MLcps: machine learning cumulative performance score for classification problems. GigaScience, (12):giad108, Dezember 2023. [PUMA: MLcps Xack Yaff cumulative learning machine performance] URL
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective. KI 2023: Advances in Artificial Intelligence: 46th German Conference on AI, Berlin, Germany, September 26--29, 2023, Proceedings, 114--129, Springer-Verlag, Berlin, Heidelberg, 2023. [PUMA: Zno best conic cost-sensitive interpretable learning machine meta-learning mixed-integer optimization selection subset]
Extendable hybrid approach to detect conscious states in a CLIS patient using machine learning. SNE Simul. Notes Eur., (32)1:37--45, ARGESIM Arbeitsgemeinschaft Simulation News, 2022. [PUMA: Zno conscious hybrid learning machine patient states {CLIS}]
Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Learning Locked-In Machine Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training Zno algorithms and data learning modeling models processing]
Machine Learning Based Mobile Capacity Estimation for Roadside Parking. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (10):99--106, Copernicus GmbH, 2024. [PUMA: Based Capacity Estimation Learning Machine Mobile Parking Roadside]
Cloudy with a chance of precision: satellite’s autoconversion rates forecasting powered by machine learning. Environmental Data Science, (3)Cambridge University Press (CUP), 2024. [PUMA: autoconversion forecasting learning machine rates satellite] 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,]