In the present paper, we present the estimated impact of the free convection on oscillatory flow using data to predict the value of the attributes to promote concentration for mass transfer. Various statistical methods can predict concentration of free convection oscillatory flow. We provide exact analytic expressions for the underlying concentration profile using the regular perturbation technique and decision tree method. Moreover, we show how these expressions can help decrease the governing equations to some uncoupled and coupled linear ordinary differential equations. Results were obtained concerning the concentrations constructed and plotted graphically, taking into consideration three values of the time. We then focused on the statistical technique used to analyze the neural network problem of predicting the concentration of free convection oscillatory flow. Both Decision Tree and MLP were used as the best models according to the value of sMAPE, MASE, and MAPE. The obtained results were compared with the corresponding estimation in Abo-Dahab and Hatem (2020), which matched and satisfied the boundary conditions of the free convection impact on the oscillatory flow. Finally, the exact analytical results that have several applications in chemical industries, medicine, and engineering were concluded.

在本文中，我们通过数据预测自由对流对振荡流的影响，以预测属性值以促进质量转移。各种统计方法可以预测自由对流振荡流的浓度。我们使用常规的扰动技术和决策树方法为基础浓度曲线提供精确的解析表达式。此外，我们展示了这些表达式如何帮助将控制方程简化为一些未耦合和耦合的线性常微分方程。考虑到时间的三个值，获得了有关所构建浓度并以图形方式绘制的结果。然后，我们将重点放在用于分析神经网络问题的统计技术上，以预测自由对流振荡流的浓度。根据sMAPE，MASE和MAPE的值，决策树和MLP均被用作最佳模型。将获得的结果与Abo-Dahab和Hatem（2020）中的相应估计值进行比较，这些估计值匹配并满足了自由对流对振荡流的边界条件。最后，得出了在化学工业，医学和工程学中有多种应用的精确分析结果。满足并满足了自由对流对振荡流的边界条件。最后，得出了在化学工业，医学和工程学中有多种应用的精确分析结果。满足并满足了自由对流对振荡流的边界条件。最后，得出了在化学工业，医学和工程学中有多种应用的精确分析结果。