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- W4310257756 abstract "Ray, Partha PratimAnomaly detection for multivariate noisy environments is a challenging task. This paper presents the implementation of the Gaussian Markov random field mixture (GMRFM) for a simulated Internet of Things (IoT)-based sensor dataset. The implied process, i.e., IoTGMRFM aims to find variable-wise anomalies based on the variational Bayesian inference. Thus, a sparse mixture of sparse graphical Gaussian models (GGM) is obtained that can remove nuisance variables from the simulated IoT sensor dataset and handles multi-operational modes. This paper aims to find a number of key parametric assessments based on the GMRFM technique such as, (i) combined multivariate anomaly scores computation, (ii) iteration time of execution, (iii) variation of execution time at constant iteration 500, and (iv) anomaly scoring time measurement. The major contribution of this work is to deploy the technique on IoT-based ecosystem, i.e., Raspberry Pi for assessing various parametric features on such environment." @default.
- W4310257756 created "2022-11-30" @default.
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- W4310257756 date "2022-11-25" @default.
- W4310257756 modified "2023-09-23" @default.
- W4310257756 title "IoTGMRFM: Internet of Things-Based Variable-Wise Anomaly Detection" @default.
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- W4310257756 doi "https://doi.org/10.1007/978-981-19-5292-0_26" @default.
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