An Approach of Dempster-Shafer Decision Fusion to Diagnose COVID-19 in Chest X-ray Imagery by Using Controlled Asymmetric Training of the two CNNs Ensemble

Neagoe, Victor-Emil; Ghenea, Gabriela-Loredana

doi:10.1109/ecai54874.2022.9847505

Cited by 4 publications

(4 citation statements)

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“…Industrial applications of FDS are reported in the detection of safety equipment [1], fault diagnosis in machines [3] [28], and prescriptive maintenance [23]. Current literature contributions propose decision assistance system architectures that address most of the components and even present methodologies to combine several (data-based) FDS at the decision level [29] [30]. However, no contribution addresses the fusion of n number of FDS within a general framework regardless of the model type.…”

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

“…Information fusion on the decision level is a common practice using data-based models (e.g., supervised classifiers in the case of bagging) [33]. The use of information fusion and databased models is reported in [29] [30], in which evidence theory combines models at the decision level. Information fusion using evidence theory provides an additional feature: the uncertainty quantification [13].…”

Section: B Information Fusionmentioning

confidence: 99%

“…Fig. 8 displays the EC M3 performance for each class while effectuating variations on the detection patience (5,15,30) for the anomalies (7,8,15). The best performance corresponds to the anomaly (8) using detection patience pt = 15, in which the EC M3 detects the fault cases (0,1,2,6,12) mostly correct and has a limited anomaly detection.…”

Section: B: Effects Of the Window Sizementioning

confidence: 99%

“…Anomaly detection results of MC EC M3 using the fault cases (0,1,2,6,12), the anomalies(7,8,15), patience variations(5,15,30), threshold (250), memory size(20), and F1-score…”

mentioning

confidence: 99%

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Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

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Fault detection systems support the operator, providing insight during the decision-making while having an (unknown) fault. Data-based models are a common option for a detection system. However, systems that rely purely on data-based models are normally trained with a specific set of data, which cannot necessarily prevent data drift. Thus, an anomaly or unknown condition detection mechanism is required to handle data with new fault cases. Besides, the model's capability to adapt to the unknown condition is equally important to anomaly detection-in other words, its capability to update itself automatically. Alternatively, expert-centered models are powered by the knowledge of operators, which provides the models with production context and expert domain knowledge. The challenge lies in combining both systems and which framework can be used to achieve this fusion. We propose an adaptive information fusion methodology to define fault detection systems using evidence theory and uncertainty quantification. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

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Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

Section: B Information Fusionmentioning

confidence: 99%

Section: B: Effects Of the Window Sizementioning

confidence: 99%

“…Anomaly detection results of MC EC M3 using the fault cases (0,1,2,6,12), the anomalies(7,8,15), patience variations(5,15,30), threshold (250), memory size(20), and F1-score…”

mentioning

confidence: 99%

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