IMP‐DETR: Optimization model for defect detection of injection‐moulded products by Anzhan Liu, Lei Han

“…The model constructs a feature extraction backbone network with the inverted residual mobile block module to extract key information and reduce interference from irrelevant backgrounds while maintaining lightweight. …”
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Clothing classification method based on attention mechanism and transfer learning by CHEN Jinguang, HUANG Xiaoju, MA Lili

“…Convolutional block attention module (CBAM) was added to the ResNet50-based network, and attention of different region of clothing was improved from both channel and spatial dimensions in turn. Then the feature expression capability was enhanced. The validation was performed on two datasets of CD and IDFashion with different background interference. …”
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Fault diagnosis method of telecom cloud platform based on deep CNN model by Qingpu Hu, Jian Hu

“…The research has verified the anti-interference ability and feature preservation advantages of the WCNN model in strong noise environments, providing an efficient solution for fault diagnosis in telecommunications cloud platforms.…”
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DASNet a dual branch multi level attention sheep counting network by Yini Chen, Ronghua Gao, Qifeng Li, Hongtao Zhao, Rong Wang, Luyu Ding, Xuwen Li

“…DASNet is shown to be effective in handling challenging scenarios, such as dense flocks and background noise, due to its dual–branch feature enhancement and global multi–level feature fusion. …”
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Enhancing Crack Segmentation Network with Multiple Selective Fusion Mechanisms by Yang Chen, Tao Yang, Shuai Dong, Like Wang, Bida Pei, Yunlong Wang

“…Finally, to tackle class imbalance, a multi-scale monitoring and selective output module is introduced to enhance the model’s focus on crack features and suppress the interference from background and irrelevant information. …”
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Design of a Nested Hollow-Core Anti-Resonant Fiber Sensor for Simultaneous Measurement of Temperature and Strain by Yueyu Xiao, Jiayao Cheng

“…Numerical investigations demonstrate the shifts of the feature wavelengths of the resonance coupling effect, and the intermodal interference shows different velocities with temperature and strain, while a simultaneous measurement of temperature and strain can be realized with high sensitivities (3.36 nm/°C and −0.003 nm/με to temperature and strain, respectively). …”
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AAD-YOLO: An Improved YOLOv8 Model for Complex Remote Sensing Scenarios by Yue Hong, Yi Shu, Shuo Guo

“…Additionally, the Attention-based Intrascale Feature Interaction (AIFI) module, which incorporates a multi-head self-attention mechanism, focuses on salient object regions while suppressing background interference. …”
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Experimental evaluation methods of multi-frequency electromagnetic radiation effects by Xiao-Peng Li, Guang-Hui Wei, Hong-Ze Zhao, Jiang-Ning Sun, Xu-Xu Lyu

“…Objective evaluation of equipment immunity to electromagnetic interference is central to studying complex electromagnetic environmental effects. …”
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An Improved YOLOv8 Model for Detecting Four Stages of Tomato Ripening and Its Application Deployment in a Greenhouse Environment by Haoran Sun, Qi Zheng, Weixiang Yao, Junyong Wang, Changliang Liu, Huiduo Yu, Chunling Chen

“…A multi-dimensional feature neck network was integrated to enhance feature fusion, and three Semantic Feature Learning modules (SGE) were added before the detection head to minimize environmental interference. …”
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WTSM-SiameseNet: A Wood-Texture-Similarity-Matching Method Based on Siamese Networks by Yizhuo Zhang, Guanlei Wu, Shen Shi, Huiling Yu

“…To address this, a concurrent attention mechanism was designed, which reduces interlayer interference by using a dual-stream parallel structure that enhances the ability to capture features. …”
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Diagnosis of abnormal sound in loudspeakers by integrated attention mechanism convolutional neural network by ZHOU Jinglei, WANG Xiaoming, LI Limin

“…Secondly, the feature data was input into the 1DCNN-BiLSTM network for initial feature extraction. …”
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GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

“…In the data preprocessing phase, packet-level images within sessions were generated from the payloads of data packets to reduce information interference. In the classification phase, the images were firstly processed by the packet group mix transformer (PCMT) module, which utilized the transformer and GMA to capture global features. …”
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Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform by Tao Sun, Xiaolong He, Jiajun Zhang

“…By introducing convolutional block attention module to optimize feature extraction and focus on key areas, a weighted bi-directional feature pyramid network is taken to fuse multi-scale features, and gradient optimized Hough transform is used to optimize the accuracy of target localization. …”
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Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm by Xue Mei, Zhenhua Li, Songsong Xu, Xiaoyan Guo

“…Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point’s translational transformation method to realize coarse registration. …”
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Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. by Hao Cao, Xiaoyan Du

“…This model addresses the challenges of feature redundancy and noise interference in elevator operation data, improving the stability and accuracy of fault predictions. …”
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A Method for Predicting Trajectories of Concealed Targets via a Hybrid Decomposition and State Prediction Framework by Zhengpeng Yang, Jiyan Yu, Miao Liu, Tongxing Peng, Huaiyan Wang

“…Accurate trajectory prediction of concealed targets in complex, interference-laden environments present a formidable challenge for millimeter-wave sensor tracking systems. …”
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Attention-fused residual transformer CNN for robust lower limb movement recognition by A. Anitha, D. Jeraldin Auxillia

“…Conventional machine-learning approaches depend on manual feature extraction, which consumes more time and is susceptible to noise interference and class imbalance. …”
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CR-Mask RCNN: An Improved Mask RCNN Method for Airport Runway Detection and Segmentation in Remote Sensing Images by Meng Wan, Guannan Zhong, Qingshuang Wu, Xin Zhao, Yuqin Lin, Yida Lu

“…Furthermore, the method incorporates an attention mechanism into the backbone feature extraction network to allocate attention to different airport runway feature map scales, which enhances the extraction of local feature information, captures detailed information more effectively, and reduces issues of false positives and false negatives when detecting airport runway targets. …”
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Small Ship Detection Based on Improved Neural Network Algorithm and SAR Images by Jiaqi Li, Hongyuan Huo, Li Guo, De Zhang, Wei Feng, Yi Lian, Long He

“…By adaptively aggregating the features extracted by large-size convolution kernels to fully obtain context information, at the same time, key features are enhanced and noise interference is suppressed. …”
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Hash-Guided Adaptive Matching and Progressive Multi-Scale Aggregation for Reference-Based Image Super-Resolution by Lin Wang, Jiaqi Zhang, Huan Kang, Haonan Su, Minghua Zhao

“…This module utilizes dynamic decoupling filters to simultaneously perceive texture information in both spatial and channel domains, extracting key information more accurately and effectively suppressing irrelevant texture interference. In addition, this module enhances the robustness of the model to large-scale biases by gradually adjusting features at different scales, ensuring the accuracy of texture transfer. …”
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