A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters. Journal of Composites Science, (6)10:318, MDPI, 2022. [PUMA: Shear Driven Composites Experimental Data Dependent Thermoplastic Strain 3D Approach Characterisation Rate Fibre Modelling Deformation topic_engineering Failure Reinforced]
A standardized clinical data harmonization pipeline for scalable ai application deployment (fhir-dhp): Validation and usability study. JMIR Medical Informatics, (11):e43847, JMIR Publications Toronto, Canada, 2023. [PUMA: scalable pipeline clinical data ai topic_neuroinspired application standardized (fhir-dhp) deployment harmonization]
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]
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]
Breaking Barriers: Workshop on Open Data Practices in AutoUI Research. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 227--230, 2023. [PUMA: AutoUI Data Research Workshop Open topic_visualcomputing Practices]
Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, (35):93–101, IEEE, September 2023. [PUMA: Study Learning Computer Data Self-Supervised Unlabelled Vision. Comparative] URL
ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: Tabular Convex LoRAS GAN Imbalanced data learning space] URL
Destination Prediction Based on Partial Trajectory Data. 2020. [PUMA: Destination Partial Prediction Data Trajectory Based on] URL
Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces. Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 81--92, 2023. [PUMA: User Interfaces Data Interactions topic_visualcomputing ICEBOAT Analytics Big Automotive]
Graph based process models as basis for efficient data driven surrogates - Expediting the material development process. engrXiv : engineering archive, Open Engineering Inc., 16.11.2024. [PUMA: FIS_scads making modeling, decision Surrogate Data development, Process topic_engineering driven]
Graph Mining for Complex Data Analytics. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 1316--1319, Dezember 2016. [PUMA: analysis;Data algorithms;Conferences;Graph design Intelligence and Data mining;Business;Algorithm Mining;Business models;Libraries;Partitioning]
ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces. Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Association for Computing Machinery, New York, NY, USA, 2022. [PUMA: Design Interaction Visualization Driving Tools Data In-Vehicle Naturalistic Information topic_visualcomputing Human-Computer System] URL
Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, Februar 2020. [PUMA: decision-making; workflow; Haematology; Support Mathematical therapy Clinical Data Routine treatment management; simulation; modelling; system optimization; Individual Computer planning; Model-based]
Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)Januar 2024. [PUMA: problems; topic_federatedlearn visualization learning; data machine analysis; AutoML; classification]
Multi-source dataset of e-commerce products with attributes for property matching. Data Brief, (41)107884:107884, Elsevier BV, April 2022. [PUMA: integration; engineering; Data Ontology; Property matching area_bigdata]
Parallel Discrete Convolutions on Adaptive Particle Representations of Images. IEEE Transactions on Image Processing, (31):4197--4212, Wiley-IEEE Press, 01.01.2022. [PUMA: FIS_scads resolution, processing, Convolution, topic_lifescience reconstruction, Microscopy, Data resolution structures, Signal Image]
PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 19.04.2023. [PUMA: FIS_scads visualization data augmented/mixed Analytics, referents, In-situ analysis, Immersive affordance, movement topic_visualcomputing physical reality,]
Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Networks Predicting low gross production data primary Neural weather]
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
TmoTA: Simple, Highly Responsive Tool for Multiple Object Tracking Annotation. In Albrecht Schmidt, Kaisa Väänänen, EditoTesh Goyal, Per Ola Kristensson, Anicia Peters, Stefanie Mueller, Julie R. Williamson, und Max L. Wilson (Hrsg.), CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, 19.04.2023. [PUMA: FIS_scads sequence labeling data video topic_visualcomputing labeling, manual] URL