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: FIS_scads Humans, Factual, Learning, Health topic_lifescience Medical Electronic Informatics, Records Databases, Machine]
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: FIS_scads Fiber modeling, area_architectures plastics, reinforced Constitutive Machine identification learning, networks, Parameter topic_engineering Neural]
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: a disease; HCC, or intelligence; liver multivariable non-alcoholic multimodal ML, artificial WSIs, images; prediction support integration TACE, DICOM, AI, network; deep neural Digital Diagnosis; MVI, transarterial fatty topic_lifescience microvascular convolutional in learning; imaging; Communications invasion; NAFLD, chemoembolisation; hepatocellular Reporting steatohepatitis; Transparent Individual slide machine Prognosis Artificial data for NASH, whole Medicine; TRIPOD, of CNN, and Imaging carcinoma; diagnostic system; model]
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: ep FOS: Machine sciences (cs.LG), Security Learning (cs.CV), and Vision Computer (cs.CR), Cryptography information Pattern area_bigdata area_responsibleai Recognition]
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: learning; single-objective analysis; black-box exploratory optimization; continuous machine optimization. Automated selection; algorithm landscape]
Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: feature-based learning; metalearning data analysis; optimisation; automated streams.; exploratory combinatorial continuous machine approaches; Automated selection; algorithm configuration; landscape]
Brief Summary of Existing Research on Students’ Conceptions of AI. 1--2, October 2022. [PUMA: FIS_scads models, education, mental preconceptions artificial ideas, beliefs, intelligence, k-12 learning, machine conceptions, area_responsibleai]
CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation. Genome medicine, (15)1BioMed Central, London, Aug 10, 2023. [PUMA: Animals, FIS_scads Infarction, Macrophages, Cardiac/metabolism, Macrophages/metabolism, Mice, Myocytes, C57BL, Machine learning, Myocardial topic_lifescience Immunocompromised, Single-cell, infarction, therapy, Inbred Cell Monocytes/metabolism]
Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113--144, Elsevier, 2023. [PUMA: agent particles, Optical Multi control Machine Active Feedback learning, Reinforcement topic_physchemistry reinforcement control,] URL
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: rates forecasting machine autoconversion learning satellite] URL
Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, December 2021. [PUMA: problems; coding extraction; feature sequence; machine lncRNA; learning classification]
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: mixed-integer selection optimization meta-learning machine learning Zno conic interpretable cost-sensitive best subset]
Data-driven determination of plant growth stages for improved weather index insurance design. Agricultural Finance Review, Emerald Group Publishing, Bingley, 2024. [PUMA: FIS_scads Generalized insurance additive model, index risk, basis Machine stages, Plant learning, Weather Temporal growth topic_engineering]
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: Convolutional FIS_scads Fiber area_architectures neural plastics rate dependency, reinforced Direct Strain Machine learning, networks, identification, parameter topic_engineering] URL
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: models Consciousness Prediction Syndrome Predictive processing modeling Theory algorithms data learning Information Modeling Complexity Processing Machine Learning Training Neuroscience Measures and Locked-In Zno Signal Computational]
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, learning, explainability, machine topic_visualcomputing trust] URL
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: hybrid machine patient {CLIS} learning Zno states conscious]
Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, October 2021. [PUMA: disease; prediction; disease learning; Network; oligogenic Diseases clinical digenic rare machine topic_lifescience Undiagnosed UDN;]
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: FIS_scads models, students mental misconceptions, artificial research, intelligence, qualitative study, learning, machine interview area_responsibleai conceptions]
Impact of pre- and post-processing steps for supervised classification of colorectal cancer in hyperspectral images. Cancers (Basel), (15)7April 2023. [PUMA: convolutional learning; cancer networks; imaging; classification; pre-processing post-processing; cancer; filter; colorectal median machine hyperspectral topic_lifescience]
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: Learning Capacity Parking Estimation Roadside Based Mobile Machine]
Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: problems; topic_federatedlearn visualization learning; data machine analysis; AutoML; classification]
MLcps: machine learning cumulative performance score for classification problems. GigaScience, (12):giad108, December 2023. [PUMA: MLcps machine Xack learning performance cumulative Yaff] URL
MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: problems; topic_federatedlearn score unified learning; package; evaluation machine Python evaluation; model classification]
Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging. NeuroImage Clin., (37)103320:103320, Elsevier BV, January 2023. [PUMA: MRI; Neurodegeneration; learning; unit_test Diagnosis; Volumetry topic_lifescience topic_neuroinspired Dementia; Machine]
NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: Near-data Modeling, topic_federatedlearn exploration Design processing, learning, space Machine Prediction,] URL
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: mass index; skeletal learning; Appendicular mass; Machine Proteomics muscle Fat fat Muscle Body]
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: FIS_scads Shape learning Machine computing, topic_federatedlearn Runtime, vision, algorithms, architecture, Embedded Computer Scalability,] URL
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: FIS_scads PAR statistical proteins, equations, learning selection, sparse differential regression, learning, machine topic_lifescience stability theory]
Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (physics.data-an), Analysis, Probability Data sciences, Numerical Statistics FOS: Machine Physical sciences Analysis (cs.LG), Learning (math.NA), Mathematics, and Computer information] URL
Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J. Med. Imaging (Bellingham), (10)4:044502, SPIE-Intl Soc Optical Eng, July 2023. [PUMA: stroke medical learning; neural networks; imaging; machine prediction multi-modal topic_visualcomputing graph]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, March 2021. [PUMA: convolutional (SHM) monitoring learning; neural networks; rotors; health dense composites; connected structural composite machine fully]
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: FIS_scads Learning, Health Diseases/therapy, Germany, Interoperability/standards, Information Electronic Machine Humans, Eye Records/standards, topic_lifescience Ophthalmology Seven/standards, Level]