Expert Systems with Applications最新文献

{"title":"Adaptive shape imitation and selective semantic guidance for industrial surface defect detection","authors":"Xiao Liang ,&nbsp;Yuechen Li ,&nbsp;Xuewei Wang ,&nbsp;Pengfei Liu ,&nbsp;Yongjun Shen ,&nbsp;Jingbo Guo","doi":"10.1016/j.eswa.2025.127334","DOIUrl":"10.1016/j.eswa.2025.127334","url":null,"abstract":"<div><div>Surface defect detection is increasingly valued within the realm of manufacturing industries, not only for ensuring the manufacturing quality and service life of industrial products but also for facilitating the repairing and re-manufacturing of defective surfaces. Despite significant advancements made by existing works, issues such as blurred boundaries, irregular shapes, and scale variances of surface defects continue to pose substantial challenges to industrial surface defect detection tasks. We believe the more adaptive and effective representation of the complex and diverse defects is the key to addressing these challenges. To this end, this work proposes an innovative feature extractor with flexible shape imitation capability and a novel feature fuser incorporating strong semantic guidance capability. Concretely, to adaptively represent irregularly-shaped defects, we design a plug-and-play shape-imitation convolutional kernel to yield flexible receptive fields and model long-distance dependencies with a partial computation strategy and rapid feature-memory retrieval mechanism. Meanwhile, to fully characterize weak and scale-varying defects, we construct a selective semantic-guided feature pyramid structure to contextually guide the network’s attention to crucial features and dynamically cross-fuse different levels of defect features. Four lite decoupled heads with wise IoU-based losses are employed to further enhance the detector’s accuracy. Overall, this work develops an effective and efficient method that significantly improves the performance of industrial surface defect detection while maintaining a favorable accuracy-speed balance and high scene adaptability. Extensive experiments on six industrial defect datasets (steel strips, steel sheets, rails, aluminum profiles, optics, and circuit boards) demonstrate the superiority of our method over other competitive approaches. The proposed method achieves either the best or at least second-best detection accuracy with lightweight model parameters and a real-time inference speed.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"279 ","pages":"Article 127334"},"PeriodicalIF":7.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769243","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":"Gwo-ga-xgboost-based model for Radio-Frequency power amplifier under different temperatures","authors":"Shaohua Zhou","doi":"10.1016/j.eswa.2025.127439","DOIUrl":"10.1016/j.eswa.2025.127439","url":null,"abstract":"<div><div>To improve the modeling accuracy and modeling speed of the XGBoost model, a Gray Wolf Optimization (GWO)-Genetic Algorithm (GA)-XGBoost model is proposed in this paper and is applied to model radio frequency (RF) power amplifiers at different temperatures. The experimental results show that compared to XGBoost, GA-XGBoost, and GWO-XGBoost, the modeling accuracy of GWO-GA-XGBoost can be improved by one order of magnitude or more. Compared to XGBoost, GA-XGBoost, and GWO-XGBoost, GWO-GA-XGBoost has also increased the modeling speed by one magnitude or more. In addition, compared to the classic machine learning algorithms gradient boosting, random forest, and AdaBoost, the proposed GWO-GA-XGBoost can improve modeling accuracy by two or more orders of magnitude while also increasing modeling speed by one or more.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127439"},"PeriodicalIF":7.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734877","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":"Cluster-Boosted Artificial Neural Networks: Theory, implementation, and performance evaluation","authors":"George Papazafeiropoulos","doi":"10.1016/j.eswa.2025.127332","DOIUrl":"10.1016/j.eswa.2025.127332","url":null,"abstract":"<div><div>This study introduces a new clustering technique to boost Artificial Neural Networks’ (ANNs’) performance. The term “Cluster-Boosted Artificial Neural Networks” (“CBANNs”) is coined for ANNs using this technique. By adding cluster identifiers as extra input features, CBANNs enhance conventional ANNs and improve the model’s ability to identify underlying patterns in complicated data landscapes. This method offers a solution to some limitations of standard ANNs, which often struggle with high-dimensional data, local minima, and nonlinear relationships. Without the need for manual feature engineering or in-depth domain knowledge, CBANNs greatly increase prediction accuracy by employing unsupervised clustering, using k-medoids, to build a more structured input space. Various numerical results are presented which validate the superior predictive ability of CBANNs across nine benchmark functions, including De Jong’s 5th, Griewank, and Rastrigin functions. Compared to conventional ANNs with identical hyperparameters, CBANNs achieve error reductions of up to 98%, consistently demonstrating higher performance on functions with intricate geometries and multiple minima. Furthermore, CBANNs are applied to a terrain modeling problem, which proved that CBANNs can reduce the prediction error by up to 95% compared to standard ANNs, indicating their potential for high-precision applications. These findings underscore the CBANN’s ability to generalize effectively in challenging datasets, suggesting its broader applicability in fields that demand accuracy in the presence of complex data distributions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127332"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724872","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":"IoT-enabled smart farming: A cloud-based approach for polyhouse automation","authors":"Rajesh Kumar Mishra ,&nbsp;Ashish Ranjan Dash ,&nbsp;Anup Kumar Panda","doi":"10.1016/j.eswa.2025.127358","DOIUrl":"10.1016/j.eswa.2025.127358","url":null,"abstract":"<div><div>The increasing demand for sustainable agriculture necessitates advanced automation in polyhouse farming to optimize resource utilization and enhance productivity. Traditional polyhouse management relies heavily on manual monitoring, leading to inefficiencies in irrigation, climate control, and energy consumption. This study presents an Internet of Things (IoT) and cloud-integrated automation system designed to streamline polyhouse operations. Unlike existing solutions, the proposed system offers real-time sensor-driven decision-making, remote monitoring via a mobile/web interface, and dynamic control of irrigation, temperature, and humidity. The proposed system enables users to monitor and control multiple polyhouses from a single interface. The system is designed to be scalable, cost-effective, and adaptable to various irrigation setups, including grid-connected and off-grid solutions. Performance evaluation demonstrates significant improvements in response time, energy efficiency, and overall cost-effectiveness compared to conventional approaches. This work contributes to smart agriculture by providing an intelligent, cloud-integrated framework for sustainable farming practices.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"279 ","pages":"Article 127358"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769240","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":"Localized Adaptive Style Mixing for feature statistics manipulation in medical image translation with limited Data","authors":"Zhong Wang ,&nbsp;Jia-Xuan Jiang ,&nbsp;Hao-Ran Wang ,&nbsp;Ling Zhou ,&nbsp;Yuee Li","doi":"10.1016/j.eswa.2025.127217","DOIUrl":"10.1016/j.eswa.2025.127217","url":null,"abstract":"<div><div>Medical image translation synthesizes missing modalities to aid clinical diagnoses, yet Generative Adversarial Networks (GANs) often overfit in limited data scenarios. This work introduces Localized Adaptive Style Mixing (LASM), a novel regularization strategy addressing this challenge. Unlike global statistical mixing, LASM segments discriminator feature maps into grids and blends localized high-order statistics (mean, variance, skewness, kurtosis) from reference and input images. This forces the discriminator to focus on structural content rather than style, effectively mitigating overfitting. Experiments on brain T1- to-CT, pelvic T1-to-CT, and T2-FLAIR synthesis tasks demonstrate that LASM-equipped GANs outperform state-of-the-art methods, achieving 54.84 FID (vs. 131.54 baseline) with only 10% training data. Notably, LASM requires no transfer learning and integrates seamlessly into existing frameworks. Our approach advances data-efficient medical image translation, particularly for rare diseases with scarce datasets. Code is available at <span><span>here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"277 ","pages":"Article 127217"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697238","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 multi-feature selection fused with investor sentiment for stock price prediction","authors":"Kehan Zhen ,&nbsp;Dan Xie ,&nbsp;Xiaochun Hu","doi":"10.1016/j.eswa.2025.127381","DOIUrl":"10.1016/j.eswa.2025.127381","url":null,"abstract":"<div><div>The stock market data has the characteristics of high dimensionality and multiple noise, and the investment behavior of investors is easily influenced by emotions, which poses a great challenge to stock price prediction. To improve the accuracy of stock price prediction, this study proposes a combined modeling approach based on multiple feature selection algorithms and incorporating investor sentiment. First, we collected stock trading data of the Chinese A-share market from 2018 to 2022, and three types of investor sentiment data sourced from social media, Internet news and newspaper news. Then, we used five feature selection algorithms to select dozens of important features from hundreds of features in the stock trading data. Based on three types of investor sentiment data, five sentiment indicators were constructed and included in the subsequent feature selection along with the previously selected important features. Finally, five deep learning models were used to predict stock prices using feature sets with sentiment indicators. A total of 1030 stocks from 10 industries such as pharmaceutical and biological, leisure service, food and beverage were selected for the experiment. The results show that in 10 industries, LSTM-CNN-Attention model has the best effect on stock price prediction, and after incorporating the sentiment indicator constructed by the principal component, the effect of the model is significantly improved, and the performance is the best in 7 industries. This method explores a new way of stock price prediction by integrating investor sentiment, and can provide further reference for the research of stock price intelligent prediction.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127381"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-session transformers and multi-attribute integration of items for sequential recommendation","authors":"Jiahao Hu ,&nbsp;Ruizhen Chen ,&nbsp;Yihao Zhang ,&nbsp;Yong Zhou","doi":"10.1016/j.eswa.2025.127266","DOIUrl":"10.1016/j.eswa.2025.127266","url":null,"abstract":"<div><div>Modeling sequential dependencies plays a significant role in simulating the dynamic changes in users’ interests, and the introduction of deep learning can address long sequence data to some extent, thereby enabling more precise capture of these changes. However, most existing models still struggle to train sequences with insufficient interaction information or overly long sequences, and they also fail to capture the genuine intentions of users reflected by the interaction behaviors. Additionally, they overlook the characteristic that items interacted with by users are not strictly ordered and are highly homogeneous within a certain period, while the items between different periods are likely to be heterogeneous. In this paper, we propose a sequential recommendation model based on Multi-session Transformers and multi-attribute integration of items (MTMISRec), which enriches the missing interaction information of sparse data by integrating items’ attributes with users’ historical interaction sequences and distinguishes the true intentions of users under similar interactions. Furthermore, we set a time threshold to partition items with interaction intervals within this threshold into a session, thereby capturing homogeneous relationships within each session. We employ the dual attention mechanism to perform local attention within each session and introduce the learned type weights of each session into the complete interaction sequence to perform global attention, thereby blurring the sequential relationships within sessions and integrating global relevance with local details to handle overly long sequences precisely. We conducted extensive experiments on four datasets, and the results demonstrate that MTMISRec surpasses advanced sequential models on sparse and dense datasets.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127266"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724079","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":"Grouped convolution dual-attention network for time series forecasting of water temperature in offshore aquaculture net pen","authors":"Xiaoyi Sun ,&nbsp;Wenqiang Liu ,&nbsp;Mengqi Wang ,&nbsp;Jingsen Zhang ,&nbsp;Ferrante Neri ,&nbsp;Yang Wang","doi":"10.1016/j.eswa.2025.127438","DOIUrl":"10.1016/j.eswa.2025.127438","url":null,"abstract":"<div><div>As a novel open aquaculture technique that approaches ecological farming, offshore aquaculture net pen provides significant value for the sustainable development of aquaculture. Water temperature, being a critical water quality parameter, directly influences the growth and development of fish. Moreover, trends in water temperature can guide the timing of relay in terrestrial-marine aquaculture models. Therefore, real-time monitoring and accurate multi-step prediction of water temperature can effectively ensure the safety of fish production and avoid severe economic losses due to weather changes. However, the openness of the offshore aquaculture net pen environment makes water temperature susceptible to spatial and temporal impacts of external factors, characterized by non-linearity, dynamics, and complexity, making accurate water temperature prediction challenging. This paper proposes a Grouped Convolution Dual-Attention Network (CDANet) framework for multivariate time series prediction based on grouped dual-attention convolution, which fully considers the spatiotemporal correlation between climate conditions and water quality parameters in the pen area, the spatial distribution of water body, and the temporal dependency of historical periods in sequence data. The framework includes a global attention feature extraction module to focus on complex relationships between various factors and a local attention feature extraction module that can overcome the shortcomings of attention mechanisms and handle anomalies. When applied to predict water temperature in offshore aquaculture net pen, the model achieved RMSEs of 0.1314, 0.1525, and 0.2002 for future 2, 6, and 12 time steps, respectively, representing improvements of 44.05%, 29.33%, and 31.56% compared to the other models in the comparative experiments.</div><div>Ablation experiments show that each component of the CDANet model can extract different information patterns from training data, demonstrating structural effectiveness. The experimental results indicate that the proposed method can accurately predict water temperature changes in offshore aquaculture net pen.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127438"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748631","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 mutual learning network for multimodal fake news detection","authors":"Wei Cui ,&nbsp;Xuerui Zhang ,&nbsp;Mingsheng Shang","doi":"10.1016/j.eswa.2025.127407","DOIUrl":"10.1016/j.eswa.2025.127407","url":null,"abstract":"<div><div>Multimodal fake news is more deceptive than unimodal content and often has adverse social and economic impacts. However, most existing methods learn modal features from a single perspective, without considering simultaneously learning and sharing knowledge across modalities from different perspectives. Our work presents a novel Multi-View Mutual Learning (MVML) network for multimodal fake news detection, which explores the semantic relationship between words and scenes, as well as words and objects, and investigates the semantic connection that exists between global concepts and local objects from multiple perspectives. We first construct text-scenes and text-objects graphs respectively, and perform intra-graph inference to obtain multi-view features. Then, the multi-view fusion layer based on the cross-view attention mechanism interactively models the cross-view dependencies between the image and text. The extracted features before feeding into the final classifier are further processed via a feed-forward attention module, which adaptively reweights and aggregates the features for redundancy reduction. In addition, to exploit the respective advantages of two multi-view classifiers, we propose a mutual learning mechanism that allows them to perform knowledge distillation and align the learning targets. The proposed MVML is thoroughly assessed on four publicly available benchmark datasets, and the findings demonstrate that it outperforms the existing standard approaches.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"279 ","pages":"Article 127407"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760694","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":"Reliable forecasting non-linear triaxial mechanical response of recycled aggregate concrete by knowledge-enhanced, modified, explainable and replicable machine learning algorithms","authors":"Hao-Yu Zhu ,&nbsp;Ming-Zhi Guo ,&nbsp;Yan Zhang","doi":"10.1016/j.eswa.2025.127326","DOIUrl":"10.1016/j.eswa.2025.127326","url":null,"abstract":"<div><div>The constitutive modelling is the only method to describe the triaxial stress–strain behavior of cement-based materials while its theoretical deduction, modelling parameters determination and numerical calibrations made it difficult to be further applied. To overcome the limitation of constitutive modelling, a comprehensive machine learning (ML) approach, including Artificial Neural Network (ANN), Gaussian Process (GP), Gradient Boosting (GB) and Optimized Gaussian Process (OGP) was firstly proposed to predict triaxial mechanical behavior of recycled aggregate concrete (RAC). The data augment technology was employed to increase the training data size from 249 to 580, effectively improving the generalization performance. The performance statistics of the aforementioned ML models were compared and validated by R², MAE, RMSE, and Taylor diagram, showing that the OGP had the best study ability and prediction accuracy. The 99 % prediction results generated by the OGP model concentrated within the ± 10 % confidence interval (R² = 0.991, MAE = 1.04, RMSE = 0.122). Furthermore, to address the black box nature of ML models, the shapley additive explanation and partial dependence analysis were employed to elucidate the underlying arithmetic mechanism. Finally, the best OGP model was compared with previous constitutive method and further utilized to validate its applicability. Unlike classical constitutive modeling, which requires specialized expertise, the proposed ML approach, available as open source at <span><span>https://doi.org/10.13140/RG.2.2.15784.89608</span><svg><path></path></svg></span>, offered an accessible and effective solution for predicting triaxial behavior with experimental data.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"278 ","pages":"Article 127326"},"PeriodicalIF":7.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724877","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}