Expert Systems最新文献

LLM4Geopolitics: A Framework Leveraging Large Language Models for Predicting Geopolitical Events 地缘政治：利用大型语言模型预测地缘政治事件的框架

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-10 DOI: 10.1111/exsy.70258

Amira Mouakher, Nuno Morgado, Farah Ftouhi

{"title":"LLM4Geopolitics: A Framework Leveraging Large Language Models for Predicting Geopolitical Events","authors":"Amira Mouakher, Nuno Morgado, Farah Ftouhi","doi":"10.1111/exsy.70258","DOIUrl":"10.1111/exsy.70258","url":null,"abstract":"<div>\u0000 \u0000 <p>The accelerating infusion of advanced computational methods into geopolitical analysis has created new opportunities to anticipate unrest, economic shocks and diplomatic shifts. Traditional machine learning pipelines can extract statistical patterns from large event corpora, but they often struggle to incorporate real-time contextual information or explain their predictions in language accessible to decision-makers. This study proposes a comprehensive framework, <span>LLM4Geopolitics</span>, that couples a domain-adapted large language model with a retrieval-augmented generation mechanism grounded in a structured knowledge graph. The forecasting component employs a transformer architecture tailored to sparse, irregular event streams, while the generative component translates model outputs into dialogue-ready assessments enriched with up-to-date economic and peace-index indicators. Experiments conducted on the <span>Gdelt</span> dataset demonstrate that the integrated approach improves event-severity prediction and generates fact-consistent narratives compared with baseline time series and text-only models. These findings highlight the potential of combining specialised sequence models, on-demand knowledge retrieval and generative reasoning to deliver timely and interpretable insights for geopolitical forecasting.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM4Geopolitics: A Framework Leveraging Large Language Models for Predicting Geopolitical Events 地缘政治：利用大型语言模型预测地缘政治事件的框架

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-10 DOI: 10.1111/exsy.70258

Amira Mouakher, Nuno Morgado, Farah Ftouhi

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引用次数: 0

Imbalance-Aware Credit Card Fraud Detection Using Multi-Autoencoders and Generative Ensemble Learning 基于多自编码器和生成集成学习的不平衡感知信用卡欺诈检测

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-09 DOI: 10.1111/exsy.70256

Sultan Alharbi, Khalid Alahmadi, Xianzhi Wang

{"title":"Imbalance-Aware Credit Card Fraud Detection Using Multi-Autoencoders and Generative Ensemble Learning","authors":"Sultan Alharbi, Khalid Alahmadi, Xianzhi Wang","doi":"10.1111/exsy.70256","DOIUrl":"https://doi.org/10.1111/exsy.70256","url":null,"abstract":"<p>Credit card fraud detection remains a challenging research problem due to the class imbalance issue caused by the rarity of fraudulent transactions. Classical oversampling techniques such as SMOTE, ADASYN and their variants help balance data but do not reflect the nonlinear structure of real-world fraud, leading to poor generalization. Recent state-of-the-art hybrid frameworks that combine deep generative models and ensemble learning improve performance but treat representation learning, augmentation and fusion as disconnected stages. To address these limitations, we propose a unified multistage framework that integrates representation learning, generative augmentation and intelligent ensemble fusion. Our framework first extracts autoencoder-based latent representations to capture discriminative and interpretable features; then, a label-conditioned VAE-GAN uses these embeddings to generate realistic synthetic fraud samples; finally, the enriched features are projected into a fusion space and classified using a pool of diverse learners, whose outputs are consolidated through an embedding-aware intelligent ensemble and a meta-ensemble layer. We benchmark the framework against two categories of baselines: oversampling-based methods and state-of-the-art hybrid fraud detection systems. Experiments on the European cardholder dataset show that our approach achieves a macro F1-score of 95.15% and balanced accuracy of 92.85%, outperforming both baseline categories by 2.8%. Additional experiments on the IEEE-CIS Fraud Detection dataset further validate the generalizability of the proposed framework on large-scale, heterogeneous and feature-rich fraud data. The results demonstrate that the proposed framework not only improves detection accuracy under severe imbalances but also maintains interpretability, offering a robust and scalable foundation for reliable financial risk control.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

引用次数: 0

Imbalance-Aware Credit Card Fraud Detection Using Multi-Autoencoders and Generative Ensemble Learning 基于多自编码器和生成集成学习的不平衡感知信用卡欺诈检测

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-09 DOI: 10.1111/exsy.70256

Sultan Alharbi, Khalid Alahmadi, Xianzhi Wang

{"title":"Imbalance-Aware Credit Card Fraud Detection Using Multi-Autoencoders and Generative Ensemble Learning","authors":"Sultan Alharbi, Khalid Alahmadi, Xianzhi Wang","doi":"10.1111/exsy.70256","DOIUrl":"10.1111/exsy.70256","url":null,"abstract":"<p>Credit card fraud detection remains a challenging research problem due to the class imbalance issue caused by the rarity of fraudulent transactions. Classical oversampling techniques such as SMOTE, ADASYN and their variants help balance data but do not reflect the nonlinear structure of real-world fraud, leading to poor generalization. Recent state-of-the-art hybrid frameworks that combine deep generative models and ensemble learning improve performance but treat representation learning, augmentation and fusion as disconnected stages. To address these limitations, we propose a unified multistage framework that integrates representation learning, generative augmentation and intelligent ensemble fusion. Our framework first extracts autoencoder-based latent representations to capture discriminative and interpretable features; then, a label-conditioned VAE-GAN uses these embeddings to generate realistic synthetic fraud samples; finally, the enriched features are projected into a fusion space and classified using a pool of diverse learners, whose outputs are consolidated through an embedding-aware intelligent ensemble and a meta-ensemble layer. We benchmark the framework against two categories of baselines: oversampling-based methods and state-of-the-art hybrid fraud detection systems. Experiments on the European cardholder dataset show that our approach achieves a macro F1-score of 95.15% and balanced accuracy of 92.85%, outperforming both baseline categories by 2.8%. Additional experiments on the IEEE-CIS Fraud Detection dataset further validate the generalizability of the proposed framework on large-scale, heterogeneous and feature-rich fraud data. The results demonstrate that the proposed framework not only improves detection accuracy under severe imbalances but also maintains interpretability, offering a robust and scalable foundation for reliable financial risk control.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}

引用次数: 0

CausGNN: A Causal-Based Explanation Framework for Graph Neural Networks CausGNN：基于因果的图神经网络解释框架

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-06 DOI: 10.1111/exsy.70252

Hichem Debbi

引用次数: 0

CausGNN: A Causal-Based Explanation Framework for Graph Neural Networks CausGNN：基于因果的图神经网络解释框架

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-06 DOI: 10.1111/exsy.70252

Hichem Debbi

引用次数: 0

Info-YOLO: A Novel Multiscale Feature Enhancement Architecture for Remote Sensing Object Detection Info-YOLO：一种新的遥感目标检测多尺度特征增强体系结构

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-04 DOI: 10.1111/exsy.70255

Ying Wang, Yuelin Gao, Yanxiang Zhao

{"title":"Info-YOLO: A Novel Multiscale Feature Enhancement Architecture for Remote Sensing Object Detection","authors":"Ying Wang, Yuelin Gao, Yanxiang Zhao","doi":"10.1111/exsy.70255","DOIUrl":"10.1111/exsy.70255","url":null,"abstract":"<div>\u0000 \u0000 <p>The detection of dense, small objects in remote sensing imagery is significantly challenged by high altitude, complex backgrounds and ultra-high resolution, which often leads to false positives and false negatives, notably in scenes with dense and small objects. To address the aforementioned challenges, this paper proposes Info-YOLO, a novel algorithm designed to reliably identify small and densely packed targets against complex backgrounds. Our initial step is to propose an Efficient Channel Attention mechanism and apply it to C2f and SPPF in the backbone network, called Feature Enhancement and Extraction Module (FEEM) and ECA-enhanced Spatial Pyramid Pooling Fast (ECSPPF). FEEM enhances the multiscale feature extraction capability, and ECSPPF alleviates information loss associated with multistep pooling. In addition, to alleviate the problem of inaccurate detection caused by overlapping objects, we employ an improved Bidirectional Feature Pyramid Network (BiFPN) for its superior feature fusion ability, replacing the conventional Path Aggregation Network (PANet) and achieving more effective integration of multiscale features with superior performance. Furthermore, to further boost the detection accuracy for small targets, a Swin Transformer block is inserted at the transition point linking the network's neck and the prediction head. Our model achieves a new state-of-the-art mAP of 95.3% on the same dataset, surpassing all contemporary methods. To facilitate reproducibility and further research, the source code is publicly available at: https://github.com/linyuesummer/Info-YOLO-paper-code.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review for Agricultural Product Prices Forecasting: Architectural Diversity and Open Challenges 农产品价格预测的综合综述：结构多样性和开放挑战

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-04 DOI: 10.1111/exsy.70254

Binrong Wu, Jing Wang, Qilei Li, Deqian Fu, Lin Wang

{"title":"A Comprehensive Review for Agricultural Product Prices Forecasting: Architectural Diversity and Open Challenges","authors":"Binrong Wu, Jing Wang, Qilei Li, Deqian Fu, Lin Wang","doi":"10.1111/exsy.70254","DOIUrl":"10.1111/exsy.70254","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate forecasting of agricultural prices is essential for informed production planning, market stabilisation and effective policy design. This review examines 773 studies published between 2006 and 2025 to synthesise recent advances. We begin by analysing the factor systems and structural characteristics of agri-price data, and organise forecasting tasks by input–output design, temporal resolution and prediction objectives—ranging from point estimates to trend detection and probabilistic forecasting. Evaluation practices are reviewed across multiple dimensions, including error metrics, trend alignment, model selection and uncertainty estimation. We then trace the evolution of forecasting 15 methods from traditional statistical models to machine learning and deep neural 16 architectures (RNN, CNN, GNN, Transformer), as well as decomposition-based and 17 ensemble strategies. These developments are contextualised within bibliometric trends, highlighting shifts in research focus and global collaboration. Empirical evidence shows that hybrid pipelines combining decomposition, feature learning and ensemble techniques tend to outperform standalone models, while simple linear models remain competitive for long-horizon or low-frequency forecasts. Common challenges include data leakage, inconsistent testing horizons and insufficient treatment of uncertainty. Looking ahead, future research should emphasise integrating diverse data sources—such as weather, trade and policy signals—and building models that can adapt to unexpected market changes. It is equally important to understand how price dynamics respond to policy actions, improve model transferability across regions and commodities and provide well-calibrated forecasts with interpretable uncertainty estimates to enhance the practical value of agricultural price prediction.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Info-YOLO: A Novel Multiscale Feature Enhancement Architecture for Remote Sensing Object Detection Info-YOLO：一种新的遥感目标检测多尺度特征增强体系结构

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-04 DOI: 10.1111/exsy.70255

Ying Wang, Yuelin Gao, Yanxiang Zhao

{"title":"Info-YOLO: A Novel Multiscale Feature Enhancement Architecture for Remote Sensing Object Detection","authors":"Ying Wang, Yuelin Gao, Yanxiang Zhao","doi":"10.1111/exsy.70255","DOIUrl":"10.1111/exsy.70255","url":null,"abstract":"<div>\u0000 \u0000 <p>The detection of dense, small objects in remote sensing imagery is significantly challenged by high altitude, complex backgrounds and ultra-high resolution, which often leads to false positives and false negatives, notably in scenes with dense and small objects. To address the aforementioned challenges, this paper proposes Info-YOLO, a novel algorithm designed to reliably identify small and densely packed targets against complex backgrounds. Our initial step is to propose an Efficient Channel Attention mechanism and apply it to C2f and SPPF in the backbone network, called Feature Enhancement and Extraction Module (FEEM) and ECA-enhanced Spatial Pyramid Pooling Fast (ECSPPF). FEEM enhances the multiscale feature extraction capability, and ECSPPF alleviates information loss associated with multistep pooling. In addition, to alleviate the problem of inaccurate detection caused by overlapping objects, we employ an improved Bidirectional Feature Pyramid Network (BiFPN) for its superior feature fusion ability, replacing the conventional Path Aggregation Network (PANet) and achieving more effective integration of multiscale features with superior performance. Furthermore, to further boost the detection accuracy for small targets, a Swin Transformer block is inserted at the transition point linking the network's neck and the prediction head. Our model achieves a new state-of-the-art mAP of 95.3% on the same dataset, surpassing all contemporary methods. To facilitate reproducibility and further research, the source code is publicly available at: https://github.com/linyuesummer/Info-YOLO-paper-code.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comprehensive Review for Agricultural Product Prices Forecasting: Architectural Diversity and Open Challenges 农产品价格预测的综合综述：结构多样性和开放挑战

IF 2.3 4区计算机科学

Expert Systems Pub Date : 2026-04-04 DOI: 10.1111/exsy.70254

Binrong Wu, Jing Wang, Qilei Li, Deqian Fu, Lin Wang

{"title":"A Comprehensive Review for Agricultural Product Prices Forecasting: Architectural Diversity and Open Challenges","authors":"Binrong Wu, Jing Wang, Qilei Li, Deqian Fu, Lin Wang","doi":"10.1111/exsy.70254","DOIUrl":"https://doi.org/10.1111/exsy.70254","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate forecasting of agricultural prices is essential for informed production planning, market stabilisation and effective policy design. This review examines 773 studies published between 2006 and 2025 to synthesise recent advances. We begin by analysing the factor systems and structural characteristics of agri-price data, and organise forecasting tasks by input–output design, temporal resolution and prediction objectives—ranging from point estimates to trend detection and probabilistic forecasting. Evaluation practices are reviewed across multiple dimensions, including error metrics, trend alignment, model selection and uncertainty estimation. We then trace the evolution of forecasting 15 methods from traditional statistical models to machine learning and deep neural 16 architectures (RNN, CNN, GNN, Transformer), as well as decomposition-based and 17 ensemble strategies. These developments are contextualised within bibliometric trends, highlighting shifts in research focus and global collaboration. Empirical evidence shows that hybrid pipelines combining decomposition, feature learning and ensemble techniques tend to outperform standalone models, while simple linear models remain competitive for long-horizon or low-frequency forecasts. Common challenges include data leakage, inconsistent testing horizons and insufficient treatment of uncertainty. Looking ahead, future research should emphasise integrating diverse data sources—such as weather, trade and policy signals—and building models that can adapt to unexpected market changes. It is equally important to understand how price dynamics respond to policy actions, improve model transferability across regions and commodities and provide well-calibrated forecasts with interpretable uncertainty estimates to enhance the practical value of agricultural price prediction.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"43 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0