Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey.
Comput Biol Chem. 2024 Oct;112:108162. doi: 10.1016/j.compbiolchem.2024.108162. Epub 2024 Jul 25.
The motive of current investigations is to design a novel radial basis neural network stochastic structure to present the numerical representations of the Zika virus spreading model (ZVSM). The mathematical ZVSM is categorized into humans and vectors based on the susceptible S(q), exposed E(q), infected I(q) and recovered R(q), i.e., SEIR. The stochastic performances are designed using the radial basis activation function, feed forward neural network, twenty-two numbers of neurons along with the optimization of Bayesian regularization in order to solve the ZVSM. A dataset is achieved using the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) based on the process of training for solving the nonlinear ZVSM. The division of the data is categorized into training, which is taken as 78 %, while 11 % for both authentication and testing. Three different cases of the nonlinear ZVSM have been taken, while the scheme's correctness is performed through the matching of the results. Furthermore, the reliability of the scheme is observed by applying different performances of regression, MSE, error histograms and state transition.
目前的研究目的是设计一种新颖的径向基神经网络随机结构，以呈现寨卡病毒传播模型（ZVSM）的数值表示。基于易感者 S(q)、暴露者 E(q)、感染者 I(q)和康复者 R(q)，即 SEIR，将数学 ZVSM 分为人类和载体。使用径向基激活函数、前馈神经网络、二十二个神经元以及贝叶斯正则化的优化来设计随机性能，以解决 ZVSM。使用显式龙格-库塔方案获得数据集，该方案用于通过训练过程减少均方误差（MSE），以解决非线性 ZVSM。数据的划分分为训练，占 78%，认证和测试各占 11%。考虑了三种不同的非线性 ZVSM 情况，通过结果匹配来验证方案的正确性。此外，通过应用回归、MSE、误差直方图和状态转换的不同性能来观察方案的可靠性。
