KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis by Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

“…To address this limitation, we propose the Kernel-Attentive Graph Convolutional Network (KA-GCN). Our key finding is that integrating kernel-based and attention-based mechanisms to dynamically refine distances and learn the adjacency matrix within a Graph Structure Learning (GSL) framework enhances the model’s adaptability, making it particularly effective for 3D face analysis tasks and delivering strong performance in data-scarce scenarios. …”
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Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen

“…The proposed HGE-ACN framework integrates graph-based embeddings to capture dynamic spatial–temporal relationships in motion and curvature data. …”
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BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network by Wanwei Huang, Huicong Yu, Yingying Li, Xi He, Rui Chen

“…First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. …”
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Association prediction of lncRNAs and diseases using multiview graph convolution neural network by Wei Zhang, Yifu Zeng, Xiaowen Xiang, Bihai Zhao, Sai Hu, Limiao Li, Xiaoyu Zhu, Lei Wang

“…Our framework constructs a heterogeneous network combining disease semantics, lncRNA similarity, and miRNA-lncRNA-disease interactions to address isolation issues. …”
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A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…Furthermore, a lightweight cross-stage partial (CSP) layer was deployed by dual convolution for feature fusion, reducing redundant parameters to further lighten the model. …”
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FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion by Rumei Li, Liyan Zhang, Zun Wang, Xiaojuan Li

“…FCSwinU employs a UNet-like encoder–decoder framework to effectively merge spatiospectral features. …”
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Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder by Asri Rizki Yuliani, Hilman Ferdinandus Pardede, Ade Ramdan, Vicky Zilvan, Raden Sandra Yuwana, M Faizal Amri, R. Budiarianto Suryo Kusumo, Subrata Pramanik

“…The DAE is built with convolutional layers instead of traditional feed-forward networks here. …”
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Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices by Antonio Robles-Guerrero, Salvador Gómez-Jiménez, Tonatiuh Saucedo-Anaya, Daniela López-Betancur, David Navarro-Solís, Carlos Guerrero-Méndez

“…Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. …”
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Time Series Classification Using Federated Convolutional Neural Networks and Image-Based Representations by Felipe A. R. Silva, Omid Orang, Fabricio Javier Erazo-Costa, Petronio C. L. Silva, Pedro H. Barros, Ricardo P. M. Ferreira, Frederico Gadelha Guimaraes

“…This research introduces a federated hybrid TSC method that combines image-based time series representation techniques with Convolutional Neural Networks (CNNs) in a decentralized framework. …”
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A Mathematical Survey of Image Deep Edge Detection Algorithms: From Convolution to Attention by Gang Hu

“…This survey presents a mathematically grounded analysis of edge detection’s evolution, spanning traditional gradient-based methods, convolutional neural networks (CNNs), attention-driven architectures, transformer-backbone models, and generative paradigms. …”
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Vehicle load identification based on bridge response using deep convolutional neural network by Sadaqat Hussain, Syed M. Hussain, Yu Xin, Zuo-Cai Wang

“…This research highlights the efficacy of deep convolutional neural networks (DCNNs) in analyzing bridge responses. …”
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ASCDet: cross-space UAV object detection method guided by adaptive sparse convolution by Gui Cheng, Xubin Feng, Yan Tian, Meilin Xie, Chaoya Dang, Qing Ding, Zhenfeng Shao

“…ASCDet introduces a plug-and-play detection head compatible with various detection frameworks, significantly reducing computational costs. …”
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Region search based on hybrid convolutional neural network in optical remote sensing images by Shoulin Yin, Ye Zhang, Shahid Karim

“…Compared with traditional region search methods, such as region-based convolutional neural network and newest feature extraction frameworks, our proposed methods show better robustness with complex context semantic information and backgrounds.…”
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Residual learning based convolution neural network for improved channel estimation for VehA channel by Sunita Khichar, Yahui Meng, Abhishek Sharma, Muhammad Saadi, Amir Parniarifard, Sushank Chaudhary

“…To address these challenges, this paper proposes a novel convolutional neural network (CNN)-based channel estimation framework utilizing residual learning and iterative refinement. …”
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DBANet: a dual-branch convolutional neural network with attention enhancement for motor imagery classification by Dandan Liang, Brendan Z. Allison, Ruiyu Zhao, Andrzej Cichocki, Jing Jin

“…Finally, the combined features are applied for classification.Results The subject-dependent results of our proposed framework on the three datasets (BCI Competition IV dataset 2b, 2a and ECUST dataset) are 85.19%, 85.15% and 75.24%, respectively.Comparison with existing methods We conduct an extensive study between the proposed framework and five State-of-the-Art models, including FBCSP, ShallowConvNet, EEGNet, FBCNet, and IFNet.Conclusions for research articles We certificate the superiority of the proposed framework by conducting comparative experiments on three datasets with advanced MI decoding algorithms.…”
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ChaMTeC: CHAnnel Mixing and TEmporal Convolution Network for Time-Series Anomaly Detection by Ibrahim Delibasoglu, Deniz Balta, Musa Balta

“…This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. …”
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Medical Image Retrieval Based on Ensemble Learning using Convolutional Neural Networks and Vision Transformers by Ahmed Yahya, Dalya Khaled, Waleed Al-Azzawi, Tawfeeq Alghazali, H. Sabah Jabr, R. Madhat Abdulla, M. Kadhim Abbas Al-Maeeni, N. Hussin Alwan, S. Saad Najeeb, Kh. T. Falih

“…Our proposed framework can be very effective in retrieving multimodal medical images with the images of different organs in the body.…”
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Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation by Sercan Yalcin, Zuhal Cetin Yalcin, Muhammed Yildirim, Bilal Alatas

“…This article introduces a novel multi-task advanced convolutional neural network (MTA-CNN) framework for ALL detection in medical imaging data by simultaneously performing, expression classification, and disease detection. …”
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Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models by Dongmin Seo, Daekyeom Lee, Sekil Park, Sangwoo Oh

“…This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. …”
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Inverse binary optimization of convolutional neural network in active learning efficiently designs nanophotonic structures by Jaehyeon Park, Zhihao Xu, Gyeong-Moon Park, Tengfei Luo, Eungkyu Lee

“…In this paper, we introduce an inverse binary optimization (IBO) scheme that optimizes a surrogate function based on a convolutional neural network (CNN) within an active learning framework. …”
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