Expert Systems with Applications最新文献

{"title":"Multi-source multi-label feature selection with missing features","authors":"Yabo Shi ,&nbsp;Peipei Li ,&nbsp;Xiulan Yuan ,&nbsp;You Wu ,&nbsp;Haiping Wang","doi":"10.1016/j.eswa.2025.129879","DOIUrl":"10.1016/j.eswa.2025.129879","url":null,"abstract":"<div><div>Feature dimensionality reduction on Multi-Source Multi-Label (MSML) data is a critical and challenging task. Because practical situations always produce massive MSML data, but they usually contain more missing feature values in the high-dimensional feature space and present severely skewed label distributions in the multi-label space, which aggravate the difficulties in the tackling of high-dimensional feature selection on MSML data. However, much attention in feature selection has been directed either toward multi-label data or multi-source data, while little attention is focused on MSML data, not to mention those containing missing features. Motivated by this, we present a new feature selection method for MSML data with missing features, called MMFSMF. Specifically, to overcome the issue of feature missing, we first supplement the feature matrix by constructing a feature correlation matrix during the modeling process of multi-label learning. At the meanwhile, we utilize a multi-label oversampling mechanism to address the persistent problem of label skewness in multi-label data. Secondly, in terms of the above processing, we introduce a refined infinite feature selection algorithm to perform feature dimensionality reduction in each multi-label data source, considering both label correlations and label-specific features. Thirdly, to address feature redundancy among multiple data sources, we apply a new inter-source feature fusion method. Finally, experiments conducted on nine synthetic MSML datasets with missing features demonstrate that MMFSMF achieves superior performances compared to all competing ones.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129879"},"PeriodicalIF":7.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222010","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}

{"title":"LFRSCNet: Skin defect detection based on lightweight flexible residual separable convolutional network","authors":"Zhenyu Lu ,&nbsp;Jue Wang ,&nbsp;Jiteng Zhu ,&nbsp;Yuwen Sun","doi":"10.1016/j.eswa.2025.129811","DOIUrl":"10.1016/j.eswa.2025.129811","url":null,"abstract":"<div><div>Aircraft skin is prone to surface damage, such as cracks and dents, during long-term service or manufacturing processes. These defects not only threaten structural integrity but may also pose potential safety hazards. The industrial sector continually explores more efficient and precise detection methods to address this issue. Therefore, this paper proposes a skin defect detection method based on a lightweight and flexible residual separation convolutional network to improve detection accuracy and efficiency. Therefore, this paper proposes a skin defect detection method based on a lightweight flexible residual separable convolution network to improve detection accuracy and efficiency. First, a lightweight flexible residual separable convolution module (LFRCM) is designed, which effectively integrates multi-modal features by combining multi-scale receptive fields with an adaptive channel attention mechanism; at the same time, a lightweight backbone network based on PP-LCNet is constructed, employing a collaborative optimization strategy of depthwise separable convolutions and the h-swish activation function to significantly enhance inference speed while maintaining detection accuracy; finally, the MPDIoU metric criterion is introduced, which effectively improves target localization accuracy by implementing a center point offset penalty mechanism. Experiments on the self-built professional dataset SD-DET and the public dataset GC10-DET show that the model achieves [email protected] of 99.5% and 86.2%, respectively, demonstrating significant advantages over mainstream detection models. Systematic ablation experiments confirm the synergistic effect of various innovative modules. Finally, verification experiments are conducted on the AIRCRAFT skin defect dataset, achieving an [email protected] of 30.7%. Quantitative analysis and comparative experiments verify that LFRSCNet can achieve detection accuracy breakthroughs while maintaining low parameter counts and computational costs. Its balanced accuracy-efficiency characteristics provide an efficient and reliable solution for surface defect detection in industrial scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129811"},"PeriodicalIF":7.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221880","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}

{"title":"A machine learning-based medical device recall initiator prediction framework: From supply chain risk management and resilience view","authors":"Yang Hu ,&nbsp;Davy Monticolo ,&nbsp;Pezhman Ghadimi","doi":"10.1016/j.eswa.2025.129922","DOIUrl":"10.1016/j.eswa.2025.129922","url":null,"abstract":"<div><div>Persistent quality problems with medical devices and the associated recall present potential health risks to users, bringing extra costs and disturbances to the supply chain. Classical medical device recall strategy neglects the significance of the failure detection process in the premarket phase, increasing the medical device recall risks. This research first established the theoretical foundation for the medical device recall reasons detection problem by reconstructing the medical device recall strategy from the supply chain risk and resilience view and reinforced the importance of failure detection and quality inspection work in the premarket stage. Moreover, existing medical device failure reason prediction research was limited in practicality and scalability. To address this problem, we developed a machine learning-based medical device recall initiator prediction system framework to conduct proactive failure detection based on the industrial case. By redesigning in dataset, clustering method and input feature selection, an accuracy rate of 88.85% is achieved, which indicates the potential of the proposed framework in assisting manufacturers with asset predictive failure detection for reducing recall. A comparative analysis of prediction performance between our framework and the most similar research that utilized the same prediction algorithms was presented. The comparison results showed that our distinctive design in the dataset, clustering method, and key input features chosen are valid and efficient. Before redesigning the prediction algorithms that require higher technical investment, our elaborate research design in selecting the dataset, cluster method, and key input features can be the antecedents of better prediction performance for manufacturers. The proposed predictive framework obtains higher accuracy, scalability, practicality, with accessibility.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129922"},"PeriodicalIF":7.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222006","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}

{"title":"Modeling Trust in human–Robot Collaborative Construction: An Improved Cloud Bayesian Network","authors":"Lei Wang ,&nbsp;Mingyu Zhang ,&nbsp;Heng Li ,&nbsp;Yinong Hu ,&nbsp;Jie Ma ,&nbsp;Waleed Umer ,&nbsp;Xin Fang","doi":"10.1016/j.eswa.2025.129928","DOIUrl":"10.1016/j.eswa.2025.129928","url":null,"abstract":"<div><div>Trust developed by workers towards robotic systems is critical to the successful implementation of human-robot collaboration (HRC) in construction, directly influencing operational efficiency and safety outcomes. To accurately evaluate trust risks within HRC scenarios, this study proposes an integrated method combining an improved Cloud Model (CM) with Bayesian Networks (BNs) for dynamic trust risk analysis. Initially, key factors influencing trust risks in HRC were identified through literature review and expert elicitation. The improved CM was then employed to capture inherent uncertainties and fuzziness in trust state definitions, facilitating the discretization of continuous expert evaluations into appropriate risk states. Subsequently, the BN was developed to perform forward reasoning, sensitivity analysis, and backward diagnosis, enabling proactive trust risk prediction, critical factor identification, and targeted interventions. The primary contributions of this research include: (a) identifying 11 trust factors from human, organizational, and robotic perspectives, offering a comprehensive basis for analyzing HRC trust risk in construction; (b) employing an optimized cloud entropy approach to accurately capture fuzziness and randomness in expert evaluations, thereby producing robust prior probabilities; and (c) developing a hybrid CBN framework to assess HRC trust risk in construction, demonstrating superior performance in risk perception, analysis, and control. Overall, this study provides valuable insights into safer and more effective HRC through dynamic evaluation of trust risk.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129928"},"PeriodicalIF":7.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222004","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}

{"title":"Life cycle cost reliability assessment for strategic real estate decision-making","authors":"Elin A. Eldars ,&nbsp;Amin A. Sorour","doi":"10.1016/j.eswa.2025.129329","DOIUrl":"10.1016/j.eswa.2025.129329","url":null,"abstract":"<div><div>Many real estate projects prioritize minimizing initial development costs while often overlooking the long-term financial implications of their decisions. This short-term focus frequently leads to increased operational, maintenance, and renewal expenses, ultimately reducing overall profitability. Life Cycle Costing (LCC) provides a comprehensive approach to evaluating total project costs over time; however, its adoption remains limited due to challenges such as data constraints, uncertainty about future cost savings, and the lack of standardized performance measurement tools. To tackle these issues, this paper proposes a structured LCC reliability assessment model designed for real estate decision-makers. The model systematically identifies and analyzes key cost factors across all project phases, including construction, operation, renewal, maintenance, and end-of-life, while integrating technical, economic, environmental, and social dimensions. A structured survey was employed to quantify and prioritize these cost factors, facilitating the development of category-specific LCC models and a standardized evaluation framework. Additionally, a benchmarking scale was created to measure the reliability of input factors. Although this study emphasizes the reliability dimension, it establishes a foundation for the future integration of an optimization module to enhance decision-making and maximize life cycle cost efficiency. The proposed model has been automated to improve usability and accessibility, allowing stakeholders to make informed investment decisions that promote long-term financial sustainability in real estate development.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129329"},"PeriodicalIF":7.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221476","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}

{"title":"Towards large-scale multi-objective feature selection: A two-stage evolutionary algorithm guided by dual feature weightings","authors":"Gaohui Li ,&nbsp;Zefeng Chen ,&nbsp;Yuren Zhou ,&nbsp;Zhengxin Huang ,&nbsp;Xiaoyun Xia","doi":"10.1016/j.eswa.2025.129823","DOIUrl":"10.1016/j.eswa.2025.129823","url":null,"abstract":"<div><div>Feature Selection (FS) is a critical task in high-dimensional data processing, aiming to identify the most discriminative subset of features to improve model performance and reduce computational complexity. In recent years, multi-objective evolutionary algorithms have been widely applied to FS problems due to their ability to simultaneously optimize multiple objectives (i.e., classification accuracy and subset size for an FS problem). However, when dealing with large-scale multi-objective FS problems, existing algorithms often suffer from the vast search space and limited search capability, which makes them prone to local optima. To address these challenges, this paper proposes a two-stage evolutionary algorithm guided by dual feature weightings, named TSEA/DFW. In the first stage, an evolutionary search is performed under the guidance of the filter-based feature weighting strategy. The key features are then identified based on the population distribution and optimal solutions, thereby shrinking the search space. In the second stage, a refined search is conducted in the shrunken feature space to boost search efficiency and solution quality. To this end, a novel weighting strategy named Pareto-based hierarchical feature weighting is proposed, which captures the variation in feature performance across different non-dominated levels, reinforces the contribution of high-quality solutions, and preserves useful information from suboptimal solutions. Additionally, a novel offspring reproduction procedure guided by stage-specific feature weights is designed to further enhance search capability. Experimental results on 13 real-world datasets show that the proposed TSEA/DFW performs best on 10 datasets in terms of HV metric and on 11 datasets in terms of IGD, demonstrating the significant superiority of TSEA/DFW over seven state-of-the-art feature selection methods. The performance improvements stem from the two-stage evolutionary framework guided by dual feature weighting, which enables the early identification of important features, thereby effectively reducing the search space and enhancing search efficiency. In addition, further analysis demonstrates that the proposed TSEA/DFW has strong generality across diverse classifiers, and the developed two-stage evolutionary framework in TSEA/DFW is a general powerful framework that can integrate any mainstream FS algorithm into its second stage, exhibiting robust applicability and scalability.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129823"},"PeriodicalIF":7.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220940","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}

{"title":"A two-stage hybrid heuristic approach combining genetic algorithm and variable neighborhood descent for the clustered electric vehicle routing problem","authors":"Yuheng Jin,&nbsp;Xiaoguang Bao,&nbsp;Zhaocai Wang","doi":"10.1016/j.eswa.2025.129848","DOIUrl":"10.1016/j.eswa.2025.129848","url":null,"abstract":"<div><div>This paper considers a new variant of the Electric Vehicle Routing Problem (EVRP), termed the Clustered Electric Vehicle Routing Problem (CluEVRP). In CluEVRP, all customers are pre-divided into clusters, and each charging station is either located within a cluster or independent of any cluster. Each electric vehicle must complete service for all customers within the current cluster before proceeding to the next cluster or returning to the depot. Electric vehicles can charge at any available charging station while serving a cluster, but incur a penalty cost upon entering each cluster. The objective is to minimize the total logistics cost, comprising vehicle startup costs, cluster entry penalty costs, and energy consumption costs. To solve CluEVRP, a two-stage hybrid heuristic combining a Genetic Algorithm (GA) and Variable Neighborhood Descent (VND) is proposed (HGA-VND), where GA ensures population diversity and VND enhances local search capability. To evaluate the algorithm’s performance, 75 test instances are adapted from classic Clustered Vehicle Routing Problem (CluVRP) dataset, incorporating electric vehicle characteristics. Computational results demonstrate that HGA-VND consistently obtains high-quality solutions within reasonable time for both CluVRP and CluEVRP instances, exhibiting good performance. Furthermore, sensitivity analysis indicates that moderately increasing vehicle capacity, optimizing battery configuration, and adopting lightweight designs can significantly reduce total operating costs. This study extends traditional EVRP research by introducing clustered customer distribution, enriching solutions for routing problems in practical logistics networks, particularly for “milk run” models in industrial parks, and providing significant managerial insights.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129848"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222076","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}

{"title":"EEG-based emotion identification from nerve conduction mechanisms: A gustatory-emotion coupling model combined with multiblock attention module","authors":"Wenbo Zheng ,&nbsp;Yong Peng ,&nbsp;Ancai Zhang ,&nbsp;Quan Yuan","doi":"10.1016/j.eswa.2025.129855","DOIUrl":"10.1016/j.eswa.2025.129855","url":null,"abstract":"<div><div>Electroencephalogram (EEG)-based emotion identification enables accurate emotional interaction in brain-computer fusion by decoding brain signals, thereby enhancing the intelligence of human-computer collaboration. Data augmentation (DA) techniques offer a promising solution to the challenge of data scarcity in emotion identification. However, traditional DA methods often overlook the physiological mechanisms underlying EEG data, limiting their effectiveness and constraining the performance of emotion classification. To address this, a DA model based on human nerve conduction mechanisms (NCMs), named the gustatory-emotion coupling model and multiblock attention module (GECM-MBAM), is proposed to improve the performance of emotion identification. First, the 1/<em>f</em> characteristics and synchronization of brain responses are reproduced in the GECM output when stimulated by EEG. The bionic performance of the model in EEG processing is validated, demonstrating brain-like perception of EEG signals via the GECM. Second, the MBAM is designed based on the characteristics of the GECM output, facilitating data augmentation of emotion-related EEG. Comparative experiments demonstrate that GECM-MBAM remarkably outperforms multiple existing DA models in recognition accuracy (<em>p</em> &lt; 0.05), confirming its effectiveness and superiority in EEG data augmentation. Finally, when compared with state-of-the-art algorithms and in ablation studies, GECM-MBAM demonstrates superior performance in emotion recognition. Specifically, GECM-MBAM attains accuracies of 96.91 % and 94.52 %, recalls of 96.23 % and 93.86 %, and kappa coefficients of 95.45 % and 94.29 % on the SEED and SEED-IV datasets, respectively. In conclusion, the performance of emotion identification is improved using the GECM-MBAM, offering a novel bionic processing approach for affective computing.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129855"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222085","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}

{"title":"A belief rule-based system for online and centralized collaborative performance assessment of networked physical systems subject to nonideal channels","authors":"Haoran Zhang ,&nbsp;Lining Xing ,&nbsp;Jian Wu ,&nbsp;Ruohan Yang ,&nbsp;Zhichao Feng","doi":"10.1016/j.eswa.2025.129803","DOIUrl":"10.1016/j.eswa.2025.129803","url":null,"abstract":"<div><div>Networked physical systems (NPSs) are widely applied in modern engineering practices characterized by intensive domain knowledge and imperfect observational data. Meanwhile, collaborative performance assessment provides strong support for them to operate safely and stably over a long period of time. For a specific NPS and its corresponding online and centralized collaborative performance assessment system, the existence of interference and noise in the real-world channel that is rather nonideal inevitably obstructs the smooth progress of the assessment. To this end, in this paper, a symbolic systematic solution is proposed resorting to an improved version of the belief rule base with continuous inputs (BRB-CI). First, the extrapolation module is enhanced by integrating a matched filtering-based link. Second, the existing robustness analysis for systems based on the fundamental belief rule base is extended to systems based on the BRB-CI. Third, the optimization module is ameliorated by designing a multimetric-balanced pattern of the grey wolf optimizer with interpretability reinforcement. Ultimately, by choosing an instance of NPSs in the field of aerospace with continuous time dynamics, pertinent empirical studies are carried out to substantiate the good engineering practicability of our proposal. Note that this paper is the first piece inquiring into belief rule-based systems such a class of expert systems for online and centralized cooperative performance assessment of NPSs with continuous time dynamics such an application, with considerable attention paid to the nonideality of real-world channels.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129803"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222084","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}

{"title":"The knowledge-driven adaptive late acceptance iterative hill-climbing heuristics for the bus and ADR collaborative delivery problem","authors":"Lijun Pan ,&nbsp;Changshi Liu ,&nbsp;Yifan Zhang ,&nbsp;Shun Li","doi":"10.1016/j.eswa.2025.129810","DOIUrl":"10.1016/j.eswa.2025.129810","url":null,"abstract":"<div><div>Urban-rural bus transit services encounter a dilemma between the necessity for enhanced services and the challenge of low profitability due to scant travel demand. Combining freight transportation with passenger services can enhance the efficiency and profitability of buses in both urban and rural areas, while also reducing environmental impacts. A case in point is the integration of freight deliveries into rural bus networks in China. Concurrently, with the advancement of autonomous delivery robot (ADR) technology, there is a growing deployment of ADRs for last-mile delivery purposes. In this paper, we have studied a new collaborative passenger and freight transportation problem involving buses and ADRs, namely, the bus and ADR collaborative delivery problem (BACDP). In this scenario, a bus route transports several ADRs, which carry multiple parcels, to distribution regions for door-to-door delivery, each ADR boards a bus to reach the sub-region and then boards another bus to return to the distribution center. We have proposed a mathematical model for BACDP, which can be decomposed into a master problem and a sub-problem. and the condition that the optimal solution to the master problem is also the optimal solution to the original problem has been proved. To tackle the BACDP effectively, we designed a novel three-stage iterative method, guided by adaptive late acceptance hill-climbing heuristics (ALAHH). Specifically, at the first stage, the k-means++ and Hamiltonian graph-guided algorithms are used to cluster customers; at the second stage, the variable neighborhood search plans the ADRs’ routes; at the third stage, we utilize the solver to address subproblems, and the evaluation and invocation mechanism is proposed to achieve the efficient utilization of solvers. Extensive experiments have been conducted on synthetic instances of varying scales to investigate the efficiency of ALAHH. The experimental results demonstrate that the objective values and the computation time are significantly lower than those of SA and LAHC, and our algorithm has achieved the best solutions for 16 problems to date. Additionally, the impacts of two key parameters and mechanisms have been analyzed, and further validation of the robustness of the algorithm parameters and the effectiveness of the mechanisms.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129810"},"PeriodicalIF":7.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221875","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}