Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma, Wenchao Sun

“…Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…In addition to the deep convolutional neural network architecture model 4, the research contribution obtained from this research is the use of filter size variations of 3x3, 2x2, and 1x1 with a fixed number of convolutional layers and a reduction in the number of hidden layers in the algorithm structure can reduce the number of model parameters while maintaining high detection capability. …”
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Vision Mamba and xLSTM-UNet for medical image segmentation by Xin Zhong, Gehao Lu, Hao Li

“…To address these limitations, this study introduces VMAXL-UNet, a novel segmentation network that integrates Structured State Space Models (SSM) and lightweight LSTMs (xLSTM). …”
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A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects by Zeynep Akkutay-Yoldar, Mehmet Türkay Yoldar, Yiğit Burak Akkaş, Sibel Şurak, Furkan Garip, Oğuzcan Turan, Bengisu Ekizoğlu, Osman Can Yüca, Aykut Özkul, Barış Ünver

“…AIRVIC’s hierarchical structure highlights its adaptability to virological diagnostics, providing unbiased infectivity scoring and facilitating viral isolation and antiviral efficacy testing. …”
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An improved U-net and attention mechanism-based model for sugar beet and weed segmentation by Yadong Li, Ruinan Guo, Rujia Li, Rongbiao Ji, Mengyao Wu, Dinghao Chen, Cong Han, Ruilin Han, Yongxiu Liu, Yuwen Ruan, Jianping Yang, Jianping Yang

“…To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.MethodsThe model adopts the encoder-decoder structure of UNet, utilizing MaxViT (Multi-Axis Vision Transformer) as the encoder to capture both global and local features within images. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…A notable advancement introduced in MTEPN is the cross-task feature aggregation structure. This module promotes information complementarity between tasks by implicitly modeling the global context relationships among different visual tasks. …”
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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation by Ahmad Raza Khan, Shaik Shakeel Ahamad, Shailendra Mishra, Mohd Abdul Rahim Khan, Sunil Kumar Sharma, Abdullah AlEnizi, Osama Alfarraj, Majed Alowaidi, Manoj Kumar

“…MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. …”
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Enhancing Object Detection in Underground Mines: UCM-Net and Self-Supervised Pre-Training by Faguo Zhou, Junchao Zou, Rong Xue, Miao Yu, Xin Wang, Wenhui Xue, Shuyu Yao

“…We propose the ESFENet backbone network, incorporating a Global Response Normalization (GRN) module to enhance feature capture stability while employing depthwise separable convolutions and HGRNBlock modules to reduce parameter volume and computational complexity. …”
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Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…These included user perception evaluation (UPE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and the performance of disease recognition within object detection frameworks. …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…To address these limitations, this paper presents a novel attention-enhanced hybrid AMC framework that synergistically integrates specialized convolutional layers for efficient temporal feature extraction with a compact transformer encoder for global sequence modeling. …”
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A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…To address these issues, this study proposes a novel picking point localization method for table grapes based on an instance segmentation network called Progressive Global-Local Structure-Sensitive Segmentation (PGSS-YOLOv11s) and a simple combination strategy of morphological operators. …”
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Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Finally, a multi-layer graph convolutional network with a hierarchical attention mechanism was designed, in which local and global features were hierarchically extracted and fused through a multi-level neighborhood aggregation strategy, significantly enhancing the model’s capability to identify key features. …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. …”
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HTCNN-Attn: a fine-grained hierarchical multi-label deep learning model for disaster emergency information intelligent extraction from social media by Shanshan Li, Qingjie Liu, Xiaoling Sun

“…It integrates a three-level tree-structured labeling architecture, Transformer-based global feature extraction, convolutional neural network (CNN) layers for local pattern capture, and a hierarchical attention mechanism. …”
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Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. by Tingyuan Zhang, Changsheng Zhang, Zhongyi Yang, Meng Wang, Fujie Zhang, Dekai Li, Sen Yang

“…The RSCD-Net architecture consists of 16 layers of SCR-Blocks, structured into four convolutional stages with 3, 4, 6, and 3 units, respectively. …”
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EFINet: Efficient Feature Interaction Network for Real-Time RGB-D Semantic Segmentation by Zhe Yang, Baozhong Mu, Mingxun Wang, Xin Wang, Jie Xu, Baolu Yang, Cheng Yang, Hong Li, Rongqi Lv

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Lightweight Road Environment Segmentation using Vector Quantization by J. Kwag, A. Yilmaz, C. Toth

“…Numerous works based on Fully Convolutional Networks (FCNs) and Transformer architectures have been proposed to leverage local and global contextual learning for efficient and accurate semantic segmentation. …”
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Two-dimensional spatial orientation relation recognition between image objects by Gong Peiyong, Zheng Kai, Jiang Yi, Zhao Huixuan, Huai Honghao, Guan Ruijie

“…A dedicated fusion module synthesizes features from both branches, generating a structured triple list that documents detected objects, their inter-object spatial orientations, and associated confidence scores. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). …”
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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…Methods: Our proposed DTC-m6Am model first represents RNA sequences by One-Hot coding to capture base-based features and provide structured inputs for subsequent deep learning models. …”
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