Search

In order to improve the effect of classroom teaching and realize the improvement of mathematics teaching quality, this article combines data-driven decision-making technology to design a mathematics teaching plan system and applies data-driven decision-making technology to the design of mathematics teaching plan by improving the big data algorithm. In addition, this paper designs a mathematics teaching plan design system based on data-driven decision-making technology. The system learning module displays the knowledge points in a chapter-sequential navigation mode and stores data or information in the nodes, which are connected to form a network structure through chains. Finally, this paper verifies the designed system with experiments. From the results of the experimental research data, it can be seen that the mathematical teaching plan design system based on the data-driven decision-making technology constructed in this article has good practical effects. On this basis, the system constructed in this article can be verified through further practice. [ABSTRACT FROM AUTHOR]

Elimination ideals are regarded as a special type of Borel type ideals, obtained from degree sequence of a graph, introduced by Anwar and Khalid. In this paper, we compute graphical degree stabilities of K n ∨ C m and K n ∗ C m by using the DVE method. We further compute sharp upper bound for Castelnuovo–Mumford regularity of elimination ideals associated to these families of graphs. [ABSTRACT FROM AUTHOR]

This paper aims to address the human action recognition issue by using convolutional long short-term memory networks (Conv-LSTM) and fully-connected LSTM (FC-LSTM) with different attentions. To this end, the spatial-temporal dual-attention network (STDAN), which is mainly composed of feature extraction, attention and fusion modules, is designed. Different from the features of high-level fully-connected layer mostly used in previous work, the features of convolution and fully-connected layers of convolutional neural network (CNN) are both extracted in STDAN, which can enrich the initial level of video representation. Besides, the Conv-LSTM and FC-LSTM are employed to handle the long-duration sequential features with different temporal context information. To reinforce the spatial-temporal attention ability, a temporal attention module (TAM) and a joint spatial-temporal attention module (JSTAM) are implemented. Through the principle components analysis (PCA) and features fusion, the potential of STDAN is effectively explored and weighted. Finally, the experimental results show that the proposed STDAN has better recognition performance than existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]