Applied Soft Computing最新文献

{"title":"Circular intuitionistic fuzzy Dombi Bonferroni mean aggregation operators and MEREC-RAFSI approach for optimizing vehicle routing software","authors":"Freeha Qamar ,&nbsp;Iqra Zareef ,&nbsp;Muhammad Riaz ,&nbsp;Muhammad Aslam ,&nbsp;Vladimir Simic ,&nbsp;Dragan Pamucar","doi":"10.1016/j.asoc.2026.114642","DOIUrl":"10.1016/j.asoc.2026.114642","url":null,"abstract":"<div><div>For the purpose of optimizing their routes, last mile delivery (LMD) organizations use vehicle routing software (VRS). The topic of VRS selection is of the utmost significance for corporations that deal with managing deliveries at the last mile. This study presents an innovative VRS selection methodology specifically designed for LMD businesses. We regulate the issue within the confines of multi-criteria decision-making (MCDM). Criteria for assessment are based on solid literature, mathematical formulation, and professional judgments. The utilization of circular intuitionistic fuzzy set (CIFS) for this model offers a more adaptable and evocative method for expressing unclear and conflicting data. A novel operator termed as a CIFDBM operator is presented to serve as an aggregation operator to boost the effectiveness of aggregation, influenced by the fundamental Bonferroni mean (BM) operator and centered on Dombi t-norm and t-conorm. Our research sets out a novel hybrid structure that integrates CIFS-based decision-making along with MEREC (method based on the removal effects of criteria) as a weighting technique and RAFSI (ranking of alternatives through functional mapping of criterion subintervals into a single interval) as a ranking approach. A robust MCDM hybrid approach named CIFS-MEREC-RAFSI is designed, which provides a reliable, competent, and quality decision for VRS selection problems containing inconsistent, uncertain, and vague data. The system outperforms state-of-the-art CIF-based MCDM approaches by 17%–22% in terms of ranking stability, resulting in more consistent and dependable rankings for all options.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114642"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039854","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 novel method for large-scale group decision-making with application to e-commerce software system evaluation","authors":"Chuan Yue","doi":"10.1016/j.asoc.2026.114595","DOIUrl":"10.1016/j.asoc.2026.114595","url":null,"abstract":"<div><h3>BACKGROUND</h3><div>Large-scale group decision-making (LSGDM) in big data environments faces challenges in robust data center construction, objective expert weighting, and efficient information fusion.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to develop a novel LSGDM framework integrating a Golden Ratio-based data center and an inversion-based data quality metric to improve ranking stability and decision reliability.</div></div><div><h3>METHODS</h3><div>A GR-based data center was introduced to replace conventional mean/median centers, alongside an inversion-number-driven quality metric for expert weighting and a scalable aggregation technique for converting crisp data into intuitionistic fuzzy matrices. The framework was validated through dynamic experiments and sensitivity analysis.</div></div><div><h3>RESULTS</h3><div>The GR-based center outperformed mean/median centers in 95% of test scenarios. The inversion-based method achieved perfect ranking consistency (Kendall’s <span><math><mi>τ</mi><mo>=</mo><mn>1</mn></math></span>), showing a 50% improvement over entropy-based methods (<span><math><mi>τ</mi><mo>=</mo><mn>2</mn><mrow><mo>/</mo></mrow><mn>3</mn></math></span>), and maintained 100% ranking stability under parameter variations—20 times higher than entropy-based approaches.</div></div><div><h3>CONCLUSION</h3><div>The proposed framework offers a robust, quantitatively validated solution for LSGDM in data-intensive environments, with significant advantages in consistency and scalability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114595"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039869","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":"Small aerial object detection through FPN-DETR integrated with a novel alignment proposal network","authors":"Usman Ahmad ,&nbsp;Tianlei Ma ,&nbsp;Jing Liang ,&nbsp;Ponnuthurai Nagaratnam Suganthan ,&nbsp;Kunjie Yu ,&nbsp;Faisal Mehmood ,&nbsp;Farhad Banoori","doi":"10.1016/j.asoc.2026.114620","DOIUrl":"10.1016/j.asoc.2026.114620","url":null,"abstract":"<div><div>In the domain of aerial imagery analysis, Small Aerial Object Detection (SAOD) presents significant challenges due to extensive scale variations, diverse orientations, and cluttered arrangements. Existing methods rely on anchor-based boxes or dense points, which involve complex manual steps such as anchor generation, transformation, and non-maximum suppression reasoning. This study proposes an innovative model that incorporates a Feature Pyramid Network into a Detection Transformer (FPN-DETR) to effectively address these challenges. The proposed approach employs the strengths of detection Transformers in modeling long-range dependencies, enabling a more comprehensive understanding of the aerial scene context, extraction of rich and scale-invariant features, and enhancement of detection accuracy for small aerial objects across varied scales and orientations. Additionally, this study develops a Novel Alignment Proposal Network (APN) with a novel loss function to further enhance FPN-DETR, resulting in the creation of the FPN-DETR+APN model. This network eliminates the time-consuming process of creating hand-crafted rotational anchors, which leads to computational complexity in existing conventional oriented detectors. Furthermore, FPN-DETR+APN generates oriented proposals that adeptly capture the diverse orientations of small objects in aerial scenes and provides improved positional priors for feature pooling, thereby enhancing cross-attention modulation in the Transformer decoder. The proposed method demonstrates superior performance in detecting small aerial objects, surpassing state-of-the-art detection frameworks in terms of precision, recall, and overall detection robustness. This study achieves 83.25 % mAP on DOTA-v1.0, 63.07 % mAP on DOTA-v2.0, 97.53 % on HRSC2016, 95.98 % on SSDD, 60.78 % on VisDrone2021, and 94.80 % on HRSID datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114620"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039913","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":"Development of federated learning neural networks with combined horizontal and vertical data partitioning","authors":"Amir Anees ,&nbsp;Matthew Field ,&nbsp;Lois Holloway","doi":"10.1016/j.asoc.2026.114734","DOIUrl":"10.1016/j.asoc.2026.114734","url":null,"abstract":"<div><div>Federated learning trains a global machine learning model without moving data that is distributed geographically, thereby preserving privacy. Most of the existing works focus on either horizontal- or vertical-federated learning. This paper proposes two new federated learning frameworks, addressing different dataset setups, for combined horizontal and vertical data-partitioned scenarios based on neural networks. The data is horizontally partitioned for a subset of clients, referred to as horizontal clients, and it is further vertically partitioned with another client, labelled as a vertical client. In the first proposed framework, referred to as ‘Horizontal-OutputFed’, the output feature is stored at clients with horizontal partitioning, while in the second proposed framework, referred to as ‘Vertical-OutputFed’, the output feature is stored at the vertical client. The accuracy, loss and convergence rate of the Horizontal-OutputFed are the same as those achieved with a horizontally partitioned federated learning framework. Furthermore, the impact of non-identically and independently distributed data is the same in both the Horizontal-OutputFed and purely horizontal federated learning setups for combined features. Compared to a centrally trained model, the globally trained model in the Vertical-OutputFed has the same performance metrics such as accuracy and convergence rate. Moreover, there is no effect of imbalanced data on the performance of the Vertical-OutputFed, contrary to some of the previous works on individual horizontal- or vertical-federated learning. Privacy attack analysis were conducted for active, passive, white-box, and black-box attacks demonstrating no risk of input and output feature leakage, nor any data inference from the resulting models or while sharing hidden layers’ outputs for either of the combined federated approaches.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114734"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191516","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":"Optimal scheduling of integrated energy systems with power-to-hydrogen for flexible supply-demand balance via Q-learning-enhanced grey wolf optimizer","authors":"Kuiyuan Pan ,&nbsp;Tianhe Sun ,&nbsp;Xinfu Pang ,&nbsp;Zedong Zheng ,&nbsp;Haibo Li","doi":"10.1016/j.asoc.2026.114795","DOIUrl":"10.1016/j.asoc.2026.114795","url":null,"abstract":"<div><div>The integrated energy system (IES) has garnered increasing attention as an effective means of optimizing the utilization of multiple energy sources. With the growing share of renewable energy, enhancing the flexibility of system operation has become a critical issue. This paper proposes an optimal scheduling model for IES that incorporates flexible supply-demand balance constraints. Firstly, a quantitative model for flexible resources is introduced, followed by the formulation of a flexible supply-demand balance constraint model. Secondly, focusing on minimizing operating costs, an optimal scheduling model for IES is developed, incorporating power-to-hydrogen (P2H) technology and considering the flexibility of supply-demand balance constraints. Finally, a grey wolf optimizer (GWO) based on Q-learning (Q-GWO) is designed to solve the optimal scheduling model. The Q-learning algorithm is used to dynamically adjust the search factor of the Q-GWO, thereby enhancing the algorithm's search capability. Simulation results under various scenarios demonstrate that the proposed optimization model significantly improves the flexibility and economy of system operation while reducing the wind curtailment rate. The superiority of the Q-GWO algorithm in solving this problem is confirmed by comparing its convergence performance with that of other algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114795"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191850","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":"Soft-label guided multi-granularity prompts learning for human-object interaction detection","authors":"Xiaoqian Han ,&nbsp;Xiaowei Zhang ,&nbsp;Guanglin Niu ,&nbsp;Mingliang Zhou ,&nbsp;Zhenkuan Pan","doi":"10.1016/j.asoc.2026.114765","DOIUrl":"10.1016/j.asoc.2026.114765","url":null,"abstract":"<div><div>Vision-language models (VLMs) have driven substantial progress in human-object interaction (HOI) detection. However, existing VLM-based HOI detectors typically rely on coarse multimodal prompts for knowledge transfer, which makes it difficult to comprehensively capture interaction-relevant contextual cues and consequently weakens generalization to HOI detection. Meanwhile, hard-label supervised learning fundamentally ignores semantic correlations among interaction categories, which tends to suppress knowledge transfer due to misalignment with the continuous semantic similarity structure encoded by VLM representations in the embedding space. To address these challenges, we propose SMPL, a <strong>S</strong>oft-label guided <strong>M</strong>ulti-granularity <strong>P</strong>rompt <strong>L</strong>earning model for HOI detection, which facilitates prompt learning by jointly capturing multi-level interaction cues and providing semantically calibrated supervision aligned with VLM embeddings. Specifically, we design multi-granularity visual and textual prompts to capture interaction cues at different levels of detail, thereby improving generalization to interaction categories. Moreover, we introduce soft-label learning to jointly optimize interaction classification with the hard-labels and soft-label supervision, which naturally reflects interaction-level semantic similarity, enabling the model to learn implicit interaction relations without additional annotations. Extensive experiments demonstrate that SMPL achieves 38.97 mAP on the HICO-DET dataset and improves performance by 2.64 mAP over the current state of the art on the challenging Rare split. SMPL also performs strongly under multiple zero-shot HOI settings, demonstrating excellent generalization to unseen interactions. The code and models are available at <span><span>https://github.com/hxqstree/SMPL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114765"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191780","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":"An intelligent over-sampling framework with physics-driven guidance for noise robust imbalanced rolling bearing fault diagnosis","authors":"Weidong Jiao ,&nbsp;Daxuan Lin ,&nbsp;Zhilin Dong ,&nbsp;Jianfeng Sun ,&nbsp;Wanxiu Xu ,&nbsp;Xiaohao Chen ,&nbsp;Siyu Liu ,&nbsp;Yonghua Jiang","doi":"10.1016/j.asoc.2026.114791","DOIUrl":"10.1016/j.asoc.2026.114791","url":null,"abstract":"<div><div>Driven by advances in deep learning, intelligent fault diagnosis is widely deployed in predictive maintenance and reliability assurance for rotating machinery. However, these methodologies hinge on noiseless, balanced data—an ideal that rarely aligns with real-world engineering scenarios. To address this issue, a Physics-driven Feature Space Synthetic Minority Over-Sampling Technique (PFS-SMOTE) is introduced that augments minority-class samples in the feature space by incorporating physical prior knowledge, thereby improving the performance of intelligent fault-diagnosis models. Initially, a Neural Blind Deconvolution Mechanism (NBDM) is proposed, which utilizes adaptive fuzzy kernels optimized via complex frequency domain vectors to effectively extract fault impulse features. Next, we construct the Multi-Resolution Time-Frequency Convolution (MRTFConv) layer, utilizing Short-Time Fourier Transform-inspired kernels with adaptive Gaussian temporal envelopes and learnable oscillatory components for physically interpretable time-frequency representations. Building upon these innovations, a deep Siamese Network, integrating NBDM and MRTFConv, maps original data into a physically separable feature space, guided by a physics-informed loss function encompassing contrastive loss, time-frequency consistency loss, and fuzzy kernel consistency loss. Ultimately, PFS-SMOTE generates synthetic fault samples in this well-separated space, ensuring a balanced dataset. Experimental validation across two bearing datasets demonstrates that PFS-SMOTE achieves 100 % accuracy under an imbalance ratio (IR) of 1:10 and maintains 93 % accuracy at SNR= 0 dB. Even under extreme conditions with IR= 1:40 and SNR= 4 dB, the method still achieves an accuracy rate of 89.5 %.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114791"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191781","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":"Semantic enhancement in graph contrastive learning by combining topology and features","authors":"Bowen Mao ,&nbsp;Wenjun Wang ,&nbsp;Wei Yu ,&nbsp;Qiang Tian ,&nbsp;Wenkai Wang ,&nbsp;Jiye Liu ,&nbsp;Han Bao ,&nbsp;Nannan Wu","doi":"10.1016/j.asoc.2026.114773","DOIUrl":"10.1016/j.asoc.2026.114773","url":null,"abstract":"<div><div>Graph contrastive learning (GCL), which learns node representations by contrasting two augmented graphs, has attracted significant research interest. The core of GCL is to obtain high-quality positive and negative samples as contrastive pairs, thereby learning the latent semantic information of the original graph. Existing GCL methods typically use random augmentation strategies to generate augmented views. In existing methods, the embeddings of the same node across two augmented graph views are treated as positive pairs, while embeddings of different nodes are regarded as negative pairs. Nevertheless, random graph augmentations may inadvertently distort the inherent structural and semantic properties of the graph, thereby inducing semantic drift for the same node between the two augmented views. Additionally, two different nodes of the same class may have similar semantics after augmentation. Therefore, we argue that the previous method of defining positive and negative pairs faces a bottleneck due to potential inauthenticity between the pairs. To address this issue, we propose a method called Semantic-Aware Graph Contrastive Learning with Positive Sampling (GCLPS), which mines latent semantic information in the graph, incorporating truly semantically similar node pairs as positive pairs. This approach reduces the impact of false positive pairs and helps the GNN learn the semantic relationships between nodes. Specifically, in the topology aspect, we select one-hop neighbors of each node in the original graph as positive pairs. In the feature aspect, we represent nodes on augmented views using subgraph-level graph kernels and choose positive pairs based on their similarity. Finally, we combine these two aspects to form the final set of positive pairs for contrastive loss calculation. Extensive experiments on six benchmark datasets demonstrate that GCLPS achieves superior performance in various downstream tasks compared to state-of-the-art methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114773"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190767","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 differential evolutionary algorithm improvement framework based on artificial potential fields","authors":"Zhe Yang ,&nbsp;Libao Deng ,&nbsp;Xianxin Mao ,&nbsp;Lili Zhang","doi":"10.1016/j.asoc.2026.114714","DOIUrl":"10.1016/j.asoc.2026.114714","url":null,"abstract":"<div><div>The Artificial Potential Field (APF) method, widely utilized in path planning, uses virtual environmental forces derived from potential field gradients for guidance. While Differential Evolution (DE) algorithms are recognized for their simplicity and robust global optimization capabilities, they often suffer from premature convergence and reduced search efficiency in complex scenarios. Motivated by the similarity between APF pathfinding and DE optimization, this study proposes an APF-inspired framework that utilizes virtual forces to guide the population. Attractive forces guide individuals toward potential optima, while repulsive forces generated by treating inferior solutions as virtual obstacles mitigate the risk of stagnation. This mechanism effectively steers the population toward promising areas, thereby enhancing search efficiency. Furthermore, an integrated adaptive strategy enhances performance by dynamically adjusting key parameters based on problem characteristics. Comprehensive experiments on the CEC2020 benchmark suite and 36 real-world engineering problems validate the effectiveness of the framework. The results indicate performance improvements over baseline algorithms and competitiveness in comparative studies.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114714"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096146","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 swarm based metaheuristic approach in passive compensator tuning strategy incurring system loadability improvement","authors":"Debanjan Mukherjee ,&nbsp;Subhajit Mukherjee ,&nbsp;Sourav Mallick ,&nbsp;Ratan Mandal ,&nbsp;Soumya Chatterjee","doi":"10.1016/j.asoc.2026.114740","DOIUrl":"10.1016/j.asoc.2026.114740","url":null,"abstract":"<div><div>Optimization issues in practical engineering are frequently nonconvex, discontinuous, and nonlinear. Most of the standard derivative-based optimization techniques either cannot provide the expected outcome for such real issues or do so only after relaxing the nonlinearities. Therefore, due to derivative-free nature, population-based metaheuristic approaches have gained popularity. They may not be totally free from the local optima trapping limitation, despite the fact that they are oblivious to the complexity of the situation. Henceforth, a suitable strategy must be adopted to create a new metaheuristic algorithm for effectively solving the said problems with appreciable accuracy without high linearization of the actual problem. Given this, the recent algorithm of the Levy Flight motivated Adaptive Particle Swarm Optimization (APSOLF) technique is enhanced by the Self-Pollination (SP) strategy; this results in proposing the SP Enhanced APSOLF (EAPSOLF) algorithm which is specifically designed for applying and testing on a nonlinear, complex, constrained optimization problem like single tuned passive compensator tuning problem. Here, two standard power systems i.e., IEEE 5 and IEEE 118 bus systems are considered as the test platforms. Consequently, the system losses are minimized after optimizing the filter settings which also result in improving the bus voltage profile and the enhancement in systems’ Maximum Loadability Limit (MLL) in a remarkable extent. Additionally, the EAPSOLF's effectiveness in solving such real-time engineering issue is compared with other state-of-the-art metaheuristics using different statistical analytical tools. The proposed technique has showed its effectiveness in all test cases.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114740"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191836","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}