Applied Soft Computing最新文献

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Optimizing renewable transportation framework by an extended spherical fuzzy rough multi-criteria group decision making method 基于扩展球面模糊粗糙多准则群决策方法的可再生能源运输框架优化
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113599
Maheen Sultan , Muhammad Akram , Cengiz Kahraman
{"title":"Optimizing renewable transportation framework by an extended spherical fuzzy rough multi-criteria group decision making method","authors":"Maheen Sultan ,&nbsp;Muhammad Akram ,&nbsp;Cengiz Kahraman","doi":"10.1016/j.asoc.2025.113599","DOIUrl":"10.1016/j.asoc.2025.113599","url":null,"abstract":"<div><div>Regional transportation infrastructure plays a crucial role in driving economic growth by facilitating the smooth movement of goods, services, and people. It strengthens trade connections, links markets, attracts investment, and promotes job creation and business development. A well-developed transportation network enhances accessibility, reduces transportation costs, and increases overall productivity. Additionally, it supports tourism, fosters regional integration, and ensures the efficient distribution of resources. Collectively, these factors contribute to sustainable economic progress and enhance regional competitiveness. However, major challenges in developing efficient transportation infrastructure include insufficient funding and a lack of effective coordination among stakeholders. Additionally, political instability and regulatory challenges can delay or hinder project implementation. To address the issue of sustainable regional transport architecture, this research study aims to introduce a novel multi-criteria group decision making method based on various dominating and preference relations among criteria of available options for optimization of renewable transportation infrastructure. The presented outranking approach involves elimination of certain inferior options and then comparing the remaining alternatives through outranking relationships. This process helps to identify the most suitable choices while accounting for conflicting criteria and their relative significance. The Step-wise Weight Assessment Ratio Analysis method is used to assess the relative importance of criteria weights through sequential computation. The proposed method is further integrated with spherical fuzzy rough numbers to manage uncertainty by incorporating both lower and upper approximations. To illustrate the effectiveness of the approach, it is applied to a case study aimed at optimizing the regional transportation framework in Africa, with detailed computational steps provided. The alternative that consistently outranks others through both downward and upward distillation processes is identified as the optimal solution. The methods validity and reliability are confirmed by comparing its results with those obtained from other well-established techniques, where the consistent identification of the same optimal choice demonstrates the robustness of our approach. Finally, the advantages, limitations, and potential future applications of the proposed method are discussed.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113599"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703047","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-Scale Sub-graph View Generation and Siamese Contrastive Learning for Graph Representations 图表示的多尺度子图视图生成与暹罗对比学习
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113608
Rende Hong , Kaibiao Lin , Binsheng Hong , Zhaori Guo , Fan Yang
{"title":"Multi-Scale Sub-graph View Generation and Siamese Contrastive Learning for Graph Representations","authors":"Rende Hong ,&nbsp;Kaibiao Lin ,&nbsp;Binsheng Hong ,&nbsp;Zhaori Guo ,&nbsp;Fan Yang","doi":"10.1016/j.asoc.2025.113608","DOIUrl":"10.1016/j.asoc.2025.113608","url":null,"abstract":"<div><div>Graph Contrastive Learning (GCL) is an essential technique in extracting structural and node-related information in graph representation learning. Most existing GCL methods rely on data augmentation to generate multiple views of a graph, aiming to maintain consistency across them via contrastive learning. However, these approaches usually have two limitations: (1) the views generated by the random perturb strategy often disrupt the critical information of the graph, and (2) the graph contrastive strategy is challenging to comprehensively construct contrastive samples between views. To address the challenges mentioned above, we propose an innovative GCL method called the Multi-Scale Sub-graph View Generation and Siamese Contrastive Learning for Graph Representations method (M3SGCL), which consists of three modules. First, the view generation module generates two novel augmented views by introducing multiple structure views and sampled sub-graph sets, which prevents the original graph structure from being damaged, providing a deep understanding of global graph information. Second, the Siamese Network module processes multiple sub-graph views using an online encoder and a target encoder, generating multi-scale representations that enrich the selection of high-quality positive and negative sample pairs for contrastive learning. Third, to further reduce the risk of the information loss and incomplete sample construction, the contrastive learning module establishes multiple contrastive paths through the Siamese Network and employs a multi-scale loss function to learn robust and informative representations. In addition, we perform comprehensive experiments on five real-world datasets, and the results show that M3SGCL significantly outperforms ten state-of-the-art baselines, especially achieving an improvement of 19.76% compared to the second-best method on the Wisconsin dataset. These results demonstrate that our method effectively captures more nuanced and informative graph information by constructing subgraph views and introducing an enhanced multi-scale comparison strategy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113608"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694852","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
Explainable GMDH-type neural networks for decision making: Case of medical diagnostics 用于决策的可解释gmdh型神经网络:以医学诊断为例
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113607
L. Jakaite, V. Schetinin
{"title":"Explainable GMDH-type neural networks for decision making: Case of medical diagnostics","authors":"L. Jakaite,&nbsp;V. Schetinin","doi":"10.1016/j.asoc.2025.113607","DOIUrl":"10.1016/j.asoc.2025.113607","url":null,"abstract":"<div><div>In medical diagnostics, the use of interpretable artificial neural networks (ANN) is crucial to enabling healthcare professionals to make informed decisions that consider risks, especially when faced with uncertainties in patient data and expert opinions. Despite advances, conventional ANNs often produce complex, not transparent models that limit interpretability, particularly in medical contexts where transparency is essential. Existing methods, such as decision trees and random forests, provide some interpretability but struggle with inconsistent medical data and fail to adequately quantify decision uncertainty. This paper introduces a novel Group Method of Data Handling (GMDH)-type neural network approach that addresses these gaps by generating concise, interpretable decision models based on the self-organizing concept. The proposed method builds multilayer networks using two-argument logical functions, ensuring explainability and minimizing the negative impact of human intervention. The method employs a selection criterion to incrementally grow networks, optimizing complexity while reducing validation errors. The algorithm’s convergence is proven through a bounded, monotonically decreasing error sequence, ensuring reliable solutions. Having been tested in complex diagnostic cases, including infectious endocarditis, systemic red lupus, and postoperative outcomes in acute appendicitis, the method achieved high expert agreement scores (Fleiss’s kappa of 0.98 (95% CI 0.97-0.99) and 0.86 (95% CI 0.83-0.89), respectively) compared to random forests (0.84 and 0.71). These results demonstrate statistically significant improvements (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>), highlighting the method’s ability to produce interpretable rules that reflect uncertainties and improve the reliability of decisions. Having demonstrated a transparent and robust framework for medical decision-making, the proposed approach bridges the gap between model accuracy and interpretability, providing practitioners with reliable insights and confidence estimates required for making risk-aware decisions.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113607"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694368","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 selective memory attention mechanism for chaotic wind speed time series prediction with auxiliary variable 带辅助变量的混沌风速时间序列预测的选择性记忆注意机制
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113579
Ke Fu, Shengli Chen, Zhengru Ren
{"title":"A selective memory attention mechanism for chaotic wind speed time series prediction with auxiliary variable","authors":"Ke Fu,&nbsp;Shengli Chen,&nbsp;Zhengru Ren","doi":"10.1016/j.asoc.2025.113579","DOIUrl":"10.1016/j.asoc.2025.113579","url":null,"abstract":"<div><div>Wind speed prediction is crucial for enhancing wind energy utilization and optimizing grid integration of wind power. Its chaotic nature and the lack of correlated variables make accurate prediction difficult. Most studies rely solely on past wind speed, limiting accuracy improvements. While wind power is highly correlated with wind speed, this correlation is reversely causal. The key challenge is effectively leveraging this reverse causality between wind power and wind speed to enhance prediction precision. This study proposed SMAMnet to address the challenge mentioned, a model that establishes its backbone network via proposed new attention mechanism. The convolution operation is employed to restructure features, besides, the frequency-domain transformation and selective state space model (SSM) serves for attention weights. The novelty of SMAMnet is characterized by the development of an adaptive frequency-domain selected attention weight operator to adaptively parse meaningful information in different frequency domain intervals. Taking 15 min and 1-hour mean absolute error as the standard, the actual wind speed prediction error is reduced by 68% and 49% compared with the classic LSTM algorithm. The feasibility of mining reverse causality to improve prediction accuracy was verified.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113579"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711437","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
Disentangled Multi-view Graph Neural Network for multilingual knowledge graph completion 多语言知识图补全的解纠缠多视图神经网络
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113605
Bingbing Dong , Chenyang Bu , Ye Wang , Yi Zhu , Xindong Wu
{"title":"Disentangled Multi-view Graph Neural Network for multilingual knowledge graph completion","authors":"Bingbing Dong ,&nbsp;Chenyang Bu ,&nbsp;Ye Wang ,&nbsp;Yi Zhu ,&nbsp;Xindong Wu","doi":"10.1016/j.asoc.2025.113605","DOIUrl":"10.1016/j.asoc.2025.113605","url":null,"abstract":"<div><div>Multilingual knowledge graph completion (MKGC) uses limited seed pairs from diverse knowledge graphs (KGs) to enrich and complete a target KG. Unlike traditional knowledge graph completion (KGC) tasks that focus on a single KG, MKGC deals with multiple KGs described by diverse languages, imposing a higher level of heterogeneity due to the varying semantic meanings, syntactic structures, and regular expressions across different languages. Existing MKGC methods mainly rely on an end-to-end embedding function that maps multiple KGs into a shared latent space, using relation-aware graph neural networks (GNNs) to unify the contents of entities and relations with respect to their topological structures. However, such methods might not fully exploit the heterogeneity of multilingual KGs, as they overlook inherent details related to neighborhood entities and relations. To address these limitations, we propose a novel <strong>D</strong>isentangled <strong>M</strong>ulti-view <strong>G</strong>raph <strong>N</strong>eural <strong>N</strong>etwork (DMGNN) for MKGC. Specifically, our approach consists of two multi-view GNN modules: MKGC and multilingual KG alignment (MKGA) to facilitate knowledge transfer. Notably, DMGNN effectively captures the heterogeneity of multilingual KGs by learning graph features from three distinct views: entities, relations, and triples. Moreover, we introduce a disentangling mechanism wherein separate GNNs are employed to learn features from different views, mitigating feature interference. In addition, we incorporate an attention mechanism on each view GNN to distinguish the importance of neighborhood features. Extensive experiments on public multilingual datasets demonstrate the superiority of our proposed model over existing competitive baselines.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113605"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703050","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 soft sensor net based on the symplectic decomposition-global attention reconstruction architecture for biopharmaceutical industry 生物制药行业基于辛分解-全局注意力重构体系结构的软传感器网络
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-23 DOI: 10.1016/j.asoc.2025.113636
Simengxu Qiao, Yichen Song, Qunshan He, Shifan Chen, He Zhang, Xinggao Liu
{"title":"A soft sensor net based on the symplectic decomposition-global attention reconstruction architecture for biopharmaceutical industry","authors":"Simengxu Qiao,&nbsp;Yichen Song,&nbsp;Qunshan He,&nbsp;Shifan Chen,&nbsp;He Zhang,&nbsp;Xinggao Liu","doi":"10.1016/j.asoc.2025.113636","DOIUrl":"10.1016/j.asoc.2025.113636","url":null,"abstract":"<div><div>Non-linearity, time-varying properties, and high noise levels in biopharmaceutical process data have been recognized as critical factors affecting the accuracy of data-driven soft sensors. To address these issues and enhance prediction precision, we introduce BPSN, an innovative soft sensor framework grounded in the symplectic decomposition-global attention reconstruction architecture. Symplectic geometry mode decomposition effectively adapts to data complexity and reduces noise. A reconstruction module combines global attention mechanism and reversible instance normalization to enhance sharp signal features via Manhattan distance while addressing internal drift. Experiments show that the proposed soft sensor model outperforms state-of-the-art models in predicting key indicators: bacterial concentration, viscosity, and reducing sugar content in the erythromycin fermentation process. This illustrates its practical applicability and exceptional performance in biopharmaceutical industry. The source code is available at: <span><span>https://github.com/Joss0623/BioPharmaSoftNet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113636"},"PeriodicalIF":7.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703051","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
Common neighbor-aware link weight prediction with simplified graph transformer 基于简化图转换器的共同邻居感知链路权重预测
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-22 DOI: 10.1016/j.asoc.2025.113614
Lizhi Liu
{"title":"Common neighbor-aware link weight prediction with simplified graph transformer","authors":"Lizhi Liu","doi":"10.1016/j.asoc.2025.113614","DOIUrl":"10.1016/j.asoc.2025.113614","url":null,"abstract":"<div><div>The link weight prediction holds significant importance in various fields, yet it has been less explored. Building a superior model faces two major challenges. First, the classic graph neural network can only propagate information along the adjacency connections due to the message-passing paradigm. When some edges are unobserved, learning better node representations is hindered. Second, existing methods often condense the local topological patterns into link representations by either graph pooling on enclosing subgraphs or handcrafted feature indices. The former incurs a heavy computational burden while the latter lacks flexibility. To address these challenges, we present a novel link weight prediction algorithm named CoNe. We design a simplified graph Transformer with linear complexity to simultaneously capture local and global topological structure information. Specifically, CoNe leverages a novel simplified global attention mechanism, allowing interactions to no longer be hardwired in static edges but to be flexibly and efficiently extended to arbitrary nodes. Furthermore, we propose self-attentive common neighbor aggregation to embed link heuristics into learnable pairwise representations. Experiments on real-world datasets demonstrate that CoNe outperforms state-of-the-art methods with 0.51%–14.67% improvements.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113614"},"PeriodicalIF":7.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694366","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
Enhancing EEG-based individual-generic emotion recognition through invariant sparse patterns extracted from ongoing affective processes 通过从正在进行的情感过程中提取不变的稀疏模式,增强基于脑电图的个体通用情感识别
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-22 DOI: 10.1016/j.asoc.2025.113659
Yiwen Zhu , Jiehao Tang , Hongjuan Wei , Kaiyu Gan , Jianhua Zhang , Zhong Yin
{"title":"Enhancing EEG-based individual-generic emotion recognition through invariant sparse patterns extracted from ongoing affective processes","authors":"Yiwen Zhu ,&nbsp;Jiehao Tang ,&nbsp;Hongjuan Wei ,&nbsp;Kaiyu Gan ,&nbsp;Jianhua Zhang ,&nbsp;Zhong Yin","doi":"10.1016/j.asoc.2025.113659","DOIUrl":"10.1016/j.asoc.2025.113659","url":null,"abstract":"<div><div>Emotional responses to stimuli produce distinct brain activity patterns that are often sparse in time and spatial distribution across the cortex. These neural signals also contain individual-specific features, complicating emotion recognition across diverse populations. Current approaches rarely address the dual challenge of capturing sparse emotional patterns while minimizing identity-related biases in individual-generic emotion analysis. To bridge this gap, we propose a graph-based emotion-enhancing network framework that isolates emotion-specific neural signatures by amplifying sparse temporal-spatial features and suppressing person-specific biomarkers. Evaluated on two benchmark databases for binary emotion classification, our model achieved state-of-the-art performance in individual-dependent scenarios with accuracies of 65.76 % and 65.39 % for the arousal scale, and 57.75 % and 66.74 % for the valence scale. In the individual-generic condition, the accuracies were 56.11 % and 61.02 % for arousal, and 55.21 % and 66.17 % for valence. Notably, the model’s temporal and spatial enhancement modules provide interpretable insights into emotion-related neural sparsity through learned feature weights. This framework advances emotion recognition systems by reliably identifying universal emotional patterns across individuals while improving computational generalizability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113659"},"PeriodicalIF":7.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711436","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 double-convolution-double-attention Transformer network for aircraft cargo hold fire detection 飞机货舱火灾探测用双卷积双关注变压器网络
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-22 DOI: 10.1016/j.asoc.2025.113622
Hai Li , Zhen-Song Chen , Sheng-Hua Xiong , Peng Sun , Hai-Ming Zhang
{"title":"A double-convolution-double-attention Transformer network for aircraft cargo hold fire detection","authors":"Hai Li ,&nbsp;Zhen-Song Chen ,&nbsp;Sheng-Hua Xiong ,&nbsp;Peng Sun ,&nbsp;Hai-Ming Zhang","doi":"10.1016/j.asoc.2025.113622","DOIUrl":"10.1016/j.asoc.2025.113622","url":null,"abstract":"<div><div>Traditional smoke and gas detection systems in aircraft cargo compartments tend to have high false-alarm rates, and deep learning models reliant on video imagery tend to entail substantial computation. This paper introduces a transfer learning approach, FE-DCDA-Transformer-TL. Color features are used to enhance fire images, so as to improve the recognition of fire smoke and flame targets. The Transformer network is simplified and combined with dual convolution and dual attention mechanism modules. Dual convolution reduces the number of structural parameters of the Transformer network, and dual attention enhances the features of fire smoke and flame. FE-DCDA-Transformer-TL is trained and evaluated on a custom aircraft cargo compartment fire dataset, and tested on a similar dataset. In experiments, the proposed model achieves 97.69% accuracy, 98% precision, 96.7% recall, an F1-score of 97.34%, 0.98 AUC, 3.44G FLOPS, 21.54M Params, and 0.61 FPS. Compared with state-of-the-art methods, the proposed model improves accuracy, precision, and recall by at least 32.91%, 28.60%, and 16.94%, respectively. FE-DCDA-Transformer-TL effectively solves the accuracy problem of aircraft cargo hold fire detection, providing strong support for fire detection.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"183 ","pages":"Article 113622"},"PeriodicalIF":7.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703049","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
Enhancing long-tailed software vulnerability type classification via adaptive data augmentation and prompt tuning 通过自适应数据增强和及时调优增强长尾软件漏洞类型分类
IF 7.2 1区 计算机科学
Applied Soft Computing Pub Date : 2025-07-22 DOI: 10.1016/j.asoc.2025.113612
Long Zhang , Xiaolin Ju , Lina Gong , Jiyu Wang , Zilong Ren
{"title":"Enhancing long-tailed software vulnerability type classification via adaptive data augmentation and prompt tuning","authors":"Long Zhang ,&nbsp;Xiaolin Ju ,&nbsp;Lina Gong ,&nbsp;Jiyu Wang ,&nbsp;Zilong Ren","doi":"10.1016/j.asoc.2025.113612","DOIUrl":"10.1016/j.asoc.2025.113612","url":null,"abstract":"<div><div>Software vulnerability type classification (SVTC) is essential for efficient and targeted remediation of vulnerabilities. With the rapid increase in software vulnerabilities, the demand for automated SVTC approaches is becoming increasingly critical. However, the SVTC is significantly affected by the long-tailed issues, where the distribution of vulnerability types is highly unbalanced. Specifically, a small number of head classes contain a large volume of samples, while a substantial portion of tail classes consists of only a limited number of samples. This imbalance poses a significant challenge to the classification accuracy of existing approaches. To alleviate these challenges, we propose an innovative approach VulTC-LTPF, which integrates prompt tuning with long-tailed learning to enhance the effectiveness of SVTC. Within VulTC-LTPF, an adaptive error-rate-based data augmentation strategy is developed. This strategy allows the SVTC model to dynamically augment data for tail classes types with limited sample size during training, thereby mitigating the impact of the long-tailed problem. Furthermore, VulTC-LTPF employs a hybrid prompt tuning strategy, aligning the training process more closely with pre-training, which enhances adaptability to downstream tasks. Unlike existing approaches that rely solely on either vulnerability description or source code, VulTC-LTPF leverages both sources of information. By incorporating a combination of hard and soft prompts, it facilitates a more comprehensive and effective classification strategy. Experimental results demonstrate that VulTC-LTPF achieves substantial performance improvements over four state-of-the-art SVTC baselines, with gains ranging from 26.1% to 55.1% in MCC. Ablation studies further validate the effectiveness of the adaptive data augmentation, prompt tuning, the integration of two types of vulnerability information, and the use of hybrid prompts. These findings highlight that VulTC-LTPF represents a promising advancement in the field of SVTC, offering significant potential for further progress in addressing software vulnerability type classification challenges.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"182 ","pages":"Article 113612"},"PeriodicalIF":7.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694365","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
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