Dynamic Target Time Management with Reinforcement Learning: A case study on Zurich short-haul regulated arrivals

This Master Thesis investigates the possible improvements to the Target Time Management concept to optimize the arrival flows for SWISS International Airlines. The aim is to improve operational performance based on the current model used, as well as prove that Target Time Management constitutes a valuable system to improve operations in a...

Health data sharing for clinical research : Designing a patient-centric approach

The use of Electronic Health Records (EHRs) has seen a breakthrough in clinical research for personalized treatments (Hamburg & Collins, 2010.) Despite the potential advantages of vast EHR data available, constraints of privacy and legislation hinder its use (Rieke et al., 2020.) Health data exists in an interconnected healthcare system ...

Navigating the Pedagogical Landscape: An Exploration of Machine Learning Teaching Methods

This study delves into machine learning (ML) education by conducting a comprehensive literature review, a targeted survey of ML lecturers in Dutch universities, and a comparative experiment. These methods aid in addressing the challenges of aligning teaching methods with the evolving nature of ML and the growing demands of the field, and fill in...

Learning Curves: How do Data Imbalances affect the Learning Curves using Nearest Mean Model?

This research investigates the impact of data imbalances on the learning curve using the nearest mean model. Learning curves are useful to represent the performance of the model as the training size increases. Imbalanced datasets are often encountered in real-life scenarios and pose challenges to standard classifier models impacting their...

A comparative analysis of coding approaches in machine learning among computer science students and non-computer science students

The increasing presence of Machine Learning in all fields of study requires an improvement in how it is taught. Previous research on this topic examined how to teach ML concepts and highlighted the importance of using technology and leveraging relevant pedagogical content knowledge. It did not compare the impact of previous programming knowledge...

Learning Reduced-Order Mappings between Functions: An Investigation of Suitable Inputs and Outputs

Data-driven approaches are a promising new addition to the list of available strategies for solving Partial Differential Equations (PDEs). One such approach, the Principal Component Analysis-based Neural Network PDE solver, can be used to learn a mapping between two function spaces, corresponding to a PDE. However, the practical limitations of...

What are the types of projects that Scratch users create?: How do different types of features relate to the project type?

Scratch is a block-based programming language. It is designed to be simple and syntax error avoidant. This makes Scratch an accessible platform for cultivating coding skills. Many young learners are taught about different programming skills using various project types as examples. For instance, games are used as an engagement tool, and various...

Acoustic Side-Channel Attacks on a Computer Mouse: Predicting Mouse Movements through Emitted Audio

Acoustic side-channel attacks (SCAs) use audio produced by a system to bypass traditional security measures to extract sensitive information. Human interface devices, such as keyboards, have been the focus of such attacks, however, computer mice are input devices that are currently in a research gap. This paper explores the security risks the...

Automated Carnegie Staging of the Human Embryo in 3D Ultrasound using Deep Learning

The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal...

Autonomous Sailing with Sim-to-Real Reinforcement Learning

Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown...

Improving Medical Care for Adults with Intellectual Disabilities (ID): Can Automated Processing of Electronic Health Record Clinical Text Assist in ID Detection Among the General Outpatient Population?

<b>Background</b>: Undetected Intellectual Disability (ID) can lead to chronic stress due to overestimation by society. Chronic stress can cause stress-related health issues, like hypertension, chronic fatigue and abdominal complaints. When a physician (General Practitioner (GP) or medical specialist) does not recognize that a patient has ID,...

Learning Interpretable Reduced-order Models for Jumping Quadrupeds

This work introduces a novel methodology for the development of interpretable reduced-order dynamic models specifically tailored for jumping quadruped robots. Leveraging Symbolic Regression combined with autoencoder neural networks, the framework autonomously derives symbolic equations from data and fundamental physics principles capturing the...

Prediction of Crashworthiness Performance Using Multi-Fidelity Machine Learning Techniques

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...

Reinforcement Learning for Flight Control: Evaluating Handling Qualities and Stability Properties of the PH-LAB

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...

Prediction of Aircraft Take-off Weight using Machine Learning

Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML...