Applied Soft Computing最新文献

{"title":"Intelligent compressive strength prediction of sustainable rubberised concrete using an optimised interpretable deep CNN-LSTM model with attention mechanism","authors":"Yang Yu ,&nbsp;Roshan Jayathilakage ,&nbsp;Yiyang Liu ,&nbsp;Ailar Hajimohammadi","doi":"10.1016/j.asoc.2025.113993","DOIUrl":"10.1016/j.asoc.2025.113993","url":null,"abstract":"<div><div>The increasing environmental concerns associated with waste rubber disposal, particularly from used tyres, have led to the exploration of rubberised concrete as a sustainable construction material. Rubberised concrete provides benefits like enhanced flexibility and energy absorption; however, its reduced compressive strength remains a challenge for structural applications. This study puts forward an advanced deep learning model to accurately evaluate compressive strength of rubberised concrete by combining a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network enhanced with attention mechanism, and optimised using the enhanced firefly algorithm (EFA), featuring chaotic initialisation and nonlinear learning factor for improved convergence, for hyperparameter tuning. The proposed model introduces computing novelties: attention-guided CNN-LSTM feature fusion and chaos-enhanced firefly optimisation. Then, it is trained on an extensive dataset incorporating key mix parameters, including water, cement, supplementary cementitious materials, superplasticiser, coarse and fine aggregates, crumb and chipped rubber content, and concrete age, with validation supported by experimental tests in the laboratory. The proposed model achieves superior prediction accuracy, achieving R² values of 0.967 for training and 0.943 for testing, outperforming conventional machine learning methods. Evaluation metrics showcase the superior performance of model, with root mean square error of 2.966 MPa and 3.757 MPa for training and test data, respectively. A sensitivity analysis based on SHapley Additive exPlanations (SHAP) highlights coarse aggregate, rubber content, and concrete age as the most influential variables affecting compressive strength. By providing a highly accurate, interpretable, and cost-effective predictive tool, this research facilitates the optimisation of rubberised concrete mix design, supporting its broader adoption in sustainable construction practice.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113993"},"PeriodicalIF":6.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221556","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":"Machine learning-based adaptive large neighborhood search algorithm for the integrated vessel scheduling and speed optimization problem in the compound channel","authors":"Jian Du ,&nbsp;Shan Lin ,&nbsp;Liming Guo ,&nbsp;Jianfeng Zheng","doi":"10.1016/j.asoc.2025.113980","DOIUrl":"10.1016/j.asoc.2025.113980","url":null,"abstract":"<div><div>The carbon emission from vessel navigating in the channel accounts for about 61 % of the total emissions in port areas. Considering an effective means of reducing emissions, namely, speed adjustment, this study deals with an integrated problem of vessel scheduling and speed optimization (VSSOP) in the channel. This study considers the complex structure of a compound channel, i.e., containing both one-way and two-way lanes with different navigation rules. We also focus on the effects of meteorological conditions (winds, waves and currents) on the vessel stall, and tidal restrictions on the time window for large vessels to pass through the channel. Thus, a mixed integer programming (MIP) model for the VSSOP is proposed to control the carbon emissions in the channel. Then, we develop a machine learning-based adaptive large neighborhood search (ALNS) approach, where the ALNS is used to solve the proposed MIP in real cases and the dynamic machine learning approach helps to evaluate and fit the complex effects of multiple meteorological conditions on the vessel sailing speed. The dynamic parallel mechanism is further introduced to improve the fitting accuracy of the machine learning part without increasing the running time of the ALNS. The experimental results reveal that the machine learning-based ALNS approach can be applied in practice. Additionally, valuable managerial insights for port operators are obtained to aid in vessel traffic management.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113980"},"PeriodicalIF":6.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183696","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":"FSAMLM: A few-shot adaptation multimodal large model for cross-domain fault diagnosis","authors":"Xin Zhang ,&nbsp;Shixi Liu ,&nbsp;Li Jiang ,&nbsp;Yibing Li","doi":"10.1016/j.asoc.2025.113985","DOIUrl":"10.1016/j.asoc.2025.113985","url":null,"abstract":"<div><div>Recent advances in deep learning have demonstrated remarkable performance in mechanical fault diagnosis. However, most existing approaches are designed for a single data modality or a single task, limiting their flexibility and generalization ability. Moreover, common knowledge across different tasks remains largely unexploited. Inspired by the success of large-scale models in natural language processing and computer vision, integrating multimodal data with multi-task learning strategies may significantly improve the performance of fault diagnosis models. Nonetheless, a significant gap exists between general pre-trained knowledge and domain-specific expertise, posing considerable challenges for effective integration. To address these issues, we propose a novel few-shot adaptation multimodal large model (FSAMLM) that efficiently captures shared representations through a parameter-efficient fine-tuning strategy, structured in two stages. Specifically, we first design a low-rank adaptation meta-learning (LoRAML) framework, which employs low-rank decomposition on pre-trained parameters to reduce computational complexity and improve robustness. This approach not only accelerates adaptation to new few-shot tasks but also preserves pre-training knowledge effectively. During the second fine-tuning stage, we implement target-domain training with limited samples and introduce a training-free inference option for real-world deployment. Experimental validation using six public datasets demonstrates FSAMLM’s superior domain generalization capability in few-shot fault diagnosis tasks compared to existing methods. The code repository is publicly available at: <span><span>https://github.com/ohhyeeaah/FSAMLM-for-fault-diagnosis</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113985"},"PeriodicalIF":6.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221557","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 proximal policy optimization driven hyper-heuristic for workers constrained hybrid flow shop problem","authors":"Shengnan Ding ,&nbsp;Weishi Shao ,&nbsp;Zhongshi Shao ,&nbsp;ShengTao Peng ,&nbsp;Dechang Pi ,&nbsp;Jiaquan Gao","doi":"10.1016/j.asoc.2025.113990","DOIUrl":"10.1016/j.asoc.2025.113990","url":null,"abstract":"<div><div>As the production environment becomes increasingly complex, the integration of soft computing techniques becomes essential for addressing resource-constrained scheduling problems. This paper delves into a worker constrained hybrid flow shop scheduling problem (WHFSP) that integrates worker resources at each processing stage. A mixed-integer linear programming (MILP) model is constructed which enables the use of mathematical solvers to obtain optimal solutions for small-scale instances. Additionally, a novel soft computing-based scheduling framework, namely a proximal policy optimization-based hyper-heuristic algorithm (PPO-HH), is proposed. It automatically selects the most suitable low-level heuristic strategies based on the current state and historical data, facilitating efficient exploration and exploitation of the complex decision space. Several low-level heuristics including perturbative and local search operators are developed to explore the solution space. Subsequently, a high-level control strategy based on proximal policy optimization is proposed. A solution quality evaluation function and a reward mechanism based on problem characteristics are formulated. This mechanism provides feedback to PPO-HH based on the degree of alignment between the actions taken by the agent and the objectives, gradually optimizing the selection of low-level heuristic strategies. Eventually, it generates a probability distribution for each low-level heuristic in the given environment. Comprehensive numerical experiments are conducted to evaluate the performance of both the MILP model and the components of the PPO-HH algorithm. The comparison results show that PPO-HH is effective and efficient for solving the WHFSP.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113990"},"PeriodicalIF":6.6,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183700","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":"Beyond training horizons: A physics-informed U-Net transformer for time-dependent CO₂ storage simulation in environmental applications","authors":"Ye Liu ,&nbsp;Jiahao Wang ,&nbsp;Nan Zhang ,&nbsp;Xiaodong Qian ,&nbsp;Shaojun Chai","doi":"10.1016/j.asoc.2025.113987","DOIUrl":"10.1016/j.asoc.2025.113987","url":null,"abstract":"<div><div>CO₂ geological storage is a key strategy for reducing greenhouse gas emissions, requiring accurate modeling of subsurface CO₂ migration is essential for effective storage planning and risk assessment. Conventional numerical simulations, which solve time-dependent nonlinear partial differential equations, provide detailed physical insights but are computationally demanding, especially for large-scale or long-term scenarios. To improve computational efficiency, surrogate models based on machine learning have been increasingly investigated. Methods such as deep learning and physics-Informed neural networks aim to approximate the behavior of physical systems, offering potential reductions in simulation time. However, these approaches often require extensive case-specific datasets and are typically limited to fixed time horizons defined during training, which can restrict their generalizability and practical application. This study presents a time-aware surrogate modeling framework that combines convolutional neural networks with self-attention mechanisms to address these limitations. Drawing inspiration from autoregressive forecasting used in sequential learning models, the proposed approach captures temporal dependencies through iterative prediction of system states.The framework requires only a short-term numerical simulation to initialize the physical system, after which it can generate predictions for arbitrarily extended time horizons without the need for retraining. By enabling long-term forecasting, the method improves efficiency and supports repeated scenario evaluations, such as site screening and well placement optimization. Such predictive capabilities are particularly valuable in addressing environmental sustainability goals, where rapid and scalable simulations are essential for managing long-term subsurface processes under climate-related constraints.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113987"},"PeriodicalIF":6.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222104","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":"Evaluating CEO hubris effects on sustainable performance in the IC design industry: An integrated dynamic network DEA framework with machine learning","authors":"Sheng-Wei Lin,&nbsp;Yu-Rou Lin","doi":"10.1016/j.asoc.2025.113986","DOIUrl":"10.1016/j.asoc.2025.113986","url":null,"abstract":"<div><div>This study introduces an integrated analytical framework combining dynamic network data envelopment analysis (DNDEA) with machine learning to assess the impact of CEO hubris on sustainable performance in the integrated circuit (IC) design industry. Our two-stage DNDEA model evaluates operational and R&amp;D efficiency separately, incorporating intermediate factors including profit and ESG scores. We develop a novel text-based measure of CEO hubris by analyzing the contrast between confidence and conservatism language in annual shareholder reports. This hubris measure is then incorporated into predictive models, where we compare traditional linear regression against advanced machine learning approaches—support vector regression (SVR) and random forest (RF)—using cross-validation and hyperparameter optimization. The analysis reveals a significant negative correlation between CEO hubris and operational and R&amp;D efficiency. Notably, the non-linear models (SVR and RF) demonstrate superior predictive accuracy compared to linear regression across varying levels of CEO hubris. These findings yield two primary contributions: first, they establish the critical need for monitoring hubristic leadership behavior in innovation-intensive industries, given their detrimental effect on organizational efficiency. Second, they validate the effectiveness of combining text analytics, DNDEA efficiency metrics, and machine learning for evaluating leadership impact on firm performance. This methodology provides a comprehensive framework for analyzing leadership dynamics in the IC design sector and offers an adaptable template for similar analyses across technology-driven industries.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113986"},"PeriodicalIF":6.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183699","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":"Federated deep embedded clustering under privacy protection","authors":"Xiao Xu ,&nbsp;Hong Liao ,&nbsp;Xu Yang","doi":"10.1016/j.asoc.2025.113963","DOIUrl":"10.1016/j.asoc.2025.113963","url":null,"abstract":"<div><div>Deep clustering, a prominent research focus in data mining, utilizes deeply embedded features to reveal the intrinsic statistical structure of data. However, existing deep clustering models rely on centralized datasets, which are impractical in scenarios with data silos and privacy constraints, leading to degraded clustering performance. Considering the privacy protection characteristics of federated learning, this paper incorporates the idea of federated learning into deep clustering, and proposes a federated deep embedding clustering (FDEC) model under privacy protection. FDEC follows a universal client-server structure, coordinating training between clients through a central server to obtain a unified central model. The server updates the global deep embedding and cluster centers based on a hybrid federated averaging strategy, while each client conducts two-stage deep clustering on local data without sharing raw data. To enhance robustness under non-independent and identically distributed (non-IID) conditions, the hybrid strategy improves parameter aggregation effectiveness. Experimental results on both IID and non-IID datasets demonstrate that FDEC is more robust and consistently outperforms centralized deep clustering methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113963"},"PeriodicalIF":6.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222106","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 label enhancement based positive-unlabeled hybrid network for pump bearing intelligent fault diagnosis","authors":"Jiaxing zhu,&nbsp;Junlan Hu,&nbsp;Buyun Sheng","doi":"10.1016/j.asoc.2025.113976","DOIUrl":"10.1016/j.asoc.2025.113976","url":null,"abstract":"<div><div>Bearings are important support parts for rotating machinery such as pumps, and the application of machine learning algorithms has brought the fault diagnosis of bearings to a more intelligent stage. However, with the scarcity of target fault data and the lack of accurate labeling for critical data, commonly used data-driven fault diagnosis methods had its limitations. Inspired by semi-supervised learning, hypergraph and knowledge distillation theories, a hybrid PUHGNN network based on label augmentation was proposed in this paper. Firstly, a hypergraph neural network (HGNN) structure based on the multi-resolution signal was proposed to measure the correlation at multiple scales to make difference and connections between different labels. Secondly, the HGNN network is improved by combining HGNN and Positive-Unlabeled (PU) Learning ideas to form a new PUHGNN label enhancing mechanism which will solve the lacking of labels. Lastly, a soft-label-based label selection method is proposed to dynamically judge the similarity of samples to reiterate, which will make the otherwise indistinguishable faults more explicit. In experimental session, the CWRU dataset and the enviormental protection pump bearing datasets were applied to conduct unbalance, mislabel, extreme mislabel and ablation experiments. The result shows that the label enhancement is not only necessary but significant in the unbalanced under-labeled datasets, furthermore, the PUHGNN has more obvious enhancement compared to other methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113976"},"PeriodicalIF":6.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183695","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":"T5-based anomaly-behavior video captioning using semantic relation mining","authors":"Min-Jeong Kim ,&nbsp;Kyungyong Chung","doi":"10.1016/j.asoc.2025.113923","DOIUrl":"10.1016/j.asoc.2025.113923","url":null,"abstract":"<div><div>Video data consist of a series of images that change over time. The sequence of frames in a video provides important information on the motion and continuity of the video. Therefore, this dynamic information can be used to analyze the movement and behavior patterns of objects. Video captioning, which is used to explain a video, can describe the content of the video data and provide subtitles or descriptions in various languages. It can also explain the main points in a video with complex content, facilitating the information provided to users. In captioning, semantic analysis is used to identify the overall context of the data and generate the correct captions. However, captions are usually generated by focusing on major objects and actions, making it difficult to capture the details. In this paper, we propose text-to-text transfer transformer (T5)-based abnormal behavior video capturing using semantic relation mining. The proposed method generates captions with semantic features from video data based on environmental factors and improves the accuracy of video description by identifying the similarity of each caption for similar video and caption classification. This enables the classification and search of video data and is useful in video analysis systems, such as video monitoring and media analysis.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113923"},"PeriodicalIF":6.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183694","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-branch and multi-loss learning for fine-grained image retrieval","authors":"Hongchun Lu ,&nbsp;Min Han ,&nbsp;Songlin He ,&nbsp;Xue Li ,&nbsp;Chase Wu","doi":"10.1016/j.asoc.2025.113833","DOIUrl":"10.1016/j.asoc.2025.113833","url":null,"abstract":"<div><div>To effectively address the problem of low accuracy of fine-grained image retrieval due to significant intra-class differences and small inter-class differences, we propose a novel and highly reliable fine-grained deep hashing learning framework dubbed MBLNet to accurately retrieve fine-grained images. Specifically, we propose (i) a dual-selected significant region erasure method for generating compact binary codes for fine-grained images; (ii) a dual filtering object location method for mining discriminative local features; and (iii) a new multi-stage loss function for optimizing network training. We conducted extensive experiments on three fine-grained datasets, Stanford Cars, FGVC-Aircraft, and CUB-200-2011, and achieved mAP results of 89.3%, 87.2%, and 80.6%, respectively. Additionally, the ablation study demonstrates that both the dual-selected significant region erasure method and the dual filtering object location method contribute to the improved accuracy of fine-grained image retrieval, further validating the effectiveness of the proposed method. Code can be found at <span><span>https://github.com/luhongchun/MBLNet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"185 ","pages":"Article 113833"},"PeriodicalIF":6.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221600","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}