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Neighborhood selection is an important part of a multiobjective evolutionary algorithm based on decomposition (MOEA/D) because the impetus for population evolution mainly comes from its neighborhood. However, the fixed neighborhood size used in MOEA/D may deteriorate the performance of the algorithm due to an unreasonable allocation of computational resources. To further improve the performance of MOEA/D, this paper proposes a multiobjective decomposition evolutionary algorithm with optimal history-based neighborhood adaptation and a dual-indicator selection strategy. The optimal history-based neighborhood adaptation strategy is applied to alleviate the imbalance between exploration and exploitation in the search process, while the dual-indicator selection strategy is developed to enhance the population diversity. The performance of the proposed algorithm is evaluated on the DTLZ and WFG series test problems. Experimental results show that the proposed algorithm performs competitively in comparison with several MOEA/D variants. [ABSTRACT FROM AUTHOR]

With the rapid development of internet plus education, it is increasingly important to quickly and accurately match personalized videos for learners from massive learning videos. However, the existing resource recommendation methods have two problems. On the one hand, they do not make full use of the implicit interaction data of learners in the learning process, i.e., they rarely reflect the comprehensive consideration of learners' interest preferences and cognitive level when describing learners. On the other hand, they are mostly based on collaborative filtering algorithms, using the similarity between learners or videos to recommend, ignoring the impact of semantic relations between videos on the recommendation results. In view of this, we proposed a personalized learning video matching method (IFT‐PTransE) based on heterogeneous feature data transfer and knowledge reasoning from two aspects of learning behavior data mining and knowledge graph representation learning. In this method, firstly, a learner model is constructed. By analyzing and quantifying various implicit interaction behavior data of learners, the sparse video scoring matrix is filled as auxiliary data to complete the transfer of target scoring data. Secondly, the semantic close relationship between videos is introduced. PTransE algorithm is used to mine the multi‐path relationship between entities. All entity relationships are embedded into the low dimensional vector space, so that the semantic similarity between videos can be calculated using the distance between vectors. Finally, video score similarity and semantic similarity between frequencies are fused. video sorting is performed based on the improved collaborative filtering algorithm and then recommend the top N videos to the students. Through simulation and analysis, the effectiveness of this method in personalized video matching is proved. This method makes up for the shortcomings of collaborative filtering algorithm in using implicit information, enhances the recommendation effect at the semantic level, and solves the data sparse and cold start problems to a certain extent. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this paper proposes an operational risk prediction model based on an automatic encoder and convolutional neural networks. First, we use an automatic encoder to extract features of motion risk factors and obtain feature components that can highly represent risk. Secondly, based on the causal relationship between sports risk and risk characteristics, a convolutional neural network with a dual convolution layer and dual pooling layer topology is constructed. Finally, the sports risk prediction model is established by combining the auto-coded feature components with the topology of the convolutional neural network. Compared with other algorithms, the proposed method can effectively analyze and extract risk characteristics and has a high prediction accuracy. At the same time, it promotes the integration of sports science and computer science and provides a basis for the application of machine learning in the field of sports risk prediction. [ABSTRACT FROM AUTHOR]