计算机科学最新文献

{"title":"A short-term load forecasting method considering multiple feature factors based on long short-term memory and an improved temporal convolutional network","authors":"Yu Mu,&nbsp;Lingrui Kong,&nbsp;Guoqiang Zheng,&nbsp;Zhonge Su,&nbsp;Guodong Wang","doi":"10.1016/j.engappai.2025.111649","DOIUrl":"10.1016/j.engappai.2025.111649","url":null,"abstract":"<div><div>In order to address the problems of multi-factor coupling difficulties and low prediction efficiency of existing short-term electricity load forecasting methods, in this paper a short-term load forecasting method is proposed that combines the maximum mutual information coefficient (MIC) algorithm and the Long Short-Term Memory (LSTM)-Improved Temporal Convolutional Network (ITCN) model. Second, based on the problem of low prediction efficiency of the Temporal Convolutional Network (TCN), the TCN was improved (ITCN) by using the single residual block structure and the parallel activation function structure. Finally, the LSTM-ITCN model is designed to extract the short-term temporal features of the given data using LSTM first, and extract the long-term temporal features of the given data using ITCN and make the final prediction. Comparison experiments with Convolutional Neural Network (CNN)-LSTM, CNN-Bidirectional Gated Recurrent Unit (BIGRU), and other prediction methods on different datasets are conducted, and the findings indicate that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), and Running times values of the proposed method are improved by 10.56%, 10.48%, 8.45%, and 25.64%, respectively, which significantly improves the prediction accuracy and prediction efficiency.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Bi-objective evolutionary hyper-heuristics in automated machine learning for text classification tasks","authors":"Jonathan Estrella-Ramírez ,&nbsp;Jorge de la Calleja ,&nbsp;Juan Carlos Gómez Carranza","doi":"10.1016/j.swevo.2025.102073","DOIUrl":"10.1016/j.swevo.2025.102073","url":null,"abstract":"<div><div>This paper proposes an evolutionary model based on hyper-heuristics to automate the selection of classification methods for text datasets under a bi-objective approach. The model has three nested levels. At the first level, individual methods classify datasets, recording two performances: the number of misclassifications and computational time, which are often in conflict. At the second level, hyper-heuristics, as a set of rules of the form <span><math><mrow><mi>i</mi><mi>f</mi><mo>→</mo><mi>t</mi><mi>h</mi><mi>e</mi><mi>n</mi></mrow></math></span>, select classification methods for datasets based on 16 meta-features representing the data distribution. The fitness for a hyper-heuristic is evaluated on a training group of datasets by aggregating the two low-level performances of the chosen methods. At the third level, the multi-objective evolutionary algorithm Strength Pareto Evolutionary Algorithm 2 evolves hyper-heuristic populations considering the bi-objective of minimizing the two aggregated performances. The result is a Pareto-approximated front of hyper-heuristics, which offers a range of solutions from computationally efficient to high classification performance. Finally, the model evaluates the front with an independent test group of datasets and selects those hyper-heuristics that are not dominated. We evaluated the resulting fronts through extensive experiments, measuring several quality indicators. We compare the model’s fronts with a front baseline consisting of non-dominated individual classification methods and four state-of-the-art automated machine learning tools (AutoKeras, AutoGluon, H2O, and TPOT). The proposed model yields larger, more diverse Pareto-approximated fronts that outperform the baseline front, allowing solution selection based on available resources and trade-offs between performance and cost.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102073"},"PeriodicalIF":8.2,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702703","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":"Secure industrial federated learning: Label encryption for model protection","authors":"Xuemei Yuan ,&nbsp;Hewang Nie","doi":"10.1016/j.engappai.2025.111806","DOIUrl":"10.1016/j.engappai.2025.111806","url":null,"abstract":"<div><div>Amid the widespread adoption of deep learning techniques in the Industrial Internet of Things, unauthorized extraction of trained deep learning models has emerged as a significant threat to model intellectual property protection. Existing intellectual property protection methods, such as neural network watermarking and fingerprinting, primarily focus on passive tracing, lacking proactive prevention capabilities. In this paper, we propose an encryption-based intellectual property protection framework specifically designed for federated learning scenarios in industrial settings. The primary artificial intelligence-related contribution of this framework is the design of an efficient label encryption scheme, which selectively encrypts only label information rather than entire datasets or model parameters, significantly reducing computational and communication overhead while preserving model accuracy. The primary engineering application involves integrating encryption mechanisms into the federated learning training and deployment processes to ensure proactive access control and robust passive traceability. The proposed framework employs a hierarchical access control protocol leveraging client-specific encryption keys, providing active prevention against unauthorized model use and facilitating forensic evidence collection. Additionally, the encryption mechanism protects client data privacy and clearly establishes model ownership. Through comprehensive experiments and analyses, we demonstrate that the proposed encryption-based framework effectively safeguards model intellectual property, preserves model performance, and achieves robustness suitable for resource-constrained industrial environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111806"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Retraction","authors":"","doi":"10.1111/coin.70111","DOIUrl":"https://doi.org/10.1111/coin.70111","url":null,"abstract":"<p><b>RETRACTION</b>: <span>H. Rajadurai</span> and <span>U.D. Gandhi</span>, “ <span>An Empirical Model in Intrusion Detection Systems Using Principal Component Analysis and Deep Learning Models</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>3</span> (<span>2021</span>): <span>1111</span>–<span>1124</span>, https://doi.org/10.1111/coin.12342.</p><p>The above article, published online on 05 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695773","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}

{"title":"Authors’ reply to ‘Comment on “Saturated adaptive pose tracking control of spacecraft on SE(3) under attitude constraints and obstacle-avoidance constraints” [Automatica 159 (2024) 111367]’","authors":"Zeyu Kang ,&nbsp;Qiang Shen ,&nbsp;Shufan Wu ,&nbsp;Christopher J. Damaren","doi":"10.1016/j.automatica.2025.112493","DOIUrl":"10.1016/j.automatica.2025.112493","url":null,"abstract":"","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112493"},"PeriodicalIF":4.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Lightweight Detection and Recognition Framework for cigarette laser code","authors":"Wei Zhou ,&nbsp;Honggang Li ,&nbsp;Li Zheng ,&nbsp;Shan Xiao ,&nbsp;Jun Yi","doi":"10.1016/j.engappai.2025.111777","DOIUrl":"10.1016/j.engappai.2025.111777","url":null,"abstract":"<div><div>Automatic detection and recognition of laser codes on cigarette case is important in distinguishing the authenticity of cigarettes. However, detecting and recognizing cigarette laser codes presents a challenging industrial problem due to the intricate background of cigarette images. In this paper, a Lightweight Detection and Recognition Framework (LDRF) is proposed to detect and recognize cigarette laser code. The LDRF model consists of the Lightweight Detection Network (LDNet) and the Lightweight Recognition Network (LRNet). In the LDNet stage, a lightweight feature extraction network is proposed to extract features of the cigarette code area. Furthermore, a bidirectional feature pyramid network (BiFPN) feature fusion structure is introduced to tackle the multi-scale feature fusion challenge in cigarette code detection scenarios. Notably, alignment and normalization of all features channels are conducted to reduce computational requirements in the post-processing stage, enabling precise detection performance while significantly reducing parameters and computational complexity. In the LRNet stage, an integrated network architecture is designed to enhance the fusion of visual and temporal features. Furthermore, a combination of bidirectional temporal convolutional network (BiTCN) and Transformer is employed in the feature extraction stage to differentiate between background and characters, as well as capture the interdependence among different characters. Specifically, DownSampling is utilized to adjust the size of input images and Merging or Combining methods are applied at each stage to capture multi-level features. Experimental results demonstrate that the proposed LDRF method provides better performance than state-of-the-art models, and achieves trade-off between accuracy and speed.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111777"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhanced knowledge graph cascade learning model for cyber–physical systems","authors":"Shumao Zhang,&nbsp;Jie Xu,&nbsp;Haodiao Xie,&nbsp;Qiuru Fu,&nbsp;Ke Miao,&nbsp;Shixue Cheng,&nbsp;Zelei Wu","doi":"10.1016/j.engappai.2025.111802","DOIUrl":"10.1016/j.engappai.2025.111802","url":null,"abstract":"<div><div>Recently, the application prospects of knowledge graph technology in cyber–physical systems (CPS) have attracted considerable attention. However, knowledge graph data in various CPS domains are typically collected from sensors or through manual efforts, which inevitably results in incomplete and unreliable data, thereby impacting the performance of downstream task models. This issue is often overlooked in existing studies. This paper proposes an enhanced knowledge graph cascade learning model for CPS. The model performs cascaded and iterative learning of both graph structure and graph representation. By optimizing the graph structure and incorporating hierarchical learning of graph-structured information, the proposed model enhances feature propagation and aggregation during representation learning. Experiments show that our model achieves outstanding results: compared to the baseline models, our approach achieves an average improvement of 2.7% in accuracy on the node classification task and 1.35% in MRR on the link prediction task.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111802"},"PeriodicalIF":7.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"General construction of integer codes correcting single error in t2-TQAM","authors":"T. Alexandrova ,&nbsp;H. Kostadinov ,&nbsp;N. Manev","doi":"10.1016/j.compeleceng.2025.110572","DOIUrl":"10.1016/j.compeleceng.2025.110572","url":null,"abstract":"<div><div>We propose a general construction of codes over the ring <span><math><msub><mrow><mi>Z</mi></mrow><mrow><mi>A</mi></mrow></msub></math></span> of integers modulo <span><math><mrow><mi>A</mi><mo>=</mo><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>+</mo><mn>1</mn></mrow></math></span> that are capable to correct single errors of types that are dominant in communication based on triangular quadrature amplitude modulation constellation with <span><math><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> signal points.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110572"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"An efficient industrial defect detection based on hybrid residual attention with modified generative adversarial network and convolutional neural network model","authors":"Asadulla Ashurov,&nbsp;Hongchun Qu","doi":"10.1016/j.compeleceng.2025.110580","DOIUrl":"10.1016/j.compeleceng.2025.110580","url":null,"abstract":"<div><div>Detecting and classifying industrial defects continues to pose significant issues in Industry 4.0 era, principally due to constraints in managing data scarcity, variability in fault characteristics, and the inadequacy of traditional models to adapt to different and dynamic situations. Contemporary approaches frequently encounter difficulties in producing dependable synthetic data, effectively extracting essential features, and attaining robust performance in industrial applications. This paper presents a hybrid residual attention generative adversarial network with convolutional neural networks (RAtGAN-CNN) model to address these constraints. The RAtGAN-CNN framework combines residual blocks and attention mechanisms with a generative adversarial network to produce high-quality synthetic samples that replicate the complex distributions of real defects. This approach effectively addresses data scarcity and is trained concurrently with a discriminator through adversarial learning, thereby enhancing data diversity and reducing overfitting in situations with limited labeled data. The lightweight design guarantees appropriateness for real-time industrial applications, fulfilling the demands of computationally limited situations. The model employs a lightweight convolutional neural network (CNN) that utilizes a modified residual block to boost feature extraction, while its attention mechanism concentrates on critical defect areas to improve detection accuracy. These methodologies empower the RAtGAN-CNN to operate effectively across many datasets and settings, particularly excelling in situations with sparse or highly variable input data. The framework is evaluated on a binary image classification dataset of industrial casting defects, attaining a competitive accuracy above 99% on the validation set, with a lightweight model size of 12.5 MB and an average inference time of 18.5 ms per image on a single GPU. Metrics including precision, recall, and F1-score illustrate the approach’s robustness, underpinned by thorough evaluation via confusion matrices and loss-accuracy curves.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110580"},"PeriodicalIF":4.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 graph regularized overlapping community discovery framework with three-way decisions","authors":"Xiaoyang Zou ,&nbsp;Jinxin Cao ,&nbsp;Hengrong Ju ,&nbsp;Weiping Ding ,&nbsp;Lu Liu ,&nbsp;Fuxiang Chen ,&nbsp;Di Jin","doi":"10.1016/j.ins.2025.122525","DOIUrl":"10.1016/j.ins.2025.122525","url":null,"abstract":"<div><div>Community detection is essential for complex network analysis. Most existing approaches focus on hard community partitioning, and a few have investigated overlapping community structures, which are important but difficult to handle in practical applications. This paper presents a graph regularization-based framework for overlapping community detection, which integrates topological information and applies a theoretical three-way decision method to handle uncertain knowledge. The proposed models, <span><math><mrow><mtext>GNMFO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, <span><math><mrow><mtext>GYNMFO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, and <span><math><mrow><mtext>GAEO</mtext><mi>_</mi><mtext>TW</mtext></mrow></math></span>, employ NMF, YNMF, and AEs with graph regularization terms for initial partitioning. The membership degrees of each node across different communities are then used for re-partitioning through three-way decisions. These models apply subspace clustering principles to incorporate basic network structure. To address the limitations caused by sparse network topology, the graph regularization terms encourage similar community membership among connected or nearby nodes, resulting in more coherent communities. In addition, three-way decisions, guided by node structural similarity, detect overlapping clusters and participating vertices. The proposed models not only identify community memberships but also reveal the overlapping community structures within networks. Empirical evaluations across both artificial and empirical networks indicate that our method outperforms existing advanced overlapping community detection techniques.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"720 ","pages":"Article 122525"},"PeriodicalIF":8.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703725","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}