Applied Intelligence最新文献

{"title":"A novel drift detection method using parallel detection and anti-noise techniques","authors":"Qian Zhang,&nbsp;Guanjun Liu","doi":"10.1007/s10489-024-05988-9","DOIUrl":"10.1007/s10489-024-05988-9","url":null,"abstract":"<div><p>With the rapid development of the Internet industry, a large amount of streaming data with significant application value will be generated on the Internet. The distribution of stream data is evolving over time compared to traditional data, posing a significant challenge in the learning process from streaming data. In order to adapt the change of data distribution, concept drift detection methods are proposed to pinpoint when the concept drift occurs. Most existing drift detection methods, however, overlook the improvement of the current classifier and the influence of noise data on drift detection. This oversight leads to a decrease in the effectiveness of drift detection. In this paper, we propose a novel adaptation drift detection method to overcome the shortcomings of previous algorithms, such as error detection and lack of anti-noise capability. Meanwhile, stream computing and parallel computing are used to enhance the efficiency of our algorithm. The results of a simulation experiment on 9 synthetic stream data and 6 real-world stream data, all exhibiting concept drift, demonstrate that our method is more effective in handling concept drift compared to other state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373221","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}

{"title":"Boundary-sensitive Adaptive Decoupled Knowledge Distillation For Acne Grading","authors":"Xinyang Zhou,&nbsp;Wenjie Liu,&nbsp;Lei Zhang,&nbsp;Xianliang Zhang","doi":"10.1007/s10489-025-06260-4","DOIUrl":"10.1007/s10489-025-06260-4","url":null,"abstract":"<div><p>Acne grading is a critical step in the treatment and customization of personalized therapeutic plans. Although the knowledge distillation architecture exhibits outstanding performance on acne grading task, the impact of non-label classes is not considered separately, resulting in low distillation efficiency for non-label classes. Such insufficiency will cause the misclassification of the acne images located on the edge of the decision boundary. To address this issue, a novel method named Adaptive Decoupled Knowledge Distillation (ADKD) which considers the uniqueness of the acne images is proposed. In order to explore the influence of non-label classes and enhance the model’s distillation efficiency on them, ADKD splits the traditional KD loss into two parts: non-label class knowledge distillation (NCKD), and label class knowledge distillation (LCKD). Additionally, it dynamically adjusts the NCKD based on the distance between the sample and each non-label class. This allows the model to allocate different learning intensities to various non-label classes, reducing the overrecognition of classes near the sample and the underrecognition of distant classes. The proposed method enables the model to better learn the fuzzy features between acne images, and more accurately classify the samples located on the decision boundary. To verify the proposed method, extensive experiments were carried out on ACNE04 dataset, ACNEHX dataset, and DermaMnist dataset. The experimental results demonstrate the effectiveness of this method, and its performance surpasses that of current state-of-the-art (SOTA) method.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373217","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}

{"title":"Deep random walk inspired multi-view graph convolutional networks for semi-supervised classification","authors":"Zexi Chen,&nbsp;Weibin Chen,&nbsp;Jie Yao,&nbsp;Jinbo Li,&nbsp;Shiping Wang","doi":"10.1007/s10489-025-06322-7","DOIUrl":"10.1007/s10489-025-06322-7","url":null,"abstract":"<div><p>Recent studies highlight the growing appeal of multi-view learning due to its enhanced generalization. Semi-supervised classification, using few labeled samples to classify the unlabeled majority, is gaining popularity for its time and cost efficiency, particularly with high-dimensional and large-scale multi-view data. Existing graph-based methods for multi-view semi-supervised classification still have potential for improvement in further enhancing classification accuracy. Since deep random walk has demonstrated promising performance across diverse fields and shows potential for semi-supervised classification. This paper proposes a deep random walk inspired multi-view graph convolutional network model for semi-supervised classification tasks that builds signal propagation between connected vertices of the graph based on transfer probabilities. The learned representation matrices from different views are fused by an aggregator to learn appropriate weights, which are then normalized for label prediction. The proposed method partially reduces overfitting, and comprehensive experiments show it delivers impressive performance compared to other state-of-the-art algorithms, with classification accuracy improving by more than 5% on certain test datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373171","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}

{"title":"Correction To: Dirichlet stochastic weights averaging for graph neural networks","authors":"Minhoi Park,&nbsp;Rakwoo Chang,&nbsp;Kyungwoo Song","doi":"10.1007/s10489-024-06099-1","DOIUrl":"10.1007/s10489-024-06099-1","url":null,"abstract":"","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361780","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}

{"title":"Correction to: Dynamic interactive weighted feature selection using fuzzy interaction information","authors":"Xi-Ao Ma,&nbsp;Hao Xu,&nbsp;Yi Liu","doi":"10.1007/s10489-025-06273-z","DOIUrl":"10.1007/s10489-025-06273-z","url":null,"abstract":"","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361779","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}

{"title":"Cross-attention fusion and edge-guided fully supervised contrastive learning network for rail surface defect detection","authors":"Jinxin Yang,&nbsp;Wujie Zhou","doi":"10.1007/s10489-025-06314-7","DOIUrl":"10.1007/s10489-025-06314-7","url":null,"abstract":"<div><p>In recent years, there has been significant research focus on efficiently and accurately detecting defects on rail surfaces using computer vision. Utilizing depth information from the rail surface has emerged as an effective approach for detecting visually insignificant types of defects that are unique in nature. However, previous methods have typically overlooked the long-distance dependency between the two modalities when fusing them using conventional convolutional network methods. Additionally, these methods have often relied on traditional cross-entropy loss for edge supervision without considering the intra and inter-pixel relationships associated with edge features. To address these limitations, we propose a novel approach called CECLNet (cross-attention fusion and edge-guided fully supervised contrastive learning network) for rail surface defect detection (RSDD). The proposed CECLNet incorporates a module for inter-modal cross-attention fusion, which effectively explores the complementary information by considering the long-range relationship. Furthermore, we introduce a progressive aggregation-based multiscale feature interactions decoder to promote sufficient information interaction between multiscale features, thus facilitating the generation of final predictions. Finally, we propose a pixel-level fully supervised contrastive learning approach to enhance the efficiency of utilizing edge-assisted information. Extensive experiments conducted on the industrial NEU RGB-D RSDDS-AUG dataset demonstrate the superiority of our proposed CECLNet over 17 state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361781","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}

{"title":"Correction to: Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models","authors":"Francisco de Arriba-Pérez,&nbsp;Silvia García-Méndez,&nbsp;Javier Otero-Mosquera,&nbsp;Francisco J. González-Castaño","doi":"10.1007/s10489-024-06169-4","DOIUrl":"10.1007/s10489-024-06169-4","url":null,"abstract":"","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-024-06169-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361782","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}

{"title":"TADST: reconstruction with spatio-temporal feature fusion for deviation-based time series anomaly detection","authors":"Bin Yang,&nbsp;Tinghuai Ma,&nbsp;Huan Rong,&nbsp;Xuejian Huang,&nbsp;Yubo Wang,&nbsp;Bowen Zhao,&nbsp;Chaoming Wang","doi":"10.1007/s10489-025-06310-x","DOIUrl":"10.1007/s10489-025-06310-x","url":null,"abstract":"<div><p>Anomaly detection is crucial in time series analysis for identifying abnormal events. To address the limitations of traditional methods in integrating spatiotemporal correlations and modeling normal patterns, we propose a Time Series Anomaly Detection Model Based on Spatio-Temporal Feature Fusion (TADST). First, the Spatio-Temporal Feature Fusion Network (STF) combines temporal convolutional networks and graph attention influence networks to capture temporal dynamic dependencies and attribute correlations respectively, facilitating joint spatiotemporal feature modeling. Then, the Time Series Reconstruction Network (TSR) employs a multi-layer encoder-decoder architecture to learn the normal sample distribution and amplify discrepancies between reconstructed and anomalous data. Finally, the Anomaly Detection Mechanism (ADM) identifies anomalies by fitting the tail distribution of reconstruction deviations. When the anomaly score exceeds a predefined threshold, the mechanism updates the parameters of the Generalized Pareto Distribution, keeping the detection criteria adaptive. Experiments demonstrate that the proposed TADST achieves state-of-the-art results on five publicly available datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361778","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}

{"title":"Reusability of Bayesian Networks case studies: a survey","authors":"Nikolay Babakov,&nbsp;Adarsa Sivaprasad,&nbsp;Ehud Reiter,&nbsp;Alberto Bugarín-Diz","doi":"10.1007/s10489-025-06289-5","DOIUrl":"10.1007/s10489-025-06289-5","url":null,"abstract":"<div><p>Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06289-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361831","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}

{"title":"Cross-contextual stress prediction: Simple methodology for comparing features and sample domain adaptation techniques in vital sign analysis","authors":"Samson Mihirette,&nbsp;Enrique A. De la Cal,&nbsp;Qing Tan,&nbsp;Javier Sedano","doi":"10.1007/s10489-025-06277-9","DOIUrl":"10.1007/s10489-025-06277-9","url":null,"abstract":"<div><p>Stress significantly impacts individuals, particularly in professions like nursing and driving, leading to severe health risks and accidents. Accurate stress measurement is critical for effective interventions, yet research is hindered by incomplete datasets and inconsistent methodologies, slowing the development of reliable predictive models. This paper introduces a framework for cross-contextual stress prediction, enabling the generation of general stress prediction models adaptable to specific domain challenges. The methodology leverages two general daily life datasets and three domain-specific datasets, employing steps such as dataset selection, feature extraction, significant feature identification, feature preprocessing, fine-tuning, domain adaptation, and application to specific contexts. Through this framework, key vital signs were identified as significant predictors of stress, including electrocardiography (ECG), heart rate (HR), heart rate variability (HRV) - low frequency (LF), electrodermal activity (EDA), body temperature (TEMP), and skin conductance response (SCR). The experiments conducted include: 1) Utilizing HR and HRV-LF through domain adaptation from general to automobile driving datasets; 2) Applying EDA, HR, and TEMP from general to specific nurse activity datasets; and 3) Adapting ECG, HR, and TEMP from general to automobile driving datasets. Results demonstrate the potential of the proposed framework for cross-contextual stress prediction, with HR and HRV-LF identified as pivotal features. When applied to target datasets specific to stress scenarios, the model achieved a 62% F1 score, demonstrating the effectiveness of the feature-based Correlation Alignment (CORAL) technique combined with Random Forest models in transferring learned knowledge across domains. These findings highlight the robustness of the approach in adapting general stress prediction models to specific contexts, paving the way for real-world applications such as stress monitoring in driving and nursing during high-stress periods like COVID-19.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06277-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362123","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}