CSC-GCN: Contrastive semantic calibration for graph convolution network by Xu Yang, Kun Wei, Cheng Deng

“…In this paper, we propose a simple yet effective contrastive semantic calibration for graph convolution network (CSC-GCN), which integrates stochastic identity aggregation and semantic calibration to overcome these weaknesses. …”
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Spectral-Spatial Convolutional Hybrid Transformer for Hyperspectral Image Classification by Haixin Sun, Jingwen Xu, Fanlei Meng, Mengdi Cheng, Qiuguang Cao

“…First, the spectral pyramid 3D convolution and 2D convolution are combined to extract joint and detailed spectral-spatial features. …”
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YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes by Yiliang Zeng, Xiuhong Wang, Jinlin Zou, Hongtao Wu

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Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections by Asadulla Y. Ashurov, Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, Mohammed Saleh Ali Muthanna

“…This study proposes an advanced method for plant disease detection utilizing a modified depthwise convolutional neural network (CNN) integrated with squeeze-and-excitation (SE) blocks and improved residual skip connections. …”
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Hypergraph Convolution Network Classification for Hyperspectral and LiDAR Data by Lei Wang, Shiwen Deng

“…To overcome these limitations, we propose HGCN-HL, a novel multimodal deep learning framework that integrates hypergraph convolutional networks (HGCNs) with lightweight CNNs. …”
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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…To address these limitations, we propose an adaptive spatiotemporal dynamic graph convolutional network (AST-DGCN) for traffic prediction. …”
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SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification by Shandong Yuan, Zili Zou, Han Zhou, Yun Ren, Jianping Wu, Kai Yan

“…Sentence classification constitutes a fundamental task in natural language processing. Convolutional Neural Networks (CNNs) have gained prominence in this domain due to their capacity to extract n-gram features through parallel convolutional filters, effectively capturing local lexical correlations. …”
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A novel hybrid convolutional and transformer network for lymphoma classification by Mohamed Yacin Sikkandar, Sankar Ganesh Sundaram, Muteb Nasser Almeshari, S. Sabarunisha Begum, E. Siva Sankari, Yousef A. Alduraywish, Waeal J. Obidallah, Fahad Mansour Alotaibi

“…A Feature Fusion and Enhancement Module (FFEM) is introduced to dynamically integrate features from both pathways. …”
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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

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Convolutional Neural Networks for Automatic Identification of Individuals at Terrestrial Terminals by Darwin Yarango-Farro, Alex Mondragon-Fernandez, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…Extracted features and data were stored in a MySQL database. …”
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Crude Oil and Hot-Rolled Coil Futures Price Prediction Based on Multi-Dimensional Fusion Feature Enhancement by Yongli Tang, Zhenlun Gao, Ya Li, Zhongqi Cai, Jinxia Yu, Panke Qin

“…Additionally, a data augmentation framework leveraging multi-dimensional feature engineering has been established. The technical indicators, volatility indicators, time features, and cross-variety linkage features are integrated to build a prediction system, and the lag feature design is used to prevent data leakage. …”
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Near-real-time wildfire detection approach with Himawari-8/9 geostationary satellite data integrating multi-scale spatial–temporal feature by Lizhi Zhang, Qiang Zhang, Qianqian Yang, Linwei Yue, Jiang He, Xianyu Jin, Qiangqiang Yuan

“…The two modules are combined into multiple streams to integrate the multi-scale spatial–temporal features, and the multi-stream features are then fused to generate the fire classification map. …”
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A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics by Xinle Zhang, Guowei Zhang, Shengqi Zhang, Hongfu Ai, Yongqi Han, Chong Luo, Huanjun Liu

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VMDU-net: a dual encoder multi-scale fusion network for polyp segmentation with Vision Mamba and Cross-Shape Transformer integration by Peng Li, Jianhua Ding, Chia S. Lim

“…Furthermore, Depthwise Separable Convolutions are introduced to facilitate multi-scale feature extraction and improve convergence efficiency by leveraging the inductive bias of convolution.ResultsExtensive experiments were conducted on five widely-used polyp segmentation datasets. …”
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FruitsMultiNet: A deep neural network approach to identify fruits through multi-scale feature fusion using mobile interface by Tasauf Mim, Md Mahbubur Rahman, Jahanur Biswas, Ahmad Shafkat, Khandaker Mohammad Mohi Uddin

“…The data were preprocessed before the feature extraction phase to achieve more accurate outcomes. …”
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Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection by Sabina Umirzakova, Shakhnoza Muksimova, Abrayeva Mahliyo Olimjon Qizi, Young Im Cho

“…In response to these issues, we propose RASST—a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve high-precision detection under fully and semi-supervised conditions. RASST integrates a hybrid Vision Transformer architecture augmented with rotationally aware patch embeddings, adaptive rotational convolutions, and a multi-scale feature fusion (MSFF) module that employs cross-scale attention to enhance detection across object sizes. …”
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Landslide Identification from Post-Earthquake High-Resolution Remote Sensing Images Based on ResUNet–BFA by Zhenyu Zhao, Shucheng Tan, Yiquan Yang, Qinghua Zhang

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DA-ResNeXt50 method for radio frequency fingerprint identification based on time-frequency and bispectral feature fusion by CHEN Mengdi, ZHANG Wei, SHEN Lei, LEI Fuqiang, ZHANG Jiafei

“…To address the problems that a single feature in radio frequency fingerprint recognition could not fully represent the integrity of the signal and that the differences between features of different classes were small, which limited the recognition accuracy, a DA-ResNeXt50 (ResNeXt50 with dense connection and ACBlock) method for radio frequency fingerprint identification based on time-frequency and bi-spectral feature fusion was proposed. …”
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Diagnosis of Commutation Failure in a High- Voltage Direct Current Transmission System Based on Fuzzy Entropy Feature Vectors and a PCNN-GRU by Cao Ruirui, Yang Taigang, Li Guohui, Chen Shilong

“…Subsequently, the PCNN-GRU architecture performs deep feature extraction through two distinct mechanisms: the PCNN branch employs dual-path convolutional kernels of varying sizes for multidimensional feature mining, whereas the GRU network enhances temporal feature extraction capabilities. …”
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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. …”
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