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Applying Machine Learning on the Daily Life Data of the TrackYourTinnitus mHealth Crowdsensing Platform Predicts the Mobile Operating System with High Accuracy

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Towards self-configuration and management of e-service provisioning in dynamic value constellations., , , and . SAC, page 566-571. ACM, (2008)Evaluation Patterns for Analyzing the Costs of Enterprise Information Systems., and . CAiSE, volume 5565 of Lecture Notes in Computer Science, page 379-394. Springer, (2009)Anomaly Detections for Manufacturing Systems Based on Sensor Data - Insights into Two Challenging Real-World Production Settings., , , , , and . Sensors, 19 (24): 5370 (2019)Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform, , , , , , , and . International Journal of Data Science and Analytics, (2018)Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus--stress associations based on the TrackYourTinnitus mobile platform, , , , , , , and . International Journal of Data Science and Analytics, 8 (4): 327--338 (Nov 1, 2019)CPD: Crowd-based Pothole Detection., , , and . VEHITS, page 33-42. SciTePress, (2019)Towards Quantifying the Effects of Robotic Process Automation., and . EDOC Workshops, page 11-19. IEEE, (2020)Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain, , , , , , , , and . Frontiers in Neuroscience, (2020)Deriving Event Logs from Legacy Software Systems., , , and . ICPM Workshops, volume 468 of Lecture Notes in Business Information Processing, page 409-421. Springer, (2022)An Approach to quantify the Costs of Business Process Intelligence., , and . EMISA, volume P-75 of LNI, page 152-165. GI, (2005)