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: topic_visualcomputing XAI, explainability, learning, machine trust] URL
NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: topic_federatedlearn Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL
Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, und Zilong Wu (Hrsg.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: topic_physchemistry Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL
A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP. Computational Materials Science, (244 (2024))Elsevier Science B.V., September 2024. [PUMA: area_architectures topic_engineering Constitutive FIS_scads Fiber Machine Neural Parameter identification learning, modeling, networks, plastics, reinforced]
Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, (3):1252--1259, European Society for Composite Materials (ESCM), 02.07.2024. [PUMA: area_architectures topic_engineering Convolutional Direct FIS_scads Fiber Machine Strain dependency, identification, learning, networks, neural parameter plastics rate reinforced] URL
A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data. Scientific reports, (14)1Nature Publishing Group, 27.01.2024. [PUMA: Databases, Electronic FIS_scads Factual, Health Humans, Informatics, Learning, Machine Medical Records topic_lifescience]
Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care. Studies in health technology and informatics, (317):139--145, IOS Press, Amsterdam u. a., 30.08.2024. [PUMA: topic_lifescience Diseases/therapy, Electronic Eye FIS_scads Germany, Health Humans, Information Interoperability/standards, Learning, Level Machine Ophthalmology Records/standards, Seven/standards,]
CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome medicine, (15)1BioMed Central, London, 10.08.2023. [PUMA: topic_lifescience Animals, C57BL, Cardiac/metabolism, Cell FIS_scads Immunocompromised, Inbred Infarction, Machine Macrophages, Macrophages/metabolism, Mice, Monocytes/metabolism Myocardial Myocytes, Single-cell, infarction, learning, therapy,]
Stability selection enables robust learning of differential equations from limited noisy data. Proceedings of the Royal Society of London : Series A, Mathematical, physical and engineering sciences, (478)2262Royal Society Publishing, Juni 2022. [PUMA: topic_lifescience FIS_scads PAR differential equations, learning learning, machine proteins, regression, selection, sparse stability statistical theory]
Data-driven determination of plant growth stages for improved weather index insurance design. Agricultural Finance Review, Emerald Group Publishing, Bingley, 2024. [PUMA: topic_engineering FIS_scads Generalized Machine Plant Temporal Weather additive basis growth index insurance learning, model, risk, stages,]
Identifying Secondary School Students' Misconceptions about Machine Learning: An Interview Study. WiPSCE '24: Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research, 1--10, Association for Computing Machinery, 16.09.2024. [PUMA: area_responsibleai FIS_scads artificial conceptions intelligence, interview learning, machine mental misconceptions, models, qualitative research, students study,]
Brief Summary of Existing Research on Students’ Conceptions of AI. 1--2, Oktober 2022. [PUMA: area_responsibleai FIS_scads artificial beliefs, conceptions, education, ideas, intelligence, k-12 learning, machine mental models, preconceptions]