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In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]

As China's distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model's input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economic development, the demand for technical talents in all walks of life is becoming more and more urgent. Therefore, the research on the intelligent method of vocational education information is becoming more and more important. In this research, the cross-attention fusion module and attention mechanism are introduced into the knowledge map recommendation algorithm to build an algorithm model. The attention mechanism is used to give corresponding attention to each neighbour node of the head node in the knowledge map, and a weight matrix is established to represent different importances of the additional information contained by each neighbour node, which further improves the interpretability of the recommendation. Finally, the model is analysed experimentally. The results show that CAF is superior to other algorithms in Recall and NDCG, which further verifies that attention mechanism plays a significant role in communication. It can be seen that CAF optimisation model is superior to other algorithms in many tests, and is superior to a similar algorithm MKR, which further verifies the effectiveness and superiority of cross-attention fusion module. The CAF model can still maintain its stability in the case of sparse data. [ABSTRACT FROM AUTHOR]