Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells. by Mehran Ghafari, Justin Clark, Hao-Bo Guo, Ruofan Yu, Yu Sun, Weiwei Dang, Hong Qin

“…We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. …”
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Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks by Skowron Maciej

“…This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. …”
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Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 by Liping Bai, Chenglei Xia, Fei Liu, Xing Yang, Tai Zhang

“…In this study, we enhanced the YOLOv8 architecture by replacing the traditional backbone with an EfficientNetV2 feature extraction network and using ODConv instead of the standard convolution. The loss function was modified with a dynamic nonmonotonic focusing mechanism, and WiseIoU was introduced to replace the traditional CIoU. …”
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Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation by Reyhaneh Dehghan, Marjan Naderan, Seyed Enayatallah Alavi

“…To evaluate the proposed method, different measures such as accuracy, sensitivity (recall) and f1-score are used. …”
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Resilient Temporal Graph Convolutional Network for Smart Grid State Estimation Under Topology Inaccuracies by Seyed Hamed Haghshenas, Mia Naeini

“…This paper studies these scenarios under topology uncertainties and evaluates the impact of the topology uncertainties on the performance of a Temporal Graph Convolutional Network (TGCN) for state estimation in power systems. …”
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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

“…Abstract This study aims to explore the potential application of artificial intelligence in ethnic dance action instruction and achieve movement recognition by utilizing the three-dimensional convolutional neural networks (3D-CNNs). In this study, the 3D-CNNs is introduced and combined with a residual network (ResNet), resulting in a proposed 3D-ResNet-based ethnic dance movement recognition model. …”
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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

“…This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. …”
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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 paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. …”
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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

“…This study proposes a novel inversion method based on Temporal Convolutional Networks (TCNs), integrating finite element simulation with deep learning to effectively identify random ice loads. …”
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