Computational Intelligence最新文献

{"title":"Data Driven Long Short-Term Load Prediction: LSTM-RNN, XG-Boost and Conventional Models in Comparative Analysis","authors":"Waqar Waheed,&nbsp;Qingshan Xu","doi":"10.1111/coin.70084","DOIUrl":"https://doi.org/10.1111/coin.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>The precise prediction of power demand is of utmost importance for optimizing power system operations, particularly in the domain of the increasing integration of renewable energy resources. Conventional statistical and machine learning techniques encounter difficulties in capturing complex temporal correlations within load data. The objective of this research is to examine the utilization of Long Short-Term Memory – Recurrent Neural Networks (LSTM-RNNs) in load prediction and perform an extensive comparison analysis with the well-established XG-Boost and other conventional techniques. The incorporation of demand response and distributed renewable energy sources is of paramount importance in ensuring the stability of smart grids and the accurate assessment of power demand. However, the task of making precise energy forecasts faces various obstacles that stem from climate conditions, societal influences, and seasonal variations. The precision of our LSTM-RNN model is evaluated using actual demand data obtained from a prominent utility company in Germany. The findings indicate that the LSTM-RNN model consistently exhibits superior performance compared to standard machine learning techniques and XG-Boost in both short-term (1–24 h) and long-term (yearly) load forecasting. The LSTM-RNN has a notable level of resilience in generating accurate predictions, particularly when confronted with inadequate or noisy input data. The aforementioned results highlight the potential of LSTM-RNN in enhancing load forecasting in smart grids, hence enabling the efficient incorporation of demand response mechanisms and renewable energy sources. This study offers valuable insights and presents a comprehensive methodology for improving power demand estimation in contemporary power systems.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300242","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":"Deep Learning for Visible Watermark Removal: A Survey","authors":"Peixian Su,&nbsp;Yong Zhang","doi":"10.1111/coin.70072","DOIUrl":"https://doi.org/10.1111/coin.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>With the advancement of deep learning technology, deep learning methods are increasingly applied to image restoration, especially in the field of visible watermark removal from images. These methods play an important role and have achieved remarkable success. However, there is a scarcity of literature summarizing the application of different deep learning methods in the field of image watermark removal. In this paper, we present a comparative study of image watermark removal methods from different perspectives. First, we take a look at the development of image restoration techniques. Second, we present the popular architectures of deep learning networks for image applications. Then, we analyze deep learning-based watermark removal methods from both supervised and unsupervised perspectives and provide insights into the motivation and principle of various deep learning methods, which will be analyzed by integrating different network architectures and methodological frameworks. Thirdly, we compare the performance of these popular watermark removal methods on public watermarked datasets in terms of quantitative and qualitative analysis. Finally, we highlight the challenges and potential research directions of current watermarking methods. We review and summarize deep learning-based methods for visible watermark removal, aiming to help evaluate existing removal techniques and advance the field of image watermarking.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300243","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":"Neural Network-Based Adaptive Sliding Mode Control for Upper Limb Rehabilitation With Disturbance Observer","authors":"Changlin Yu,&nbsp;Jiacong Li,&nbsp;Baozhen Nie,&nbsp;Zhongbo Sun,&nbsp;Keping Liu","doi":"10.1111/coin.70075","DOIUrl":"https://doi.org/10.1111/coin.70075","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a neural network-based adaptive sliding mode controller combined with a nonlinear disturbance observer to enhance the stability and precision of the upper limb rehabilitation robot in uncertain environments. The upper limb movement intention is initially captured using an optical motion capture system and a surface electromyography acquisition system. An adaptive sliding mode control method, powered by a neural network, dynamically adjusts the controller's parameters to effectively address system uncertainties and external disturbances. The nonlinear disturbance observer in the controller helps identify and mitigate disturbances from the external environment, including Fourier-type, power-type, and mixed disturbances. Furthermore, the stability of the human-machine interaction controller is rigorously verified using the Lyapunov theorem. Simulation results demonstrate that the proposed neural network-based adaptive sliding mode control method significantly improves the performance and robustness of the upper limb rehabilitation robot.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292845","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":"Exploring Siamese-Based Self-Supervised Learning for Sleep Apnea Detection","authors":"Chandra Bhushan Kumar,&nbsp;Amit Bhongade,&nbsp;Bijaya Ketan Panigrahi,&nbsp;Tapan Kumar Gandhi","doi":"10.1111/coin.70080","DOIUrl":"https://doi.org/10.1111/coin.70080","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Obstructive sleep apnea (OSA) is a common and serious sleep disorder characterized by periodic interruptions in breathing lasting more than 10 s (apnea episodes) during sleep. OSA significantly affects quality of life and overall health, highlighting the critical need for an accurate and timely diagnosis. Polysomnography (PSG) is the standard diagnostic technique for OSA, involving the collection of respiratory, oxygen saturation, biometric, and physiological signals. However, manual analysis of these extensive sleep recordings by medical professionals is labor-intensive and time-consuming. To address this challenge, we propose a Siamese Network-based Self-Supervised Learning (SSSL) model for the automatic identification of SA episodes from single-channel electrocardiogram (ECG) signals. Unlike conventional self-supervised methods, our approach does not require a momentum encoder, large batch sizes, or negative-positive pair construction. The model is evaluated using the PhysioNet Apnea-ECG database and employs a two-stage training strategy. In the first stage, the encoder is trained on unlabeled data to learn robust signal representations. In the second stage, the pre-trained encoder and classifier are fine-tuned using labelled data for optimal classification performance. The proposed model achieved high accuracy of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;73&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 73% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;77&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 77% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;86&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 86% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; when fine-tuned with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 10% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 50% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;100&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 100% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of the labelled training data, respectively, for the classification per segment. These results demonstrate the model's effectiveness in both offline and online diagnostic settings, outperforming state-of-the-art method","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256327","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":"Contextualized Cross-Domain Aspect Sentiment Transformer: A Fine-Grained Aspect-Centric Approach for Enhanced Context-Aware Sentiment Analysis","authors":"Gaurav Dubey,&nbsp;Anupama Chadha,&nbsp;Amrita Jyoti,&nbsp;Gaurav Raj,&nbsp;Kamaljit Kaur,&nbsp;Anil Kumar Dubey","doi":"10.1111/coin.70081","DOIUrl":"https://doi.org/10.1111/coin.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>Context-aware sentiment analysis (CASA) is increasingly critical due to the complex nature of sentiments in digital communication. Traditional sentiment analysis often fails to capture the nuances in context-rich environments, facing challenges like disentangling sentiments, adapting to dynamic contexts, and handling cross-domain variations. Additionally, data sparsity and subjectivity in sentiment interpretation complicate CASA. To address these challenges, this paper proposed a Contextualized Cross-Domain Aspect Sentiment Transformer Network (CC-ASTN) that integrates BERT-based embeddings with aspect-specific embeddings for nuanced contextual and aspect-specific sentiment details. A core feature of CC-ASTN is its fine-grained sentiment analysis, which begins with word-level analysis to discern subtle emotional cues and modifiers, enabling the model to detect sentiment nuances. A novel dual attention mechanism dynamically adjusts focus based on relevance, resolving ambiguities. Advanced domain adversarial training and transfer learning techniques ensure effective cross-domain adaptation, while data augmentation and few-shot learning strategies tackle data sparsity. A hierarchical approach for sentiment analysis breaks down complex sentiments into granular components. The model's robustness is enhanced through dropout, layer normalization, and noise contrastive estimation (NCE), ensuring stability and performance consistency. A composite loss function balances multiple objectives, facilitating precise, domain-neutral sentiment analysis. Additionally, the model integrates real-time feedback mechanisms and leverages a multi-modal approach by incorporating textual, visual, and contextual data for holistic analysis. The CC-ASTN model demonstrates significant efficiency, with training typically taking ˜5 h. Experimental results validate the model's effectiveness, showing significant improvements over existing methods on the SemEval2014 Task 4 and SentiHood datasets. The model achieves inference times of ˜2 s, highlighting its suitability for real-time applications. These findings underscore CC-ASTN's efficacy as an advanced solution for context-aware sentiment analysis, capturing sentiment variations and aspect-level nuances with high precision and efficiency. Its adaptability to rapidly changing trends and real-time feedback integration enhance its applicability in dynamic, real-world scenarios, making it an effective tool for sentiment analysis across a range of fields.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256328","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":"Short-Term Wind Speed Prediction Model Based on Hybrid Decomposition Method and Deep Learning","authors":"Xueqiong Yuan,&nbsp;Feiyu Hu,&nbsp;Zehui Zhu","doi":"10.1111/coin.70078","DOIUrl":"https://doi.org/10.1111/coin.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Wind power, as an important component of distributed power grid integration, plays a vital role in the establishment of a robust power grid. However, the size and direction of wind speeds are random and intermittent, posing significant challenges to the integration of wind power into the grid. To address this issue, this article proposes a highly accurate hybrid optimized wind speed prediction model (HOWSPM) by combining techniques such as data noise processing methods, intelligent optimization algorithms, and deep learning models. First, HOWSPM utilizes the Rime optimization algorithm (RIME) to optimize the variational modal decomposition (VMD) and obtain the RIME-VMD data decomposition model. Second, the RIME-VMD decomposition model is employed to preprocess the nonlinear wind power data, resulting in 10 modal eigencomponents. Additionally, the fruit fly optimization algorithm (FOA) is applied to determine the optimal hyperparameters of the bidirectional long-short memory network (Bi-LSTM), leading to an optimized Bi-LSTM network. Finally, experiments are conducted using the optimized Bi-LSTM network for feature extraction and training on the 10 types of modal data. The experimental results show that the RMSE, MAE, MAPE, and <i>R</i>² of HOWSPM were improved by an average of 36.04%, 42.42%, 23.65%, and 3.09%, respectively, across the four sites. Experimental results indicate that the proposed HOWSPM model effectively enhances the accuracy of wind speed prediction, thereby improving the efficiency of wind power grid integration.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244823","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":"An Innovative Sentiment Influenced Stock Market Prediction Based on Dual Scale Adaptive Residual Long Short Term Memory With Attention Mechanism","authors":"R. Gnanavel,&nbsp;J. M. Gnanasekar","doi":"10.1111/coin.70073","DOIUrl":"https://doi.org/10.1111/coin.70073","url":null,"abstract":"<div>\u0000 \u0000 <p>The stock market is extremely unpredictable and impulsive because of a variety of reasons, including public opinion, economic conditions, and so on. Each second, many Petabytes of data emerge from various sources, impacting the stock marketplace. A fair and effective merging of those sources of information (factors) into knowledge is predicted to improve the precision of stock market predictions. However, combining these characteristics from multiple sources of data into a single dataset to supply market evaluation is considered difficult since they are presented in various formats. This paper recommends a deep learning framework for performing prediction in the stock market by considering the sentiment text and historical information from social media. Initially, the required sentiment text and data are collected from the social media platform. From the database, the historical data of the company and the sentiment text from the user uploaded in the social media and news articles are collected. After that, the collected sentiment texts are preprocessed to remove the unwanted data. The preprocessed sentiment texts are given to the Bidirectional Encoder Representations from Transformers (BERT) model for retrieving the first set of features from the positive and negative sentiments. On the other hand, the deep features are retrieved from the data using a One-Dimensional Convolutional Neural Network (1DCNN), which is considered a second feature set from historical data. The two sets of features retrieved from the sentiment text and data are passed to the Dual Scale Adaptive Residual Long Short-Term Memory with Attention Mechanism (DSAResLSTM-AM) for stock market price prediction, where the attributes of the ResLSTM are tuned using Enhanced Deep Sleep Optimizer (EDSO). Here, the sentiment text having positive and negative sentiments helps to predict the stock market price of the company effectively to be less or high along with the analysis of previous data. The recommended model helps to perform the accurate stock market prediction, and it is used to enhance the return and reduce the investment. Finally, experimental validations are conducted to find the performance of the developed model in the stock market prediction.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232464","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":"AIoT Fault Detection for Firefighting Pump Maintenance Services Based Metaheuristics and Combined Deep Learning Methodologies","authors":"Thanh-Phuong Nguyen","doi":"10.1111/coin.70071","DOIUrl":"https://doi.org/10.1111/coin.70071","url":null,"abstract":"<div>\u0000 \u0000 <p>Firefighting pumps are vital components in fire safety systems, and their proper maintenance is essential for operational reliability. Conventional maintenance methods significantly depend on manual inspection and labor-intensive procedures, which are time-consuming and require significant personnel and capital expenses, particularly in large infrastructures. This paper introduces a novel fault detection framework leveraging artificial intelligence of things (AIoT) technology to enhance firefighting pump maintenance services. An advanced hybrid deep learning approach, IPSO-GRU-CNN, is developed to improve failure classification accuracy. The improved particle swarm optimization (IPSO) methodology is employed for hyperparameter optimization of the gated recurrent unit and convolutional neural network (GRU-CNN) model, demonstrating superior performance to conventional optimization methods such as PSO and random search. The IPSO-GRU-CNN model is extensively compared with various deep learning architectures, including recurrent neural networks (RNN), CNN, long short-term memory (LSTM), GRU, and CNN-GRU, to assess its classification accuracy and efficiency. The suggested AIoT framework optimizes the fault detection process and demonstrates a practical and scalable solution for industrial applications, significantly reducing labor costs and capital expenses associated with the maintenance services of firefighting pumps. Experimental results demonstrated that the developed framework outperforms conventional techniques in terms of classification accuracy and error. Comparing across conventional techniques, IPSO-GRU-CNNs acquire the most significant enhancements of 73.37% loss, 98.88% validating loss, 25.84% CP, 89.72% validating CP, 74.64% MAE, 97.36% validating MAE, 74.21% MSE, 99.9% validating MSE, 5.8% PRE, 5.78% validating PRE, 5.06% REC, and 5.2% validating REC. This framework offers a robust and efficient solution for predictive maintenance in firefighting pump systems, facilitating early fault detection and reducing downtime.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220149","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.70068","DOIUrl":"https://doi.org/10.1111/coin.70068","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>P. Sathishkumar</span>, <span>M. Gunasekaran</span>, “ <span>An Improved Vertical Fragmentation, Allocation and Replication for Enhancing E-Learning in Distributed Database Environment</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>1</span> (<span>2021</span>): <span>253</span>–<span>272</span>, \u0000https://doi.org/10.1111/coin.12401.</p><p>The above article, published online on 31 August 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 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191012","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.70067","DOIUrl":"https://doi.org/10.1111/coin.70067","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>S.G.R. Chinnaraj</span>, <span>R. Kuppan</span>, “ <span>Optimal Sizing and Placement of Multiple Renewable Distribution Generation and DSTATCOM in Radial Distribution Systems Using Hybrid Lightning Search Algorithm-Simplex Method Optimization Algorithm</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>4</span> (<span>2021</span>): <span>1673</span>–<span>1690</span>, \u0000https://doi.org/10.1111/coin.12402.</p><p>The above article, published online on 23 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 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 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191011","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}