesis 2024
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Liscio, E. (author)
Human values are the abstract motivations that drive our opinions and actions. AI agents ought to align their behavior with our value preferences (the relative importance we ascribe to different values) to co-exist with us in our society. However, value preferences differ across individuals and are dependent on context. To reflect diversity in...
doctoral thesis 2024
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Raftopoulou, M. (author)
Following the trend of previous years, the number of devices, and hence the traffic in cellular networks is increasing. Moreover, new applications with stringent requirements are envisioned. Examples of such applications include collaborative learning and coverage extension with drones. To accommodate the traffic with its respective Quality of...
doctoral thesis 2024
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Vermeer, Jort (author)
The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use...
master thesis 2024
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Potter, George (author)
Learning new concepts is a difficult task for autonomous robots. These robots can adapt to changes in the situations. To adapt to a situation, they should be able to determine the usefulness of objects around them. The usefulness of objects is highly dependent on situational context, making pre-programming of adaptation behaviour to all possible...
master thesis 2024
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Jansen, Hidde (author)
Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research...
master thesis 2024
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de Bie, Melissa (author)
Introduction<br/>Patient-ventilator asynchrony (PVA) poses a significant challenge in the management of mechanically ventilated patients, contributing to adverse clinical outcomes. Current methods of detecting PVA rely on visual assessment by clinicians, leading to subjectivity and inconsistency. Therefore, there is a need for automated...
master thesis 2024
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Pozzi, G. (author)
The advancement of AI-based technologies, such as machine learning (ML) systems, for implementation in healthcare is progressing rapidly. Since these systems are used to support healthcare professionals in crucial medical practices, their role in medical decision-making needs to be epistemologically and ethically assessed. However, a central...
doctoral thesis 2024
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Koronaios, Panagiotis (author)
This study investigates the development and application of meta-models for crashworthiness assessment of helicopter structures and components. It aims to address the challenges associated with scarcity of data from computationally expensive simulations and experimental drop-tests, and enable the use of surrogates in a crashworthiness...
master thesis 2024
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Chin-A-Pauw, Laurens (author)
In this thesis, we aim to improve the application of deep reinforcement learning in portfo- lio optimization. Reinforcement learning has in recent years been applied to a wide range of problems, from games to control systems in the physical world and also to finance. While reinforcement learning has shown success in simulated environments (e.g....
master thesis 2024
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Verburg, Corné (author)
This thesis addresses the challenge of segmenting ultra-high-resolution images. Limitations of current approaches to segment these are that either detailed spatial contextual information is lost or many redundant computations are necessary. To overcome these issues, we propose a novel approach combining the U-Net architecture with domain...
master thesis 2024
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Kappé, Jeroen (author)
The city of Amsterdam faces the challenge of monitoring and assessing 200 kilometers of historic quay walls, of which much is deemed to be in poor condition. A key monitoring technique used is photogrammetry resulting in deformation testing. The fundamental data source forming the basis of this deformation analysis is a collection of overlapping...
master thesis 2024
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Liang, M. (author)
Since the introduction of cementitious materials, shrinkage-induced earlyage cracking (EAC) has emerged as a significant issue that negatively influences the function, durability, and aesthetics of concrete structures like dams, tunnels, and underground garages. This thesis aims to develop new experimental and modelling techniques that help...
doctoral thesis 2024
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Wang, Sunny (author)
While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through Certified Learning (CL), which combines a learning-based controller with formal methods to provide safety guarantees. This thesis...
master thesis 2024
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Nadeem, A. (author)
Understanding the behavior of cyber adversaries provides threat intelligence to security practitioners, and improves the cyber readiness of an organization. With the rapidly evolving threat landscape, data-driven solutions are becoming essential for automatically extracting behavioral patterns from data that are otherwise too time-consuming to...
doctoral thesis 2024
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