Applied Soft Computing最新文献

A knowledge-driven fuzzy logic framework for supporting decision-making entities 支持决策实体的知识驱动模糊逻辑框架

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-17 DOI: 10.1016/j.asoc.2025.113415

David Muñoz-Valero , Juan Moreno-Garcia , Julio Alberto López-Gómez , Enrique Adrian Villarrubia-Martin , Luis Jimenez-Linares

{"title":"A knowledge-driven fuzzy logic framework for supporting decision-making entities","authors":"David Muñoz-Valero , Juan Moreno-Garcia , Julio Alberto López-Gómez , Enrique Adrian Villarrubia-Martin , Luis Jimenez-Linares","doi":"10.1016/j.asoc.2025.113415","DOIUrl":"10.1016/j.asoc.2025.113415","url":null,"abstract":"<div><div>Decision support systems enable decision makers (whether individuals, systems, or other agents) to select the most suitable options by integrating expert knowledge with computational intelligence. Accurate modeling of these decision makers is crucial to ensure optimal decision making in complex and uncertain environments. Embedding expert knowledge in these models is challenging, as experts often lack familiarity with the underlying techniques. Therefore, there is a need for frameworks that are intuitive for experts and enable them to seamlessly integrate their knowledge into decision support systems. This paper presents a novel framework for the automatic generation of fuzzy decision models based on expert knowledge, designed to support decision-making scenarios. The proposed approach leverages the Takagi–Sugeno–Kang Fuzzy Inference System (TSK FIS) to model qualitative human reasoning and automatically induce decision models through expert-defined parameters that model the expert knowledge. This framework represents decision variables using linguistic terms, and introduces a weighted co-occurrence mechanism that captures variable interactions, enabling the generation of cumulative fuzzy decision rules that produce robust and interpretable outcomes. It simplifies expert data input through an intuitive method for defining relationships between variables, eliminating the need for extensive knowledge of fuzzy logic. The flexibility of the proposed framework is demonstrated through two practical case studies: passenger train ticket selection, and weapon choice optimization in video games, showcasing its effectiveness across varied domains. Experimental results validate the system’s capacity to generate tailored decision models that adapt to specific user profiles and objectives, while maintaining both decision-making accuracy and interpretability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113415"},"PeriodicalIF":7.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322520","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

Bi-stage restriction-handling method for the preventive maintenance of a complex machine using differential evolution algorithm 基于差分进化算法的复杂机械预防性维修双阶段限制处理方法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-17 DOI: 10.1016/j.asoc.2025.113427

Xiang Wu , Jinxing Lin

{"title":"Bi-stage restriction-handling method for the preventive maintenance of a complex machine using differential evolution algorithm","authors":"Xiang Wu , Jinxing Lin","doi":"10.1016/j.asoc.2025.113427","DOIUrl":"10.1016/j.asoc.2025.113427","url":null,"abstract":"<div><div>With the increasing complexity of manufacturing equipment, reliability is facing increasingly serious challenges. To ensure its safe operation, this paper considers the preventive maintenance scheme of a complex machine considering production, variable maintenance instants, and deterioration. To begin with, a stochastic dynamical system is proposed to describe the machine’s deterioration process. Further, this problem is modeled as a stochastic dynamical system optimal control model (SDSOCM) including restrictions. It is challenging to directly achieve a high-quality solution of the SDSOCM due to its non-convexity, strong non-linearity, and randomness. To obtain a global optimal solution, the SDSOCM is analytically transformed into a deterministic dynamical system optimal control model (DDSOCM) with restrictions. Following that, a differential evolution algorithm with bi-stage restriction-handling method (DEA-BSRHM) is proposed to solve this DDSOCM via integrating the exterior point approach (EPA) and the interior point approach (IPA) into a differential evolution algorithm (DEA). In the first stage, the EPA involving a dynamic penalty parameter is proposed for comparing candidate members to drive them into the feasible domain. To enhance the search capability and decrease the calculation cost, the IPA including a dynamic penalty parameter is employed for choosing the candidate members in the second stage. Finally, the validity of the proposed method is illustrated via comprehensive experiments and comparative studies. Numerical results on a machine preventive maintenance problem and test functions from IEEE CEC 2010, IEEE CEC 2017, and IEEE CEC 2020 show that compared with other algorithms, DEA-BSRHM can obtain a better solution with smaller standard deviation and less number of function evaluations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113427"},"PeriodicalIF":7.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient heuristics for the obnoxious planar p-median problem with variable sizes 可变尺寸平面p中值问题的高效启发式算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-17 DOI: 10.1016/j.asoc.2025.113401

Sergio Salazar , Oscar Cordón , J. Manuel Colmenar

引用次数: 0

Class-specific intuitionistic fuzzy kernel ridge regression classifier 类专用直觉模糊核岭回归分类器

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-16 DOI: 10.1016/j.asoc.2025.113490

Barenya Bikash Hazarika , Deepak Gupta

{"title":"Class-specific intuitionistic fuzzy kernel ridge regression classifier","authors":"Barenya Bikash Hazarika , Deepak Gupta","doi":"10.1016/j.asoc.2025.113490","DOIUrl":"10.1016/j.asoc.2025.113490","url":null,"abstract":"<div><div>In real-world data classification problems, class imbalance learning, noise and outliers are the major problems. The conventional kernel ridge regression (KRR) cannot efficiently deal with these challenges because all the samples are provided equal importance irrespective of their contribution to decision-making. Hence, to address this problem, we suggest a novel class-specific intuitionistic fuzzy KRR (CS-IFKRR) model for classification. CS-IFKRR provides appropriate weights to the samples for effective decision-making. CS-IFKRR classifier is designed to tackle the challenge of class imbalance in classification tasks, which generally leads to biased predictions and poor generalization for minority classes. Moreover, time efficiency is a secondary but significant advantage of CS-IFKRR, as it solves systems of linear equations. In addition to that intuitionistic fuzzy score values consider sample distance and heterogeneity to determine appropriate weights. The experimental investigation is carried out over a few popular datasets. The classification performance of the proposed CS-IFKRR model is contrasted with that of support vector machine (SVM), twin SVM, intuitionistic fuzzy SVM (IFSVM), IF twin SVM (IFTSVM), KRR and intuitionistic fuzzy KRR (IFKRR). The results, based on accuracy, F1 score and G-mean reveal the superiority of CS-IFKRR over other relevant models. Further statistical analysis is carried out based on Friedman test and posthoc Nemenyi analysis.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113490"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313356","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

Learning-based stochastic multi-objective optimizer for uncertain power system scheduling 基于学习的不确定电力系统调度随机多目标优化算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-16 DOI: 10.1016/j.asoc.2025.113402

B. Deng, M.S. Li, T.Y. Ji, Q.H. Wu

{"title":"Learning-based stochastic multi-objective optimizer for uncertain power system scheduling","authors":"B. Deng, M.S. Li, T.Y. Ji, Q.H. Wu","doi":"10.1016/j.asoc.2025.113402","DOIUrl":"10.1016/j.asoc.2025.113402","url":null,"abstract":"<div><div>Power system scheduling with renewable energy sources poses significant challenges due to high computational complexity and uncertainty in operating conditions. Multi-period and multi-scenario modeling further escalates these issues, creating large-scale optimization problems that overwhelm traditional Stochastic Optimization Algorithm (SOA) with slow convergence and limited solution diversity. To tackle these challenges, we propose the Feature-Driven Multi-Objective Group Search Optimizer (FDMOGSO), a novel SOA for large-scale power systems scheduling. FDMOGSO employs the Self-Learning Method of Solution Space Feature (SLMSPF) to extract key features, reducing computational complexity by focusing exploration on promising regions. A Multi-Block Network (MBNet) classifier further enhances robustness by prioritizing high-quality solutions under uncertainty, while an enhanced Multi-Objective Group Search Optimizer (EMOGSO) adapts search strategies to improve convergence and solution diversity. Experimental results on IEEE 9-bus and IEEE 118-bus systems show that FDMOGSO significantly outperforms classical SOAs, including MOGSO, NSGA-II, MOPSO, and EMOGSO, on the Cumulative IGD Efficiency (CIGDE) metric, with improvements of 96.20%, 95.61%, 98.83%, and 94.68%, respectively. This demonstrates that FDMOGSO can find high-quality solutions for large-scale optimization problems with limited evaluations, enhancing the practical application potential of SOAs in complex power system scheduling.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113402"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313220","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

Differential evolution based on two-stage mutation strategy and multi-stage parameter control 基于两阶段突变策略和多阶段参数控制的差分进化

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-16 DOI: 10.1016/j.asoc.2025.113387

Huarong Xu, Shengke Lin, Zhiyu Zhang, Qianwei Deng

{"title":"Differential evolution based on two-stage mutation strategy and multi-stage parameter control","authors":"Huarong Xu, Shengke Lin, Zhiyu Zhang, Qianwei Deng","doi":"10.1016/j.asoc.2025.113387","DOIUrl":"10.1016/j.asoc.2025.113387","url":null,"abstract":"<div><div>The Differential Evolution (DE) algorithm is an advanced evolutionary method for tackling global optimization challenges, yet designing effective parameter control generation methods and mutation strategies remains a significant challenge. In response, this paper introduces a differential evolution based on <strong>T</strong>wo-<strong>S</strong>tage Mutation Strategy and <strong>M</strong>ulti-<strong>S</strong>tage Parameter Control (TSMS-DE). Firstly, a multi-stage parameter control is proposed, in the early stage, a larger step size is used to enhance exploration, in the mid stage, the scaling factor is dynamically adjusted based on individual ranking, and in the late stage, a Cauchy distribution is applied to improve parameter adaptability. Secondly, an external archive optimization method utilizing a Two-Stage Mutation Strategy is developed to effectively eliminate individuals with suboptimal fitness values, ensuring the archive consistently retains high-quality individuals. Third, TSMS-DE employs an Opposite-Based Learning Strategy to generate sample points in the solution space, enabling more comprehensive coverage of the search space and enhancing overall search performance. We conducted comparative experiments on 100 benchmark test suites from the Congress on Evolutionary Computation (CEC) competitions, including CEC2013, CEC2014, CEC2017 and CEC2022. In order to rigorously evaluate the performance of the algorithms, statistical validation was carried out using a variety of tests. Compared to several advanced Differential Evolution variants and heuristic algorithms, the results demonstrate that our algorithm exhibits significant advantages in convergence, diversity, and accuracy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113387"},"PeriodicalIF":7.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290590","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 dual-layer task planning algorithm based on UAVs-human cooperation for search and rescue 基于无人机-人协同的搜救双层任务规划算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-15 DOI: 10.1016/j.asoc.2025.113488

Guang Yang , Yadong Mo , Chengyu Lv , Ying Zhang , Jian Li , Shimin Wei

{"title":"A dual-layer task planning algorithm based on UAVs-human cooperation for search and rescue","authors":"Guang Yang , Yadong Mo , Chengyu Lv , Ying Zhang , Jian Li , Shimin Wei","doi":"10.1016/j.asoc.2025.113488","DOIUrl":"10.1016/j.asoc.2025.113488","url":null,"abstract":"<div><div>To address the issues of low efficiency and difficult localization in search and rescue, a dual-layer task planning algorithm based on UAVs-human cooperation for search and rescue is proposed, which mainly includes the search layer for UAVs and the execution layer for rescuers. Firstly, in the search layer, to solve the problems of uneven task allocation and redundant coverage paths of heterogeneous UAVs, a coverage path optimization based on cluster algorithm (CPOC) is adopted. It applies the K-means algorithm with the proportional constraint to allocate the appropriate task-area for each UAV, and uses the non-redundant exact cellular decomposition method to achieve more efficient planning of the subregion coverage paths, meanwhile, those paths are connected by the Min-Max Ant System. Secondly, in the execution layer, the Rapidly-exploring Random Tree star with dynamic guidance mechanism (DG-RRT*) is introduced to improve the performance of path planning for rescuers in the indoor environment. By comparing the different levels of the target locations, this mechanism guides the RRT to explore purposefully to avoid the algorithm being trapped in the local optimum. Finally, compared with the classical algorithm, the total task time of CPOC in the two examples is reduced by 7.3 % and 27.8 % respectively. DG-RRT* can obtain the effective solution in a shorter time under the premise of ensuring the optimal path length. The results indicate that our algorithm can improve the efficiency of search and rescue route planning as well as the accuracy of the solutions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113488"},"PeriodicalIF":7.2,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322523","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

Post-earthquake infectious disease risk assessment approach using AHP and MULTIMOORA with decomposed fuzzy sets 基于AHP和MULTIMOORA分解模糊集的震后传染病风险评价方法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-14 DOI: 10.1016/j.asoc.2025.113484

Kübra Yazici Sahin , Alev Taskin

{"title":"Post-earthquake infectious disease risk assessment approach using AHP and MULTIMOORA with decomposed fuzzy sets","authors":"Kübra Yazici Sahin , Alev Taskin","doi":"10.1016/j.asoc.2025.113484","DOIUrl":"10.1016/j.asoc.2025.113484","url":null,"abstract":"<div><div>This study introduces a novel approach to post-earthquake infectious disease risk assessment, a critical component of emergency planning for public health and safety. Focusing on the districts of the European side of Istanbul, the risk assessment is based on the opinions of three experts. The novel two-level methodology, which integrates Decomposed Fuzzy Set Analytic Hierarchy Process (DFS-AHP) with Decomposed Fuzzy Multi-objective Optimization by Ratio Analysis Plus the Full Multiplicative Form (DFS-MULTIMOORA), aims to improve decision-making under linguistic uncertainty and ambiguity. By integrating DFS-AHP with DFS-MOORA, the problems of precision and consistency in expert judgments are addressed. Criteria importance weights determined using DFS-AHP enable successful district ranking with DFS-MOORA. The results reveal that \"Adequacy of housing conditions\" is the most important sub-criteria, while Bağcılar district is identified as the highest risk due to its crowded living spaces and poor health conditions after the earthquake. Beşiktaş exhibits the lowest risk. Among the contributions are the introduction of the DFS-AHP integrated DFS-MOORA methodology, the development of criteria and importance weights for risk assessment, and the pioneering of the implementation Multiple Criteria Decision Making (MCDM) approaches for post-earthquake infectious disease risk assessment. This study provides decision makers with a quick and effective procedure for assessing post-earthquake infectious disease risk.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"181 ","pages":"Article 113484"},"PeriodicalIF":7.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-granularity ensemble sample selection and label correction for classification with noisy labels 基于噪声标签分类的多粒度集成样本选择与标签校正

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-14 DOI: 10.1016/j.asoc.2025.113266

Kecan Cai , Hongyun Zhang , Witold Pedrycz , Duoqian Miao , Chaofan Chen

{"title":"Multi-granularity ensemble sample selection and label correction for classification with noisy labels","authors":"Kecan Cai , Hongyun Zhang , Witold Pedrycz , Duoqian Miao , Chaofan Chen","doi":"10.1016/j.asoc.2025.113266","DOIUrl":"10.1016/j.asoc.2025.113266","url":null,"abstract":"<div><div>Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113266"},"PeriodicalIF":7.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291171","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 communication-efficient federated learning approach via dynamic mutual distillation for image recognition 基于动态互蒸馏的高效通信联邦学习图像识别方法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-06-14 DOI: 10.1016/j.asoc.2025.113286

Youhuizi Li , Yu Chen , Yuyu Yin , Haitao Yu

{"title":"A communication-efficient federated learning approach via dynamic mutual distillation for image recognition","authors":"Youhuizi Li , Yu Chen , Yuyu Yin , Haitao Yu","doi":"10.1016/j.asoc.2025.113286","DOIUrl":"10.1016/j.asoc.2025.113286","url":null,"abstract":"<div><div>Federated learning is a promising approach to protect data privacy in the image recognition field, enabling collaborative model training across distributed edge participants without compromising local data. However, the privacy-preserving feature comes at the cost of significant communication overhead due to frequent model parameter exchanges. In the current edge computing environment, clients are usually deployed on edge devices with limited bandwidth, the communication delay greatly influences the training efficiency of federated learning. Hence, the paper proposes a communication-efficient federated learning approach FedDMS based on mutual distillation and dynamic client selection for image recognition. It improves the convergence efficiency through client-side dynamic distillation and increases task accuracy through fine-tuning. In addition, the server adaptively selects participation clients through periodic gradient evaluation, thereby reducing the communication overhead. FedDMS is evaluated from the aspects of performance and parameter sensitivity on two public datasets. The experimental results show that compared with other federated algorithms, FedDMS can save 73% of communication costs, significantly improving efficiency. Furthermore, FedDMS’s performance remains stable in different network structures, demonstrating its strong adaptability and optimization potential. At a cost, it requires additional computing resources on the client side.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"180 ","pages":"Article 113286"},"PeriodicalIF":7.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298345","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