Publicaties

We research how deep learning convolutional neural networks can be used to automatically classify the unique data set of black-and-white naval ships images from the Wright and Logan photographic collection held by the National Museum of the Royal Navy. We contrast various types of deep learning methods: pretrained models such as ConvNeXt, ResNet and EfficientNet, and ConvMixer. We also thoroughly investigate the impact of data preprocessing and ...

In academic literature and in business communication digital platforms are categorized into different types including, but not limited to, multi-sided platform, digital marketplace, on-demand platform and sharing economy platform. Observing the substantial literature on these platform types, both in academia and professional contexts, there is lack of consensus on the definition of these digital platform types. On top of that, there is a lack of ...

A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the ...

Several machine learning applications, including genetics and fraud detection, suffer from incomplete label information. In such applications, a classifier can only train from positive and unlabeled (PU) examples in which the unlabeled data consist of both positive and negative examples. Despite a substantial presence of PU learning in the literature, few works have considered a class imbalance setting. Hence, we propose a novel two-step method ...

Middle managers are often operating in complex and fast-moving environments, resulting in having to deal with enormous challenges in a short span of time, maneuvering diverse and numerous “givens” and aiming to achieve specific goals. These challenges are very diverse and require support tailored to the needs and characteristics of middle management. Due to the lack of tailored tools, middle managers often use methods that were originally ...

Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variable. Recurrent neural networks (RNNs), and more specifically long short-term memory nets (LSTMs), stand out in terms of popularity. In this work, we investigate the capabilities of such an LSTM to ...

With the growing popularity of web applications, there is a corresponding need to ensure that they comply with relevant regulations and standards, such as the General Data Protection Regulation (GDPR), which mandates strict guidelines for processing the personal data of European Union data subjects. In this paper, we leverage machine learning and natural language processing techniques to gather a data set of web applications to evaluate their ...