Titlebook: Big Data; 11th CCF Conference, Enhong Chen,Yang Gao,Wanqi Yang Conference proceedings 2023 The Editor(s) (if applicable) and The Author(s),

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Interlocking

草本植物

Sara K. Howe,Antonnet Renae Johnsonty question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called “Long-term and Short-term perception in knowledge tracing (LSKT)

Commonplace

Sara K. Howe,Antonnet Renae Johnson domains such as energy consumption, network traffic, and solar radiation. The framework is compared with the conventional self-built MVMD-hybrid framework in terms of ARIMA model fitting time and normalized root mean square error (NRMSE) for forecasting accuracy. The results demonstrate that the pr

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Sara K. Howe,Antonnet Renae Johnsonperimental results show that the accuracy, specificity and AUC of the GA-DCNN reach 0.91, 0.94 and 0.93, respectively. Compared with traditional CNN, GA-DCNN can capture the detailed features of DR lesions and integrate the classification results of the multiple DCNNs, effectively improving the dete

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Sara K. Howe,Antonnet Renae Johnsoneliability of high-level feature information are maintained. 2) Attention pyramid: pass the detailed information of low-level features in a bottom-up path to enhance the feature representation; 3) ROI feature refinement: dropblock and zoom-in are used for feature refinement to effectively eliminate

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PAC

carotid-bruit

Scrutinizing the Disabled Body in e classifier’s own features on model performance, which is integrated in a deep graph convolutional network that contains multiple layers of the same simplified graph network architecture and a nonlinear function that can be recursively optimized. Extensive experiments show that our approach still y