A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery by Jianming Hu, Xiyang Zhi, Bingxian Zhang, Tianjun Shi, Qi Cui, Xiaogang Sun

“…The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. …”
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Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems by Saud S. Alotaibi, Turki Ali Alghamdi

“…This study presents a League Championship Algorithm Feature Selection with Optimal Deep Learning based Cyberattack Detection (CLAFS-ODLCD) technique for securing the digital ecosystem. …”
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HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system by S. Veluchamy, R. Bhuvaneswari, K. Ashwini, Samah Alshathri, Walid El-Shafai

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Primary Fixation Feature Design Does Not Influence Total Ankle Tibial Component Stability When Implanted with Press-Fit in High Density Bone by Joshua E. Johnson PhD, Gabriel A. Clarke MS, Cesar de Cesar Netto MD, PhD, Donald D. Anderson PhD

“…Implant positioning and interference fit (100 µm) were set according to surgical guidelines (Fig. 1A). …”
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DRST-Net: A Dual-Branch Feature Fusion Network Combining ResNet50 and Swin Transformer for Welding Light Strip Recognition by Yuan Lu, Qingjiu Huang

“…To address the challenges of strong arc light noise, metal spatter, and smoke interference in weld seam recognition, we propose DRST-Net, a dual-branch cross-attention feature fusion network. …”
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An Underwater Acoustic Communication Signal Modulation-Style Recognition Algorithm Based on Dual-Feature Fusion and ResNet–Transformer Dual-Model Fusion by Fanyu Zhou, Haoran Wu, Zhibin Yue, Han Li

“…This paper proposes a dual-feature ResNet–Transformer model with two innovative breakthroughs: (1) A dual-modal fusion architecture of ResNet and Transformer is constructed using residual connections to alleviate gradient degradation in deep networks and combining multi-head self-attention to enhance long-distance dependency modeling. (2) The time–frequency representation obtained from the smooth pseudo-Wigner–Ville distribution is used as the first input branch, and higher-order statistics are introduced as the second input branch to enhance phase feature extraction and cope with channel interference. …”
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Staphylococcal enterotoxin-like X (SElX) is a unique superantigen with functional features of two major families of staphylococcal virulence factors. by Ries J Langley, Yi Tian Ting, Fiona Clow, Paul G Young, Fiona J Radcliff, Jeong Min Choi, Richard P Sequeira, Silva Holtfreter, Heather Baker, John D Fraser

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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

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A lithium-ion batteries SOH estimation method based on extracting new features during the constant voltage charging stage and improving BPNN. by Yanhua Xian, Mingyang Li, Jiayin Huang

“…The complexity and noise interference of battery data make it difficult to accurately extract health features, and it is necessary to develop effective methods to process the data and extract representative features. …”
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The library of reference models of the physical layer signals by Sergey Gennadiyevich Rassomakhin, Sergey Gennadiyevich Veklich, Oleg Pavlovich Malofey

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An enhanced YOLOv8 model for accurate detection of solid floating waste by Juxing Di, Kaikai Xi, Yang Yang

“…This results in the development of an enhanced model that integrates feature enhancement, interference suppression, and localization optimization. …”
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Subsea Nodule Recognition and Deployment Detection Method Based on Improved YOLOv8s by Jixin Li, Junchao Li, Bin Su, Yuxin Cui

“…These modifications enhance feature extraction capabilities in the presence of uneven lighting and background interference, optimizing nodule segmentation in complex backgrounds and improving small-target detection performance. …”
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Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model by Lv Deguo

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Remote sensing image protection using CTRSU-Net, SegNet + and ensemble learning by De Li, Chao Song, Xun Jin

“…To extract features with strong anti-interference ability, we propose a Convolutional block attention module-based Transformer Remote Sensing U-Net (CTRSU-Net) model and a SegNet + model. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…This framework model consists of three parts: a backbone network, a multi-scale feature representation network, and a detection head. …”
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SE-ResUNet Using Feature Combinations: A Deep Learning Framework for Accurate Mountainous Cropland Extraction Using Multi-Source Remote Sensing Data by Ling Xiao, Jiasheng Wang, Kun Yang, Hui Zhou, Qianwen Meng, Yue He, Siyi Shen

“…The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. …”
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CGDU-DETR: An End-to-End Detection Model for Ship Detection in Day–Night Transition Environments by Wei Wu, Xiyu Fan, Zhuhua Hu, Yaochi Zhao

“…., strong reflections, low light), we designed a novel CG-Net model based on cascaded group attention and introduced a dynamic feature upsampling algorithm, effectively enhancing the model’s ability to extract multi-scale features and detect targets in complex backgrounds. …”
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LEM-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection by Xinwen Zhou, Xiang Li, Wenfu Huang, Ran Wei

“…To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. …”
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CD4C: Change Detection for Remote Sensing Image Change Captioning by Xiliang Li, Bin Sun, Zhenhua Wu, Shutao Li, Hu Guo

“…The C-Stream leverages the visual change information provided by the mask to enhance the ability of CD4C to capture foreground visual change features at both the image and feature levels. The N-Stream incorporates a pseudofeature generation module designed to mitigate the interference caused by poor change detection results. …”
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DMSA-Net: a deformable multiscale adaptive classroom behavior recognition network by Chunyu Dong, Jing Liu, Shenglong Xie

“…Moreover, there are occlusions and scale differences in the front and back rankings, which can easily cause confusion and interference with target features in the detection process, greatly limiting the accurate recognition ability of existing visual algorithms for classroom behavior. …”
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