Computational Intelligence最新文献

{"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}

{"title":"RETRACTION","authors":"","doi":"10.1111/coin.70065","DOIUrl":"https://doi.org/10.1111/coin.70065","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>M.A. Khan</span>, <span>K.A. Abuhasel</span>, “ <span>Advanced Metameric Dimension Framework for Heterogeneous Industrial Internet of Things</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>3</span> (<span>2021</span>): <span>1367</span>–<span>1387</span>, \u0000https://doi.org/10.1111/coin.12378.</p><p>The above article, published online on 13 July 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 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.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191161","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.70064","DOIUrl":"https://doi.org/10.1111/coin.70064","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>A. Ancy Mergin</span>, <span>M.S. Godwin Premi</span>, “ <span>Implementation Analysis of Pixel-Level Image Processing Based on Multiscale Transforms</span>,” <i>Computational Intelligence</i> <span>37</span> <span>no. 3</span> (<span>2021</span>): <span>1415</span>–<span>1427</span>, \u0000https://doi.org/10.1111/coin.12384.</p><p>The above article, published online on 03 August 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.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191013","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.70066","DOIUrl":"https://doi.org/10.1111/coin.70066","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>K. A. Abuhasel</span>, <span>M. Khadr</span>, <span>M.M. Alquraish</span>, “ <span>Analyzing and Forecasting COVID-19 Pandemic in the Kingdom of Saudi Arabia using ARIMA and SIR Models</span>,” <i>Computational Intelligence</i> <span>38</span> no. <span>3</span> (<span>2022</span>): <span>770</span>–<span>783</span>, \u0000https://doi.org/10.1111/coin.12407.</p><p>The above article, published online on 05 October 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 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.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191162","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.70070","DOIUrl":"https://doi.org/10.1111/coin.70070","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>R. Mathi</span>, <span>S. Jayalalitha</span>, “ <span>Influence of Renewable Energy Sources on the Scheduling on Thermal Power Stations and its Optimization for <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>CO</mtext>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{CO}}_2 $$</annotation>\u0000 </semantics></math> Reduction</span>,” <i>Computational Intelligence</i> <span>38</span> no. <span>3</span> (<span>2022</span>): <span>903</span>–<span>920</span>, https://doi.org/10.1111/coin.12477.</p><p>The above article, published online on 10 October 2021 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.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191010","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.70069","DOIUrl":"https://doi.org/10.1111/coin.70069","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>C. Wang</span>, <span>Y. Dong</span>, <span>Y. Xia</span>, <span>G. Li</span>, <span>O. Sanjuan Martinez</span>, <span>R. Gonzalez Crespo</span>, “ <span>Management and Entrepreneurship Management Mechanism of College Students Based on Support Vector Machine Algorithm</span>,” <i>Computational Intelligence</i> <span>38</span> no. <span>3</span> (<span>2022</span>): <span>842</span>–<span>854</span>, \u0000https://doi.org/10.1111/coin.12430.</p><p>The above article, published online on 25 December 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.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191009","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}