Anomaly Detections for Manufacturing Systems Based on Sensor Data - Insights into Two Challenging Real-World Production Settings. - Klaus Kammerer; Burkhard Hoppenstedt; Rüdiger Pryss; Steffen Stökler; Johannes Allgaier; Manfred Reichert (Sensors, 2019)

Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data*. - Johannes Allgaier; Patrick Neff; Winfried Schlee; Stefan Schoisswohl; Rüdiger Pryss (EMBC, 2021)

Corona Health - A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. - Felix Beierle; Johannes Schobel; Carsten Vogel; Johannes Allgaier; Lena Mulansky; Fabian Haug; Julian Haug; Winfried Schlee; Marc Holfelder; Michael Stach; Marc Schickler; Harald Baumeister; et al. (CoRR, 2021)

Self-Assessment of Having COVID-19 With the Corona Check mHealth App. - Felix Beierle; Johannes Allgaier; Carolin Stupp; Thomas Keil; Winfried Schlee; Johannes Schobel; Carsten Vogel; Fabian Haug; Julian Haug; Marc Holfelder; Berthold Langguth; Jana Langguth; et al. (IEEE J. Biomed. Health Informatics, 2023)

How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare. - Johannes Allgaier; Lena Mulansky; Rachel Lea Draelos; Rüdiger Pryss (Artif. Intell. Medicine, 2023)

Machine Learning Explainability on Multi-Modal Data using Ecological Momentary Assessments in the Medical Domain (Erklärbarkeit von maschinellem Lernen unter Verwendung multi-modaler Daten und Ecological Momentary Assessments im medizinischen Sektor) - Johannes Allgaier (2024)

Cross-Validation Visualized: A Narrative Guide to Advanced Methods. - Johannes Allgaier; Rüdiger Pryss (Mach. Learn. Knowl. Extr., 2024)