Computational Intelligence最新文献

{"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}

{"title":"A Method for Constructing Open-Channel Velocity Field Prediction Model Based on Machine Learning and CFD","authors":"Bo Li,&nbsp;Cheng Jin,&nbsp;Ruixiang Lin,&nbsp;Xinzhi Zhou,&nbsp;Mingjiang Deng","doi":"10.1111/coin.70043","DOIUrl":"https://doi.org/10.1111/coin.70043","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapid and accurate prediction of the sectional velocity field of the channel is of great significance to the design and maintenance of open channels and the improvement of irrigation efficiency. During the water delivery process of Renmin Canal of Dujiangyan irrigation system, the water level of the main canal changes rapidly and in a large range, which is the biggest difficulty in real-time prediction of its velocity field. Therefore, based on machine learning, this paper proposes a new method to construct a real-time velocity field prediction model, which can directly predict the velocity field of the channel according to the water level. According to this method, the computational fluid dynamics (CFD) technology is used to simulate the target open channel, and a machine learning model that can adaptively optimize the characteristics of the velocity field data is designed as the velocity field prediction model, which is experimented in the main canal of Renmin Canal of Dujiangyan irrigation system. The results suggest that the predictions are in line with the general features of flow velocity distribution in open channels and have high precision. Therefore, this method is of high value for engineering application and theoretical research.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632922","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":"Violence Detection in Video Using Statistical Features of the Optical Flow and 2D Convolutional Neural Network","authors":"Javad Mahmoodi,&nbsp;Hossein Nezamabadi-Pour","doi":"10.1111/coin.70034","DOIUrl":"https://doi.org/10.1111/coin.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid growth of video data has resulted in an increasing need for surveillance and violence detection systems. Although such events occur less frequently than normal activities, developing automated video surveillance systems for violence detection has become essential to minimize labor and time waste. Detecting violent activity in videos is a challenging task due to the variability and diversity of violent behavior, which can involve a wide range of actions, motions, and interactions between people and objects. Currently, researchers employ deep learning models to detect violent behaviors. In fact, a large number of deep learning approaches are based on extracting spatio-temporal information from a video by exploiting a 3D Convolutional Neural Network (CNN). Despite their success, these techniques require a lot more parameters than 2D CNNs and have high computational complexity. Therefore, we focus on exploiting a 2D CNN to encode spatio-temporal information. Actually, statistical features of the optical flow changes are used to give this ability to a 2D CNN. These features are designed to make attention to regions of a video clip with much more motion. Accordingly, the optical flow of an input video is calculated. To determine meaningful changes in the optical flow, the optical flow magnitude of a current frame is compared with its predecessor. After that, statistical features of these changes are extracted to summarize a video clip to a 2D template, which feeds a 2D CNN. Experimental results on four benchmark datasets observe that the suggested strategy outperforms baseline ones. In particular, we make a better estimation of the spatio-temporal features in a video by shortening a video clip into a 2D template.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595139","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":"Real-Time Solutions for Dynamic Complex Matrix Inversion and Chaotic Control Using ODE-Based Neural Computing Methods","authors":"Cheng Hua,&nbsp;Xinwei Cao,&nbsp;Bolin Liao","doi":"10.1111/coin.70042","DOIUrl":"https://doi.org/10.1111/coin.70042","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a robust dual-integral structure zeroing neural network (ZNN) design framework, effectively overcoming the limitations of existing single-integral enhanced ZNN models in completely suppressing linear noise. Based on this design framework, a complex-type dual-integral structure ZNN (DISZNN) model with inherent linear noise suppression capability is constructed for computing dynamic complex matrix inversion (DCMI) online. The stability, convergence, and robustness of the proposed DISZNN model are ensured via rigorous theoretical analyses. In three distinct experiments involving DCMI (including cases with only imaginary parts, both real and imaginary parts, and high-dimensional scenarios), the state trajectories of the DISZNN model are well and quickly fitted to the dynamic trajectories of the theoretical solutions with very low residual errors in various linear noise environments. More specifically, the residual errors of the DISZNN model for online computation of DCMI under linear noise environments are consistently below the order of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>3</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ 1{0}^{-3} $$</annotation>\u0000 </semantics></math>, representing one-thousandth of the residual errors in existing noise-tolerant ZNN models. Finally, the DISZNN design framework is applied to construct a controlled chaotic system of a permanent magnet synchronous motor (PMSM) with uncertainties and external disturbances based on real-world modeling. Experimental results demonstrate that the three state errors of the controlled PMSM chaotic system converge to zero quickly and stably under various conditions (system parameters, external disturbances, and uncertainties), further highlighting the superiority and generalizability of the DISZNN design framework.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581675","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":"Improving Neural Machine Translation Through Code-Mixed Data Augmentation","authors":"Ramakrishna Appicharla,&nbsp;Kamal Kumar Gupta,&nbsp;Asif Ekbal,&nbsp;Pushpak Bhattacharyya","doi":"10.1111/coin.70033","DOIUrl":"https://doi.org/10.1111/coin.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper studies neural machine translation (NMT) of code-mixed (CM) text. Specifically, we generate synthetic CM data and how it can be used to improve the translation performance of NMT through the data augmentation strategy. We conduct experiments on three data augmentation approaches viz. CM-Augmentation, CM-Concatenation, and Multi-Encoder approaches, and the latter two approaches are inspired by document-level NMT, where we use synthetic CM data as context to improve the performance of the NMT models. We conduct experiments on three language pairs, viz. Hindi–English, Telugu–English and Czech–English. Experimental results demonstrate that the proposed approaches significantly improve performance over the baseline model trained without data augmentation and over the existing data augmentation strategies. The CM-Concatenation model attains the best performance.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564804","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}