A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data. Scientific reports, (14)1Nature Publishing Group, Jan 27, 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., Aug 30, 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, Aug 10, 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, June 2022. [PUMA: topic_lifescience FIS_scads PAR differential equations, learning learning, machine proteins, regression, selection, sparse stability statistical theory]
RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures. 2023 26th Euromicro Conference on Digital System Design (DSD), 585--592, IEEE, Sep 8, 2023. [PUMA: topic_federatedlearn Computer Embedded FIS_scads Machine Runtime, Scalability, Shape algorithms, architecture, computing, learning vision,] URL
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,]
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: topic_engineering area_architectures 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), Jul 2, 2024. [PUMA: topic_engineering area_architectures Convolutional Direct FIS_scads Fiber Machine Strain dependency, identification, learning, networks, neural parameter plastics rate reinforced] URL
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, Sep 16, 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, October 2022. [PUMA: area_responsibleai FIS_scads artificial beliefs, conceptions, education, ideas, intelligence, k-12 learning, machine mental models, preconceptions]
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences. Earth's Future, (12)7Wiley, Hoboken, July 2024. [PUMA: FIS_scads XAI big data, discovery, interpretability, interpretable knowledge learning, machine]
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: area_responsibleai area_bigdata (cs.CR), (cs.CV), (cs.LG), Computer Cryptography FOS: Learning Machine Pattern Recognition Security Vision and ep information sciences]
Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: topic_physchemistry Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement] URL
Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] 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
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]
Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: classification coding extraction; feature learning lncRNA; machine problems; sequence;]
Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning. Evol. Comput., (27)1:99--127, MIT Press, 2019. [PUMA: Automated algorithm analysis; black-box continuous exploratory landscape learning; machine optimization. optimization; selection; single-objective]
Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.;]
Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study. J. Cachexia Sarcopenia Muscle, (12)4:1011--1023, Wiley, August 2021. [PUMA: Appendicular Body Fat Machine Muscle Proteomics fat index; learning; mass mass; muscle skeletal]