SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture by Taixing Qiu, Jiawei Li, Yan Guo, Limin Jiang, Jijun Tang

“…Additionally, SVEA integrates multi-head self-attention mechanisms and multi-scale convolution modules, enhancing its ability to capture global context and multi-scale features. …”
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Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction by Hongkai Zhang, Hongxuan Yuan, Minghao Shao, Junxin Wang, Suhong Liu

“…By decoupling detailed feature extraction from global context modeling, the proposed framework more faithfully represents complex road structures. …”
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Urban Land Use Classification Model Fusing Multimodal Deep Features by Yougui Ren, Zhiwei Xie, Shuaizhi Zhai

“…The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. …”
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YOLO-AFR: An Improved YOLOv12-Based Model for Accurate and Real-Time Dangerous Driving Behavior Detection by Tianchen Ge, Bo Ning, Yiwu Xie

“…YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. …”
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Self-Supervised Social Recommendation Algorithm Fusing Residual Networks by WANG Yujie, YANG Zhe

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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. …”
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Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation by Wei-Jong Yang, Chih-Chen Wu, Jar-Ferr Yang

“…In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps. …”
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An explainable hybrid feature aggregation network with residual inception positional encoding attention and EfficientNet for cassava leaf disease classification by M. Sundara Srivathsan, S. Alden Jenish, K. Arvindhan, R. Karthik

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Advanced AI techniques for classifying Alzheimer’s disease and mild cognitive impairment by Sophie Tascedda, Sophie Tascedda, Sophie Tascedda, Pierfrancesco Sarti, Pierfrancesco Sarti, Veronica Rivi, Claudia Savia Guerrera, Giuseppe Alessio Platania, Mario Santagati, Filippo Caraci, Filippo Caraci, Johanna M. C. Blom, Johanna M. C. Blom

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Single-Image Superresolution for RGB Remote Sensing Imagery via Multiscale CNN-Transformer Feature Fusion by Xudong Yao, Haopeng Zhang, Sizhe Wen, Zhenwei Shi, Zhiguo Jiang

“…MSTB generates multiscale tokens with multiple convolutional layers to obtain multiscale global information. …”
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Quantifying Interdisciplinarity in Scientific Articles Using Deep Learning Toward a TRIZ-Based Framework for Cross-Disciplinary Innovation by Nicolas Douard, Ahmed Samet, George Giakos, Denis Cavallucci

“…Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. …”
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Directed Knowledge Graph Embedding Using a Hybrid Architecture of Spatial and Spectral GNNs by Guoqiang Hou, Qiwen Yu, Fan Chen, Guang Chen

“…To address this limitation, a directed spectral graph transformer (DSGT), a hybrid architecture model, is constructed by integrating the graph transformer and directed spectral graph convolution networks. The graph transformer leverages multi-head attention mechanisms to capture the global connectivity of the feature graph from different perspectives in the spatial domain, which bridges the gap between frequency responses and, further, naturally couples the graph transformer and directed graph convolutional neural networks (GCNs). …”
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Graph-Based Feature Crossing to Enhance Recommender Systems by Congyu Cai, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou, Yuanguo Lin

“…Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. …”
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Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network by Anparasy Sivaanpu, Kumaradevan Punithakumar, Rui Zheng, Michelle Noga, Dean Ta, Edmond H. M. Lou, Lawrence H. Le

“…It is integrated into the encoder-decoder structure, allowing the model to focus on both local and global texture structural information. …”
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ROPGCViT: A Novel Explainable Vision Transformer for Retinopathy of Prematurity Diagnosis by Mustafa Yurdakul, Kubra Uyar, Sakir Tasdemir, Irfan Atabas

“…GCViT was enhanced with Squeeze-and-Excitation (SE) block and Residual Multilayer Perceptron (RMLP) structures to effectively learn local and global context information. …”
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Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model by Santosh Kumar Henge, Nikhil Reddy Viraati, Musaed Alhussein, Ajay Shriram Kushwaha, Khursheed Aurangzeb, Ravleen Singh

“…The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. …”
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Multi-scale Information Aggregation for Spoofing Detection by Changtao Li, Yi Wan, Feiran Yang, Jun Yang

“…In this paper, we propose a spoofing detection system built on SincNet and Deep Layer Aggregation (DLA), which leverages speech representations at different levels to distinguish synthetic speech. DLA is totally convolutional with an iterative tree-like structure. …”
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Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms by Yiheng Qian, Zhiyong Xiao, Zhaohong Deng

“…Additionally, a Transformer block is integrated to overcome the limitations of traditional convolutional approaches in capturing global contextual information. …”
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Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications by Yiheng Ding, Ziqiang Wei, Chaoyun Wang, Xinyue Li, Bingbing Li, Xueting Liu, Zhijie Fu, Hongwei Mo, Hong Zhang

“…BackgroundAs a disease with high global incidence, hypertension is known to cause systemic vasculopathy. …”
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PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction From High-Resolution Remote Sensing Images by Yaoteng Zhang, Julin Zhang, Guangshuai Wang, Jiwei Deng, Hui Sheng, Yasir Muhammad, Shiqing Wei

“…However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. …”
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