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Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification

Głomb Przemysław, Cholewa Michał, Foszner Paweł, Bularz Jakub

Annals of Computer Science and Information Systems

2023

Vol. 35

259--264

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.

Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation

Kozielski Michał, Henzel Joanna, Tobiasz Joanna, Gruca Aleksandra, Foszner Paweł, Zyla Joanna, Bach Małgorzata, Werner Aleksandra, Jaroszewicz Jerzy, Polańska Joanna, Sikora Marek

Bulletin of the Polish Academy of Sciences. Technical Sciences

2021

Vol. 69, nr 4

art. no. e137349

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.