Applied Soft Computing最新文献_第10页

Transparent and bias-resilient AI framework for recidivism prediction using deep learning and clustering techniques in criminal justice 在刑事司法中使用深度学习和聚类技术进行累犯预测的透明和抗偏见的人工智能框架

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113160

Muhammed Cavus , Muhammed Nurullah Benli , Usame Altuntas , Mahmut Sari , Huseyin Ayan , Yusuf Furkan Ugurluoglu

{"title":"Transparent and bias-resilient AI framework for recidivism prediction using deep learning and clustering techniques in criminal justice","authors":"Muhammed Cavus , Muhammed Nurullah Benli , Usame Altuntas , Mahmut Sari , Huseyin Ayan , Yusuf Furkan Ugurluoglu","doi":"10.1016/j.asoc.2025.113160","DOIUrl":"10.1016/j.asoc.2025.113160","url":null,"abstract":"<div><div>This paper presents the Recidivism Clustering Network (RCN), an effective approach for predicting repeat offenses using deep learning (DL), clustering, and explainable AI (XAI). The RCN improves offender profiling for more accurate and interpretable recidivism predictions, aligning with key legal principles like fair sentencing, transparency, and non-discrimination. The RCN employs machine learning (ML) models optimized with a Keras tuner, using the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. With about 75% accuracy, the model shows strong recall, identifying 10,661 recidivists but producing 4,038 false positives—indicating a trade-off between sensitivity and specificity. Beyond predictions, RCN integrates clustering methods, including k-means, principal component analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE), to identify hidden patterns within offender data. Visualizations reveal distinct clusters, linking characteristics, such as age, to recidivism behaviors. SHapley Additive exPlanations (SHAP) values enhance interpretability, showing that factors like time since the last conviction and age significantly impact predictions. The RCN approach offers substantial potential for criminal justice applications by combining predictive power with actionable insights, supporting a more ethical and accountable use of ML in offender profiling and aiding in fairer recidivism prevention strategies. The code and data are publicly available on GitHub at <span><span>https://github.com/cavusmuhammed68/Recidivism-Clustering-Network-RCN-</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113160"},"PeriodicalIF":7.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894646","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

Social network group decision-making with linguistic Z-number preference relations based on personalized individual semantics and trust driven 基于个性化个体语义和信任驱动的语言z数偏好关系的社会网络群体决策

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113166

Xiao-Yun Lu , He-Cheng Li , Ze-Hui Chen

{"title":"Social network group decision-making with linguistic Z-number preference relations based on personalized individual semantics and trust driven","authors":"Xiao-Yun Lu , He-Cheng Li , Ze-Hui Chen","doi":"10.1016/j.asoc.2025.113166","DOIUrl":"10.1016/j.asoc.2025.113166","url":null,"abstract":"<div><div>Compared to preference relations (PRs) based on one-dimensional data description, linguistic Z-number (LZN) preference relations (LZPRs) exhibit more advantages in expressing uncertainty information when comparing objectives. However, the extant preference group decision-making (PGDM) with LZPRs focus on traditional group decision-making (TGDM) problems. In addition, there are certain shortcomings in the consistency analysis of LZPRs proposed in the PGDM with LZPRs. Therefore, this study will focus on discussing the PGDM with LZPRs based on dynamic social networks. Firstly, A new additively consistent concept for LZPRs is presented, and the social network structure based on LZN trust relationships is constructed. Furthermore, a synthetical personalized individual semantics (PIS) determination method based on consistency driven and social network driven is developed for the credibility of evaluation values in LZPRs. Secondly, a dynamic mixed experts weights determination method based on experts’ opinions and social network trust relationships between experts has been proposed, considering multiple indicators of experts’ opinions. Thirdly, a synthetical consensus improving algorithm based on dynamic trust-based feedback adjustment mechanism is designed by integrating optimization-based consensus strategy and identification rule (IR) and direction rule (DR) strategy. Finally, the rationality and effectiveness of the proposed method are verified through a numerical example. Meanwhile its merits are illustrated by comparison analyses.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113166"},"PeriodicalIF":7.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887256","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

Effective latent hierarchical feature fusion in multiple instance learning for Whole Slide Image classification 基于多实例学习的有效潜层特征融合全幻灯片图像分类

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113191

Qingzhi Lan , Yaozu Wu , Weiping Ding , Jingping Yuan

{"title":"Effective latent hierarchical feature fusion in multiple instance learning for Whole Slide Image classification","authors":"Qingzhi Lan , Yaozu Wu , Weiping Ding , Jingping Yuan","doi":"10.1016/j.asoc.2025.113191","DOIUrl":"10.1016/j.asoc.2025.113191","url":null,"abstract":"<div><div>Deep learning applications in computational pathology have revolutionized cancer diagnostics through histopathology tissue analysis of Whole Slide Images (WSIs). However, the gigapixel scale of WSIs presents significant challenges for traditional approaches. While Multiple Instance Learning (MIL) frameworks address these challenges by treating WSIs as bags of patches, existing methods often focus solely on information extraction modules, neglecting effective decoding of latent features. This paper introduces LHFF-MIL, a novel framework that emphasizes latent feature decoding and fusion in MIL. Our key contribution is the Latent Feature Distribution Decoder (LFDD), which efficiently decodes diverse information from high-dimensional semantics across different WSI resolutions, enabling explicit measurement of image informativeness for tumor detection. Evaluated on three real-world datasets of breast and gastric cancer, LHFF-MIL consistently outperforms competing methods, demonstrating statistically significant diagnostic accuracy improvement from 0.27% to 1.44% with at least 95% of confidence level. These improvements advance computational pathology by enhancing classification performance, potentially enabling more reliable computer-aided diagnosis systems in clinical settings. Code will be available upon acceptance.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"177 ","pages":"Article 113191"},"PeriodicalIF":7.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913149","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 diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation 基于遗传和自动学习双重搜索策略的多样性增强树种子图像分割算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113143

Xianqiu Meng , Gaochao Xu , Xu Xu , Ziqi Liu , Jiaqi Ge , Jianhua Jiang

{"title":"A diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation","authors":"Xianqiu Meng , Gaochao Xu , Xu Xu , Ziqi Liu , Jiaqi Ge , Jianhua Jiang","doi":"10.1016/j.asoc.2025.113143","DOIUrl":"10.1016/j.asoc.2025.113143","url":null,"abstract":"<div><div>Image segmentation represents a critical yet inherently complex problem in the field of image processing, with the objective of extracting significant information from visual data. Traditional methodologies often encounter difficulties in effectively retrieving pertinent information. In contrast, swarm intelligence techniques, which optimize through collaborative interaction and stochastic exploration without dependence on prior knowledge, are more adept at addressing image segmentation challenges. The Tree-Seed Algorithm (TSA), a prominent swarm intelligence optimization technique, has been extensively utilized to tackle intricate optimization issues. Nonetheless, the reliance on a singular seed generation approach may result in inadequate exploration, premature convergence, diminished diversity, and local stagnation. To address these deficiencies, a hybrid variant known as the Tree-Seed-Gene Algorithm (TSGA) is proposed, drawing inspiration from the Genetic Algorithm (GA) and incorporating a double search strategy that integrates genetic and automated learning strategies. The genetic search contains mechanisms such as elite, crossover, and mutation. Furthermore, an opposition-based learning strategy is introduced to bolster population diversity, thereby enhancing exploration capability. The efficacy of the TSGA algorithm is assessed in comparison to both classical and contemporary meta-heuristic algorithms, including their variants, utilizing benchmark functions from the IEEE CEC 2014, 2017, 2020, and 2022. The performance of the TSGA is substantiated through statistical analyses, specifically, the Wilcoxon signed-rank and Friedman tests. The findings indicate that the TSGA algorithm exhibits superior performance in resolving image segmentation issues. In conclusion, the experimental results consistently affirm the TSGA has significant potential for practical applications in the domain of image segmentation.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113143"},"PeriodicalIF":7.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894787","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

Corrigendum to “Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption” [Appl. Soft Comput. 167 (Part B) (2024) 112405] “使用区块链和同态加密的隐私保护可验证联邦学习方案”的勘误表[apple]。软计算。167 （B部分）（2024）112405]

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113165

Ganesh Kumar Mahato , Aiswaryya Banerjee , Swarnendu Kumar Chakraborty , Xiao-Zhi Gao

引用次数: 0

A periodic intervention and strategic collaboration mechanisms based differential evolution algorithm for global optimization 基于周期干预和策略协作机制的差分进化全局优化算法

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-05-01 DOI: 10.1016/j.asoc.2025.113137

Guanyu Yuan , Gaoji Sun , Libao Deng , Chunlei Li , Guoqing Yang , Lili Zhang

{"title":"A periodic intervention and strategic collaboration mechanisms based differential evolution algorithm for global optimization","authors":"Guanyu Yuan , Gaoji Sun , Libao Deng , Chunlei Li , Guoqing Yang , Lili Zhang","doi":"10.1016/j.asoc.2025.113137","DOIUrl":"10.1016/j.asoc.2025.113137","url":null,"abstract":"<div><div>Differential Evolution (DE) algorithm is a well-known metaheuristic algorithm that features a simple structure and excellent optimization performance. However, it still suffers from premature convergence or stagnation when dealing with complex optimization problems. To avoid these dilemmas in the DE algorithm, we propose a novel DE variant, abbreviated as PISCDE, which is based on periodic intervention and strategic collaboration mechanisms. PISCDE incorporates two types of operations: routine operation and intervention operation. The routine operation employs two mutation strategies with different functional positions to drive the population toward the optimal position. In contrast, the intervention operation uses two intervention strategies with distinct functional roles to restore population diversity and is executed only when a fixed number of iterations is reached. Additionally, to achieve a better balance between global exploration and local exploitation during the optimization process, we propose several strategic collaboration mechanisms. These mechanisms are based on the positioning analysis of different strategies and the interaction analysis between strategies and their corresponding control parameters. To verify the optimization performance of PISCDE, we selected nine comparison algorithms with outstanding optimization performance that have been proposed in the last five years. We used the IEEE CEC 2014 testbed to construct comparative experiments. Based on the comparative results, three conclusions can be drawn: (1) PISCDE has the best overall optimization performance among all the algorithms. (2) PISCDE performs more significantly on complex test problems. (3) PISCDE shows more impressive optimization performance when the dimension of the test problems is increased.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"176 ","pages":"Article 113137"},"PeriodicalIF":7.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887254","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

Study of occupational hazards under the environment of interval valued q-rung picture fuzzy sets 区间值q阶图像模糊集环境下的职业危害研究

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-30 DOI: 10.1016/j.asoc.2025.113185

Gulfam Shahzadi , Musavarah Sarwar , Muhammet Deveci

{"title":"Study of occupational hazards under the environment of interval valued q-rung picture fuzzy sets","authors":"Gulfam Shahzadi , Musavarah Sarwar , Muhammet Deveci","doi":"10.1016/j.asoc.2025.113185","DOIUrl":"10.1016/j.asoc.2025.113185","url":null,"abstract":"<div><div>Practical group decision-making (DM) issues typically involve challenging situations when trying to assign appropriate values to the facts because of the ambiguity and unpredictability of the surrounding conditions. In order to address the uncertainty and imprecision that arise in DM challenges, interval-valued <span><math><mi>q</mi></math></span>-rung picture fuzzy sets (IV<span><math><mi>q</mi></math></span>-RPFSs) are more widely constructed. This work integrates the criteria importance through intercriteria correlation (CRITIC), the decision-making trial and evaluation laboratory (DEMATEL) approaches, and the multi-attributive border approximation area comparison (MABAC) method separately. The MABAC technique determines the distance of each decision from the boundary approximation area, making it a very reliable and useful tool for real-world problem resolution. In the CRITIC technique, the correlations between attributes are taken into account when determining the criteria weights, and the DEMATEL methodology is recognized as the most efficient method for determining the interactions between many criteria or components. In the light of these factors, we develop the CRITIC-MABAC and DEMATEL-MABAC methods for IV<span><math><mi>q</mi></math></span>-RPFSs. This article’s main objective is to identify the occupational risk that most significantly affects hospital medical staff health by utilizing the recommended methodologies. First, we use the CRITIC technique to determine the criteria weights. Additionally, we calculate the weights of the criteria using the DEMATEL approach. To determine the most hazardous work environment for hospital employees, the applicability of the suggested methodologies is investigated. To confirm the accuracy of the suggested techniques, we compare our findings with previous studies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"177 ","pages":"Article 113185"},"PeriodicalIF":7.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913072","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

Physical-aware uncertainty prompt learning for real-world blind image restoration 物理感知的不确定性提示学习用于真实世界的盲图像恢复

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-30 DOI: 10.1016/j.asoc.2025.113173

Yuanjian Qiao , Mingwen Shao , Lingzhuang Meng , Wangmeng Zuo

{"title":"Physical-aware uncertainty prompt learning for real-world blind image restoration","authors":"Yuanjian Qiao , Mingwen Shao , Lingzhuang Meng , Wangmeng Zuo","doi":"10.1016/j.asoc.2025.113173","DOIUrl":"10.1016/j.asoc.2025.113173","url":null,"abstract":"<div><div>Recent years have witnessed notable progress in universal image restoration, which tackles multiple image degradations using a single model. However, these methods struggle to handle complex real-world scenarios due to the lack of paired real data and limited adaptability to unknown corruptions. To address these challenges, we propose a novel Physical-aware Uncertainty Prompt (PUP) paradigm for real-world blind image restoration. Specifically, instead of simply employing pre-synthesized degraded images, we develop a Physical-aware Degradation Modeling scheme (PDM) that considers multiple distortion factors to generate more authentic degraded data online during training. To adaptively handle unknown corruptions, we propose an Uncertainty-Prompted Fourier Transformer (UPFomer) for unified image restoration, which comprises two collaborative designs: Spatial-Frequency Selective Interaction (SSI) and Uncertainty Prompt Alignment (UPA). The former aggregates global frequency information and local spatial context for robust feature representation, while the latter interacts learnable prompts with SSI features via uncertainty weights to compute degradation-aware knowledge. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on real-world images while ensuring favorable model efficiency.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"177 ","pages":"Article 113173"},"PeriodicalIF":7.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922496","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

Integration of Type-2 fuzzy TOPSIS and Quality Function Deployment to address patient satisfaction in healthcare 集成2型模糊TOPSIS和质量功能部署来解决医疗保健中的患者满意度问题

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-30 DOI: 10.1016/j.asoc.2025.113187

Büşra Meniz , Sezin Ozturk Usun , Sema Akin Bas , Elif Yafez , Beyza Ahlatcioglu Ozkok

{"title":"Integration of Type-2 fuzzy TOPSIS and Quality Function Deployment to address patient satisfaction in healthcare","authors":"Büşra Meniz , Sezin Ozturk Usun , Sema Akin Bas , Elif Yafez , Beyza Ahlatcioglu Ozkok","doi":"10.1016/j.asoc.2025.113187","DOIUrl":"10.1016/j.asoc.2025.113187","url":null,"abstract":"<div><div>The healthcare sector seeks to maintain a sustainable competitive advantage through Patient Satisfaction (PS) against frequently changing global conditions. Analyzing the Voice of Patient (VoP) is very important for understanding patient preferences and developing effective strategies. This study focuses on improving current service quality and patient satisfaction by analyzing survey data collected from outpatient, inpatient, and emergency departments of a private university hospital in Türkiye. In this study, a double-sided hybrid methodology integrating Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Quality Function Deployment (QFD) is proposed utilizing Interval Type-2 Fuzzy Sets (IT2FS). This approach evaluates both patient expectations and the service components that hospitals need to develop to meet these needs. The criteria evaluated and valued by patients receiving service from different departments are not the same In this context, the findings reveal that each department needs different strategies to maximize satisfaction and quality. To the best of our knowledge, this is the first study to combine TOPSIS and QFD within IT2FS in the healthcare context and offers a versatile methodology that can be applied in various healthcare institutions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"177 ","pages":"Article 113187"},"PeriodicalIF":7.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922494","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 manifold-based adversarial autoencoder with Fourier convolution for hyperspectral unmixing 一种基于流形的傅立叶卷积对抗性自编码器，用于高光谱解混

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-04-30 DOI: 10.1016/j.asoc.2025.113176

Ziyang Guo , Meixia Xiao , Fa Zhu , Xingchi Chen , Achyut Shankar , Mazdak Zamani , Sushil Kumar Singh

{"title":"A manifold-based adversarial autoencoder with Fourier convolution for hyperspectral unmixing","authors":"Ziyang Guo , Meixia Xiao , Fa Zhu , Xingchi Chen , Achyut Shankar , Mazdak Zamani , Sushil Kumar Singh","doi":"10.1016/j.asoc.2025.113176","DOIUrl":"10.1016/j.asoc.2025.113176","url":null,"abstract":"<div><div>Hyperspectral unmixing aims to decompose each subpixel into their pure endmembers and the corresponding proportions. But existing deep autoencoder-based hyperspectral unmixing methods often suffer from obstacles like endmember variability, local respective fields and insufficient use of inner structure. In the manuscript, we build a manifold-based Fourier adversarial autoencoder which regards generative adversarial mechanism as a utilization of prior information. This method combines manifold learning with adversarial autoencoder in order to promote the performance of hyperspectral unmixing. Specifically, firstly, in order to preserve local manifold structure, we add a discriminator to the autoencoder which uses the covariance matrices of a superpixel as real samples while covariance matrices of the abundance as fake samples; secondly, we add a regularization term of Laplacian eigenmap at the loss of autoencoder to in-depth abbreviate autoencoder solution space; thirdly, Fast Fourier Convolution modules are used to enhance multi-scale information fusion. At last, comparative experiments are conducted on three popular datasets, including Jasper, Urban4 and Samson, to validate the effectiveness of the proposed method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"177 ","pages":"Article 113176"},"PeriodicalIF":7.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913075","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