Applied Intelligence最新文献_第4页

COFA: counterfactual attention framework for trustworthy wafer map failure classification

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06488-0

Kaiyue Feng, Jia Wang, Chenke Yin, Andong Li

{"title":"COFA: counterfactual attention framework for trustworthy wafer map failure classification","authors":"Kaiyue Feng, Jia Wang, Chenke Yin, Andong Li","doi":"10.1007/s10489-025-06488-0","DOIUrl":"10.1007/s10489-025-06488-0","url":null,"abstract":"<div><p>Classifying wafer map failure pattern plays a crucial role in semiconductor manufacturing, as it can help identify the underlying cause of abnormalities, thus reducing production costs. Existing works have shown that deep learning methods have great advantages in recognizing failure patterns. However, recent studies mainly focus on utilizing attention mechanisms to pinpoint critical regions as salient features, while ignoring the imperceptible underlying features and the causal relationship between prediction results and attention. This paper introduces a model-agnostic classification framework that leverages counterfactual explanations to enhance attention. Our approach consists of two steps: counterfactual example generation (Explain) and attention-based classifier refinement (Reinforce). The counterfactual explainer is designed to identify key pixel-level features, the adjustment of which could lead to different predictions. These generated counterfactual examples reveal hidden causal factors in the classifier’s decision-making process. Then the classifier utilizes these pixel features as attention, conducting reliable classification under the guidance of counterfactual examples. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed model. It achieves an accuracy of 98.125<span>(%)</span> in the defect classification task on the WM-811K dataset and 92.544<span>(%)</span> on the MixedWM38 dataset, outperforming state-of-the-art attention methods such as SENet, CBAM, and Vision Transformer by over 5%. Our results highlight the superiority of our approach and its potential for practical implementation in the semiconductor manufacturing domain.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

An integrated interpretation and clustering model based on attribute grouping

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06262-2

Liang Chen, Leming Sun, Caiming Zhong

{"title":"An integrated interpretation and clustering model based on attribute grouping","authors":"Liang Chen, Leming Sun, Caiming Zhong","doi":"10.1007/s10489-025-06262-2","DOIUrl":"10.1007/s10489-025-06262-2","url":null,"abstract":"<div><p>Clustering is a technique in unsupervised learning used to group unlabeled data. However, traditional clustering algorithms cannot provide explanations for the clustering process and its results, which limits their applicability in certain fields. Existing methods to address the lack of interpretability in clustering algorithms typically focus on explaining the results after the clustering process is complete. Few studies explore embedding interpretability directly into the clustering process, and most of these methods rely on data prototypes to express interpretability, which often leads to explanations that are not intuitive and user-friendly. To address this, a feature-based method is proposed to embed interpretability into the clustering process. This approach provides users with intuitive and easy-to-understand explanations and introduces a new direction for research on embedding interpretability into clustering. The method operates in two stages: in the first stage, all attributes are grouped; in the second stage, an optimization formula is used to complete both the clustering and the weighting of each attribute group. The proposed method was evaluated on multiple synthetic and real-world datasets and compared with other methods. The experimental results show that the method improves clustering accuracy by approximately 5 percent and interpretability by around 40 percent compared to existing approaches.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06262-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continual learning with selective nets

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06497-z

Hai Tung Luu, Marton Szemenyei

{"title":"Continual learning with selective nets","authors":"Hai Tung Luu, Marton Szemenyei","doi":"10.1007/s10489-025-06497-z","DOIUrl":"10.1007/s10489-025-06497-z","url":null,"abstract":"<div><p>The widespread adoption of foundation models has significantly transformed machine learning, enabling even straightforward architectures to achieve results comparable to state-of-the-art methods. Inspired by the brain’s natural learning process-where studying a new concept activates distinct neural pathways and recalling that memory requires a specific stimulus to fully recover the information-we present a novel approach to dynamic task identification and submodel selection in continual learning. Our method leverages the power of the learning robust visual features without supervision model (DINOv2) foundation model to handle multi-experience datasets by dividing them into multiple experiences, each representing a subset of classes. To build a memory of these classes, we employ strategies such as using random real images, distilled images, k-nearest neighbours (kNN) to identify the closest samples to each cluster, and support vector machines (SVM) to select the most representative samples. During testing, where the task identification (ID) is not provided, we extract features of the test image and use distance measurements to match it with the stored features. Additionally, we introduce a new forgetting metric specifically designed to measure the forgetting rate in task-agnostic continual learning scenarios, unlike traditional task-specific approaches. This metric captures the extent of knowledge loss across tasks where the task identity is unknown during inference. Despite its simple architecture, our method delivers competitive performance across various datasets, surpassing state-of-the-art results in certain instances.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06497-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximizing diversity in k-pattern set mining through constraint programming and entropy

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06482-6

Mohamed El Amine Douad, Noureddine Aribi, Samir Loudni, Arnold Hien, Yahia Lebbah

{"title":"Maximizing diversity in k-pattern set mining through constraint programming and entropy","authors":"Mohamed El Amine Douad, Noureddine Aribi, Samir Loudni, Arnold Hien, Yahia Lebbah","doi":"10.1007/s10489-025-06482-6","DOIUrl":"10.1007/s10489-025-06482-6","url":null,"abstract":"<div><p>Extracting diverse and frequent closed itemsets from large datasets is a core challenge in pattern mining, with significant implications across domains such as fraud detection, recommendation systems, and machine learning. Existing approaches often lack flexibility and efficiency, and struggle with initial itemset selection bias and redundancy. This paper addresses these research gaps by introducing a compact and modular constraint programming model that formalizes the search for diverse patterns. Our approach incorporates a novel global constraint derived from a relaxed Overlap diversity measure, using tighter lower and upper bounds to improve filtering capabilities. Unlike traditional methods, we leverage an entropy-based optimization framework that combines joint entropy maximization with top-k pattern mining to identify the maximally k-diverse pattern set. Our approach ensures more comprehensive and informative pattern discovery by minimizing redundancy and promoting pattern diversity. Extensive experiments validate the effectiveness of the proposed method, demonstrating significant performance gains and superior pattern quality compared to state-of-the-art approaches. Implemented in both sequential and parallel versions, the framework offers an efficient and adaptable solution for anytime pattern mining tasks in various domains.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Machine sound anomaly detection based on dual-channel feature fusion variational auto-encoder

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06449-7

Chen Zhang, Yongkang Wei, Xiaofeng Wang, Xiaoxuan Wu, Xuhui Zhu

{"title":"Machine sound anomaly detection based on dual-channel feature fusion variational auto-encoder","authors":"Chen Zhang, Yongkang Wei, Xiaofeng Wang, Xiaoxuan Wu, Xuhui Zhu","doi":"10.1007/s10489-025-06449-7","DOIUrl":"10.1007/s10489-025-06449-7","url":null,"abstract":"<div><p>With the increasing intelligence and automation of industrial equipment, the technology for detecting equipment anomalies has become increasingly important. Compared to image-based anomaly detection methods, sound-based anomaly detection methods have the advantages of being non-intrusive, real-time and having lower data collection costs. These advantages make them highly valuable for research. Currently, deep learning methods that focus on spectrogram reconstruction have become widely utilized in the field of machine sound anomaly detection research. However, previous methods only attempted to mitigate the impact of noise without enabling the model to fully learn the distribution of sound features during the reconstruction process. In this paper, a novel Dual-Channel Feature Fusion Variational Autoencoder (DCFF-VAE) is proposed to effectively improve its reconstruction ability and help it better learn the normal sound features. In this method, the deep features extracted from the convolution layer and bidirectional gated cycle unit in the encoder are integrated by means of concatenation to make full use of the important features in the sound. Subsequently, grouped deconvolution is applied in the decoder to reduce model complexity while enhancing its perceptual ability for features. Additionally, during the anomaly detection phase, anomaly scores are calculated based on the Mahalanobis distance to better capture the differences between normal and abnormal sounds. Anomaly detection experiments conducted on five types of machines demonstrate that DCFF-VAE not only achieves the best stability but also surpasses the best comparison method by 3.14% and 1.21% in AUC and pAUC metrics, respectively.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Decomposition dynamic multi-graph convolutional recurrent network for traffic forecasting

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-29 DOI: 10.1007/s10489-025-06503-4

Longfei Hu, Lai Wei, Yeqing Lin

{"title":"Decomposition dynamic multi-graph convolutional recurrent network for traffic forecasting","authors":"Longfei Hu, Lai Wei, Yeqing Lin","doi":"10.1007/s10489-025-06503-4","DOIUrl":"10.1007/s10489-025-06503-4","url":null,"abstract":"<div><p>Accurate traffic flow prediction is crucial for urban traffic management. Traffic data is typically collected from sensors deployed along roadways, which often record both valid and erroneous data. However, most existing studies assume that the collected data is perfectly accurate, overlooking the existence of erroneous data. Meanwhile, graph neural networks are widely applied in traffic forecasting due to their ability to effectively capture correlations between nodes in a network. However, existing methods often rely solely on either static or dynamic graph structures, which may not accurately reflect the complex spatial relationships between nodes. To address these issues, we propose a decomposition dynamic multi-graph convolutional recurrent network (DDMGCRN). DDMGCRN utilizes a residual decomposition mechanism to separate erroneous data from valid data, thereby mitigating its impact. Additionally, DDMGCRN introduces sensor-specific spatial identity embeddings and timestamp embeddings to construct dynamic graphs. It further integrates static graphs for multi-graph fusion, facilitating more effective spatial feature extraction. Furthermore, to address the limitations of RNN-based models in capturing global temporal dependencies, DDMGCRN incorporates a global temporal attention module. Experimental results on four real-world datasets show that DDMGCRN outperforms all baseline models on the PEMS08 dataset, achieving a mean absolute error (MAE) of 14.13, which improves performance by approximately 4.85% compared to the best baseline model. The source code is available at https://github.com/hulongfei123/DDMGCRN.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 deep reinforcement learning approach and its application in multi-USV adversarial game simulation

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-28 DOI: 10.1007/s10489-025-06380-x

Jinjun Rao, Cong Wang, Mei Liu, Jinbo Chen, Jingtao Lei, Wojciech Giernacki

{"title":"A deep reinforcement learning approach and its application in multi-USV adversarial game simulation","authors":"Jinjun Rao, Cong Wang, Mei Liu, Jinbo Chen, Jingtao Lei, Wojciech Giernacki","doi":"10.1007/s10489-025-06380-x","DOIUrl":"10.1007/s10489-025-06380-x","url":null,"abstract":"<div><p>With the progression of unmanned surface vehicle (USV) intelligence and the maturation of cluster control technologies, intelligent decision-making methods for multi-USV adversarial games have become pivotal technological focuses. Deep reinforcement learning (DRL), a prominent subset of artificial intelligence, has recently achieved notable advancements, heralding significant potential for this field. The intrinsic curiosity module (ICM), self-play (SP), and posthumous credit assignment (POCA) are integrated with proximal policy optimization (PPO) to address the challenges of sparse reward, low sample utilization, and credit assignment in multi-USV adversarial games, and a novel proximal policy optimization algorithm (PPO-ICMSPPOCA) is finally constructed. The algorithm generates intrinsic rewards through iterative training during multi-USV adversarial games while simultaneously addressing the evaluation of each USV's specific contribution to the team and the challenge of varying numbers of USVs. A perturbation mathematical model for a USV with three degrees of freedom is established, considering the influence of external environmental disturbances and variations in the USV's state on its hydrodynamic performance in this paper. With the Unity3D and ML-Agents toolkit platforms, multi-USV adversarial game simulation scenes, which can integrate and load various reinforcement learning (RL) algorithms, have been developed. Symmetric or asymmetric game experiments of different scales are conducted in adversarial games. The experiments show that the red teams with our algorithms can learn adversarial tactics more quickly, such as troop dispersion and coordinated attacks. Over 100 episodes, the red teams with ICM, SP, and POCA achieved win rates of 88.25%, 86.75%, and 91.33%, respectively, exhibiting higher game intelligence while obtaining higher cumulative rewards.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

DRHGNN: a dynamic residual hypergraph neural network for aspect sentiment triplet extraction

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-28 DOI: 10.1007/s10489-025-06466-6

Peng Guo, Zihao Yu, Chao Li, Jun Sun

{"title":"DRHGNN: a dynamic residual hypergraph neural network for aspect sentiment triplet extraction","authors":"Peng Guo, Zihao Yu, Chao Li, Jun Sun","doi":"10.1007/s10489-025-06466-6","DOIUrl":"10.1007/s10489-025-06466-6","url":null,"abstract":"<div><p>Aspect Sentiment Triple Extraction (ASTE) is an emerging sentiment analysis task. Many existing methods focus on designing a new labeling scheme to enable end-to-end operation of the model. However, these methods overlook the relationships between words in the ASTE task. In this paper, we propose the Dynamic Residual Hypergraph Neural Network (DRHGNN), which fully considers the relationships between words. Specifically, based on the pre-defined ten types of word pair relationships, we employ a graph attention network to model sentence features as a relational graph matrix. Subsequently, we use a dynamic hypergraph network to learn deep features from the transformed graph structure, then constructing relation-aware node representations. Furthermore, we integrate a residual connection to improve the performance of our DRHGNN model. Finally, we design a relationship constraint to dynamically control the number of hyperedges, thereby enhancing the effectiveness of the dynamic hypergraph neural network. Extensive experimental results on benchmark datasets show that our proposed model significantly outperforms state-of-the-art methods, demonstrating the effectiveness and robustness of the model.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Automated text annotation: a new paradigm for generalizable text-to-image person retrieval

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-28 DOI: 10.1007/s10489-025-06487-1

Delong Liu, Peng Wang, Zhicheng Zhao, Fei Su

{"title":"Automated text annotation: a new paradigm for generalizable text-to-image person retrieval","authors":"Delong Liu, Peng Wang, Zhicheng Zhao, Fei Su","doi":"10.1007/s10489-025-06487-1","DOIUrl":"10.1007/s10489-025-06487-1","url":null,"abstract":"<p>Retrieving specific person images based on textual descriptions, known as Text-to-Image Person Retrieval (TIPR), has emerged as a challenging research problem. While existing methods primarily focus on architectural refinements and feature representation enhancements, the critical aspect of textual description quality remains understudied. We propose a novel framework that automatically generates stylistically consistent textual descriptions to enhance TIPR generalizability. Specifically, we develop a dual-model architecture employing both captioning and retrieval models to quantitatively evaluate the impact of textual descriptions on retrieval performance. Comparative analysis reveals that manually annotated descriptions exhibit significant stylistic variations due to subjective biases among different annotators. To address this, our framework utilizes the captioning model to generate structurally consistent textual descriptions, enabling subsequent training and inference of the retrieval model based on automated annotations. Notably, our framework achieves a <b>18.60%</b> improvement in Rank-1 accuracy over manual annotations on the RSTPReid dataset. We systematically investigate the impact of identity quantity during testing and explore prompt-guided strategy to enhance image caption quality. Furthermore, this paradigm ensures superior generalization capabilities for well-trained retrieval models. Extensive experiments demonstrate that our approach improves the applicability of TIPR systems.</p><p>Comparison framework of manual and automated annotation performance. The left panel illustrates the process of generating automated annotations and the details of captioner training and testing. The right panel demonstrates the training and testing processes using different image-text pairs and compares the final results on the RSTPReid dataset. This results show that the performance of automated annotations surpasses that of manual annotations on this dataset</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 novel classification method based on an online extended belief rule base with a human-in-the-loop strategy

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-27 DOI: 10.1007/s10489-025-06434-0

Jinyuan Li, Guangyu Qian, Wei He, Hailong Zhu, Guohui Zhou

{"title":"A novel classification method based on an online extended belief rule base with a human-in-the-loop strategy","authors":"Jinyuan Li, Guangyu Qian, Wei He, Hailong Zhu, Guohui Zhou","doi":"10.1007/s10489-025-06434-0","DOIUrl":"10.1007/s10489-025-06434-0","url":null,"abstract":"<div><p>Classification methods, such as fault diagnosis and intrusion detection, are widely used in modeling complex systems. The accuracy and credibility of these methods directly affect the reliability of the modeling results, which in turn determines the effectiveness of engineering decisions. Additionally, the model's ability to be dynamically updated should be considered, given the intricate and ever-changing nature of engineering environments. For online models, adding new training samples without considering their suitability can lead to problems such as poor model performance and increased rule base complexity. Furthermore, amid constantly arriving new samples in a dynamic environment, modeling based only on initial expert knowledge can result in new samples not being fully used. Therefore, a novel classification method based on an online extended belief rule base with a human-in-the-loop strategy (OEBRB-H) is proposed in this paper. First, a fuzzy c-means algorithm based on expert knowledge (FBE) is designed to evaluate model parameters online. Second, a human-in-the-loop strategy for dividing the new sample set and a domain-value-based rule updating method are proposed for model optimization. Finally, two case studies, namely, aeroengine inter-shaft bearing fault diagnosis and industrial control intrusion detection, are performed. The results indicate that the model proposed in this paper can maintain both credibility and high accuracy in dynamic environments.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06434-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0