Detection of untrustworthy iot measurements using expert knowledge of their joint distribution

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Abstract

The aim of this work is to discuss abnormality detection and explanation challenges motivated by Medical Internet of Things. First, any feature is a measurement taken by a sensor at a time moment, so abnormality detection also becomes a sequential process. Second, an anomaly detection process could not rely on having a large collection of data records, but instead there is a knowledge provided by the experts.

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Nouretdinov, I., Darwish, S., & Wolthusen, S. (2018). Detection of untrustworthy iot measurements using expert knowledge of their joint distribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10898 LNCS, pp. 310–316). Springer Verlag. https://doi.org/10.1007/978-3-319-94523-1_31

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