Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction by Linlong Chen, Linbiao Chen, Hongyan Wang, Jian Zhao

“…Additionally, we design an adversarial graph convolutional framework, which optimizes the loss through adversarial training, thereby reducing the trend discrepancy between predicted and actual values. …”
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Advancing Breast Cancer Detection: SE-Conformer Framework for Malignancy Detection in Histopathology Images by Lekha S. Nair, K. R. Amarnath, Jyothisha J. Nair

“…To enhance feature representation in convolutional neural networks, we introduced an improved convolutional block, termed the SE-Res-Conv Block, which incorporates Squeeze-and-Excitation (SE) attention mechanisms within a residual convolutional framework. The extracted features are then processed by a Conformer block, which further refines them by emphasizing the most relevant regions in the input feature map. …”
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MRDDA: a multi-relational graph neural network for drug–disease association prediction by Congzhou Chen, Yaozheng Zhou, Yinghong Li, Jin Xu, Demin Li, Lingfeng Wang

“…First, we design a hybrid graph convolutional framework to capture both local and global representations of drugs and diseases. …”
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Lightweight Domestic Pig Behavior Detection Based on YOLOv8 by Kaining Zhang, Yu Zhang, Hongli Xu

“…Subsequently, a Grouped Convolution module is integrated into the convolution framework, followed by incorporating the SE module to diminish the recognition error rate further. …”
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Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites by Yue Yu, Shuang Xiang, Minghao Wu

“…Following this, M6A-SAI employs a self-correlation fusion graph convolution framework to merge information from both the similarity and awareness graphs, thus producing enriched sequence representations. …”
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