The stock market is characterized by its high nonlinearity and complexity, making traditional methods ineffective in capturing its nonlinear features and complex market dynamics. This paper proposes a novel stock price forecasting model—the Variational Mode Decomposition—Triangulated Maximally Filtered Graph—Long Short-Term Memory (VMD–TMFG–LSTM) combined model—aimed at improving prediction accuracy, stability, and computational efficiency. The proposed model first employs Variational Mode Decomposition (VMD) to decompose the stock price time series into multiple smooth intrinsic mode functions (IMFs), reducing data complexity and mitigating noise interference. Subsequently, the TMFG algorithm is utilized for feature selection, simplifying the input data and accelerating the iterative convergence process. Finally, the filtered features are modeled and predicted using a Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the VMD–TMFG–LSTM model significantly outperforms AutoRegressive Integrated Moving Average (ARIMA), Neural Network (NN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), as well as single LSTM, TMFG–LSTM, and VMD–LSTM models in forecasting the closing prices of multiple stocks. Specifically, for Shanghai International Airport Co., Ltd. (sh600009), the VMD–TMFG–LSTM model achieves a 69.76% reduction in Root Mean Squared Error (RMSE), a 71.41% reduction in Mean Absolute Error (MAE), a 46.28% reduction in runtime, and an improvement of 0.2184 in R-squared (R²), indicating significantly higher prediction accuracy. In conclusion, the combined model proposed in this paper enhances the accuracy, efficiency, and stability of stock price prediction, providing a robust and efficient solution for forecasting stock market trends.

The financial condition of an enterprise is a critical factor in determining its viability and long-term prospects, and it also directly affects the interests of stakeholders such as investors and creditors. So extra attention is needed to evaluate and forecast the financial risk of an enterprise. This paper proposes a novel enterprise financial risk evaluation and prediction model integrating Synthetic Minority Over-sampling and Edited Nearest Neighbors (SMOTE-ENN) and Natural Gradient Boosting (NGBoost). The CRITIC-TOPSIS method is first employed to conduct a comprehensive evaluation of the financial risk of the listed companies based on their annual financial statements. Subsequently, the obtained evaluation results are subjected to a clustering analysis to categorize the companies into different levels of financial risk. To address the issue of imbalanced samples, the study introduces the SMOTE-ENN combined sampling method. This technique is applied to mitigate the imbalance in the number of samples across different categories. Lastly, NGBoost is utilized to develop a prediction model for corporate financial risk based on the clustered data. The analysis reveals that companies with low levels of financial risk typically exhibit a balanced financial profile, robust operational performance, and substantial growth potential. Conversely, companies with high levels of financial risk are characterized by weak profitability and debt-paying ability. The experimental findings show that the model has a 97.18% accuracy rate, thereby suggesting that the model is both reasonable and practicable.

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway. First Published Online: 07 Jul 2011 Keyword : support vector machine, statistical learning theory, least square support vector machine, regressive model, passenger flow, prediction This work is licensed under a Creative Commons Attribution 4.0 International License.

The financial status of an enterprise is related to its healthy and long-term development, and whether the interests of investors and bank loans can be guaranteed. To improve the prediction accuracy of corporate financial risk, this paper proposes a prediction model for corporate financial risk that integrates GRA-TOPSIS and SMOTE-CNN. First, using GRA-TOPSIS to make a comprehensive evaluation of the financial situation of listed companies. Second, the evaluation results are clustered to obtain the scientific level and interval of financial risk, which lays the foundation for the supervised learning of the convolutional neural network. Then, the SMOTE algorithm is introduced to solve the problem of data imbalance of enterprises at all levels, and the focal loss function is used instead of the cross-entropy loss function to further balance the data. Finally, the listed companies in A shares are randomly selected, and experiments were designed to verify the performance of the model built in this paper. The results show that the prediction accuracy of the financial risk prediction model based on GRA-TOPSIS and SMOTE-CNN is 98.57%, which indicates that the model is feasible and has certain reference value.

We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of image patches to fit the input form of the DeiT encoder, which was distilled by RegNet. Then, we used the Transformer decoder to generate descriptions. Compared to “CNN + LSTM” models, our proposed model is entirely convolution-free and has high training efficiency. On the Rice2k dataset created by us, the model achieved a 47.3 BLEU-4 score, 65.0 ROUGE_L score, and 177.1 CIDEr score. The extensive experiments demonstrate the effectiveness and the strong robustness of our model. It can be better applied to automatically generate descriptions of similar crop disease characteristics.