Browsing by Author "Zhu Y"

Now showing 1 - 4 of 4
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    Accounting students’ online engagement, choice of course delivery format and their effects on academic performance
    (Taylor and Francis Group, 2023-09-24) Hu Y; Nath N; Zhu Y; Laswad F
    This study examines the effects of synchronous and non-synchronous online engagement on the academic performance of accounting students at a New Zealand university based on their choice of course delivery format – either distance learning or face-to-face learning with online components (F2F+). We track accounting students as they complete three financial accounting courses over three consecutive years. Drawing on social constructivism theory, we find that both synchronous and non-synchronous student online engagement are positively related to their academic performance, and this positive effect varies across assessment types. The positive effect of synchronous online engagement on student performance is more pronounced when students choose to learn via F2F+ rather than via distance learning. Further analyses show that the positive effect persists among students with different characteristics. These findings highlight the useful role of student online engagement in learning and provide support for universities to allow students to choose their preferred course delivery format.
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    Completed sample correlations and feature dependency-based unsupervised feature selection
    (Springer Science+Business Media, LLC, 2023-04) Liu T; Hu R; Zhu Y
    Sample correlations and feature relations are two pieces of information that are needed to be considered in the unsupervised feature selection, as labels are missing to guide model construction. Thus, we design a novel unsupervised feature selection scheme, in this paper, via considering the completed sample correlations and feature dependencies in a unified framework. Specifically, self-representation dependencies and graph construction are conducted to preserve and select the important neighbors for each sample in a comprehensive way. Besides, mutual information and sparse learning are designed to consider the correlations between features and to remove the informative features, respectively. Moreover, various constraints are constructed to automatically obtain the number of important neighbors and to conduct graph partition for the clustering task. Finally, we test the proposed method and verify the effectiveness and the robustness on eight data sets, comparing with nine state-of-the-art approaches with regard to three evaluation metrics for the clustering task.
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    Hierarchical graph learning with convolutional network for brain disease prediction
    (Springer Nature, 2024-10-23) Liu T; Liu F; Wan Y; Hu R; Zhu Y; Li L
    In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However, the existing methods either ignore the uniqueness of different subjects across the functional connectivities or neglect the commonality of the same disease for the functional connectivity of each subject, resulting in a lack of capacity of capturing a comprehensive functional model. To solve the issues, we develop a hierarchical graph learning with convolutional network that not only considers the unique information of each subject, but also takes the common information across subjects into account. Specifically, the proposed method consists of two structures, one is the individual graph model which selects the representative brain regions by combining each subject feature and its related brain region-based graph. The other is the population graph model to directly conduct classification performance by updating the information of each subject which considers both the subject itself and the nearest neighbours. Experimental results indicate that the proposed method on four real datasets outperforms the state-of-the-art approaches.
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    Initialization-similarity clustering algorithm
    (Springer Science+Business Media, LLC, 2019-12) Liu T; Zhu J; Zhou J; Zhu Y; Zhu X
    Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.