Computational Intelligence最新文献

{"title":"Reinforcement Learning Driven Cross-Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect","authors":"Taixin Li,&nbsp;Chenxi Ye,&nbsp;Lang Wu,&nbsp;Feng Liu,&nbsp;Chengxiao Yu","doi":"10.1111/coin.70048","DOIUrl":"https://doi.org/10.1111/coin.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>As manufacturing faces evolving customer demands, the integration of Industrial Internet of Things (IIoT) networks is crucial for enhancing production flexibility. In this context, the Seru Production System (SPS) has emerged as a highly adaptable production mode and emphasizes the strategic assignment of cross-trained workers, particularly in hybrid configurations combining divisional and rotating serus. This paper proposes a novel bi-objective mathematical model incorporating learning effects to minimize makespan and balance workloads among workers. With the development of Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have emerged in industrial manufacturing decision-making. These models utilize deep learning for foundational content processing and leverage reinforcement learning to optimize strategies. This process provides robust support for achieving efficient decision optimization. Building on the concepts of AIGC big models training, this study employs reinforcement learning to refine the results of multi-objective genetic algorithms, thereby improving the solution capability of the bi-objective model. Experimental results demonstrate that the proposed algorithm effectively provides optimal strategies for tuning crossover and mutation operations. Additionally, numerical experiments offer insights into the formation of hybrid SPS configurations.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689282","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}

{"title":"Motor Imagery Classification Using fNIRS Brain Signals: A Method Based on Synthetic Data Augmentation and Cosine-Modulated Attention","authors":"Cheng Peng,&nbsp;Baojiang Li,&nbsp;Haiyan Wang,&nbsp;Xinbing Shi,&nbsp;Yuxing Qin","doi":"10.1111/coin.70044","DOIUrl":"https://doi.org/10.1111/coin.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional near-infrared spectroscopy (fNIRS), renowned for its high spatial resolution, shows substantial promise in brain-computer interface (BCI) applications. However, challenges such as lengthy data acquisition processes and susceptibility to noise can limit data availability and reduce classification accuracy. To overcome these limitations, we introduce the CosineGAN-transformer network (CGTNet), which integrates a dual discriminator GAN for generating high-quality synthetic data with a Transformer-based classification network. Equipped with a multi-head self-attention mechanism, this network excels at capturing the intricate spatiotemporal relationships inherent in high-resolution fNIRS signals. The dual discriminator framework ensures that both the temporal and spatial aspects of the synthetic data closely resemble the original signals, thereby enhancing data diversity and fidelity. Experimental results on a publicly available fNIRS dataset, comprising 30 participants performing motor imagery tasks (right-hand tapping, left-hand tapping, and foot tapping), demonstrate that CGTNet achieves an accuracy of 82.67%, outperforming existing methods. Key contributions of this work include the use of multi-head self-attention for refined feature extraction and a dual discriminator Generative Adversarial Networks (GAN) framework that maintains data quality and consistency. These advancements significantly improve the robustness and accuracy of BCI systems, offering promising applications in neurorehabilitation and assistive technologies.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689240","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}

{"title":"RETRACTION","authors":"","doi":"10.1111/coin.70040","DOIUrl":"https://doi.org/10.1111/coin.70040","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>J. Mao</span>, <span>Q. Sun</span>, <span>X. Wang</span>, <span>B. Muthu</span>, <span>S. Krishnamoorthy</span>, “ <span>The Importance of Public Support in the Implementation of Green Transportation in the Smart Cities</span>,” <i>Computational Intelligence</i> <span>40</span> no. <span>1</span> (<span>2024</span>): e12326, https://doi.org/10.1111/coin.12326.</p><p>The above article, published online on 26 April 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.</p><p>The authors disagree with the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689489","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":"RETRACTION","authors":"","doi":"10.1111/coin.70039","DOIUrl":"https://doi.org/10.1111/coin.70039","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>H. Yuan</span>, <span>H. Zhang</span>, <span>X. Liu</span>, <span>X. Jiao</span>, “ <span>Traffic Wave Model Based on Vehicle-Infrastructure Cooperative and Vehicle Communication Data</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>4</span> (<span>2020</span>): 1755-1772, \u0000https://doi.org/10.1111/coin.12346.</p><p>The above article, published online on 27 May 2020 in Wiley Online Library (\u0000wileyonlinelibrary.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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689488","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":"A Modified U-Shaped Transfer Function: Applied to Classify Parkinson'S Disease","authors":"Suvita Rani Sharma,&nbsp;Birmohan Singh,&nbsp;Manpreet Kaur","doi":"10.1111/coin.70036","DOIUrl":"https://doi.org/10.1111/coin.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>Transfer functions have a very important role in metaheuristic optimization-based feature selection algorithms as these functions map the continuous search space into binary space. The U-shaped transfer function (UTF) is one of the transfer functions used to solve the problem of feature selection. However, the UTF requires the selection of parametric values, which can vary for different types of data. To address this issue, an approach to select the parameters of the UTF has been proposed based on a time-varying adaption method, resulting in the modified U-shaped transfer function (MUTF). Furthermore, a methodology has been proposed to enhance feature selection and classification for Parkinson's disease by utilizing z-score normalization in conjunction with a modified U-shaped transfer function and the binary self-adaptive bald eagle search (MUTF-SABES) optimization algorithm. The z-score normalization has been used to mitigate issues caused by outliers. Also, the performance of the k nearest neighbor classifier is improved by selecting an optimal parameter value using the proposed MUTF-SABES algorithm. The effectiveness of the proposed methodology is validated on seven different Parkinson's disease datasets and compared with five state-of-the-art optimization algorithms: Salp Swarm algorithm, Harris Hawks optimization, equilibrium optimizer, aquilla optimizer, and Honey Badger algorithm, to evaluate its performance superiority. The results achieved using the proposed approach have been superior or analogous to the erstwhile algorithms for performance comparability. Friedman's mean rank test is used to check the statistical significance of the propounded approach. The lowest Friedman's mean rank value obtained using the proposed approach indicates that the proposed approach has the potential to become an alternative to other well-known strategies.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689461","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}

{"title":"One Unsupervised Feature Selection Method for the Classical Linear Classifier in Land Coverage Classification With PolSAR Imagery","authors":"Kun Tian,&nbsp;Xichao Liu,&nbsp;Dapeng Tao,&nbsp;Jun Ni","doi":"10.1111/coin.70025","DOIUrl":"https://doi.org/10.1111/coin.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>Land coverage mapping and classification is one of the critical information-based tools for sustainable agricultural development, enabling relevant departments to carry out agricultural resource adjustments, yield predictions, and other tasks in advance. As a vital means of acquiring land cover and usage information, SAR sensors have become an important research direction due to their all-weather and all-day working capabilities. Nevertheless, traditional classification methods in PolSAR image classification often input a combination of various scattering features, i.e., high-dimensional feature combination, into classifiers, leading to mutual interference among different features and consequently degrading classification performance, especially for linear classifiers such as NRS and SVM. To mitigate this interference, this paper proposed an unsupervised feature selection based on spectral clustering (FSSC) that constructs a targeted approach by leveraging the linear expression capabilities of high-dimensional features. In this method, the linear relationships between different features are first analyzed, and the linear similarity between features can be quantitatively expressed using Pearson correlation coefficients, forming a feature similarity matrix. Subsequently, the similarity matrix undergoes unsupervised similarity partitioning through spectral clustering, dividing the features into distinct combinations. Features within clustering subsets can be considered as combinations with high linear similarity. Therefore, KL divergence is applied to select the most representative features within each cluster, and the resulting representative feature combinations from different clustering subsets are combined to form an optimal feature set, achieving the purpose of feature selection. This method maps high-dimensional feature combinations into low-dimensional ones while preserving the essential attributes of the original data, thereby retaining the valuable feature information and enhancing classification performance. Experimental outcomes conclusively show that the proposed method enhances the overall accuracy (OA) of SVM by 4.51% and the OA of NRS by 2.34% in the Flevoland Dataset, underscoring its efficacy in PolSAR image classification, especially for linear classifiers.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689491","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}

{"title":"RETRACTION","authors":"","doi":"10.1111/coin.70038","DOIUrl":"https://doi.org/10.1111/coin.70038","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>C. Ming</span>, <span>S. Kadry</span>, <span>A.A. Dasel</span>, “ <span>Automating Smart Internet of Things Devices in Modern Homes Using Context-Based Fuzzy Logic</span>,” <i>Computational Intelligence</i> <span>40</span> no. <span>1</span> (<span>2024</span>): e12370, \u0000https://doi.org/10.1111/coin.12370.</p><p>The above article, published online on 02 September 2020 in Wiley Online Library (\u0000wileyonlinelibrary.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 publication, it has come to the attention of the journal that this article was accepted solely on the basis of a compromised peer review process. Therefore, a decision has been made to retract this article. The authors have been informed of the decision to retract.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689062","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":"RETRACTION","authors":"","doi":"10.1111/coin.70041","DOIUrl":"https://doi.org/10.1111/coin.70041","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>X. Xu</span>, <span>W. Sun</span>, <span>G.N. Vivekananda</span>, <span>A. Shankar</span>, “ <span>Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access</span>,” <i>Computational Intelligence</i> <span>40</span> no. <span>1</span> (<span>2024</span>): e12296, \u0000https://doi.org/10.1111/coin.12296.</p><p>The above article, published online on 03 March 2020 in Wiley Online Library (\u0000wileyonlinelibrary.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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689063","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":"Reb-DINO: A Lightweight Pedestrian Detection Model With Structural Re-Parameterization in Apple Orchard","authors":"Ruiyang Li,&nbsp;Ge Song,&nbsp;Shansong Wang,&nbsp;Qingtian Zeng,&nbsp;Guiyuan Yuan,&nbsp;Weijian Ni,&nbsp;Nengfu Xie,&nbsp;Fengjin Xiao","doi":"10.1111/coin.70035","DOIUrl":"https://doi.org/10.1111/coin.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>Pedestrian detection is crucial in agricultural environments to ensure the safe operation of intelligent machinery. In orchards, pedestrians exhibit unpredictable behavior and can pose significant challenges to navigation and operation. This demands reliable detection technologies that ensures safety while addressing the unique challenges of orchard environments, such as dense foliage, uneven terrain, and varying lighting conditions. To address this, we propose ReB-DINO, a robust and accurate orchard pedestrian detection model based on an improved DINO. Initially, we improve the feature extraction module of DINO using structural re-parameterization, enhancing accuracy and speed of the model during training and inference decoupling. In addition, a progressive feature fusion module is employed to fuse the extracted features and improve model accuracy. Finally, the network incorporates a convolutional block attention mechanism and an improved loss function to improve pedestrian detection rates. The experimental results demonstrate a 1.6% improvement in Recall on the NREC dataset compared to the baseline. Moreover, the results show a 4.2% improvement in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mtext>mAP</mtext>\u0000 </mrow>\u0000 <annotation>$$ mathrm{mAP} $$</annotation>\u0000 </semantics></math> and the number of parameters decreases by 40.2% compared to the original DINO. In the PiFO dataset, the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mtext>mAP</mtext>\u0000 </mrow>\u0000 <annotation>$$ mathrm{mAP} $$</annotation>\u0000 </semantics></math> with a threshold of 0.5 reaches 99.4%, demonstrating high detection accuracy in realistic scenarios. Therefore, our model enhances both detection accuracy and real-time object detection capabilities in apple orchards, maintaining a lightweight attributes, surpassing mainstream object detection models.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645916","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}

{"title":"RETRACTION","authors":"","doi":"10.1111/coin.70037","DOIUrl":"https://doi.org/10.1111/coin.70037","url":null,"abstract":"<p>\u0000 <b>RETRACTION:</b> <span>M. Li</span>, <span>K. Xu</span>, <span>S. Huang</span>, “ <span>Evaluation of Green and Sustainable Building Project Based on Extension Matter-Element Theory in Smart City Application</span>,” <i>Computational Intelligence</i> <span>40</span>, no. <span>1</span> (<span>2024</span>): e12286, \u0000https://doi.org/10.1111/coin.12286.</p><p>The above article, published online on 12 February 2020 in Wiley Online Library (\u0000wileyonlinelibrary.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 disagree with the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645917","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}