MSALNet: a multi-scale adaptive learning network for high-resolution remote sensing scene classification by Chao Yang, Chengbo Wei, Yiming Zhao, Liming Wang, Peigang Xu, Kunlun Qi, Yuanzheng Shao, Huayi Wu

“…The MSALNet begins by extracting original features using dilated convolution, effectively capturing information from objects of diverse sizes. …”
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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

“…Subsequently, a spatial-temporal module to extract the spatial-temporal features is employed. Then, a transpose attention module is integrated to extract distinct features from various brain regions and temporal windows. …”
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Crack Identification for Bridge Condition Monitoring Combining Graph Attention Networks and Convolutional Neural Networks by Feiyu Chen, Tong Tong, Jiadong Hua, Chun Cui

“…In this paper, we present a novel approach for crack detection and bridge condition monitoring by integrating convolutional neural networks (CNNs) with graph attention networks (GATs). …”
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PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu

“…It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…During the feature fusion stage, a dual parallel attention feature fusion mechanism is designed to fully integrate the extracted multiscale features. …”
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DMoC-UNet: A Dynamic Mixture-of-Convolution Network for Enhanced Pathological Image Segmentation by Jingwei Zhu, Lining Qin, Zixin Teng, Xiaomin Li, Kuiwu Li, Haoran Chu

“…This paper proposes a novel pathological image segmentation method, DMoC-UNet, which integrates Dynamic Mixture-of-Convolution (DMoC) modules, Haar wavelet downsampling, and Dual Attention Fusion (DAF) modules to enhance multi-scale feature extraction and fine-grained boundary segmentation. …”
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A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis by Jiamao Yu, Hexuan Hu

“…Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. …”
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An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection by Sarah Tipper, Hany F. Atlam, Harjinder Singh Lallie

“…Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. …”
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Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network by Shouyi Yang, Yongxin Wu

“…An adaptive dilated convolution module with 3 × 3 deformable kernels dynamically captures multi-scale lesion features. …”
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Tea leaf disease detection using segment anything model and deep convolutional neural networks by Ananthakrishnan Balasundaram, Prem Sundaresan, Aryan Bhavsar, Mishti Mattu, Muthu Subash Kavitha, Ayesha Shaik

“…Also, the images were fed into a custom Convolutional Neural Network (CNN) model to extract the relevant features. …”
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Topological Attention-Based Convolution Neural Networks in Analyzing and Predicting Particulate Matter Pollution Level by Zixin Lin, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin, R. U. Gobithaasan

“…Objective To improve the prediction of hourly PM10 pollution levels by integrating topological data analysis (TDA) with attention-based convolutional neural networks (ABCNNs), focusing on classifying air quality into eight severity levels. …”
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Color Night-Light Remote Sensing Image Fusion With Two-Branch Convolutional Neural Network by Jie Wang, Yanling Lu, Yuefeng Wang, Jianwu Jiang

“…The framework also integrates a multilevel feature fusion module and a residual learning mechanism, further improving the fusion performance of CNLRSI. …”
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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

“…The study establishes a generalized efficient layer aggregation network incorporating Sobel convolution and develops a novel feature focus module that effectively aggregates information from different feature map levels. …”
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Application of a Convolutional Neural Network in a Terrain-Based Tire Pressure Management System by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Emmanuel A. Salcedo, Marites B. Tabanao, Emmy Grace T. Requillo, John Paul T. Cruz

“…In this study, we integrate a terrain recognition component which uses a convolutional neural network (CNN), specifically, ResNet-18, into the TPMS to classify and detect terrain conditions and apply the correct pressure level. …”
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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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LO-MLPRNN: A Classification Algorithm for Multispectral Remote Sensing Images by Fusing Selective Convolution by Xiangsuo Fan, Yan Zhang, Yong Peng, Qi Li, Xianqiang Wei, Jiabin Wang, Fadong Zou

“…The algorithm employs parallel-connected ODC and LSK modules to adaptively adjust convolution kernel parameters across multiple dimensions and dynamically optimize spatial receptive fields, enabling multi-perspective feature fusion for efficient processing of multispectral band information. …”
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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

“…Finally, a hierarchical temporal convolution attention module is employed to integrate motion feature information across different time sequences. …”
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WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention by Jiahui Qian, Ming Chen

“…Secondly, we designed the DASPPF module by integrating deformable attention, which dynamically adjusts the attention domain to enhance feature relevance to targets, reducing irrelevant information interference and better adapting to marine benthos shape variations. …”
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DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification by Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li

“…In particular, the improved convolutional cross-substitution Transformer module effectively integrates convolution and Transformer, reducing the complexity of attention operations and enhancing the accuracy of hyperspectral image classification tasks. …”
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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. …”
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