Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients by Li Li, Wenjun Ren, Yuying Lei, Lixia Xu, Xiaohui Ning

“…Methods We developed a novel hybrid model integrating convolutional neural network (CNN), Transformer, and Whale Optimization Algorithm (WOA) for arrhythmia prediction in AMI patients. …”
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FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms by Xu Ma, Jialong Lai, Zhicheng Zhong, Feifei Cui

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MDA-MIM: a radar echo map prediction model integrating multi-scale feature fusion and dual attention mechanism by HU Qiang, GAO Yating, YIN Binli, QU Lianen

“…Dilated convolution was used to extract and integrate multi-scale features. …”
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Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks by Deependra Rastogi, Prashant Johri, Massimo Donelli, Seifedine Kadry, Arfat Ahmad Khan, Giuseppe Espa, Paola Feraco, Jungeun Kim

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An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification by Santosh Kumar Chhotray, Debahuti Mishra, Sarada Prasanna Pati, Sashikala Mishra

“…Four pre-trained models—VGG16, ResNet50, NASNet, and DenseNet121—capture distinct features, and a Custom-CNN integrates these models with the convolutional block attention module (CBAM). …”
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Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features by Mingqi Li, Pengxin Wang, Kevin Tansey, Fengwei Guo, Ji Zhou

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Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers by Natenaile Asmamaw Shiferaw, Zefree Lazarus Mayaluri, Prabodh Kumar Sahoo, Ganapati Panda, Prince Jain, Adyasha Rath, Md. Shabiul Islam, Mohammad Tariqul Islam

“…This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance recognition accuracy. …”
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CAD-ViT: Coordinate Attention-Enhanced Vision Transformer With Dilated Feature Fusion for Diabetic Retinopathy Staging by Ye Wang, Xiaofang Gou, Wenman Li

“…In this paper, we propose a deep learning method for DR diagnosis and grading based on multi-scale feature fusion and attention guidance. This approach employs dilated convolutions with varying dilation rates to expand the receptive field and integrate features at multiple scales. …”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

“…Lastly, a gating network dynamically balances temporal and frequency-domain features to achieve cross-domain information integration. …”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. …”
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PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang, Xiaohui Huang

“…Existing object detection methods focus on integrating convolutional neural networks (CNNs) and Transformer networks to explore local and global representations to improve performance. …”
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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion by Yuanbo Chu, Jiahao Wang, Longhui Ma, Chenxing Wu

“…These methods optimize convolutional computation cost and enhance multiscale information extraction, significantly reducing computational cost and parameters, while improving feature representation and fusion without sacrificing accuracy. …”
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Dynamic convolution models for cross-frontend keyword spotting by Rongqi Liu, Wenkang Chen, Xuejun Zhang

“…Abstract In this study, we propose a novel keyword spotting method that integrates a dynamic convolution model with a cross-frontend mutual learning strategy. …”
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Flood Image Classification using Convolutional Neural Networks by Olusogo Julius Adetunji, Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, Adedayo Aladejobi Sobowale Sobowale

“…This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. …”
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An Ensemble of Convolutional Neural Networks for Sound Event Detection by Abdinabi Mukhamadiyev, Ilyos Khujayarov, Dilorom Nabieva, Jinsoo Cho

“…The proposed CRNN model integrates spatial and temporal feature extraction by processing these spectrograms through convolution and bi-directional gated recurrent unit (GRU) layers. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

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Diagnosis of Lung Cancer Using Endobronchial Ultrasonography Image Based on Multi-Scale Image and Multi-Feature Fusion Framework by Huitao Wang, Takahiro Nakajima, Kohei Shikano, Yukihiro Nomura, Toshiya Nakaguchi

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Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predicti... by Yuxuan Wang, Zhouyuan Zhang, Shu Pi, Haishan Zhang, Jiatian Pi

“…The DG3L model includes a memory-based graph learning module capable of generating dynamic graphs to accurately reflect ongoing changes in spatio-temporal dependencies. By integrating the strengths of Transformer and Graph Convolutional Recurrent Unit (GCRU) technologies within its Dual-Gated Graph Convolutional Recurrent Unit architecture, DG3L provides a mechanism for fusing Transformer features with contextual features from recurrent units. …”
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An Internet-of-Things-Integrated Deep Learning Model for Fault Diagnosis in Industrial Rotating Machines by Tuan M. Le, Hieu M. Tran, Ke Wang, Hung V. Pham, Son V. T. Dao

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Augmented Graph Convolutional Network for Enhancing Label Reachability by Xiangyi Wang, Fengjun Zhang, Wei Teng, Baoda Liu

“…Graph Convolutional Networks (GCNs) have emerged as a leading approach for semi-supervised node classification. …”
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