Resilient Temporal Graph Convolutional Network for Smart Grid State Estimation Under Topology Inaccuracies by Seyed Hamed Haghshenas, Mia Naeini

“…The evaluation results indicate that while the two proposed architecture show different performance, they preserve and improve the performance of the TGCN state estimation under topology uncertainties.…”
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Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution by Gang XIE, Quanyi WANG, Xinlin XIE, Jian’an WANG

“…Aiming at the problems of discontinuous segmentation of thin strip objects that were easy to blend into the surrounding background and a large number of model parameters in the semantic segmentation algorithm of traffic scenes, a lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution was proposed.First, a multi-scale strip feature extraction module (MSEM) was constructed based on deep convolution to enhance the representation ability of thin strip target features at different scales.Secondly, a spatial detail auxiliary module (SDAM) was designed using the convolutional inductive bias feature in the shallow network to compensate for the loss of deep spatial detail information to optimize object edge segmentation.Finally, an asymmetric encoding-decoding network based on the Transformer-CNN framework (TC-AEDNet) was proposed.The encoder combined Transformer and CNN to alleviate the loss of detail information and reduce the amount of model parameters; while the decoder adopted a lightweight multi-level feature fusion design to further model the global context.The proposed algorithm achieves the mean intersection over union (mIoU) of 78.63% and 81.06% respectively on the Cityscapes and CamVid traffic scene public datasets.It can achieve a trade-off between segmentation accuracy and model size in traffic scene semantic segmentation and has a good application prospect.…”
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Ethnic dance movement instruction guided by artificial intelligence and 3D convolutional neural networks by Ni Zhen, Park Jae Keun

“…This model performs well in movement recognition tasks, showing remarkable advantages in different dance types. It exhibits good versatility and adaptability to various cultural contexts, providing advanced technical support for ethnic dance instruction. …”
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Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks by Tinuk Agustin, Indrawan Ady Saputro, Mochammad Luthfi Rahmadi

“…Future research should explore additional augmentation strategies and test the model across different datasets to further validate its effectiveness. …”
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Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction by Lei Huang, Jianxin Qin, Tao Wu

“…Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. …”
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Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises by Shaohui Zhang, Qiuying Han, Peng Wang, Junjie Li

“…Second, based on the fused topology structure, a learnable topological matrix is established for each action frame to capture subtle differences between patient movements. Finally, a hierarchical temporal convolution attention module is employed to integrate motion feature information across different time sequences. …”
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Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network by Yefeng Xu, Ruili Jiao, Qiubai Li, Minsong Huang

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Design of a Convolutional Neural Network with Type-2 Fuzzy-Based Pooling for Vehicle Recognition by Cheng-Jian Lin, Bing-Hong Chen, Chun-Hui Lin, Jyun-Yu Jhang

“…Convolutional neural networks typically employ convolutional layers for feature extraction and pooling layers for dimensionality reduction. …”
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Diagnosis Anthracnose of Chili Pepper Using Convolutional Neural Networks Based Deep Learning Models by Hae-In Kim, Ju-Yeon Yoon, Ho-Jong Ju

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Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network by Xiao Wang, Yan Li

“…Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. …”
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Application research of convolutional neural network and its optimization in lightning electric field waveform recognition by Caixia Wang, Xiaoyi Zhang, Hui Yang, Jinyuan Guo, Jia Xu, Zhuling Sun

“…The effects of various optimization terms and their different optimization orders on the training time of the model were studied. …”
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Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network by Huihui Song, Zheng Wang

“…This paper presents a semi-supervised convolutional neural network that can maintain a similarly high accuracy of classification as deep learning approaches with only 10% labeled data or less. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

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SADNet: sustained attention decoding in a driving task by self-attention convolutional neural network by Shuzhong Lai, Lin Yao, Yueming Wang

“…By combining depthwise separable convolution and self-attention mechanisms, the model applies different attention to signals in the temporal and spatial domains, extracting effective local and global channel features for attention state recognition.Results In within-subject and cross-subject experiments on publicly available datasets, SADNet achieves state-of-the-art performance with an average F1-Score of 0.8894 and 0.6156 respectively, and an average AUC of 0.9545 and 0.7024, outperforming existing models in comparative experiments. …”
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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This spatial dependency analysis enables rapid deployment in different coastal settings. Adversarial learning with gradient penalty further refines the model’s performance. …”
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Inversion Method Based on Temporal Convolutional Networks for Random Ice Load on Conical Offshore Platforms by Wei Li, Ya Guo, Shuzhao Li, Yang Gao, Yan Qu

“…The results show that the TCN model achieves goodness-of-fit (R²) values of 0.821 and 0.808 on the training and test sets, respectively, indicating strong predictive performance. Under different ice thickness and velocity conditions, the model achieves R² values close to 0.99, demonstrating high robustness. …”
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Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology by Adonay S. Nunes, Matthew R. Patterson, Dawid Gerstel, Sheraz Khan, Christine C. Guo, Ali Neishabouri

“…This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. …”
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Urban Spatiotemporal Event Prediction Using Convolutional Neural Network and Road Feature Fusion Network by Yirui Jiang, Shan Zhao, Hongwei Li, Huijing Wu, Wenjie Zhu

“…However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. …”
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