Rumor Detection Based on Knowledge Enhancement and Graph Attention Network by Wanru Wang, Yuwei Lv, Yonggang Wen, Xuemei Sun

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Lightweight and robust ship detection method driven by self-attention mechanism by Feng MA, Zihui SHI, Jie SUN, Chen CHEN, Xianbin MAO, Xinping YAN

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Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

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Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances by Chaofan Lan, Qingquan Luo, Tao Yu, Minhang Liang, Wenlong Guo, Zhenning Pan

“…Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. …”
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Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring by Joseph Farah, Jordy Davelaar, Daniel Palumbo, Michael Johnson, Jonathan Delgado

“…Developing this capability can be achieved by building a sample of n = 1 subring simulations, as well as by performing feature extraction on this high-volume sample to track changes in the geometry, which presents significant computational challenges. …”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…The experimental results show that LGR-Net outperforms other YOLO-series models in terms of overall performance, achieving optimal results in terms of precision (<i>p</i> = 98.7%), recall (R = 98.9%), mAP (99.4%), and parameter count (2,412,118). LGR-Net achieves low computational complexity and high detection accuracy, providing an efficient and effective solution for defect detection in elevator guide rail pressure plates.…”
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Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries by Yuanchen Cheng, Zichen Zhang, Yuqing Liu, Jie Li, Zhou Fu

“…Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.5 and a 6.4% increase in mAP@0.5:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. …”
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Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation by Tareq Mahmod AlZubi, Hamza Mukhtar

“…Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. …”
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Design of an Intelligent Pear Bagging End-Effector Based on Yolov8 and SGBM Algorithm by Jing Ruijun, Liu JingKai, LiXin, ZhiguoZhao

“…We describe an intelligent bagging end-effector for pears, which employs the Yolov8 algorithm for fruitlets detection and the Semi-Global Block Matching (SGBM) algorithm to acquire three-dimensional spatial information of the targets. To address the computational limitations of embedded devices in agricultural intelligent equipment, we improve the YOLOv8 model by replacing its neck component with the Asymptotic Feature Pyramid Network (AFPN) and incorporating Context Guided (CG) blocks into the C2f module. …”
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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li, Yanlei Xu

“…Additionally, the model incorporates the Ghost Module as a replacement for traditional 1 × 1 convolutions, further reducing computational overhead. Innovatively, we introduce a Channel Spatial Attention Mechanism (CSAM) that significantly enhances feature extraction and generalization aimed at rice disease detection. …”
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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

“…By incorporating quantum-specific feature extraction and latent variable disentanglement, the VAE model detects attack detection accuracy of 85–90 % with a reduction of 25 % in false positives. …”
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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

“…Firstly, the VGG16 module is used as the backbone feature extraction network to enhance the model’s generalization ability and prevent the initialization of model parameters from being too random. …”
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A survey of models for automatic assessment of similarity of student's answer to the reference answer by Nadezhda S. Lagutina, Ksenia V. Lagutina

“…A large number of authors choose large language models to solve their problems, but standard features remain in demand. It is impossible to single out a universal approach; each subtask requires a separate choice of method and adjustment of its parameters. …”
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Simulation of incremental update of electronic document information based on big data technology by Zhiyuan Jin, Qi Zhang, Tiejun Pan

“…Through first-order approximation, the number of iterations in model parameter updates is significantly reduced, thereby improving the computational efficiency of the algorithm. …”
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Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny by Tang Li, Mei Shunqi, Shi Yishan, Zhou Shi, Zheng Quan, Hongkai Jiang, Xu Qiao, Zhang Zhiming

“…The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. …”
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INTEGRATING ACOUSTIC MODULATION AND LISTENER DEMOGRAPHICS FOR ENHANCED PODCAST EMOTIONAL RESONANCE by Jun Ji, Kotchaphan Youngmee, Khachakrit Liamthaisong, Narisara Brikshavana

“…The research contradicts conventional views in previous studies by including an overall framework for the improvement of auditory features in podcasts. The underlying framework combines analytical methods, computational modelling, and machine learning in order to systematically enhance audio quality and consequently increase listener engagement.  …”
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Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework by Lin Pan, Qianqian Ren, Zilong Li, Xingfeng Lv

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Sweet Potato Yield Prediction Using Machine Learning Based on Multispectral Images Acquired from a Small Unmanned Aerial Vehicle by Kriti Singh, Yanbo Huang, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Edgar Lobaton, Johnie Jenkins, Mark Shankle

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Dense Sandstone Material Decomposition Based on Improved Convolutional Neural Network by Ran ZHANG, Huihua KONG, Jiaxin LI, Yijiao SONG

“…The proposed method uses the structure of the U-Net network and Resnet-152 as the backbone network to extract multi-scale features. Parallel asymmetric convolution is used to complete the large kernel convolution, which reduces the number of parameters and computation of the network. …”
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Lightweight Detection and Counting of Maize Tassels in UAV RGB Images by Hang Yang, Jiaji Wu, Yi Lu, Yuning Huang, Pinwei Yang, Yurong Qian

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