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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: best conic cost-sensitive interpretable learning machine meta-learning mixed-integer optimization selection subset zno]
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: conscious hybrid learning machine patient states zno {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 Locked-In Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training algorithms and data learning modeling models processing zno machine]
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 Mobile Parking Roadside yaff learning machine]
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 yaff] URL
A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, Januar 2022. [PUMA: Cluster Computer Computing, Distributed, Learning Machine Parallel, Science and myOwn] URL
Content-Aware Depth-Adaptive Image Restoration. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, Januar 2024. [PUMA: Computer Learning, Machine Pattern Recognition, Science Vision and myOwn] URL
Explainable Earth Surface Forecasting under Extreme Events. arXiv, 2024. [PUMA: (cs.LG), Computer FOS: Learning Machine and information sciences sciences, topic_earthenvironment] 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, Medical Records topic_lifescience yaff machine learning]
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: Convolutional Direct FIS_scads Fiber Strain area_architectures dependency, identification, networks, neural parameter plastics rate reinforced topic_engineering yaff machine learning] URL
Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning. arXiv preprint arXiv:2409.01329, arXiv, September 2024. [PUMA: (cs.CR) (cs.CV) (cs.LG) Computer Cryptography FOS Pattern Recognition Security Vision area_bigdata area_responsibleai ep information learning machine sciences xack yaff]
Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG) (math.NA) (physics.data-an) Analysis Computer FOS Mathematics Numerical Physical Probability Statistics data information learning machine sciences xack] 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: Appendicular Body Fat Muscle Proteomics fat index learning machine mass muscle skeletal xack]
Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am. J. Hum. Genet., (108)10:1946--1963, Elsevier BV, Oktober 2021. [PUMA: Diseases Network UDN Undiagnosed_clinical_digenic disease learning machine oligogenic prediction rare topic_lifescience zno]
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: AI 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 topic_lifescience transarterial whole zno]
Common features in lncRNA annotation and classification: A survey. Noncoding RNA, (7)4:77, MDPI AG, Dezember 2021. [PUMA: classification coding extraction feature learning lncRNA machine problems sequence yaff]
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: algorithm analysis black-box continuous exploratory landscape learning machine optimization selection single-objective zno automated]
Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: algorithm analysis approaches automated combinatorial configuration continuous data exploratory feature-based landscape learning machine metalearning optimisation selection streams zno]
Structural damage identification of composite rotors based on fully connected neural networks and convolutional neural networks. Sensors (Basel), (21)6:2005, MDPI AG, März 2021. [PUMA: (SHM) composite composites; connected convolutional dense fully health machine monitoring neural rotors; structural xack learning networks]