Expert Systems with Applications最新文献

英文中文

Expert system for extracting keywords in educational texts and textbooks based on transformers models

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-26 DOI: 10.1016/j.eswa.2025.127735

Irene Cid Rico, Jordán Pascual Espada

Automated keyword extraction is widely used for tasks like classification and summarization, but generic methods often fail to address domain-specific requirements. In education, texts are designed to help students grasp and retain key concepts needed for exercises and resolve questions. Despite the variety of existing keyword extraction algorithms, none are specifically adapted to the unique structure and purpose of educational materials like textbooks or lecture notes.Supervised methods have demonstrated their effectiveness in various domains through advanced techniques like contextual embeddings and domain-specific fine-tuning, Our study proposes a novel solution leveraging pretrained transformer models, specifically BERT, to adapt to the structure of educational materials for effective keyword extraction. Our research demonstrates that by fine-tuning BERT models to the specific characteristics of educational texts, we can achieve more accurate and relevant keyword extraction. YodkW, our adapted model, outperforms traditional algorithms in identifying the key concepts that are essential for educational purposes. Performance is quantified using the F1 score relative to text books key terms list, Preliminary results demonstrate that our approach can improve the identification of key concepts pertinent to student understanding and facilitate the automatic generation of test questions.

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引用次数: 0

A consensus-based group decision-making method for multidisciplinary team meeting under q-rung orthopair fuzzy environment

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-24 DOI: 10.1016/j.eswa.2025.127761

Aihui Ye , Runtong Zhang , Wei Cai , Yang Liu , Cui Shang , Xiaomin Zhu

In response to the complex treatment process and evolving medical needs of multimorbidity, multidisciplinary team (MDT) is dedicated to integrating the diagnosis opinions of experts and providing optimal treatment plans. Reaching consensus on disease treatment plans involves a dynamic and iterative group decision-making process, in which traditional methods for MDT meetings fail to address the standardized decision-making procedure, interactive trust relationships, and fuzzy information integration. Given the challenges, this study proposes a dynamic consensus framework based on dual-path feedback mechanism with q-rung orthopair fuzzy set (q-ROFS). A hybrid trust evolution model is first established within MDT, in which the trust degree is composed of inherent trust and preference similarity in each round. Then the opinion dynamics model is also introduced to the fuzzy environment. Based on trust evolution and opinion dynamics, the dual-path feedback mechanism is employed to provide references for preference adjustment and weight adjustment. Correspondingly, the calculation methods for consensus measure, preference similarity and alternative selection with q-ROFS are proposed. Additionally, a case study about vascular MDT meeting is used to illustrate the effectiveness of the proposed method. The simulation experiments are performed to verify the impact of consensus threshold, group size, individual self-confidence, and trust evolution on the proposed method. The results of the comparative analysis show that increasing the q value can expand the fuzzy information expression space while ensuring the consensus level, and the proposed method is superior to other methods in terms of more efficient and high-quality consensus results.

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引用次数: 0

Generalizing fuzzy k-nearest neighbor classifier using an OWA operator with a RIM quantifier

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-24 DOI: 10.1016/j.eswa.2025.127795

Mahinda Mailagaha Kumbure, Pasi Luukka

This paper proposes a new fuzzy k-nearest neighbor (FKNN) method, called the ordered weighted averaging (OWA) with regular increasing monotone quantifier-based fuzzy k-nearest neighbor (OWARIM-FKNN) classifier. The proposed method aims at enhancing the classification performance of the KNN rule-base variants, especially the local mean-based approaches, while dealing with outlier and data uncertainty issues. In the proposed method, the OWA operator is used to generalize the multi-local mean vectors from each class. The resulting

k

multi-local OWA vectors are then used to create the class representative pseudo nearest neighbors. Lastly, the new sample is classified into the class with the highest membership degree measured using the weighted distance between the new sample and the pseudo nearest neighbor. The classification performance of the proposed method was examined using one artificial and twenty-seven real-world data sets compared with the results obtained from eight related KNN variants. Experimental results showed that the proposed OWARIM-FKNN classifier achieves the highest average accuracy of 87.59% with an average confidence interval of

\pm

0.64, outperforming all baseline methods. Using the Friedman and Nemenyi tests, the analysis further confirms that the proposed method shows statistically significant performance improvements.

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引用次数: 0

Developing an automated framework for eco-label information categorization using web crawling and Natural Language Processing techniques

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-24 DOI: 10.1016/j.eswa.2025.127688

Ho Anh Thu Nguyen , Duy Hoang Pham , Byeol Kim , Yonghan Ahn , Nahyun Kwon

Eco-labels are extensively employed to assess the environmental performance of building materials. However, their management is often fragmented across disparate online databases with inconsistent data structures, presenting significant challenges for efficient information acquisition and management. This study explores the application of web crawling techniques, Natural Language Processing (NLP), and machine learning (ML) models to collect and categorize eco-label information, with the objective of advancing the automation of information management processes. The results demonstrate that the categorization models exhibit high performance, achieving F1-scores exceeding 0.95 on the test set and at least 0.76 when validating datasets incorporating temporally updated information. However, the limited availability of data for certain eco-labels, such as Forest Stewardship Council certification and Green Screen, substantially degrades model performance with updated data. Notably, traditional ML models leveraging manual feature engineering outperform deep learning models with automatic feature extraction when applied to web-crawled data. Furthermore, the TF-IDF feature extraction technique surpasses other n-gram-based approaches, with model performance declining as n-gram length increases. This study establishes a systematic framework that informs the selection of reliable data sources, feature engineering strategies, and ML algorithms for integrating web crawling, thereby enhancing the automation of eco-label information management.

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引用次数: 0

Selective Guidance Network with edge and texture awareness for polyp segmentation

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-24 DOI: 10.1016/j.eswa.2025.127772

Qiaohong Chen, Zhenyang Xu, Xian Fang, Qi Sun, Xin Wang

Colorectal polyp segmentation plays a crucial role in preventing colorectal cancer through colonoscopic image screening. However, most existing methods overlook uncertain regions in colonoscopic images, particularly the blurred boundary areas where polyps closely resemble colon fold structures. To address this challenge, we propose the Selective Guidance Network with edge and texture awareness for polyp segmentation (SGNet). SGNet consists of three essential modules, namely the Edge and Texture Awareness Module (ETAM), the Prior Enhancement Module (PEM), and the Hierarchical Feature Fusion Module (HFFM). ETAM integrates Laplacian operators with spatial attention mechanisms to enhance feature perception, allowing for precise extraction of polyp boundaries and adaptive amplification of texture patterns. PEM strengthens multi-scale contextual perception through dilated convolutions while refining backbone features through dual prior-driven feature rectification. HFFM employs multi-level attention gating to achieve cross-scale feature integration while effectively combining low-level edge cues with high-level semantic representations. Experimental results on five public datasets demonstrate that SGNet outperforms 16 state-of-the-art methods across six evaluation metrics, highlighting its superior segmentation performance and robustness.

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引用次数: 0

TransCORALNet: A two-stream transformer CORAL networks for supply chain credit assessment cold start

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-23 DOI: 10.1016/j.eswa.2025.127581

Jie Shi, Arno P.J.M. Siebes, Siamak Mehrkanoon

Supply chain credit assessment is critical for financial decision-making due to limited historical data for new borrowers and the domain shift between segment industries. Existing models often struggle with challenges such as domain shift, cold start, imbalanced classes, and lack of interpretability. This paper proposes an interpretable two-stream transformer CORAL network (TransCORALNet) for supply chain credit assessment, designed to address these challenges. The two-stream domain adaptation architecture with correlation alignment (CORAL) loss serves as the core model and is equipped with a transformer, which provides insights into the learned features and allows efficient parallelization during training. Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domains is minimized. Furthermore, we employ Local Interpretable Model-agnostic Explanations (LIME) to provide additional insights into the model predictions and identify the key features contributing to supply chain credit assessment decisions. Experimental results on a real-world dataset demonstrate the superiority of TransCORALNet over several state-of-the-art baselines in terms of accuracy. The code is available on GitHub.¹

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引用次数: 0

HinMAD3R: Representation learning on heterogeneous information networks via multiple attentions with dual dropout and dual residual

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-23 DOI: 10.1016/j.eswa.2025.127674

Ting Zhang , Lihua Zhou , Xinchao Lu , Pei Zhang , Lizhen Wang

Heterogeneous information networks (HINs) contain rich semantic information, and effectively utilizing this information can enhance the quality of representation learning. Existing models based on the message-passing paradigm typically focus on either node-type or edge-type information, neglecting the synergistic effect of various heterogeneous information. Moreover, these models are prone to over-smoothing as network depth increases, which degrades both performance and generalization ability. To address these issues, we propose a novel multiple attention mechanism that simultaneously considers node-features, node-types, and edge-types, aiming to maximize the utilization of diverse semantic information. Additionally, we introduce dual dropout and dual residual strategies to mitigate the over-smoothing problem and enhance the model’s generalization capability. Extensive experiments conducted on seven datasets demonstrate that the proposed model outperforms state-of-the-art baselines.

异构信息网络（HIN）包含丰富的语义信息，有效利用这些信息可以提高表征学习的质量。现有的基于信息传递范式的模型通常只关注节点型或边缘型信息，忽视了各种异构信息的协同效应。此外，这些模型容易随着网络深度的增加而过度平滑，从而降低性能和泛化能力。为了解决这些问题，我们提出了一种新颖的多重关注机制，该机制同时考虑节点特征、节点类型和边缘类型，旨在最大限度地利用各种语义信息。此外，我们还引入了双剔除和双残差策略，以缓解过度平滑问题，增强模型的泛化能力。在七个数据集上进行的广泛实验证明，所提出的模型优于最先进的基线模型。

引用次数: 0

Non-destructive detection and quantification of corrosion damage in coated steel components with different illumination conditions 在不同照明条件下对涂层钢部件的腐蚀损伤进行无损检测和量化

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-23 DOI: 10.1016/j.eswa.2025.127854

Caiwei Liu , Libin Tian , Pengfei Wang , Qian-Qian Yu , Li Song , Jijun Miao

Existing deep learning-based detection methods for corrosion damage in steel structures are mostly applicable under normal lighting conditions and lack an association between detection results and damage levels. Focuses on coated corrosion steel components under various illumination conditions, this paper presents a YOLOv8s-G network tailored for pixel-level image segmentation and quantification of corrosion damage. A dataset of 1299 images of corroded steel components with different illumination conditions was captured in a field steel structure workshop. Furthermore, the ability of network to extract multi-scale corrosion features across various illumination conditions was enhanced by integrating the C2f-S module and fusion splicing method. The advancement and generalization of YOLOv8s-G were verified through comparisons with other state-of-the-art networks and tests on public datasets. Finally, the ratio of the corrosion area to the cross-sectional area of the steel component was calculated using morphological image operations, quantifying the relative area occupied by corrosion. The accuracy of this quantification method was further validated through comparison with filed measurements. Our research can enhance the reliability of decision-making regarding steel structural corrosion damage.

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引用次数: 0

Monocular visual semantic understanding system for real-time internal damage detection

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-23 DOI: 10.1016/j.eswa.2025.127734

Bian Xu , Tian Biwan , Yu Yangyang

To quickly and non-destructively obtain internal damage data of components, endoscope technology based on machine vision has been widely utilized. However, it is still challenging to measure the internal damage of equipment intelligently and accurately in real time, especially for the internal damage of precision equipment. Therefore, an intelligent endoscope detection system based on monocular visual semantic understanding is proposed in this paper. In this system, a self-developed microprobe structure is used to construct a multi-frame full convolutional network model through multi-scale feature coupling mechanism, which effectively overcomes the feature degradation caused by low contrast imaging and uneven illumination during internal detection. As a result, it enables the automatic identification of the target region and the high − precision measurement of regional geometric parameters. Experimental results demonstrate that the average absolute error of damage size measurement is 0.029 mm, with a standard deviation of 0.0236. The average mIoU is at least 3.3 % higher than other detection methods covered in this article, and the accuracy of damage measurement is improved by about 10 %. It can realize automatic and intelligent defect identification and measurement, and meet the requirements of real-time measurement on site.

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引用次数: 0

A Survey on recent advances in reinforcement learning for intelligent investment decision-making optimization

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2025-04-23 DOI: 10.1016/j.eswa.2025.127540

Feng Wang , Shicheng Li , Shanshui Niu , Haoran Yang , Xiaodong Li , Xiaotie Deng

Reinforcement learning (RL) has emerged as a powerful tool for optimizing intelligent investment decision-making. With the rapid evolution of financial markets, traditional approaches often struggle to effectively analyze the vast and complex datasets involved. RL-based methods address these challenges by leveraging neural networks to process large-scale financial data, dynamically interacting with market environments to refine strategies, and designing tailored reward functions to achieve diverse investment objectives. This paper provides a comprehensive review of recent advancements in RL for investment decision-making, with a focus on four key areas, i.e., portfolio selection, trade execution, options hedging, and market making. These four problems represent highly challenging instances of multi-stage , multi-objective decision optimization in investment, highlighting the strengths of RL-based methods in effectively balancing trade-offs among different objectives over time. Detailed comparison work of state-of-the-art RL-based methods is presented, analyzing the action spaces, state representations, reward structures, and neural network architectures. Finally, the paper discusses some new challenges and point out some directions for future research in the field.

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