## 4 Discussion  
In this study, a combined machine-type batch-type grain drying system with counter and current drying sections was used to design corn kernel drying experiments and gather researchable data. Then, based on UVE, sensitive variables were screened from temperature and humidity data, and six outgoing corn moisture content prediction models for the corn drying process based on MLR, ELM, and LSTM methods were proposed to provide theoretical support for improving the moisture content prediction capability and drying efficiency of the corn drying process. These models were developed based on an analysis of temperature and humidity data from mainstream drying models' simulation experiments, providing significant reference value for the actual production of counter-current drying towers [44; 45]. The constructed prediction models for the moisture content of corn drying systems exhibit high fitting accuracy, small error, and an ideal validation effect, thus offering technical support for the optimization of corn drying process parameters and the automatic control of the process [46; 47].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & **Initial Learn** & \multirow{2}{*}{**Iterations**} & **RMSE** & **RMSE** & **Training** \\  & & & **(Training Set)/\%** & **(Validation Set)/\%** & **Duration/s** \\ \hline \multirow{4}{*}{30} & & 50 & 0.774 & 0.749 & 2 \\  & & 100 & 0.711 & 0.697 & 2 \\  & & 500 & 0.690 & 0.723 & 9 \\  & & 1000 & 0.649 & 0.793 & 17 \\  & & 50 & 0.997 & 0.833 & 1 \\  & & 100 & 0.755 & 0.714 & 2 \\
50 & 0.01 & 500 & 0.769 & 0.809 & 4 \\  & & 1000 & 0.731 & 0.752 & 9 \\  & & 50 & 1.057 & 1.233 & 1 \\
100 & 0.01 & 100 & 1.201 & 1.141 & 2 \\  & & 500 & 0.862 & 0.915 & 5 \\  & & 1000 & 0.740 & 0.819 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of UVE-LSTM network model with varying training parameters.  
Figure 9: Prediction model of moisture content of corn outlet based on UVE-LSTM.  
First, we discuss future directions for model construction. Due to the high demand for LSTM models in terms of sample size, it is important to consider further exploring the deep analysis capability of such models by enriching the experimental design and increasing the training samples and feature data [48; 49]. Moreover, to further improve the generalization ability of machine learning models and reduce the overfitting problem, model optimization can be carried out by considering the use of cross-validation [50], integrated learning [51], and swarm intelligence [52; 53; 54] to improve the prediction accuracy of the model. The problem of multicollinearity in the fitting process is more prominent in the MLR model. Integrating the model with UVE effectively screened sensitive variables and mitigated the issue, albeit at a certain cost to the model's predictive accuracy. In future research, we should consider combining the model with principal component analysis to effectively deal with the problem of multicollinearity [55; 56; 57] or using structural equation modelling, which can also incorporate multiple variables while not being restricted by data types [58; 59].  
Second, we also consider future directions for the experimental design aspect. It would be beneficial to consider enriching the experimental design by adding control variable gradients, increasing experimental varieties, accumulating training samples, and upgrading data complexity, so as to further improve the generalization and prediction capabilities of the model [60; 61]. At the same time, there is limited experience in the validation of large-scale corn drying systems, and it is necessary to consider carrying out validation experiments to explore the application effect of the model in actual drying production [62].