EFCNet: Expert Feature-Based Convolutional Neural Network for SAR Ship Detection by Zheng Chen, Yuxiang Zhang, Jing Bai, Biao Hou

“…In this paper, we revisit the relationship between SAR expert features and network abstract features, and propose an expert-feature-based convolutional neural network (EFCNet). …”
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Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network by Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li, Yijia Zhang

“…To tackle this problem, we propose ConvLSTM-TFN (temporal feature network), an innovative blind-recognition network that integrates convolutional layers, long short-term memory (LSTM) networks, and a self-attention mechanism. …”
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Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks by Ran Wang, Ruyu Shi, Xiong Hu, Changqing Shen

“…Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. …”
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A path aggregation network with deformable convolution for visual object detection by Chengming Rao, Zunhao Hu, QiMing Zhao, Min Shan, Li Mao

“…In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable convolution and path aggregation network (DePAN), is an integration of a path aggregation network with a deformable convolution block added to the feature fusion branch to improve the flexibility of feature point sampling. …”
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Underwater object detection algorithm integrating image enhancement and deformable convolution by Lijia Guo, Xiangchun Liu, Dongsheng Ye, Xuebao He, Jianxin Xia, Wei Song

“…In addition, the separated and enhancement attention module (SEAM) is integrated to better capture features of occluded targets. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

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PIONet: A Positional Encoding Integrated Onehot Feature-Based RNA-Binding Protein Classification Using Deep Neural Network by Mahmood A. Rashid, Mayank Chaturvedi, Kuldip K. Paliwal

“…Here we present PIONet, a deep learning method based on a convolutional neural network (CNN) that accurately classifies RBPs. …”
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Integration of Convolutional Neural Network and Image Processing for Pulp Fibril Detection and Measurement by Tanachot Chirakitsakul, Pakaket Wattuya, Phichit Somboon, Panthira Jansakra, Chakrit Watcharopas

“…This study proposes a novel method that integrates deep learning with image processing techniques to automate fibril detection and fibrillation index computation. …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model’s precision, thereby enhancing the feature extraction network. …”
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A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition by Lianglian Gu, Guangzhi Di, Danju Lv, Yan Zhang, Yueyun Yu, Wei Li, Ziqian Wang

“…To address this issue, we propose a multi-scale hybrid convolutional attention mechanism model (MUSCA). This method combines depthwise separable convolution and traditional convolution for feature extraction and incorporates self-attention and spatial attention mechanisms to refine spatial and channel features, thereby improving the effectiveness of multi-scale feature extraction. …”
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Graph convolution-based adaptive feature fusion method for MRI brain tumor segmentation by Ye ZHANG, Muqing ZHANG, Xuegang YUAN, Datian NIU

“…The algorithm was based on 3D U-Net,incorporating a graph convolution inference module to capture additional long-range contextual features. …”
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An XAI Approach to Melanoma Diagnosis: Explaining the Output of Convolutional Neural Networks with Feature Injection by Flavia Grignaffini, Enrico De Santis, Fabrizio Frezza, Antonello Rizzi

“…This field is still unexplored; thus, in this paper, we aim to provide a method to explain, qualitatively and quantitatively, a convolutional neural network model with feature injection for melanoma diagnosis. …”
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A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. …”
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Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features by Meng Wang, Juanle Wang, Mingming Yu, Fei Yang

“…This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. …”
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DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images by Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou

“…This study proposes a dual-branch semantic aggregation network (DBSANet) by integrating ResNet and a Swin Transformer. A Feature Fusion Module (FFM) is designed to effectively integrate semantic information extracted from the ResNet and Swin Transformer branches. …”
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Diagnosis of abnormal sound in loudspeakers by integrated attention mechanism convolutional neural network by ZHOU Jinglei, WANG Xiaoming, LI Limin

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YOLO-CSMD: Integrating Improved Convolutional Techniques for Manhole Cover Defect Detection by Zhiwang Xu, Haowei Luo, Huijie Zhu, Wanfa Sun, Shengying Yang

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Photonic neuromorphic accelerator for convolutional neural networks based on an integrated reconfigurable mesh by Aris Tsirigotis, George Sarantoglou, Stavros Deligiannidis, Erica Sánchez, David Sanchez, Ana Gutierrez, Adonis Bogris, Jose Capmany, Charis Mesaritakis

“…On the other hand, upscaling integrated photonic circuits to meet the demands of state-of-the-art machine learning schemes such as convolutional layers, remains challenging. …”
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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis by S. Edwin Raja, J. Sutha, P. Elamparithi, K. Jaya Deepthi, S.D. Lalitha

“…To overcome these hurdles, this study presents two integrated approaches namely, – Attention-Guided Convolutional Neural Networks (AG-CNNs), and Genomic Feature Analysis Module (GFAM). …”
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Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks by Feiyan Zhou, Duanshu Fang

“…However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). …”
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