Expert Systems with Applications最新文献_第2页

A multi-task optimization algorithm via reinforcement learning for multimodal multi-objective optimization 基于强化学习的多任务优化算法用于多模态多目标优化

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-30 DOI: 10.1016/j.eswa.2025.127862

Jie Cao , Yuze Yang , Jianlin Zhang , Zuohan Chen , Zongli Liu

{"title":"A multi-task optimization algorithm via reinforcement learning for multimodal multi-objective optimization","authors":"Jie Cao , Yuze Yang , Jianlin Zhang , Zuohan Chen , Zongli Liu","doi":"10.1016/j.eswa.2025.127862","DOIUrl":"10.1016/j.eswa.2025.127862","url":null,"abstract":"<div><div>Solving multimodal multi-objective optimization problems (MMOPs) via evolutionary algorithms has recently garnered increasing attention. Maintaining diversity in both decision and objective spaces is crucial for effectively handling MMOPs. However, most traditional multimodal multi-objective evolutionary algorithms (MMEAs) prioritize convergence in the objective space, often eliminating poorly converged solutions which could enhance diversity in the decision space. To address this issue, this paper proposes a novel MMEA, named QLMTMMEA. Specifically, a multi-task optimization framework comprises a main task and three auxiliary tasks based on different strategies for MMOPs is designed. Then, Q-Learning (QL) is utilized to the adaptively selects optimal auxiliary tasks in the evolution process. In addition, a new diversity enhancement technique is proposed for objective space and decision space by dynamically adjusting the relaxation factor to maintain high quality solutions. Seven state-of-the-art MMEAs are adopted to make comparisons for demonstrating the performance of QLMTMMEA, experimental results show that QLMTMMEA is competitive compared to others MMEAs on 34 complex MMOPs.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"283 ","pages":"Article 127862"},"PeriodicalIF":7.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886215","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}

引用次数: 0

An evolutionary multitasking algorithm based on k-nearest neighbors pre-selection strategy for constrained multi-objective optimization 基于k近邻预选策略的约束多目标优化进化多任务算法

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-30 DOI: 10.1016/j.eswa.2025.127768

Mengqi Jiang , Xiaochuan Gao , Qianlong Dang , Junhu Ruan

{"title":"An evolutionary multitasking algorithm based on k-nearest neighbors pre-selection strategy for constrained multi-objective optimization","authors":"Mengqi Jiang , Xiaochuan Gao , Qianlong Dang , Junhu Ruan","doi":"10.1016/j.eswa.2025.127768","DOIUrl":"10.1016/j.eswa.2025.127768","url":null,"abstract":"<div><div>Evolutionary algorithms for solving constrained multi-objective optimization problems have attracted considerable attention in recent years. These algorithms typically involve population initialization and evaluation, offspring generation, and environmental selection. However, many existing algorithms fail to improve solving efficiency due to the neglect of promising infeasible solutions. To address this issue, we propose a constrained multi-objective evolutionary algorithm that integrates a k-nearest neighbors (KNN)-based pre-selection strategy into the evolutionary multitasking framework (CMOEAKNN). Specifically, a KNN classifier is designed and trained to pre-select offspring with superior performance before performing environmental selection, thereby minimizing unnecessary evaluation efforts and retaining promising infeasible solutions, which improves the solving efficiency of the algorithm. The algorithm incorporates a reverse learning mutation strategy to improve population diversity and global exploration capability. The experiment results on three test suites and seven engineering application problems demonstrate that the proposed CMOEAKNN has significant competitiveness and superior performance compared to the other nine comparative algorithms.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"283 ","pages":"Article 127768"},"PeriodicalIF":7.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895667","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}

引用次数: 0

GLSCL: Graph local similarity contrastive learning for recommendation 面向推荐的图局部相似度对比学习

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-30 DOI: 10.1016/j.eswa.2025.127855

Zhou Zhou, Zheng Hu, Shi-Min Cai, Tao Zhou

{"title":"GLSCL: Graph local similarity contrastive learning for recommendation","authors":"Zhou Zhou, Zheng Hu, Shi-Min Cai, Tao Zhou","doi":"10.1016/j.eswa.2025.127855","DOIUrl":"10.1016/j.eswa.2025.127855","url":null,"abstract":"<div><div>Graph Contrastive Learning (GCL) has recently gained widespread adoption in recommendation systems owing to its outstanding performance and capability to alleviate data sparsity issues. GCL mitigates data sparsity issues by learning more uniformly distributed user and item representations. However, current recommendation approaches based on GCL have a significant drawback, which is to overlook the relationships between homogeneous nodes (i.e., users to users and items to items). The potential of contrastive learning in these contexts remains largely untapped because contrastive learning often focuses only on the user–item interaction space, missing the fine-grained, contextual similarities that exist within homogeneous nodes. These overlooked relationships can significantly improve recommendation accuracy by providing richer, more context-sensitive embeddings. To address this problem, we propose a Graph Local Similarity Contrastive Learning (GLSCL) framework, which enhances embedding uniformity and constructs contrast pairs based on local similarity. Specifically, to avoid the loss of original information, we employ a random perturbation contrastive task to enhance embedding uniformity and improve recommendation performance by exploring the inherent correlations among users (or items). GLSCL treats users (or items) with their local batch of most similar users (or items) as positive contrastive pairs during training, which can capture the homogenous relationships in user–user and item–item similarity relationships while maintaining embedding uniformity. To validate the proposed model, extensive experiments were conducted on three real-world datasets, including Douban-Book, Yelp, and Amazon-Book. On the three datasets, our model outperforms the suboptimal model with an average improvement of 3.23%, 3.28%, 3.55%, and 3.41% in four metrics, respectively. Extensive ablation experiments and visual analyses were conducted, providing conclusive evidence for the effectiveness of the proposed core modules.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"284 ","pages":"Article 127855"},"PeriodicalIF":7.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903275","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}

引用次数: 0

A learning search algorithm for the Restricted Longest Common Subsequence problem 受限最长公共子序列问题的学习搜索算法

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-30 DOI: 10.1016/j.eswa.2025.127731

Marko Djukanović , Jaume Reixach , Ana Nikolikj , Tome Eftimov , Aleksandar Kartelj , Christian Blum

{"title":"A learning search algorithm for the Restricted Longest Common Subsequence problem","authors":"Marko Djukanović , Jaume Reixach , Ana Nikolikj , Tome Eftimov , Aleksandar Kartelj , Christian Blum","doi":"10.1016/j.eswa.2025.127731","DOIUrl":"10.1016/j.eswa.2025.127731","url":null,"abstract":"<div><div>This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant applications in bioinformatics, particularly for identifying similarities and discovering mutual patterns and important motifs among DNA, RNA, and protein sequences. Building on recent advancements in solving this problem through a general search framework, this paper introduces two novel heuristic approaches designed to enhance the search process by steering it towards promising regions in the search space. The first heuristic employs a probabilistic model to evaluate partial solutions during the search process. The second heuristic is based on a neural network model trained offline using a genetic algorithm. A key aspect of this approach is extracting problem-specific features of partial solutions and the complete problem instance. An effective hybrid method, referred to as the learning beam search, is developed by combining the trained neural network model with a beam search framework. An important contribution of this paper is found in the generation of real-world instances where scientific abstracts serve as input strings, and a set of frequently occurring academic words from the literature are used as restricted patterns. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed approaches in solving the RLCS problem. Finally, an empirical explainability analysis is applied to the obtained results. In this way, key feature combinations and their respective contributions to the success or failure of the algorithms across different problem types are identified.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"284 ","pages":"Article 127731"},"PeriodicalIF":7.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900422","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}

引用次数: 0

Optimised conjugate prior for model structure estimation in the exponential family 指数族模型结构估计的优化共轭先验

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-30 DOI: 10.1016/j.eswa.2025.127716

Miroslav Kárný

引用次数: 0

Distribution-Aware Multi-Attention Tri-branch Networks with Feedforward Differential Features for semi-supervised medical image segmentation 基于前馈差分特征的分布感知多注意三分支网络半监督医学图像分割

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-29 DOI: 10.1016/j.eswa.2025.127687

Peilian Shi , Shuchang Zhao , Lin Guo , Dandan Wang , Shiqing Zhang , Xiaoming Zhao , Jiangxiong Fang , Guoyu Wang , Hongsheng Lu , Jun Yu

{"title":"Distribution-Aware Multi-Attention Tri-branch Networks with Feedforward Differential Features for semi-supervised medical image segmentation","authors":"Peilian Shi , Shuchang Zhao , Lin Guo , Dandan Wang , Shiqing Zhang , Xiaoming Zhao , Jiangxiong Fang , Guoyu Wang , Hongsheng Lu , Jun Yu","doi":"10.1016/j.eswa.2025.127687","DOIUrl":"10.1016/j.eswa.2025.127687","url":null,"abstract":"<div><div>Semi-supervised learning (SSL) is a challenging yet significant subject. However, previous SSL methods usually directly transfer the knowledge learned from labeled data to unlabeled data, resulting in their limited abilities to fully leverage the distribution discrepancy between labeled and unlabeled data. To tackle this issue, this work proposes a novel SSL framework called Distribution-Aware Multi-Attention Tri-branch Networks with Feedforward Differential Features (DAMATN-FDF) for semi-supervised medical image segmentation. DAMATN-FDF consists of a shared encoder and a tri-branch decoder equipped with different attention mechanisms. To bridge the distributional gap between labeled and unlabeled data, we introduce two key modules: Distribution-Aware (DA) and Integrity Supervision and Uncertainty Minimization (IS- UM). The DA module is designed to learn distribution-aware features. The IS-UM module is designed to encourage the inter-branch consistency for regularization. Besides, Feedforward Differential Features (FDF) are introduced to enhance the knowledge transferring across different decoder branches. Extensive experiments are conducted on three typical datasets like LA, Pancreas CT and BraTS-2019 datasets. Experimental results demonstrate the effectiveness of the proposed DAMATN-FDF method, significantly improving the performance over state-of-the-art methods. Code is publicly available at <span><span>https://github.com/MapleUnderTheMooon/DAMATN-FDF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"284 ","pages":"Article 127687"},"PeriodicalIF":7.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900424","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}

引用次数: 0

A comprehensive analysis of deception detection techniques leveraging machine learning 利用机器学习的欺骗检测技术的综合分析

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-29 DOI: 10.1016/j.eswa.2025.127601

Hagar Elbatanouny , Noora Al Roken , Abir Hussain , Wasiq Khan , Bilal Khan , Eqab Almajali

{"title":"A comprehensive analysis of deception detection techniques leveraging machine learning","authors":"Hagar Elbatanouny , Noora Al Roken , Abir Hussain , Wasiq Khan , Bilal Khan , Eqab Almajali","doi":"10.1016/j.eswa.2025.127601","DOIUrl":"10.1016/j.eswa.2025.127601","url":null,"abstract":"<div><div>Deception detection has recently drawn noticeable attention to an objective understanding of human behavior, which can be driven by various factors ranging from self-preservation to causing harm. Deceptive behavior can be categorized into various types based on its implications, as lies can range from being harmful to having serious consequences. Consequently, this field is paramount, particularly in critical areas such as the legal system, where accurate deception identification is essential. This paper presents a comprehensive analysis aimed at extracting and analyzing knowledge from existing literature on deception detection. Our approach aims to streamline deception detection methods based on machine learning and compares them to conventional non-machine learning approaches. Convolutional neural networks have demonstrated superior performance on real-life datasets compared to various modeling approaches. The findings reveal a common shortfall among existing studies, specifically the lack of consideration of cultural, linguistic, and gender influences on deception detection, as well as issues related to data scarcity and heterogeneity.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"283 ","pages":"Article 127601"},"PeriodicalIF":7.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886074","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}

引用次数: 0

Words of War: A hybrid BERT-CNN approach for topic-wise sentiment analysis on The Russia-Ukraine War 战争之言：一种混合BERT-CNN方法，用于对俄罗斯-乌克兰战争的主题明智的情绪分析

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-29 DOI: 10.1016/j.eswa.2025.127759

Md. Saiful Islam, Mahmuda Ferdusi, Tanjim Taharat Aurpa

引用次数: 0

Air quality forecasting in non-monitored urban areas through machine and deep-learning model 基于机器和深度学习模型的非监测城市空气质量预测

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-29 DOI: 10.1016/j.eswa.2025.127749

Fernando Illescas-Martinez, Laura Garcia, Antonio-Javier Garcia-Sanchez, Rafael Asorey-Cacheda, Joan Garcia-Haro

{"title":"Air quality forecasting in non-monitored urban areas through machine and deep-learning model","authors":"Fernando Illescas-Martinez, Laura Garcia, Antonio-Javier Garcia-Sanchez, Rafael Asorey-Cacheda, Joan Garcia-Haro","doi":"10.1016/j.eswa.2025.127749","DOIUrl":"10.1016/j.eswa.2025.127749","url":null,"abstract":"<div><div>Air pollution poses a major environmental challenge, raising concerns about human health in urban environments. It leads to diseases such as asthma, exacerbates pulmonary conditions, and creates murky skies, lowering inhabitants’ quality of life. To quantify air pollution, cost-effective IoT (Internet of Things) devices are being deployed in cities, making air quality monitoring available for a wide range of end-users, including public administrations. However, full urban coverage is unfeasible, and awareness of the carbon footprint of IoT deployments is increasing. Therefore, new techniques are needed to maximize the value of IoT networks with reduced infrastructure. To address these challenges, this paper presents an air pollution analytical forecasting solution based on deep-learning/machine-learning techniques to estimate air quality in locations without deployed devices. Different combinations of well-known deep-learning models are compared with machine-learning techniques to determine the best approach for monitoring polluting gases and airborne particles based on well-defined evaluation metrics. Additionally, two new deep-learning techniques, Multipath-CNN-LSTM (M-CNN-LSTM) and Multipath-CNN-BiLSTM (M-CNN-BiLSTM), are proposed to conduct a more exhaustive comparison. Combinations of LSTM (Long Short-Term Memory) techniques give the best results, with different models working best for each pollutant. Specifically, LSTM was optimal for O<sub>3</sub>, and combinations of CNN (Convolutional Neural Networks) and BiLSTM (Bidirectional LSTM) worked best for NO<sub>2</sub>. GRU (Gated Recurrent Unit) was more efficient for PM<sub>2.5</sub>, and BiLSTM performed best for PM<sub>10</sub>. This demonstrates that the best strategy to accurately predict the time evolution of each pollutant’s behavior depends on the selection of the most suitable machine-learning or deep-learning technique.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"284 ","pages":"Article 127749"},"PeriodicalIF":7.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900413","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}

引用次数: 0

Transformer-enhanced meta-learning for few-shot fault diagnosis of electric submersible pump 基于变压器增强元学习的电潜泵小故障诊断

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-04-29 DOI: 10.1016/j.eswa.2025.127851

Kang Li , Liangcheng Wang , Xiaoyong Gao , Laibin Zhang

{"title":"Transformer-enhanced meta-learning for few-shot fault diagnosis of electric submersible pump","authors":"Kang Li , Liangcheng Wang , Xiaoyong Gao , Laibin Zhang","doi":"10.1016/j.eswa.2025.127851","DOIUrl":"10.1016/j.eswa.2025.127851","url":null,"abstract":"<div><div>Meta-learning represents a promising approach for few-shot fault diagnosis (FSFD). However, when directly applied to the fault diagnosis of electric submersible pumps (ESPs), its diagnostic performance requires enhancement due to insufficient consideration of the long-term dependencies among ESP variables. To address this issue, we propose a novel technique termed Transformer-Enhanced Meta-Learning (TEML), in which an encoder network based on the Transformer architecture serves as an embedding module integrated into a meta-learning framework that optimizes both intraclass and interclass relationships. Specifically, we develop a multivariate time-series tokenization strategy to preprocess ESP data, facilitating its effective input into the Transformer’s encoder network. The proposed TEML method not only adeptly captures the multivariate long-term dependency characteristics inherent in ESP data but also exhibits improved discrimination performance in scenarios characterized by limited sample availability. Extensive experiments conducted on practical defective datasets collected from Energy Development Co., Ltd., China, demonstrate that the proposed TEML approach outperforms state-of-the-art techniques while yielding favorable diagnostic results.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"284 ","pages":"Article 127851"},"PeriodicalIF":7.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900423","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}

引用次数: 0