Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machine...
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With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current *** graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the applica...
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With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current *** graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much ***,in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features,which does not take full advantage of various graph neural networks(graph filters).Moreover,the traditional GNNs have the problem of *** alleviate these shortcomings,we introduce a deep hybrid multi-graph neural network(DHMG),where two different graph filters,i.e.,the spectral filter and the autoregressive moving average(ARMA)filter,are utilized in two *** former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph *** network realizes information interaction between the two branches and takes good advantage of different graph *** addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are *** dense structure satisfies the needs of different classification targets presenting different ***,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid *** experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.
The use of electronic communication has increased dramatically in recent years, opening up new avenues for mental health research. In an effort to humanize these connections, this study presents a framework for identi...
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Current text classification methods face limitations due to dataset size, hindering fast learning and generalization in few-shot scenarios. Our proposed method, Reptile-MAM, transforms multiclassification tasks into b...
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E-commerce platforms have reshaped consumer shopping, emphasizing customer experience and satisfaction. This study compares sentiment analysis techniques employing Support Vector Machine (SVM) and Random Forest Algori...
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Securing reusable hardware intellectual property (IP) cores has become increasingly critical due to their widespread application in multimedia systems and modern consumer electronics. These IP cores, particularly digi...
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Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate ...
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Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is *** recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean *** it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean *** this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center *** demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method *** results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.
Water is a necessity for the correct functioning of the human body. In the modern era, most people have hectic schedules, making it difficult for them to remember to drink enough water and track their water consumptio...
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Automatically extracting topics from large amounts of text is one of the main uses of natural language processing (NLP). The latent Dirichlet allocation (LDA) technique is frequently used to extract topics from pre-pr...
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Treatment outcomes and patient survival rates are greatly improved by early identification of ovarian cancer. However, to increase diagnostic accuracy, effective predictive modeling is required due to the biomarkers&#...
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