A Bayesian regularization intelligent computing scheme for the fractional dengue virus model

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal Pub Date : 2025-01-08 DOI:10.1016/j.eij.2024.100606

Manoj Gupta , Pattarasinee Bhattarakosol

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引用次数: 0

Abstract

This research’s goal is to investigate the numerical assessments of a fractional order dengue viral model (FO-DVM) by using the artificial intelligence procedure of Bayesian regularization neural networks (BRNNs). The FO derivatives present more precise results as compared to integer order for solving the DVM. The dynamics of the mathematical DVM form is considered into five classes. The computing stochastic BRNNs approach is presented for three variations with the selection of the data as testing 13%, authentication 11% and training 76% together with sixteen hidden neurons. The result’s comparison is accessible in the form of overlapping, which is based on the BRNNs approach and reference Adam solutions. However, minor absolute error around 10^-05 to 10^-07 enhances the worth of the proposed solver. The BRNNs approach is used to minimize the mean square error for the mathematical FO-DVM. The obtained measurements of error histograms values, and regression coefficient calculated as 1 are presented to verify the efficiency of stochastic BRNNs approach.

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分数型登革热病毒模型的贝叶斯正则化智能计算方案

本研究的目的是利用贝叶斯正则化神经网络（BRNNs）的人工智能程序研究分数阶登革热病毒模型（FO-DVM）的数值评估。与求解DVM的整数阶导数相比，FO阶导数给出了更精确的结果。数学DVM形式的动力学分为五类。提出了计算随机BRNNs的方法，其中选择数据为测试13%，认证11%，训练76%以及16个隐藏神经元。结果的比较以重叠的形式进行，这是基于BRNNs方法和参考Adam解。然而，在10-05到10-07之间的小绝对误差提高了所提出的求解器的价值。采用BRNNs方法对数学FO-DVM进行均方误差最小化。给出了得到的误差直方图值和计算为1的回归系数的测量值，验证了随机brnn方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research

CiteScore

11.10

自引率

1.90%

发文量

审稿时长

110 days

期刊介绍： The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.