Applied Soft Computing最新文献

{"title":"A multimodal small sample pipeline leak detection method based on spatio-temporal fusion","authors":"Jingyi Lu ,&nbsp;Yuhang Wang ,&nbsp;Jing Chen ,&nbsp;Yao Chen ,&nbsp;Dongmei Wang ,&nbsp;Peng Wang","doi":"10.1016/j.asoc.2026.114628","DOIUrl":"10.1016/j.asoc.2026.114628","url":null,"abstract":"<div><div>In the industry, leak detection is a critical task to ensure the safe operation of oil and gas pipelines. Due to the low frequency and short duration of pipeline leakage in actual operation, and the small leakage is easily drowned by noise, these factors affect the accuracy and generalization of the leakage diagnosis model. This study proposes a new multimodal few-shot leak detection method based on spatio-temporal fusion. First, we combine VQ-VAE and short-time Fourier transform (STFT) to enhance one-dimensional leak signals. We then build a spatio-temporal bidirectional (STB) dataset by combining the enhanced signals with images converted from time-series data, improving data reliability. Next, we design a spatio-temporal fusion (STF) network, which uses the PKO algorithm to optimize key model parameters and integrates an intra-inter module (IIM) attention mechanism to fuse multi-source feature information, thereby boosting model performance. Finally, the proposed method is validated using pipeline data under different working conditions. The results show that this method achieves an accuracy of 98.67 % in identifying various types of leak signals, comparative experiments demonstrate that this method outperforms other models, demonstrating strong robustness.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114628"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039855","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 hybrid agent based on reinforcement learning and fuzzy computing using q-rung orthopair fuzzy information for electricity purchasing negotiation in smart grids","authors":"Dimitrios K. Panagiotou,&nbsp;Anastasios Dounis","doi":"10.1016/j.asoc.2026.114683","DOIUrl":"10.1016/j.asoc.2026.114683","url":null,"abstract":"<div><div>Smart Grid electricity procurement requires efficient multi-issue negotiation under uncertainty, where participants lack complete information about opponents’ preferences and must balance individual utility with social welfare. This paper proposes a hybrid negotiation framework that integrates q-rung orthopair fuzzy sets (q-ROFS) for preference modeling, Q-learning for online estimation of opponent weights, and fuzzy inference systems for adaptive concessions and acceptance decisions. Buyer offers are ranked using a q-ROFS aggregation mechanism that captures preference vagueness, while reinforcement learning dynamically estimates opponent issue weights based on observed negotiation outcomes and Kaldor-Hicks welfare changes. The proposed model is evaluated in a simulated electricity purchasing scenario involving multiple sellers and three negotiation issues across different q values. Results demonstrate that the proposed approach consistently improves joint utility and fairness compared to a Stackelberg-inspired benchmark, achieving an average increase in joint utility of 3.8 % while producing more balanced and robust agreements that approach the Pareto frontier. These findings confirm the effectiveness of combining fuzzy preference modeling, learning-based adaptation, and welfare-oriented rewards in automated energy negotiations.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114683"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039864","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":"Using sentiment analysis and CEEMDAN to learn the preferences of consumer groups: A case study of online hotel reviews","authors":"Dan Wang ,&nbsp;Zaiwu Gong ,&nbsp;Guo Wei ,&nbsp;María Ángeles Martínez ,&nbsp;Enrique Herrera-Viedma","doi":"10.1016/j.asoc.2026.114625","DOIUrl":"10.1016/j.asoc.2026.114625","url":null,"abstract":"<div><div>With the rapid development of big data technologies, online reviews have emerged as a crucial platform where consumers can express their opinions. However, the unstructured nature, temporal characteristics, and implicit and ambiguous nature of these preferences pose formidable challenges for researchers. This paper introduces a data-driven model designed to learn and interpret consumer preferences. The approach begins with fine-grained sentiment analysis of online reviews via the deep learning model known as Bidirectional Encoder Representations from Transformers (BERT). The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is then applied to extract the residual trend component from the data. The mean value of this component serves as an approximate representation of group-consistent sentiment features, providing quantitative support for the subsequent preference modeling. Following this, the UTilités Additives (UTA) method is used to construct a preference disaggregation model that aids in inferring the classification value system of consumer groups and identifying the core factors prioritized by different consumer segments. To validate the model, experiments were conducted using real-world hotel industry review data. The results suggest that the proposed model not only enhances the interpretability of consumer group preferences but also refines and provides insights into their classification value system. Moreover, it enables hotel managers to accurately identify consumer needs, optimize resource allocation, enhance market competitiveness, and offer a scientific basis for evidence-based management decisions tailored to consumer preferences.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114625"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039863","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":"Boundary-guided large-scale vision model for unified multi-domain industrial anomaly detection","authors":"Zixuan Zhang ,&nbsp;Fan Shi ,&nbsp;Chen Jia ,&nbsp;Mianzhao Wang ,&nbsp;Xu Cheng","doi":"10.1016/j.asoc.2026.114650","DOIUrl":"10.1016/j.asoc.2026.114650","url":null,"abstract":"<div><div>Extracting shared boundary cues across different anomaly domains is critical to enhancing generalization on unseen data, thereby laying the foundation for unified industrial anomaly detection paradigms. Existing unified detection paradigms often directly extract discriminative features from multiple data domains. However, due to the inherent semantic gaps between different data sources, bridging this disparity within a shared feature representation across multiple data domains remains a key challenge. To address this challenge, we propose a boundary-guided large-scale vision model that extracts commonalities across diverse domains. Specifically, we generate initial feature embeddings by establishing a multi-domain normal sample repository and employing a parameter coupling strategy. This captures shared boundary information across different data domains, thereby reducing the inherent semantic gaps. For anomalous feature synthesis, we incorporate this boundary information into the generation process, ensuring that the synthesized features retain critical structural details while expanding the coverage of potential anomalous data distributions. Additionally, to enhance feature space separation between normal and anomalous samples, we introduce a hybrid constraint optimization mechanism that improves the discriminative ability of the model. Extensive experiments on the MVTec AD, VisA, and MPDD datasets demonstrate that our method achieves state-of-the-art performance across various industrial scenarios. Experimental results demonstrate the effectiveness of boundary-guided shared information for multi-domain anomaly detection.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114650"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039851","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":"End-to-end discrete cosine transform integration in spectral convolutional neural networks for resource-efficient deep learning","authors":"Ibrahim Yousef Alshareef ,&nbsp;Ab Al-Hadi Ab Rahman ,&nbsp;Nuzhat Khan ,&nbsp;Hasan Alqaraghuli","doi":"10.1016/j.asoc.2026.114599","DOIUrl":"10.1016/j.asoc.2026.114599","url":null,"abstract":"<div><div>Spectral convolutional neural networks using Fast Fourier Transform (FFT) often suffer from high computational complexity and memory demands due to complex valued operations and the need for inverse transforms limiting their deployment on resource constrained devices. This paper presents a novel end-to-end spectral convolutional neural network (SpCNN) architecture that operates entirely in the Discrete Cosine Transform (DCT) domain, eliminating the need for inverse transformations and complex arithmetic. Leveraging the DCT’s real valued representation and superior energy compaction, the proposed design significantly reduces computational workload and memory usage while preserving classification accuracy. Key innovations include the removal of IFFT layers, a frequency domain adaptive activation function (FReLU), and a DCT optimized spectral pooling mechanism, each tailored for deployment in low power, resource constrained environments. Experimental evaluations on MNIST and a 94-class ASCII dataset demonstrate the model’s efficiency: LeNet5-DCT achieves a 37.96% FLOPs reduction, 18.45% lower memory usage, and 96.56% test accuracy, while VGG7-DCT achieves a 33.95% FLOPs reduction, 14.32% lower memory usage, and 90.62% test accuracy. The architecture also shows strong robustness to quantization, confirming its suitability for edge AI applications and low energy inference. This work provides a scalable, hardware efficient spectral learning framework, paving the way for future hybrid spectral models optimized for embedded environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114599"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039850","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":"Multi-view feature representation learning: Integrating medical team information to enhance online physician recommendation","authors":"Yongbo Ni,&nbsp;Donghui Yang","doi":"10.1016/j.asoc.2026.114776","DOIUrl":"10.1016/j.asoc.2026.114776","url":null,"abstract":"<div><div>Recommending suitable physicians to patients in online healthcare communities remains a challenging decision-making task. While existing studies leverage physicians’ professional features (clinical expertise) or service features (patient evaluations) for recommendations, single-view feature analysis and limited physician-patient interaction data often lead to insufficient feature representation, which further results in relatively poor recommendation performance. The emergence of online medical teams (OMTs) provides patients with a novel team perspective and data source for understanding physician features. Therefore, this study aims to leverage the OMTs information to construct a multi-view feature representation learning framework to enhance the physician feature representation and improve the recommendation performance. We first integrate the OMTs information with online reviews, disease descriptions, and physician profiles to build the multi-source and multi-type medical data inputs. Based on these inputs, we leverage BERT, TextCNN, semantic analysis, and graph neural network technologies to construct the multi-view feature representation learning framework from the views of professional features, service features, and team features. Then, these multi-view features are fused through a multilayer perceptron to predict the selection probabilities. Experimental results demonstrate the multi-view features based method incorporating OMTs information shows an average improvement of 7.5 % in HR@5, 5.3 % in NDCG@5, and 7.8 % in MRR compared to other classical methods. Surprisingly, the physician service capabilities involved in the team features have a greater impact on the recommendation performance than the physician professional capabilities involved.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114776"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191514","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":"Evolving explainable neural architectures: Dynamic behavioral-novelty weighting in multi-objective neuroevolution for multimodal spam detection","authors":"Adnane Filali ,&nbsp;El Arbi Abdellaoui Alaoui ,&nbsp;Mostafa Merras","doi":"10.1016/j.asoc.2026.114790","DOIUrl":"10.1016/j.asoc.2026.114790","url":null,"abstract":"<div><div>Spam detection is effectively a moving target: as spammers shift their strategies across images, voice calls, and text, static detection systems struggle to keep pace. Artificial neural architectures, though often accurate, frequently fail to strike a workable balance between performance, generalization, and behavioral novelty in such adversarial environments. In this paper, we introduce and analyze two multi-objective variants of NeuroEvolution of Augmenting Topologies: a static weighting approach (NEAT-stMO) and a dynamic version (NEAT-dyMO) that adaptively reallocates objective priorities during the evolutionary process. Both methods simultaneously optimize for accuracy, the generalization gap, and novelty while evolving the topology and hyperparameters of artificial neural networks. We benchmark these evolutionary strategies against a broad spectrum of baselines, ranging from Pareto-based and single-objective NEAT variants to conventional classifiers, MLP, SVM, and automated deep learning frameworks, including Vision Transformers, Auto-Keras, and Hyperband. Our evaluation spans three distinct modalities—image, voice, and imbalanced SMS data—using five-fold cross-validation to rigorously assess accuracy, F1-scores, AUC, and computational efficiency—time and RAM. Importantly, we integrate SHapley Additive exPlanations to deconstruct the “black box” of how hyperparameters drive performance. Empirical results place NEAT-dyMO consistently among the top performers; notably, it offers significant efficiency advantages over heavy Transformer models while surpassing static baselines. SHAP analysis further sheds light on the mechanism, exposing that NEAT-dyMO’s dynamic weighting successfully navigates the trade-off between exploration—driven by larger architectures and dropout—and exploitation—favored by small batch sizes. These findings, therefore, suggest that dynamically evolving ANNs provide a robust, adaptive, and interpretable alternative for real-world multimodal spam defense.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114790"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191779","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":"DMEL: A novel dual-modal ensemble learning architecture for multi-step runoff prediction","authors":"Wen-chuan Wang ,&nbsp;Miao Gu,&nbsp;Zong Li,&nbsp;Yi-yang Wang,&nbsp;Yan-wei Zhao","doi":"10.1016/j.asoc.2026.114792","DOIUrl":"10.1016/j.asoc.2026.114792","url":null,"abstract":"<div><div>Accurate multi-step runoff prediction is crucial for effective water resource management and flood disaster reduction. Despite significant advancements in deep learning for hydrological predictions, identifying the modal characteristics of runoff sequences and extending the effective lead time of prediction models remain challenging. This paper introduces a novel Dual-Modal Ensemble Learning (DMEL) architecture that addresses these issues by integrating a Modal Recognition Strategy (MRS) with two deep learning models: Informer and Long Short-Term Memory (LSTM). The MRS technique first reduces the nonlinearity and non-stationarity of runoff data through feature extraction and fusion, lowering model computational complexity and mitigating cumulative errors from high-dimensional inputs. The optimized modal components are then categorized into high-frequency and low-frequency components, which are processed by the Informer and LSTM models, respectively, leveraging their unique capabilities for dual-modal ensemble learning. The final prediction is obtained by superimposing the results from all sequences. The two sites’ analysis results demonstrate the DMEL architecture’s effectiveness. Taking the Shuangpai Station as an example, in single-step prediction, the Kling-Gupta Efficiency (KGE) index of the DMEL model achieves relative improvements of 22.60 %, 28.32 %, 25.34 %, 43.90 %, 45.06 %, and 42.30 % compared to the Informer model, Transformer model, LSTM model, Recurrent Neural Network (RNN) model, TimesNet model, and PatchTST model, respectively. When the prediction step increases to the seventh step, the KGE index of the DMEL model still shows relative increases of 37.50 %, 105.71 %, 89.84 %, 146.04 %, 211.82 %, and 178.06 % compared to the Informer model, Transformer model, LSTM model, RNN model, TimesNet model, and PatchTST model, respectively. This indicates that the DMEL architecture can accurately capture critical information in daily runoff sequences, extending the model’s effective prediction period and providing a robust scientific basis for water resource management and flood disaster reduction.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114792"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191782","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":"GLUE-GAN: Global-local underwater image enhancement generative adversarial network","authors":"Jun Wang ,&nbsp;Daoyi Wei ,&nbsp;Wentao Shi ,&nbsp;Chenyang Liu ,&nbsp;Wenlong Jiang ,&nbsp;Juan Wang","doi":"10.1016/j.asoc.2026.114686","DOIUrl":"10.1016/j.asoc.2026.114686","url":null,"abstract":"<div><div>Underwater images suffer from color cast, blur, and low contrast due to wavelength-dependent absorption, scattering, and suspended particulates. Many enhancement methods restore global appearance while suppressing local features; others operate in the frequency domain or rely on auxiliary regularization but lack an explicit mechanism to fuse global and local evidence. We introduce GLUE-GAN, a generative adversarial network that globally–locally unifies enhancement through explicit cross-scale, cross-domain fusion. The model comprises: (1) an adaptive feature enhanced encoder that marries spatial context modules with grouped channel-wise self-attention to collaboratively model spatial–channel dependencies across diverse scenes; (2) a multichannel feature aggregation enhancement module that performs multi-scale extraction and alignment to uniformly recover global tone and local textures; and (3) a global–local information enhancement module that uses wavelet decomposition to separate low- and high-frequency bands, processing that mitigates local bias during global correction. Evaluations on EUVP, UIEB, and UFO-120 demonstrate consistent gains in color fidelity, contrast, and sharpness, with improved preservation of edges and fine details. By unifying spatial–channel reasoning with frequency-aware processing in a single adversarial framework, GLUE-GAN balances global color correction and local detail preservation for underwater image enhancement.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114686"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080303","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":"Diagnosis of autism disorder from rs-fMRI brain images through hierarchical YOLO and mechanism of attention (HYMA)","authors":"Saba Gholami ,&nbsp;Sara Motamed ,&nbsp;Elham Askari","doi":"10.1016/j.asoc.2026.114655","DOIUrl":"10.1016/j.asoc.2026.114655","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical communication among brain regions. Early and accurate diagnosis of ASD can significantly improve the effectiveness of behavioral interventions. In this study, we propose a novel deep learning framework called HYMA (Hierarchical YOLO with Mechanism of Attention) for classifying ASD and control subjects using resting-state functional MRI (rs-fMRI) data. The proposed model integrates the YOLO detection architecture with a Bottleneck Attention Module (BAM) to enhance spatial–temporal feature extraction, and employs both dynamic (expert combination) and static (Naïve Bayes) ensemble classifiers for final decision fusion. To address the limited-sample challenge in medical imaging, we adopted extensive data augmentation and subject-wise validation. Experimental results on the ABIDE I and II datasets demonstrate that HYMA achieves superior performance, reaching an accuracy of 99.3%, precision of 98.9%, recall of 99.1%, and F1-score of 99.0%, outperforming existing state-of-the-art methods. These results indicate that the proposed attention-enhanced YOLO ensemble framework provides a robust and generalizable approach for rs-fMRI-based ASD diagnosis.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114655"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080299","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}