RN-XCM: A Neural Network for Classifying Skill Level in Multi-Axis Tracking Tasks

The aviation industry's reliance on automation raises concerns about pilot complacency, necessitating continuous pilot proficiency measures. To that end, real-time pilot skill feedback is vital—through alerts on declining skill levels or scalable levels of autonomy. Current cybernetic methods are limited as they assume linearity and time...

Temporal Fusion Transformer for time series forecasting

The ability to accurately forecast sales volumes holds substantial significance for businesses. Current classical models struggle in capturing the impact of different variables upon the sales volume. These machine learning models are also not applicable to more than one specific product. The Temporal Fusion Transformer (TFT) is implemented to...

Assessing the applicability of Transformer-based architectures as rainfall-runoff models

Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...

An iterative deep reinforcement learning approach to solve vehicle routing problems

Vehicle routing problems have been studied for more than 50 years, and their in- terest has never been higher. It is partly due to their significant economic impact. Decreasing the traveling time, certainly for big organizations, can save costs in the range of millions of dollars and increase their service quality. Moreover, the wide variety of...

Phon Times: Improving Dutch phoneme recognition

In this research, Dutch phoneme recognition (PR) is researched and improved. The last research on Dutch PR dates back to 1995. This research presents Dutch PR in modern daylight by researching state-of-the-art techniques found in research on other languages and implementing them on Dutch PR. The goal of this research is to find the current best...

Modelling Time Delta in User-Item Interactions Using Deep Recommender Systems

Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time...