Department of Mechanical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir, Saudi Arabia.
Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, P.O 35516, Mansoura, Egypt.
Sci Rep. 2024 Oct 10;14(1):23672. doi: 10.1038/s41598-024-75336-x.
The primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.
本文的主要目的是分析人体免疫系统在遇到乙型肝炎病毒时的反应。这是通过使用微分方程的数学系统来完成的。微分方程系统有六个分量，可能代表免疫反应或病毒动力学的各个方面。在训练过程中提出了一种贝叶斯正则化神经网络。这些网络被用于寻找与乙型肝炎感染相关的不同类别或场景的解决方案。使用 Adams 方法生成参考数据集。基于贝叶斯正则化的反向传播人工神经网络使用生成的数据进行训练和验证。数据分为三组：90%用于训练，5%用于测试和验证。使用各种评估指标评估了所提出的神经网络模型的正确性和有效性。本研究中使用的指标包括均方误差 (MSE)、直方图误差和回归图。这些措施支持神经网络来近似乙型肝炎病毒的免疫反应。
