Confidence estimation in the prediction of epithermal neutron resonance self-shielding factors in irradiation samples using an ensemble neural network

Wilkinson, Ian; Bhattacharjee, Ritwik R.; Shafer, Jenifer C.; Osborne, Andrew G.

doi:10.1016/j.egyai.2021.100131

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…They demonstrated the effectiveness of their proposed method for COVID-19 cases in Vietnam. Ensemble neural networks (ENNs), an improvement of conventional neural networks, are a useful technique applied to predict future information based on a data period learned by the modules of the ENNs [15][16][17][18]. This improvement of conventional neural networks has also been applied to analyze images to detect COVID-19 infections.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A final result is achieved with the answers provided by the neural networks by combining them using an integration technique. In this work, this kind of ANN is applied to obtain an individual prediction, which is then combined with a unit integration to achieve a final prediction [16,18]. In Figure 2, an example of an ENN with three neural networks (modules) is shown.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Melín

Sánchez

Castro

et al. 2022

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In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%