Knowledge-Based Systems最新文献_第4页

Contrastive Transformer Network for Long Tail Classification 长尾分类对比变压器网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-06 DOI: 10.1016/j.knosys.2025.113607

Johannes Melsbach, Frederic Haase, Sven Stahlmann, Stefan Hirschmeier, Detlef Schoder

{"title":"Contrastive Transformer Network for Long Tail Classification","authors":"Johannes Melsbach, Frederic Haase, Sven Stahlmann, Stefan Hirschmeier, Detlef Schoder","doi":"10.1016/j.knosys.2025.113607","DOIUrl":"10.1016/j.knosys.2025.113607","url":null,"abstract":"<div><div>In the context of big data, multi-label text classification presents considerable challenges, most notably the long-tail problem, wherein a small number of labels account for the majority of instances, while the vast majority of labels occur only rarely. This imbalance creates a critical bias in classification models, leading to suboptimal performance on tail labels that significantly impacts applications such as recommender systems and search engines. We present CTN-LT (Contrastive Transformer Network for Long Tail Classification), a novel dual-encoder architecture that combines adapted loss functions, contrastive learning and reframes the multi-label text classification as a semantic similarity task to specifically enhance tail label performance. Our method achieves state-of-the-art performance on tail labels while maintaining competitive performance on head labels across multiple benchmark datasets. The model demonstrates superior few-shot and zero-shot capabilities, making it particularly valuable for dynamic environments where new categories frequently emerge. We release our code at <span><span>https://github.com/jmelsbach/CTN-LT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113607"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-STANet: A delay-enhanced spatio-temporal attention network for traffic prediction D-STANet：用于交通预测的延迟增强时空注意网络

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-06 DOI: 10.1016/j.knosys.2025.113533

Jiqiang Tang, Junjie Yang, Yuanqiong Zhang

{"title":"D-STANet: A delay-enhanced spatio-temporal attention network for traffic prediction","authors":"Jiqiang Tang, Junjie Yang, Yuanqiong Zhang","doi":"10.1016/j.knosys.2025.113533","DOIUrl":"10.1016/j.knosys.2025.113533","url":null,"abstract":"<div><div>With the accelerating pace of global urbanization, accurate traffic flow prediction has become crucial for alleviating congestion and optimizing resource allocation. However, existing methods often fail to effectively capture the complex spatio-temporal dependencies inherent in traffic data, which limits predictive accuracy. To address this challenge, we propose the D-STANet, which is an innovative traffic flow prediction model that integrates spatio-temporal attention mechanisms with a delay-aware module. Specifically, D-STANet leverages the spatio-temporal attention mechanism to adaptively select the most relevant features across different temporal and spatial scales, thereby capturing complex spatio-temporal dependencies. Additionally, the proposed delay-aware module is designed to model the temporal delay effects in traffic flow data, as predictions are not only dependent on current flow data, but also influenced by fluctuations in past traffic states. Furthermore, D-STANet incorporates a graph attention mechanism to enhance its ability to respond to dynamic changes. This module automatically adjusts the weight of each node in the graph based on the degree of association between nodes in the traffic flow data, further improving the model’s ability to capture traffic flow variations. Experimental results demonstrate that D-STANet outperforms all baseline models across multiple metrics, particularly on the HZME dataset, where its superior ability to model spatio-temporal dependencies is evident. Specifically, D-STANet achieves improvements of 31.71%, 20.48% and 5.06% in MAE, RMSE and MAPE, respectively, compared to DMSTGCN. The model’s exceptional performance in sparse traffic networks further underscores its robustness and reliability in complex traffic environments.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113533"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VIS4SL: A visual analytic approach for interpreting and diagnosing shortcut learning VIS4SL：一种用于解释和诊断快捷学习的可视化分析方法

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-06 DOI: 10.1016/j.knosys.2025.113598

Xiyu Meng , Tan Tang , Yuhua Zhou , Zihan Yan , Dazhen Deng , Yongheng Wang , Yuhan Wu , Yingcai Wu

{"title":"VIS4SL: A visual analytic approach for interpreting and diagnosing shortcut learning","authors":"Xiyu Meng , Tan Tang , Yuhua Zhou , Zihan Yan , Dazhen Deng , Yongheng Wang , Yuhan Wu , Yingcai Wu","doi":"10.1016/j.knosys.2025.113598","DOIUrl":"10.1016/j.knosys.2025.113598","url":null,"abstract":"<div><div>Shortcut learning, a phenomenon where deep neural networks inadvertently learn irrelevant features, has been extensively discussed due to its impact on model generalization and unexpected failures. Interpreting and diagnosing shortcut learning is challenging due to its diverse manifestations and multiple influencing factors. To assist data scientists in these tasks, we introduce VIS4SL, an interactive visual analytics approach that harnesses both human intelligence and computational power. VIS4SL integrates a perturbation-based method with comprehensive visualizations to facilitate an understandable analysis of learned features. We also present a set of comparative visualizations that allow for the evaluation of model explanations against robust proxies, particularly human explanations, to quantify the degree of shortcut learning and assess model components. Two case studies, involving natural image classification and visualization classification, demonstrate the efficacy of VIS4SL in practical applications. Our findings reveal that the model uses the orientation of bars to differentiate between bar charts and Pareto charts. Furthermore, we explore how interactive visualizations enhance data scientists’ understanding of shortcut learning, enabling the development of more precise deep learning models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113598"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond protection: Unveiling neural network copyright trading 超越保护：揭示神经网络版权交易

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-06 DOI: 10.1016/j.knosys.2025.113617

Xuemei Yuan , Hewang Nie

{"title":"Beyond protection: Unveiling neural network copyright trading","authors":"Xuemei Yuan , Hewang Nie","doi":"10.1016/j.knosys.2025.113617","DOIUrl":"10.1016/j.knosys.2025.113617","url":null,"abstract":"<div><div>The advent of deep learning has transformed data into invaluable intellectual property, encapsulated within trained neural network models. While copyright protection mechanisms exist, the lack of a secure, standardized platform for trading these intellectual assets limits their commercial potential. This study introduces a blockchain-based framework designed to invigorate the trading ecosystem for neural network model copyrights. A key innovation is our advanced watermarking technique, specifically developed for neural networks. This method embeds copyright information directly into the model architecture during training, providing robust protection against unauthorized use and modifications. Additionally, we have developed a decentralized blockchain marketplace tailored for the peer-to-peer exchange of authenticated model copyrights. This platform utilizes smart contracts to ensure secure, seamless copyright ownership transfers, enabling fluid exchanges within a trustless environment. By integrating cutting-edge watermarking technology with a decentralized trading venue, our framework establishes a market infrastructure that treats neural network models as a new class of freely tradable digital assets, thereby accelerating AI innovation and adoption across various sectors.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113617"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local High-order Structure-aware Graph Neural Network for motif prediction 局部高阶结构感知图神经网络的基序预测

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-06 DOI: 10.1016/j.knosys.2025.113618

Wen Yang , Xiang Li , Bin Wang , Jianpeng Qi , Zhongying Zhao , Peilan He , Yanwei Yu

{"title":"Local High-order Structure-aware Graph Neural Network for motif prediction","authors":"Wen Yang , Xiang Li , Bin Wang , Jianpeng Qi , Zhongying Zhao , Peilan He , Yanwei Yu","doi":"10.1016/j.knosys.2025.113618","DOIUrl":"10.1016/j.knosys.2025.113618","url":null,"abstract":"<div><div>Motifs, serving as fundamental building blocks in complex networks, refer to small, frequently occurring connected subgraphs. Unlike link prediction, motif prediction focuses on whether a given set of nodes will form a particular type of motif and has achieved much more attention. Motif prediction holds significant research value and demonstrates broad application potential across various fields, such as financial default prediction and social network recommendation. However, existing research methods are relatively limited and have largely overlooked the crucial role of local higher-order correlations among nodes and the enclosing subgraph-level structural information in motifs. To overcome these challenges, we propose a novel <u><strong>L</strong></u>ocal <u><strong>H</strong></u>igh-order <u><strong>S</strong></u>tructure-aware <u><strong>G</strong></u>raph <u><strong>N</strong></u>eural <u><strong>N</strong></u>etwork for motif prediction, named LHSGNN. It comprehensively predicts motifs from the perspectives of both the node level and subgraph level. LHSGNN incorporates local higher-order correlations among nodes to learn node embeddings. Then, it labels nodes from the views of node roles and distance metrics to capture complex structural information in enclosing subgraph-level motifs. Comprehensive experiments conducted on real-world datasets demonstrate the effectiveness of our LHSGNN.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113618"},"PeriodicalIF":7.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage graph spatiotemporal model with domain-class alignment for fault diagnosis under multi-source long-tailed distributions 面向多源长尾分布故障诊断的域类对齐两阶段图时空模型

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-05 DOI: 10.1016/j.knosys.2025.113698

Qianwen Cui , Shuilong He , Jinglong Chen , Chao Li , Chaofan Hu

{"title":"A two-stage graph spatiotemporal model with domain-class alignment for fault diagnosis under multi-source long-tailed distributions","authors":"Qianwen Cui , Shuilong He , Jinglong Chen , Chao Li , Chaofan Hu","doi":"10.1016/j.knosys.2025.113698","DOIUrl":"10.1016/j.knosys.2025.113698","url":null,"abstract":"<div><div>In practical engineering, monitoring data often follow multi-domain long-tailed distributions (MDLT), where label imbalance, domain shift, and cross-domain label divergence are deeply intertwined, posing significant challenges for intelligent fault diagnosis. To address these, we propose a novel two-stage decoupled graph spatiotemporal network guided by a balanced domain-class alignment loss. This framework introduces domain-class pairs and constructs a domain-class transferability graph using distance metrics. Building upon this, we propose an intensified Balanced Domain-Class Distribution Alignment (iBoDA) loss, which strengthens the similarity of intra-domain and cross-domain features within the same class while attenuating the similarity across different classes. This loss function calibrates and aligns domain-class distributions in imbalanced datasets, enhancing generalization for out-of-distribution samples. Furthermore, we design a multi-source fusion two-stage decoupled graph spatiotemporal network to extract domain-invariant, noise-resistant representations by capturing multi-dimensional spatiotemporal dependencies. Extensive experiments on three MDLT datasets, benchmarked against 15 state-of-the-art algorithms, validate the method's effectiveness, robustness, and computational efficiency in addressing MDLT challenges in industrial fault diagnosis.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113698"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing task incremental continual learning: integrating prompt-based feature selection with pre-trained vision-language model 增强任务增量持续学习：将基于提示的特征选择与预训练的视觉语言模型相结合

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-05 DOI: 10.1016/j.knosys.2025.113704

Lie Yang , Haohan Yang , Xiangkun He , Wenhui Huang , Chen Lv

{"title":"Enhancing task incremental continual learning: integrating prompt-based feature selection with pre-trained vision-language model","authors":"Lie Yang , Haohan Yang , Xiangkun He , Wenhui Huang , Chen Lv","doi":"10.1016/j.knosys.2025.113704","DOIUrl":"10.1016/j.knosys.2025.113704","url":null,"abstract":"<div><div>Task incremental continual learning is pivotal for the evolution of general artificial intelligence, enabling models to progressively acquire and integrate new knowledge. However, enhancing model plasticity while ensuring stability remains one of the most significant challenges in this field. In this study, we propose a task incremental continual learning method based on a vision-language model (TICL-VLM), which exhibits both high plasticity and good stability. First, the image encoder from a pre-trained vision-language model (VLM) is adopted for robust feature extraction, and a novel task-prompt-based feature selection module is designed to enhance the plasticity of the proposed model. Additionally, a class description constraint is introduced to further improve the performance of the method. To ensure excellent stability, we freeze the parameters of the VLM's image and text encoders and introduce distinct feature selection and classification modules for each incremental task. Furthermore, a specific dataset (LFDDE) is constructed to comprehensively evaluate the performance of task incremental continual learning algorithms. Extensive experiments have been conducted on both the LFDDE and the well-known CIFAR-100 datasets. The experimental results clearly demonstrate significant improvements in maintaining stability while efficiently incorporating new knowledge with our method.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113704"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSPT: Two-Step Prompt Tuning for class-incremental novel class discovery TSPT：类增量新类发现的两步提示调优

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-05 DOI: 10.1016/j.knosys.2025.113603

Jiayu An, Zhenbang Du, Herui Zhang, Dongrui Wu

{"title":"TSPT: Two-Step Prompt Tuning for class-incremental novel class discovery","authors":"Jiayu An, Zhenbang Du, Herui Zhang, Dongrui Wu","doi":"10.1016/j.knosys.2025.113603","DOIUrl":"10.1016/j.knosys.2025.113603","url":null,"abstract":"<div><div>In real-world applications, models often encounter a sequence of unlabeled new tasks, each containing unknown classes. This paper explores class-incremental novel class discovery (class-iNCD), which requires maintaining previously learned knowledge while discovering novel classes. We consider a more realistic and also more challenging scenario, which has a small number of initial known classes and a large number of unlabeled tasks, with the additional requirement of data privacy protection. A simple yet effective approach, Two-Step Prompt Tuning (TSPT), is proposed. TSPT tackles class-iNCD through prompt tuning, which is rehearsal-free and plug-and-play, protecting data privacy and significantly reducing the number of trainable parameters. TSPT consists of two main steps: (1) novel class discovery, which initializes the classifier using uniform clustering, and uses intra- and inter-sample consistency learning to discover novel classes; and, (2) knowledge fusion, where the prompt learned in the previous step is adapted as task-specific prompt, and additional optimal prompts are selected from a prompt pool to integrate knowledge from both old and new classes. Experiments on three datasets demonstrated the effectiveness of TSPT.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113603"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient black-box adversarial attacks via alternate query and boundary augmentation 通过交替查询和边界增强实现高效的黑盒对抗攻击

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-05 DOI: 10.1016/j.knosys.2025.113604

Jiatian Pi , Fusen Wen , Fen Xia , Ning Jiang , Haiying Wu , Qiao Liu

{"title":"Efficient black-box adversarial attacks via alternate query and boundary augmentation","authors":"Jiatian Pi , Fusen Wen , Fen Xia , Ning Jiang , Haiying Wu , Qiao Liu","doi":"10.1016/j.knosys.2025.113604","DOIUrl":"10.1016/j.knosys.2025.113604","url":null,"abstract":"<div><div>Most existing query-based black-box attacks use surrogate models as transferable priors to improve query efficiency. However, these methods still suffer from high query times and complexity due to the following three reasons. First, they usually use a transfer-based strategy to find a starting point, which is not conducive to fast optimization. Second, most of them exploit transferable priors in a complex way that severely constrains query efficiency. Third, their performance usually depends on the number of surrogate models and the more surrogate models, the better the performance. To this end, we propose an optimization framework based on fusion attack and boundary augmentation, which make full use of transfer prior and query feedback to achieve a more effective and efficient attack. Specifically, we first use the surrogate model to conduct a warm-up attack guided by query feedback, which provides a better starting point for fast optimization. Then, we introduce a data-augmentation-based transferable attack into query-based method for alternative query. Since the alternate attack framework can quickly find out the adversarial area of the target model, it improves the query efficiency. Finally, we design a decision boundary enhancement strategy to make the decision boundary of the model more diverse. This strategy can reduce the number of surrogate models used yet still achieve competitive performance. To validate the effectiveness of the proposed method, we conduct experiments with three victim models on the ImageNet dataset. Extensive experiment results show that our method achieves favorable performance against the state-of-the-art methods. While the proposed method gets a 100% attack success rate, the query times can be reduced by several orders of magnitude.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"319 ","pages":"Article 113604"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-temporal image fusion empowered convolutional neural networks for recognition of 9 common mice actions 多时相图像融合增强卷积神经网络识别9种常见的小鼠动作

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-05-05 DOI: 10.1016/j.knosys.2025.113628

Jian Li , Chen Du , Yuliang Zhao , Peng Shan , Xingqi Wang , Huawei Zhang , Ying Wang

{"title":"Multi-temporal image fusion empowered convolutional neural networks for recognition of 9 common mice actions","authors":"Jian Li , Chen Du , Yuliang Zhao , Peng Shan , Xingqi Wang , Huawei Zhang , Ying Wang","doi":"10.1016/j.knosys.2025.113628","DOIUrl":"10.1016/j.knosys.2025.113628","url":null,"abstract":"<div><div>The study of complex behaviors and social interactions necessitates precise and efficient methodologies for the recognition and tracking of animal actions. However, existing methods such as depth perception and wearable devices for mice behavior recognition pose risks of physical harm to the subjects and exhibit limited applicability across species with low precision. To redress these deficiencies, this paper proposes the multi-temporal image fusion empowered Convolutional Neural Networks (CNN), aimed at achieving accurate and efficient recognition of nine common mice actions. In this study, we employ mice at various time points as subjects and employ a multi-temporal approach to process image sequences, integrating various frame difference extraction techniques to address the limitations inherent in single-frame prediction for capturing dynamic changes in actions. Subsequently, we utilize a Deformable Convolution Network (DCN) in conjunction with multi-stacked residual units to enhance the feature extraction capacity of the CNN, particularly focusing on mice action contours, while mitigating the risk of overfitting. Furthermore, we investigate the efficacy of fused images derived from varying frame differences in representing the nine actions, culminating in the establishment of a robust mice action recognition model through ensemble learning techniques. Experimental findings demonstrate an impressive precision rate of 92.9% in recognizing mice actions. The proposed method effectively eliminates background interference and exhibits superior generalization and adaptability properties.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"320 ","pages":"Article 113628"},"PeriodicalIF":7.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0