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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)

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Debriefings: A Knowledge Management Method to Capture Lessons Learned in Large-Scale Projects, , and . Information-Mining und Wissensmanagement in Wissenschaft und Wirtschaft, page 83-96. 8. Göttinger Symposium Soft Computing, (2005)Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis., , , and . SAC, page 6-13. ACM, (2018)Tag-Aware Spectral Clustering of Music Items., , , and . ISMIR, page 159-164. International Society for Music Information Retrieval, (2009)Review of Smart Services for Tinnitus Self-Help, Diagnostics and Treatments, , , , , , and . Frontiers in neuroscience, (2018)Tree Induction over Perennial Objects., and . SSDBM, volume 6187 of Lecture Notes in Computer Science, page 640-657. Springer, (2010)Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency., , and . Discovery Science, volume 8777 of Lecture Notes in Computer Science, page 168-179. Springer, (2014)Juxtaposing 5G Coronavirus Tweets with General Coronavirus Tweets During the Early Months of Coronavirus Outbreak., and . CBMS, page 597-602. IEEE, (2021)Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data., , , , , , , , and . CBMS, page 228-233. IEEE, (2022)When Can I Expect the mHealth User to Return? Prediction Meets Time Series with Gaps., , , and . AIME, volume 13263 of Lecture Notes in Computer Science, page 310-320. Springer, (2022)Eine empirische Untersuchung und thematische Kategorisierung von Transformationsprojekten in der Telekommunikationsindustrie, , and . Braunschweig, (2012)Braunschweig.