Information Sciences最新文献_第6页

A high-order hesitancy fuzzy time series model based on improved cumulative probability distribution approach and weighted fuzzy logic relationship 基于改进的累积概率分布方法和加权模糊逻辑关系的高阶犹豫模糊时间序列模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-13 DOI: 10.1016/j.ins.2025.122262

Chuyi Zhang , Deshan Sun , Kuo Pang , Li Zou , Luis Martínez , Witold Pedrycz

{"title":"A high-order hesitancy fuzzy time series model based on improved cumulative probability distribution approach and weighted fuzzy logic relationship","authors":"Chuyi Zhang , Deshan Sun , Kuo Pang , Li Zou , Luis Martínez , Witold Pedrycz","doi":"10.1016/j.ins.2025.122262","DOIUrl":"10.1016/j.ins.2025.122262","url":null,"abstract":"<div><div>Fuzzy time series models, with their unique capability to handle uncertainty, have become crucial tools in managing complex and imprecise data environments. The proposal of hesitant fuzzy set provides an effective solution for addressing the uncertainty encountered when determining membership degrees in time series data. To enhance the credibility and forecasting accuracy of fuzzy time series model, this paper proposes a high-order hesitant fuzzy time series model based on an improved cumulative probability distribution approach (ICPDA) and weighted fuzzy logic relationship. First, the distribution characteristics and dispersion degrees of time series data are more comprehensively considered by refining the cumulative probability distribution approach with statistical measures, achieving a more adaptive partitioning of time series data. Second, triangular and Gaussian membership functions are employed to construct hesitant fuzzy sets, which are then aggregated using aggregation operators to define fuzzy time series, effectively capturing multiple uncertainties inherent in time series data. In addition, to further improve the forecasting capability of the model, weights are integrated into the fuzzy logical relations, facilitating the defuzzification output for forecasts. The model is finally applied to two real-world time series datasets: the enrollment numbers of the University of Alabama and car sells of Quebec City in Canada, with comparative experiments demonstrating the model's strong generalization ability and forecasting accuracy in uncertain environments.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122262"},"PeriodicalIF":8.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068507","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 robust TDoA-based localization and tracking method designed for intelligent recommender systems 一种基于tdoa的智能推荐系统鲁棒定位跟踪方法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122275

Hong-Yuan Mei , Zhi Zhou , Fu-Rao Shen , Jian Zhao

{"title":"A robust TDoA-based localization and tracking method designed for intelligent recommender systems","authors":"Hong-Yuan Mei , Zhi Zhou , Fu-Rao Shen , Jian Zhao","doi":"10.1016/j.ins.2025.122275","DOIUrl":"10.1016/j.ins.2025.122275","url":null,"abstract":"<div><div>In today's fast-paced society, the demand for intelligent Recommender Systems(RS) is increasing day by day. Users may hope that RSs can take their real-time situation into consideration to help them make timely decisions. To fulfill this goal, RSs need to accurately understand users' information, especially their real-time location. Therefore, localization of the users is very helpful in building an intelligent RS. Meanwhile, great efforts have been made to deal with localization based on time-difference-of-arrival (TDoA) measurements in the past decades. It is widely used in applications such as radar, sonar, sensor networks, and mobile communications. However, such a task is still challenging in complex real-world scenarios where large-scale noise and none-light-of-sight (NLoS) occur. To address this challenge, we propose a noise-tolerant method consisting of two stages: the Robust Multilateration Solver (RMS) and the Restricted Particle Filter (RPF). In the first stage, anomaly detection is adopted to eliminate outliers, and NLoS effects are mitigated by a data augmentation strategy. Then a rough estimate of the current position is obtained by combining two modified multilateration algorithms. In the second stage, the rough estimate is passed through an enhanced particle filter in order to reduce its bias caused by heavy Gaussian noise and smooth the motion trajectory. Meanwhile, localization rationality is guaranteed by confining the position to a predefined area. Sufficient testing in real indoor environments and simulation results demonstrate the effectiveness of our method and reveal its fitness for various industrial fields.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122275"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949048","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

Detecting changes and anomalies in nonstationary contextual bandits with an application to task categorisation

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122270

Edward Austin , Lucy E. Morgan

{"title":"Detecting changes and anomalies in nonstationary contextual bandits with an application to task categorisation","authors":"Edward Austin , Lucy E. Morgan","doi":"10.1016/j.ins.2025.122270","DOIUrl":"10.1016/j.ins.2025.122270","url":null,"abstract":"<div><div>As society becomes increasingly connected, the demands placed on telecommunications systems will only grow. To meet these demands network providers want to deploy automated tools that make decisions based on available network information. Furthermore, there is a need for these tools to be agile, so that they can react to changes, or identify unexpected outcomes, as they occur in this rapidly evolving digital landscape. To address this challenge the first nonstationary contextual bandit method that simultaneously monitors the observed rewards for both changes and anomalies, SCAPA-UCB, is introduced. In addition to incorporating change and anomaly detection, the proposed approach relaxes common nonstationary bandit assumptions on the reward distribution for an arm, allowing contextual information to be incorporated using a broad range of statistical models. Furthermore, the method provides a faster retraining process once a change is detected. Extensive simulation studies are performed to establish the favourable performance of SCAPA-UCB, and an application categorising maintenance tasks on a telecommunications network is presented.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122270"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943398","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

General strict interval-valued overlap functions, strict interval-valued overlap indices, and their applications in interval type-2 fuzzy systems

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122274

Xiaoyu Peng, Xiaodong Pan, Yexing Dan, Juan Dai

{"title":"General strict interval-valued overlap functions, strict interval-valued overlap indices, and their applications in interval type-2 fuzzy systems","authors":"Xiaoyu Peng, Xiaodong Pan, Yexing Dan, Juan Dai","doi":"10.1016/j.ins.2025.122274","DOIUrl":"10.1016/j.ins.2025.122274","url":null,"abstract":"<div><div>To facilitate the adaptation of general iv-overlap functions and iv-overlap indices to IT2 fuzzy systems, we initially modify the boundary conditions of <em>n</em>-ary iv-overlap functions. It enables us to introduce the notions of <em>n</em>-ary strict interval-valued overlap functions and general strict interval-valued overlap functions, and to propose several methodologies for constructing these entities. Subsequently, we introduce the notion of strict interval-valued overlap indices and investigate their relationship with iv-overlap indices. We also construct some strict interval-valued overlap indices leveraging suitable interval-valued functions, and conduct a thorough analysis of their continuity. To fully utilize input information in calculations of SISO and MISO IT2 fuzzy systems, we exploit strict interval-valued overlap indices to compute overlap intervals between input fuzzy sets and fuzzy sets in rules, and further use general strict interval-valued overlap functions to aggregate these intervals, thereby designing three models: SISO-SO, MISO-SOG and MISO-SGO, and studying their approximation properties. Lastly, we conduct comparative experiments on SISO-SO models for function approximation, and on MISO-SOG and MISO-SGO models for five time series forecasting. The experimental results indicate that the utilization of general strict interval-valued overlap functions and strict interval-valued overlap indices significantly enhances system performance, enabling our proposed models to outperform existing models.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122274"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943633","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

Data-driven enhanced belief rule base for complex system health state assessment 复杂系统健康状态评估的数据驱动增强型信念规则库

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122293

Qingxi Zhang , Zeyang Si , Jinting Shen, Hailong Zhu, Guohui Zhou, Wei He

{"title":"Data-driven enhanced belief rule base for complex system health state assessment","authors":"Qingxi Zhang , Zeyang Si , Jinting Shen, Hailong Zhu, Guohui Zhou, Wei He","doi":"10.1016/j.ins.2025.122293","DOIUrl":"10.1016/j.ins.2025.122293","url":null,"abstract":"<div><div>In complex systems, assessing the health state is crucial to ensuring safety and reliability. However, due to the complexity of these systems, acquiring a sufficient amount of useful data poses significant challenges. As a knowledge-based modeling approach, the belief rule base (BRB) utilizes expert knowledge to address these challenges. Nonetheless, in many engineering practices, obtaining sufficient expert knowledge can be equally difficult. To address this problem, this study proposes a data-driven enhanced BRB (DDE-BRB) method for initial model generation, which enhances the modeling capability when expert knowledge is insufficient. First, an antecedent attribute reference value initialization method based on fuzzy clustering is proposed. Second, a method using the Gaussian membership function is introduced to initialize the belief degrees. Finally, optimization algorithms are employed to fine-tune the remaining parameters, and evidence reasoning (ER) technology is used to infer the model. In two case studies, the results demonstrate that DDE-BRB can effectively complete the modeling process and achieve accurate assessment results even under conditions of insufficient expert knowledge.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122293"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072110","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

Mitigating spurious correlations with causal logit perturbation 减轻假相关与因果逻辑扰动

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122276

Xiaoling Zhou, Wei Ye, Rui Xie, Shikun Zhang

{"title":"Mitigating spurious correlations with causal logit perturbation","authors":"Xiaoling Zhou, Wei Ye, Rui Xie, Shikun Zhang","doi":"10.1016/j.ins.2025.122276","DOIUrl":"10.1016/j.ins.2025.122276","url":null,"abstract":"<div><div>Deep learning has seen widespread success in various domains such as science, industry, and society. However, it is acknowledged that certain approaches suffer from non-robustness, relying on spurious correlations for predictions. Addressing these limitations is of paramount importance, necessitating the development of methods that can disentangle spurious correlations. This study attempts to implement causal models via logit perturbations and introduces a novel Causal Logit Perturbation (CLP) framework to train classifiers with generated causal logit perturbations for individual samples, thereby mitigating the spurious associations between non-causal attributes (i.e., image backgrounds) and classes. Our framework employs a perturbation network to generate sample-wise logit perturbations using a series of training characteristics of samples as inputs. The whole framework is optimized by an online meta-learning-based learning algorithm and leverages human causal knowledge by augmenting metadata in both counterfactual and factual manners. Empirical evaluations on four typical biased learning scenarios, including long-tail learning, noisy label learning, generalized long-tail learning, and subpopulation shift learning, demonstrate that CLP consistently achieves state-of-the-art performance. Moreover, visualization results support the effectiveness of the generated causal perturbations in redirecting model attention towards causal image attributes and dismantling spurious associations.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122276"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936729","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

DXMODE: A dynamic explorative multi-operator differential evolution algorithm for engineering optimization problems DXMODE：一种用于工程优化问题的动态探索性多算子差分进化算法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122271

Mohamed Reda , Ahmed Onsy , Amira Y. Haikal , Ali Ghanbari

{"title":"DXMODE: A dynamic explorative multi-operator differential evolution algorithm for engineering optimization problems","authors":"Mohamed Reda , Ahmed Onsy , Amira Y. Haikal , Ali Ghanbari","doi":"10.1016/j.ins.2025.122271","DOIUrl":"10.1016/j.ins.2025.122271","url":null,"abstract":"<div><div>Traditional methods often struggle with complex, real-world problems, while Differential Evolution (DE) offers more robust and adaptable solutions. However, many DE variants intertwine exploration and exploitation within mutation operators and rely on static or blind population reduction, leading to premature diversity loss. This paper proposes Dynamic Explorative Multi-Operator Differential Evolution (DXMODE), a novel DE variant featuring Error-based Linear Population Decay (ELPD) for adaptive sizing, considering both the error improvement and the iteration count. A decoupled exploration phase is also introduced with two new operators, Aggressive Gaussian Exploration (AGE) and Multiple Nested Chaotic Exploration (MNCE), enhancing diversity and search efficiency. DXMODE is validated on CEC2020/2021 and CEC2022 benchmarks against 30 state-of-the-art algorithms, including advanced DE variants and CEC winners. Statistical analyses indicate that DXMODE consistently outperforms competing methods, securing first place across all tests with statistically significant p-values; it surpasses IMODE with a confidence of 99.29%. DXMODE is also validated on 13 Engineering optimization problems, outperforming all algorithms with significant p-values, proving its superiority across real-world problems. The source code of DXMODE is publicly available on GitHub and MATLAB File Exchange: <span><span>https://github.com/MohamedRedaMu/DXMODE-Algorithm</span><svg><path></path></svg></span>, <span><span>https://uk.mathworks.com/matlabcentral/fileexchange/181143-dxmode-algorithm</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122271"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088892","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

Bayesian inference-based stochastic group priorities acceptability analysis for group decision making with triangular fuzzy preference relations

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-09 DOI: 10.1016/j.ins.2025.122287

Jinpei Liu , Wenqian Wei , Longlong Shao , Shijuan Yang , Ligang Zhou , Feifei Jin

{"title":"Bayesian inference-based stochastic group priorities acceptability analysis for group decision making with triangular fuzzy preference relations","authors":"Jinpei Liu , Wenqian Wei , Longlong Shao , Shijuan Yang , Ligang Zhou , Feifei Jin","doi":"10.1016/j.ins.2025.122287","DOIUrl":"10.1016/j.ins.2025.122287","url":null,"abstract":"<div><div>Triangular fuzzy preference relation (TFPR) is one of the most prevalent tools utilized by decision-makers to express opinions in group decision making (GDM). However, many existing GDM methods with TFPRs not only result in information distortion caused by consistency adjustment but also lead to significant information loss during the integration process. To address these issues, this paper proposes a unique GDM method based on Bayesian inference and stochastic group priorities acceptability analysis, which samples fuzzy preference relations (FPRs) and expert weights using stochastic simulation techniques, and applies the Bayesian inference algorithm to obtain the posterior distribution of group priority vector. First, we establish an additive regression model for a given FPR, and present Bayesian inference algorithms to derive the posterior distribution of priority vector. For GDM with TFPRs, the Bayesian inference-based stochastic group priorities acceptability analysis method, which takes into account the inherent uncertainty in fuzzy preference information, is proposed to obtain the optimal ranking of all alternatives. Additionally, a new framework is constructed to facilitate the computation of descriptive measurements, thereby significantly enhancing the capacity to obtain the optimal ranking. Finally, numerical examples and comparative analysis are employed to demonstrate the applicability and benefits of our proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122287"},"PeriodicalIF":8.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943399","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

AutoML-driven optimization of variational quantum circuit 变分量子电路的自动驱动优化

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-08 DOI: 10.1016/j.ins.2025.122272

Haozhen Situ , Zhengjiang Li , Zhimin He , Qin Li , Jinjing Shi

{"title":"AutoML-driven optimization of variational quantum circuit","authors":"Haozhen Situ , Zhengjiang Li , Zhimin He , Qin Li , Jinjing Shi","doi":"10.1016/j.ins.2025.122272","DOIUrl":"10.1016/j.ins.2025.122272","url":null,"abstract":"<div><div>Variational Quantum Circuits (VQCs) offer a powerful framework for quantum machine learning models, where circuit parameters are optimized to learn specific tasks. Quantum architecture search refines VQCs by automating the design of circuit structures. Automated Machine Learning (AutoML) automates model selection, hyperparameter tuning, and optimization, enhancing accessibility for nonexperts and improving efficiency. In this work, we propose an AutoML-driven approach that automates not only the optimization of VQC structures and parameters but also the tuning of training settings, an aspect overlooked in previous studies. We pretrain a graph neural network on a large, unlabeled dataset to learn quantum circuit embeddings. The pretrained model is then fine-tuned on a small set of labeled data from a downstream task to develop a performance predictor that estimates the performance of quantum circuits based on their structures and training settings. This enables us to rank abundant circuit structures and training settings, effectively identifying the optimal configurations for a given task. Numerical experiments demonstrate a strong correlation between the true and predicted performance, as well as its efficiency in VQC optimization. These results highlight the potential of AutoML to improve both the performance and efficiency of VQCs in quantum machine learning applications.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122272"},"PeriodicalIF":8.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936728","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

ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-08 DOI: 10.1016/j.ins.2025.122273

Chien-Liang Liu , Bin Xiao , Cheng-Feng Tsai

{"title":"ECG-STAR: Spatio-temporal attention residual networks for multi-label ECG abnormality classification","authors":"Chien-Liang Liu , Bin Xiao , Cheng-Feng Tsai","doi":"10.1016/j.ins.2025.122273","DOIUrl":"10.1016/j.ins.2025.122273","url":null,"abstract":"<div><div>Accurate and timely diagnosis of cardiovascular diseases (CVDs) through automated electrocardiogram (ECG) interpretation is crucial for facilitating early clinical interventions, reducing mortality rates, and improving post-treatment patient outcomes. This paper introduces ECG-STAR (Spatio-Temporal Attention Residual) Net, a novel deep-learning model designed for multi-label classification of various ECG abnormalities, including arrhythmias and other cardiac conditions. ECG-STAR Net integrates linear layers, long short-term memory (LSTM) networks, spatial-temporal convolutions, and efficient channel attention mechanisms, thereby effectively capturing complex patterns and dependencies in ECG signals. Evaluations on three benchmark ECG datasets, PTB-XL, CPSC 2018, and G12EC, demonstrate the proposed model's high performance, achieving AUC scores of 0.9345, 0.9692, and 0.9117, respectively. Furthermore, ECG-STAR Net achieves notable F1 scores of 0.7556, 0.817, and 0.4429, coupled with low Hamming-loss values of 0.1049, 0.035, and 0.0517 across these datasets. A comprehensive ablation study further underscores the contributions of individual model components, providing additional insights into its architecture and effectiveness. These results highlight the ECG-STAR Net's potential for advancing automated ECG diagnosis and enhancing clinical decision-making.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122273"},"PeriodicalIF":8.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943397","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