Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

View Video Presentation: https://doi. org/10.2514/6.2022-1133. vid Machine learning (ML)
has proven to be the tool of choice for achieving human-like or even super-human …

With the proliferation of algorithmic decision-making, increased scrutiny has been placed on
these systems. This paper explores the relationship between the quality of the training data …

The use of machine learning (ML) allows us to automate and scale the decision-making
processes. The key to this automation is the development of ML models that generalize …

Machine Learning (ML) splits data into 3 parts, which are usually 60% for training, 20% for
validation, and 20% for testing. It just splits quantitatively instead of selecting each set of …

Deep learning systems recently achieved unprecedented success in various industries.
However, DNNs still exhibit some erroneous behaviors, which lead to catastrophic results …

Despite their increasing demand for assistant and autonomous systems, the recent shift
towards data-driven approaches has hardly reached aerial domains, partly due to a lack of …

Background Screening for influenza in primary care is challenging due to the low sensitivity
of rapid antigen tests and the lack of proper screening tests. Objective The aim of this study …

Robotic systems require holistic capabilities to sense, perceive, and act autonomously within
their application environment. A safe and trustworthy autonomous operation is essential …

With the breakthrough advancements of deep neural network technology in applications
such as image processing, autonomous driving, and speech recognition, the testing of deep …