TDP-SAR: Task-Driven Pruning Method for Synthetic Aperture Radar Target Recognition Convolutional Neural Network Model by Tong Zheng, Qing Wu, Chongchong Yu

“…Unlike conventional pruning techniques that rely on generic parameter importance metrics, our approach implements frequency domain analysis of convolutional kernels across different processing stages of SAR target recognition models. …”
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Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features by Yulin Wang, Tao Song, Yichen Yang, Zheng Hong

“…Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. …”
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Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision by Xin Bai, Zi Zhang

“…Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. …”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…It essentially solves the problem of convolution kernel parameter sharing and improves the consideration of the differential information from different locations, which significantly improves the accuracy of model recognition. …”
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3D Reconstruction of Asphalt Pavement Macro-Texture Based on Convolutional Neural Network and Monocular Image Depth Estimation by Xinliang Liu, Chao Yin

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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

“…After that, the multi-scale graph convolutional networks (MSGCNs) were employed to aggregate and enrich several different receptive information to further improve valuable features. …”
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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land by Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang

“…Subsequently, a novel model of edge-enhanced dynamic graph convolutional network (GCN) (EDG-Net) was proposed to learn the discriminative features for classification of mining land with irregular edges, different sizes, a relatively small proportion, and sparse spatial distribution. (1) Edge-enhanced multiscale attention module: it is designed to capture key multiscale features and edge details using parallel dilated convolutions with attention fusion and edge enhancement, which facilitates the identification of objects with irregular edges and different sizes. (2) Downsampling fusion module: it integrates the features obtained through spatially split learning and max-pooling to overcome the information loss issue of small objects. (3) Patch-based dynamic GCN: the input images were split into several patches as nodes, and a graph was constructed and dynamically updated by connecting the nearest neighbors. …”
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Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment by Xing Jin, Fa Zhu, Yu Shen, Gwanggil Jeon, David Camacho

“…Moreover, the graph convolution operations can effectively exploit the spatial information between different channels. …”
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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…Specifically, we first design a multiscale spatial–spectral shuffling convolution to comprehensively refine spatial–spectral feature granularities and enhance feature interactions by shuffling multiscale features across different groups. …”
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Brain age prediction from MRI images based on a convolutional neural network with MRMR feature selection layer by Mustafa Hatem Al Ghariri, Seyed Omid Shahdi

“…To do this, sophisticated algorithms and neural networks are used to scan MRI brain pictures in order to extract different brain properties, including cortical thickness and volume. …”
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EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Methods: To address these challenges, this paper proposes a multi-dimensional attention-based dynamic graph convolutional neural network (AttGraph) model. The model delves into the impact of different EEG features on emotion recognition by evaluating their sensitivity to emotional changes, providing richer and more accurate feature information. …”
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Deepfakes in Visual Art: Differentiating AI-Generated Art From Human Art Using Convolutional Neural Networks (CNN) by Ngonidzashe Tinago, Silas Formunyuy Verkijika, Kelibone Eva Mamabolo

“…This study explores the use of Convolutional Neural Networks (CNNs) to differentiate AI-generated art from human-created art. …”
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MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks by Lucas Wayne Welch, Xudong Liu

“…To automate this standard yet laborsome process, we develop a web-based system, called MarshCover, that automates the process of estimating vegetation density in marsh images using convolutional neural networks (CNNs). MarshCover, to the best of our knowledge, is the first such tool available to biologists that uses CNNs for marsh vegetation estimations. …”
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Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga by Ricky Mardianto, Stefanie Quinevera, Siti Rochimah

“…The study compares three methods, namely Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), to determine the best method for classifying mango types based on their images. …”
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Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

“…This network comprehensively captures abnormalities in brain network structures induced by AD, across different frequency bands and connectivity modes. By leveraging a multi-dimensional feature extraction and fusion strategy, the model effectively identifies EEG pattern changes associated with AD, enhancing detection accuracy. …”
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A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network by Shunxian Zhou, Sisi Chen, Jinhai Le, Yangtai Xu, Lei Wang

“…IntroductionIn recent years, lots of computational models have been proposed to infer potential lncRNA-disease associations.MethodsIn this manuscript, we introduced a novel end-to-end learning framework named CNMCLDA, in which, we first adopted two convolutional neural networks to extract hidden features of diseases and lncRNAs separately. …”
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Modeling Face Detection Application Using Convolutional Neural Network and Face-API for Effective and Efficient Online Attendance Tracking by Carles Juliandy, Ng Poi Wong, Darwin

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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

“…The proposed method designs private graph convolutional layers and shared heterogeneous attention layers, combining dynamic graph structure updates and graph structure regularization to dynamically enhance inter-modal relationships. …”
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Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening by Bo Wang, Muhammad Junaid, Wenjin Li

“…Therefore, the study tried combining molecular graph and convolutional neural networks as a way to improve the extrapolation ability of target-specific scoring functions in the face of data expanded within a certain range of chemical space. …”
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MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation by Chen Peng, Zhiqin Qian, Kunyu Wang, Lanzhu Zhang, Qi Luo, Zhuming Bi, Wenjun Zhang

“…Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. …”
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