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Unsupervised Learning Techniques for Detection of Regions of Interest in Solar Images.

, and . ICDM Workshops, page 582-588. IEEE Computer Society, (2015)

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Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations., , , , , and . ACM Trans. Spatial Algorithms Syst., 2 (3): 10:1-10:27 (2016)Standing on FURM ground - A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems., , , , , , , , , and 14 other author(s). CoRR, (2024)Usage of Dissimilarity Measures and Multidimensional Scaling for Large Scale Solar Data Analysis., and . CIDU, page 189-203. NASA Ames Research Center, (2010)On visualization techniques for solar data mining., , , , , and . Astron. Comput., (2015)Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study., , , , , , , , , and 28 other author(s). J. Am. Medical Informatics Assoc., 30 (5): 859-868 (April 2023)ACE: the Advanced Cohort Engine for searching longitudinal patient records., , , , , and . J. Am. Medical Informatics Assoc., 28 (7): 1468-1479 (2021)On the surprisingly accurate transfer of image parameters between medical and solar images., , and . ICIP, page 3669-3672. IEEE, (2011)Ten simple rules to run a successful BioHackathon., , , , , , , and . PLoS Comput. Biol., (2020)Towards automatic identification of self-reported COVID-19 tweets: Introducing a multilingual manually annotated dataset, baseline systems and exploratory evaluations., , and . IEEE Big Data, page 3736-3742. IEEE, (2023)Unsupervised Learning Techniques for Detection of Regions of Interest in Solar Images., and . ICDM Workshops, page 582-588. IEEE Computer Society, (2015)