Zero-small sample classification method with model structure self-optimization and its application in capability evaluation

Zhang, Rui; Bai, Xiaolu; Pan, Lei; Dong, Zengshou; Song, Rongfang

doi:10.1007/s10489-021-02686-8

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Recall is measured by calculating the number of correctly predicted positive cases as a proportion of all actual positive cases, which highlights the ability of the algorithmic model to detect interactions between users and unknown items [15]. At the same time, the prediction of recommendations is essentially a regression problem [27], and in order to better assess the effectiveness of the model implemen-tation, we use Root Mean Square Error (RMSE) for evaluation. In order to evaluate the TOP-K effect of the recommendations in the experiments, we set the evaluation metric as Recall@K, RMSE and set K=20, i.e.…”

Section: Evaluation Metricsmentioning

confidence: 99%

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

Gu,

Xu,

Liu

2024

ITC

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A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%.

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Section: Evaluation Metricsmentioning

confidence: 99%

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

Gu,

Xu,

Liu

2024

ITC

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“…The SSA was proposed by Xue and Shen (2020) in 2020. It uses sparrow individuals to search for food and anti-predation for iterative optimization (Zhang et al , 2020). It has the advantages of fewer adjustment parameters, fast convergence speed and simple calculation.…”

Section: Diagnosis Model Optimization Based On the Sparrow Search Str...mentioning

confidence: 99%

Ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion

Zhang

Zhao

et al. 2022

Self Cite

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Purpose This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters. Design/methodology/approach To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model. Findings The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications. Originality/value This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

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Recognizing defects in stainless steel welds based on multi-domain feature expression and self-optimization

Zhang

Zhao

et al. 2021

J Intell Manuf

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Zero-small sample classification method with model structure self-optimization and its application in capability evaluation

Cited by 8 publications

References 33 publications

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

Ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion

Recognizing defects in stainless steel welds based on multi-domain feature expression and self-optimization

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