Expert Systems最新文献

Optimising Performance Curves for Ensemble Models through Pareto Front Analysis of the Decision Space 基于决策空间Pareto前分析的集成模型性能曲线优化

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-29 DOI: 10.1111/exsy.70075

Alberto Gutierrez-Gallego, Oscar Garnica, Daniel Parra, J. Manuel Velasco, J. Ignacio Hidalgo

{"title":"Optimising Performance Curves for Ensemble Models through Pareto Front Analysis of the Decision Space","authors":"Alberto Gutierrez-Gallego, Oscar Garnica, Daniel Parra, J. Manuel Velasco, J. Ignacio Hidalgo","doi":"10.1111/exsy.70075","DOIUrl":"https://doi.org/10.1111/exsy.70075","url":null,"abstract":"<p>Receiver operating characteristic curves are commonly used to evaluate the performance of machine learning ensemble classification models that combine multiple classifiers through a voting procedure. Although these models have many parameters, standard ROC analyses typically vary only the voting threshold, limiting their potential for improvement. In this paper, we propose <span>Performance Curve Mapping</span>, a new method that redefines the ROC curve as the Pareto front of a multi-objective optimisation problem. The method maps the multidimensional space of all ensemble parameters (Decision space) into a two-dimensional Objective space defined by classification performance metrics. We employ an algorithm based on NSGA-II to explore the Decision space and validate the proposal on two different classification problems: (1) predicting car insurance claims in a highly imbalanced dataset (<span>Insurance</span> dataset), and (2) predicting obesity risk in a balanced clinical dataset (<span>GenObIA</span> dataset). We compare our method with alternative ensemble optimisation approaches, using visual assessment, the area under the curve and the Youden index as performance measures. In the <span>Insurance</span> dataset, <span>Performance Curve Mapping</span> achieves an average improvement of 46.4% in AUC-ROC and 26.1% in the Youden index. In the <span>GenObIA</span> dataset, it achieves an average improvement of 29.7% in AUC-ROC and 11.9% in the Youden index. All improvements are calculated relative to the maximum achievable improvement.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171891","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

Enhancing Smart Tourism With Chatbots: Focus on the Metamodel of Domain-Specific Language and Emerging Technologies 用聊天机器人提升智慧旅游：关注领域特定语言和新兴技术的元模型

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-29 DOI: 10.1111/exsy.70083

Lamya Benaddi, Adnane Souha, Charaf Ouaddi, Abdellah Chehri, Abdeslam Jakimi, Brahim Ouchao

{"title":"Enhancing Smart Tourism With Chatbots: Focus on the Metamodel of Domain-Specific Language and Emerging Technologies","authors":"Lamya Benaddi, Adnane Souha, Charaf Ouaddi, Abdellah Chehri, Abdeslam Jakimi, Brahim Ouchao","doi":"10.1111/exsy.70083","DOIUrl":"https://doi.org/10.1111/exsy.70083","url":null,"abstract":"<p>The tourism sector is adopting smart solutions to offer visitors more personalised and sustainable experiences. By leveraging urban infrastructure and new technologies, tourist destinations aim to enhance the interaction between travellers and their environment. Artificial intelligence (AI) and natural language processing (NLP) play a key role in this transformation, particularly through chatbots. They are AI-driven applications designed to simulate human-like conversations, enabling users to interact with digital services through text or voice interfaces. In the tourism sector, they facilitate real-time access to information and services, improving the visitors' experience. These applications typically rely on intent recognition APIs, which may be proprietary, requiring access fees and potentially leading to high implementation costs. This study explores the use of a domain-specific language (DSL) dedicated to chatbot development for smart tourism. The first contribution comprises various research topics and emerging technologies used to improve smart tourism experiences and their impact on key tourism components such as attractions, accessibility, amenities, activities, available packages, and ancillary services. Second, this work aims to present the key concepts of model-driven engineering involved in constructing a DSL and to introduce our approach to building a DSL, with a focus on presenting the DSL metamodel. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171892","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

On the Significance of Graph Neural Networks With Pretrained Transformers in Content-Based Recommender Systems for Academic Article Classification 带预训练变压器的图神经网络在基于内容的学术文章分类推荐系统中的意义

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-27 DOI: 10.1111/exsy.70073

Jiayun Liu, Manuel Castillo-Cara, Raúl García-Castro

{"title":"On the Significance of Graph Neural Networks With Pretrained Transformers in Content-Based Recommender Systems for Academic Article Classification","authors":"Jiayun Liu, Manuel Castillo-Cara, Raúl García-Castro","doi":"10.1111/exsy.70073","DOIUrl":"https://doi.org/10.1111/exsy.70073","url":null,"abstract":"<p>Recommender systems are tools for interacting with large and complex information spaces by providing a personalised view of such spaces, prioritising items that are likely to be of interest to the user. In addition, they serve as a significant tool in academic research, helping authors select the most appropriate journals for their academic articles. This paper presents a comprehensive study of various journal recommender systems, focusing on the synergy of graph neural networks (GNNs) with pretrained transformers for enhanced text classification. Furthermore, we propose a content-based journal recommender system that combines a pretrained Transformer with a Graph Attention Network (GAT) using title, abstract and keywords as input data. The proposed architecture enhances text representation by forming graphs from the Transformers' hidden states and attention matrices, excluding padding tokens. Our findings highlight that this integration improves the accuracy of the journal recommendations and reduces the transformer oversmoothing problem, with RoBERTa outperforming BERT models. Furthermore, excluding padding tokens from graph construction reduces training time by 8%–15%. Furthermore, we offer a publicly available dataset comprising 830,978 articles.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140623","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

T-VAE: Transformer-Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data 基于变分自编码器的多变量时间序列数据异常感知

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-23 DOI: 10.1111/exsy.70078

Chang Li, Yeo Chai Kiat, Jiwu Jing, Chun Long

{"title":"T-VAE: Transformer-Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data","authors":"Chang Li, Yeo Chai Kiat, Jiwu Jing, Chun Long","doi":"10.1111/exsy.70078","DOIUrl":"https://doi.org/10.1111/exsy.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Anomaly perception in multivariate time series data has crucial applications in various domains such as industrial control and intrusion detection. In real-world scenarios, the sequence information in multivariate time series data, which encompasses the temporal order and dependencies among high-dimensional samples and features, can be complex and nonlinear. Additionally, the time series data often exhibit high volatility and are interspersed with noise data. These factors make anomaly perception in multivariate time series challenging. Despite the recent development of deep learning methods, only a few are able to address all of these challenges. In this paper, we propose a Transformer-based Variational AutoEncoder (T-VAE) for anomaly perception in multivariate time series data. The T-VAE consists of two sub-networks, the Representation Network and the Memory Network, and achieves end-to-end jointly optimisation. The Representation Network leverages self-attention mechanisms and residual network structures to capture sequence information and metaphorical patterns from multivariate time series data. The Memory Network employs a Variational AutoEncoder to learn the distribution of normal data. It employs Maximum Mean Discrepancy to approximate the distribution of high-volatility and noisy data to the distribution of the normal data. We evaluate T-VAE on five datasets, showing superior performance and validating its effectiveness and robustness through comprehensive ablation studies and sensitivity analyses.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125896","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

Application of Artificial Intelligence in Rock Tunnel Engineering: A Survey on Where and How 人工智能在岩巷工程中的应用：地点与方式综述

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-23 DOI: 10.1111/exsy.70080

Xiaojie Yu, Ben-Guo He, Xu Xu, Yicong Zhou, Miguel A. Diaz, Junxin Chen, David Camacho

{"title":"Application of Artificial Intelligence in Rock Tunnel Engineering: A Survey on Where and How","authors":"Xiaojie Yu, Ben-Guo He, Xu Xu, Yicong Zhou, Miguel A. Diaz, Junxin Chen, David Camacho","doi":"10.1111/exsy.70080","DOIUrl":"https://doi.org/10.1111/exsy.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in-depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in-depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125897","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

Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models 利用伦理叙事增强LLM-AutoML生成的机器学习模型

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-21 DOI: 10.1111/exsy.70072

Jordan Nelson, Michalis Pavlidis, Andrew Fish, Nikolaos Polatidis, Yannis Manolopoulos

{"title":"Leveraging Ethical Narratives to Enhance LLM-AutoML Generated Machine Learning Models","authors":"Jordan Nelson, Michalis Pavlidis, Andrew Fish, Nikolaos Polatidis, Yannis Manolopoulos","doi":"10.1111/exsy.70072","DOIUrl":"https://doi.org/10.1111/exsy.70072","url":null,"abstract":"<p>The growing popularity of generative AI and large language models (LLMs) has sparked innovation alongside debate, particularly around issues of plagiarism and intellectual property law. However, a less-discussed concern is the quality of code generated by these models, which often contains errors and encourages poor programming practices. This paper proposes a novel solution by integrating LLMs with automated machine learning (AutoML). By leveraging AutoML's strengths in hyperparameter tuning and model selection, we present a framework for generating robust and reliable machine learning (ML) algorithms. Our approach incorporates natural language processing (NLP) and natural language understanding (NLU) techniques to interpret chatbot prompts, enabling more accurate and customisable ML model generation through AutoML. To ensure ethical AI practices, we have also introduced a filtering mechanism to address potential biases and enhance accountability. The proposed methodology not only demonstrates practical implementation but also achieves high predictive accuracy, offering a viable solution to current challenges in LLM-based code generation. In summary, this paper introduces a new application of NLP and NLU to extract features from chatbot prompts, feeding them into an AutoML system to generate ML algorithms. This approach is framed within a rigorous ethical framework, addressing concerns of bias and accountability while enhancing the reliability of code generation.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100882","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

Linearformer: Tri-Net Multi-Layer DVF Medical Image Registration 线性变换器：三网多层DVF医学图像配准

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-20 DOI: 10.1111/exsy.70077

Muhammad Anwar, Zhiyue Yan, Wenming Cao

引用次数: 0

A Critical Analysis of Generative Adversarial Networks in Anomaly Detection for Time Series 生成对抗网络在时间序列异常检测中的关键分析

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-19 DOI: 10.1111/exsy.70065

Marcelo Bozzetto, Maurício Cagliari Tosin, Tiago Oliveira Weber, Alexandre Balbinot

{"title":"A Critical Analysis of Generative Adversarial Networks in Anomaly Detection for Time Series","authors":"Marcelo Bozzetto, Maurício Cagliari Tosin, Tiago Oliveira Weber, Alexandre Balbinot","doi":"10.1111/exsy.70065","DOIUrl":"https://doi.org/10.1111/exsy.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>Anomaly detection has applications across different knowledge domains and is intricately linked to numerous problems, such as fault detection for industrial and measurement systems. However, the usual completely unsupervised nature of the problem complicates and restricts the application of various intelligent models. In this context, solutions based on GANs for modelling distributions and arbitrary processes with unsupervised data show potential in anomaly detection. This work addresses a solution based on the TadGAN architecture in the unsupervised detection of anomalies in time series. Initially, a brief review of the state of the art on essential concepts about anomalies in time series is provided, as well as the main works involving GANs in this respective area. Subsequently, the TadGAN architecture is assessed utilising the proposed methodology, wherein its principles and primary limitations are discussed, such as the absence of standardisation in performance evaluation metrics. As an innovation, we assess TadGAN using experimental data and propose new metrics to quantify the anomalous state from both the model and the data. The obtained results confirm the significant potential of GANs in detecting anomalies in time series.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085458","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

Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification 二元分类中高度关联模糊搅拌模式的可解释性

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-19 DOI: 10.1111/exsy.70066

D. Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin

{"title":"Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification","authors":"D. Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin","doi":"10.1111/exsy.70066","DOIUrl":"https://doi.org/10.1111/exsy.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasises not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-<i>k</i> HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 7","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085466","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

Nested Named Entity Recognition: A Survey of Latest Research 嵌套命名实体识别：最新研究综述

IF 3 4区计算机科学

Expert Systems Pub Date : 2025-05-19 DOI: 10.1111/exsy.70052

Lixia Ji, Yiping Dang, Yunlong Du, Wenzhao Gao, Han Zhang

引用次数: 0