Overview

Abstract Outlier detection is a crucial analytical tool in various fields. In critical systems like manufacturing, malfunctioning outlier detection can be costly and safety-critical. Therefore, there is a significant need for explainable artificial intelligence (XAI) when deploying opaque models in such environments. This study focuses on manufacturing time series data... [Read More]

Abstract Explainable Artificial Intelligence is critical in unraveling decision-making processes in complex machine learning models. LIME (Local Interpretable Model-agnostic Explanations) is a well-known XAI framework for image analysis. It utilizes image segmentation to create features to identify relevant areas for classification. Consequently, poor segmentation can compromise the consistency of the... [Read More]

In the realm of data analytics, the ability to pinpoint anomalies or outliers is invaluable, particularly when dealing with complex datasets. This study takes us into the depths of using convolutional autoencoders (CAE) for anomaly detection in univariate time series, bridging the gap between advanced AI techniques and practical application.... [Read More]

During my tenure as a working student at Bosch, I was entrusted with a range of responsibilities pivotal to AI model development, data mining, and collaborative innovation. My role built on my previous experience and work of Link, Link and Link. [Read More]

As a Research Assistant, I specialized in the field of explainable Artificial Intelligence (AI), particularly in enhancing the interpretability of neural networks. [Read More]