WT-HMFF: Wavelet Transform Convolution and Hierarchical Multi-Scale Feature Fusion Network for Detecting Infrared Small Targets by Siyu Li, Jingsi Huang, Qingwu Duan, Zheng Li

“…WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. …”
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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. …”
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Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention by Huimin Qu, Haiyan Xie, Qianying Wang

“…The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). …”
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An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. In the AAPW-YOLO model, we improve the standard convolution and the CSP Bottleneck with 2 Convolutions (C2f) structure in the You Only Look Once v8 (YOLOv8) backbone network by using Alterable Kernel Convolution (AKConv), which improves the network’s proficiency in capturing features across various scales while considerably lowering the model’s parameter count. …”
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Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. …”
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A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, Dongni Li

“…To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). …”
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Combining convolutional neural network with transformer to improve YOLOv7 for gas plume detection and segmentation in multibeam water column images by Wenguang Chen, Xiao Wang, Junjie Chen, Jialong Sun, Guozhen Zha

“…However, the gas plume in the image is often affected by the seafloor environment and exhibits sparse texture and changing motion, making traditional detection and segmentation methods more time-consuming and labor-intensive. The emergence of convolutional neural networks (CNNs) alleviates this problem, but the local feature extraction of the convolutional operations, while capturing detailed information well, cannot adapt to the elongated morphology of the gas plume target, limiting the improvement of the detection and segmentation accuracy. …”
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CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery by Wenqing Liu, Hongtao Huo, Luyan Ji, Yongchao Zhao, Xiaowen Liu, Jialei Xie

“…Finally, the encoder of our network adopts a parallel structure of depthwise separable convolution and state-space module to capture local detailed features and global semantic features of PV panels layer by layer. …”
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Application of 3D ELD_MobileNetV2 Incorporating Attention Mechanism and Dilated Convolution in Hepatic Nodules Classification by SUN Haoyun, WANG Lijia

“…Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. …”
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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into the gated recurrent network to simulate the diffusion of information within a dynamic spatial structure. …”
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3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐window self‐attention by Bin Yu, Quan Zhou, Li Yuan, Huageng Liang, Pavel Shcherbakov, Xuming Zhang

“…Abstract Convolutional neural network (CNN) with the encoder–decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global feature. …”
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YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases by Tianyue Jiang, Xu Du, Ning Zhang, Xiuhan Sun, Xiao Li, Siqing Tian, Qiuyan Liang

“…Additionally, Omni-dimensional Dynamic Convolution was employed to optimize the model’s basic convolutional structure, bottleneck structure, and C2f (Faster Implementation of CSP (Cross Stage Partial) Bottleneck with two convolutions) module, improving feature fusion quality and reducing computational complexity. …”
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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

“…Traditional CNNs which sturggle to capture critical global contextual information often fail to distinguish the complex tissue structures in OSCC images.MethodsTo address these challenges, we propose a novel architecture called gamUnet, which integrates the Global Attention Mechanism (GAM) to enhance the model's ability to capture global cross-modal information. …”
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Authenticity Detection of Egg White Powder Using Near-Infrared Spectroscopy Based on Improved One-Dimensional Convolutional Neural Network Model by ZHU Zhihui, LI Wolin, HAN Yutong, JIN Yongtao, YE Wenjie, WANG Qiaohua, MA Meihu

“…An improved one-dimensional convolutional neural network (1D-CNN) model for the authenticity detection of egg white powder was constructed based on near-infrared spectroscopy (NIRS). …”
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Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach by Justice Williams Asare, Emmanuel Akwah Kyei, Seth Alornyo, Emmanuel Freeman, Martin Mabeifam Ujakpa, William Leslie Brown‐Acquaye, Alfred Coleman, Forgor Lempogo

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An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory by Yanbo Sun, Wenxing Bao, Wei Feng, Kewen Qu, Xuan Ma, Xiaowu Zhang

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A Computational Approach to Understanding Agglutinative Structures in Urdu by Muhammad Shoaib Tahir, Mahnoor Amjad

“…This study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. …”
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Transition state structure detection with machine learningś by Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

“…Abstract Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research. …”
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Multiscale Feature Fusion for Salient Object Detection of Strip Steel Surface Defects by Li Zhang, Xirui Li, Yange Sun, Yan Feng, Huaping Guo

“…For the first step, MFF uses ResNet (convolutions with downsampling operations) instead of sampling techniques to generate multiscale features because convolution excels at extracting local regional features (e.g., edge and contour information). …”
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