Publications

Frank Cichos, Santiago Mui�os Landin, and Ravi Pradip. Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL

Veronia Iskandar, Mohamed A. Abd El Ghany, and Diana Goehringer. NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL

Philippe Krajsic, and Bogdan Franczyk. Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams. Procedia Computer Science, (207):235-244, 2022. [PUMA: business classification, explainability learning, online process,] URL

Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, and Olaf Wolkenhauer. ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Convex GAN, Imbalanced LoRAS, Tabular data data, learning, space] URL

Mersedeh Sadeghi, Daniel Pöttgen, Patrick Ebel, and Andreas Vogelsang. Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks. Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 36–46, Association for Computing Machinery, New York, NY, USA, 2024. [PUMA: XAI, explainability, learning, machine trust] URL