Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification

Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification

Abstract To foster greater trust and adoption of machine learning models, particularly neural networks, it is essential to develop approaches that quantify and report epistemic uncertainties alongside random uncertainties, which often affect the accuracy of Recurrent Neural Networks (RNNs). Addressi...

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Bibliographic Details
Main Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Hybrid model
Classification
Nanoparticules
BN
RNN
Transformer
Online Access:https://doi.org/10.1038/s41598-025-91057-1
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https://doi.org/10.1038/s41598-025-91057-1

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