Gazehi Wejden, Loukil Rania, Besbes Mongi
Higher institute of Information and Communication Technologies, University of Carthage, Tunis, Tunisia.
Laboratory of Robotics Informatics, and Complex Systems, University of Tunis El Manar, Tunis, Tunisia.
Sci Rep. 2025 Mar 7;15(1):7954. doi: 10.1038/s41598-025-91057-1.
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). Addressing these challenges, this study proposes a hybrid approach integrating Bayesian techniques and deep learning to improve the classification of nanocomposites with a focus on evaluating their conductivity properties. The proposed framework begins with a Bayesian Network (BN) model, which provides probabilistic insights into the conductive behavior of nanocomposites by analyzing the distribution and interaction of their constituent nanoparticles. This probabilistic foundation is complemented by a Recurrent Neural Network (RNN) based on the Transformer architecture, which enhances classification accuracy by capturing sequential dependencies and complex data patterns. The hybrid model combines the probabilistic reasoning capabilities of BNs with the deep learning strengths of RNNs, yielding a more robust and adaptable classification methodology. While this study primarily focuses on methodological advancements, experimental results demonstrate that the hybrid model significantly outperforms individual approaches in terms of key evaluation metrics. This integrated framework thus represents a promising step toward improving the predictive classification of nanocomposite conductivity, offering a balance between probabilistic interpretability and data-driven accuracy.
为了促进对机器学习模型，特别是神经网络的更多信任和采用，开发能够量化并报告认知不确定性以及随机不确定性的方法至关重要，因为这些不确定性常常会影响循环神经网络（RNN）的准确性。针对这些挑战，本研究提出了一种将贝叶斯技术与深度学习相结合的混合方法，以改进纳米复合材料的分类，重点是评估其导电性能。所提出的框架始于一个贝叶斯网络（BN）模型，该模型通过分析纳米复合材料组成纳米颗粒的分布和相互作用，为其导电行为提供概率性见解。这种概率基础由基于Transformer架构的循环神经网络（RNN）进行补充，该网络通过捕捉序列依赖性和复杂数据模式来提高分类准确性。混合模型将BN的概率推理能力与RNN的深度学习优势相结合，产生了一种更强大、更具适应性的分类方法。虽然本研究主要关注方法上的进步，但实验结果表明，在关键评估指标方面，混合模型明显优于单独的方法。因此，这个集成框架代表了朝着改进纳米复合材料导电性预测分类迈出的有希望的一步，在概率可解释性和数据驱动的准确性之间实现了平衡。
