Computational Intelligence最新文献

{"title":"Zeroing Neural Network for Real-Time Operational Research and Computational Intelligence: An Ordinary Differential Equation Based Approach","authors":"Xinwei Cao,&nbsp;Penglei Li,&nbsp;Yufei Wang,&nbsp;Cheng Hua,&nbsp;Ameer Tamoor Khan","doi":"10.1111/coin.70099","DOIUrl":"https://doi.org/10.1111/coin.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>The zeroing neural network (ZNN), a canonical recurrent neural network, was developed in previous studies to address time-varying problem-solving scenarios. Numerous practical applications involve time-varying linear equations and inequality systems that demand real-time solutions. This article proposes a ZNN model specifically designed to solve such time-varying linear systems. Innovatively, it incorporates a new non-negative slack variable that transforms complex time-varying inequality systems into more easily solvable time-varying equation systems. By using an exponential decay formula and establishing an indefinite error function, the ZNN model is built. The suggested ZNN model's convergence properties are validated by theoretical research. Results from comparative simulations further support the superiority and effectiveness of the ZNN model in resolving inequality systems and time-varying linear equations.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550934","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":"AutoMathKG: The Automated Mathematical Knowledge Graph Based on LLM and Vector Database","authors":"Rong Bian,&nbsp;Yu Geng,&nbsp;Zijian Yang,&nbsp;Bing Cheng","doi":"10.1111/coin.70096","DOIUrl":"https://doi.org/10.1111/coin.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>A mathematical knowledge graph (KG) presents knowledge within mathematics in a structured manner. Constructing a math KG using natural language is an essential but challenging task. Existing methods have two major limitations: Incomplete knowledge due to limited corpora and a lack of fully automated integration of diverse sources. This paper proposes AutoMathKG, a high-quality, wide-coverage, and multi-dimensional math KG capable of automatic updates. AutoMathKG regards mathematics as a vast directed graph composed of Definition, Theorem, and Problem entities, with their reference relationships as edges. It integrates knowledge from ProofWiki, textbooks, arXiv papers, and TheoremQA, enhanced through large language models (LLMs) for data augmentation. To search for similar entities, MathVD, a vector database, is built through two designed embedding strategies. To automatically update, two mechanisms are proposed. For knowledge completion, Math LLM is developed to interact with AutoMathKG, providing missing proofs or solutions. For knowledge fusion, MathVD is used to retrieve similar entities, and LLM is used to determine whether to merge with a candidate or add a new entity. Extensive experiments demonstrate the advanced performance of the AutoMathKG system, including superior reachability query results in MathVD compared to five baselines and robust mathematical reasoning capability in Math LLM.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536844","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":"Correction to “Short-Term Wind Speed Prediction Model Based on Hybrid Decomposition Method and Deep Learning”","authors":"","doi":"10.1111/coin.70100","DOIUrl":"https://doi.org/10.1111/coin.70100","url":null,"abstract":"<p>X. Yuan, F. Hu, and Z. Zhu, “Short-Term Wind Speed Prediction Model Based on Hybrid Decomposition Method and Deep Learning,” <i>Computational Intelligence</i> 41 (2025): e70078, https://doi.org/10.1111/coin.70078.</p><p>In the paper by Yuan and Zhu et al. (2025), the affiliation address “School of Electronic Engineering, Hunan College of Information, Xiangtan, China” of Xueqiong Yuan was incorrect. This should have read: “School of Electronic Engineering, Hunan College of Information, Changsha, China” (Xiangtan has been changed to Changsha).</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514979","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":"Spprnet: A Robust CNN for Library Material Recognition via Spatial Pyramid Pooling and Heterogeneous Convolution","authors":"Li FeiFei,&nbsp;Meng Qi,&nbsp;Hong Bo,&nbsp;Zhang Lixiang,&nbsp;Ji Wen","doi":"10.1111/coin.70094","DOIUrl":"https://doi.org/10.1111/coin.70094","url":null,"abstract":"<div>\u0000 \u0000 <p>In library environments, the diverse scales of materials and batch instability caused by inconsistent scanning conditions pose challenges for image recognition tasks. Traditional ResNet architectures, due to their fixed input size constraints, may reduce their recognition accuracy for images of arbitrary sizes. In this study, we introduce a novel heterogeneous convolution strategy, adjust batch normalization operations, and incorporate a spatial pyramid pooling module based on the ResNet18 network to eliminate these limitations. This new architecture, termed SPPRNet, supports flexible processing of arbitrary-sized inputs and combines multi-scale convolution kernels (3 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> 3, 5 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> 5, 7 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> 7) to simultaneously capture fine-grained features and global contextual patterns. Quantitative results on general datasets demonstrate that our method achieves a 25.47% Top-1 error rate on ImageNet (compared to 30.55% for ResNet18) and attains 92.95% mAP on the Caltech-101 dataset for object detection tasks, outperforming mainstream models such as VGG-16 and MobileNet. The robust performance of our method in image tasks can be extended to existing approaches to further improve the quality of image recognition in library scenarios.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482068","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":"Frequency-Driven Diffusion: A Hierarchical Attention Weighting Framework for Underwater Image Restoration","authors":"Longxiang Deng,&nbsp;Laibin Chang,&nbsp;Wei Liu","doi":"10.1111/coin.70095","DOIUrl":"https://doi.org/10.1111/coin.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>Underwater images often suffer from visual degradation, affecting downstream tasks. While recent underwater image enhancement (UIE) techniques have made some advances benefiting from deep neural networks, challenges remain in restoring fine details and achieving computational efficiency. Inspired by the success of diffusion models in image generation, we propose the Underwater Laplacian-Guided Diffusion Model (ULDM), which enhances image features layer-by-layer based on the hierarchical structure of the Laplacian pyramid transform to achieve both high-quality and efficient UIE. The Laplacian pyramid decomposes the degraded image into high- and low-frequency components, enabling the model to denoise the low-frequency spectrum and address global image degradation, thereby reducing computational overhead. To efficiently enhance high-frequency details, we introduce the Hierarchical Attention Weighted Module (HAWM) that leverages the strong pixel correlations in high-frequency sub-images at different levels, adjusting them layer-by-layer to better capture fine details. These high-frequency sub-images exhibit strong pixel correlation and consistent texture features across different layers, and their hierarchical pattern ensures effective detail restoration. Extensive experiments demonstrate that ULDM outperforms state-of-the-art methods in both quantitative and qualitative evaluations.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482069","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":"Privacy-Preserving and Efficient Pneumonia Diseases Detection System Based on Federal Intelligent Edges","authors":"Haoda Wang,&nbsp;Chen Qiu,&nbsp;Guowei Liu,&nbsp;Chunhua Su","doi":"10.1111/coin.70076","DOIUrl":"https://doi.org/10.1111/coin.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>As pneumonia cases continue to rise worldwide, rapid diagnostic capabilities are essential for effective treatment. However, traditional medical systems often lack efficiency and coordinated management. In response, we propose an AI-driven biomedical diagnosis platform for real-time detection and swift intervention. Leveraging privacy-preserving deep learning on the edge, users can promptly obtain automated diagnoses by uploading chest CT images. To further enhance accuracy, we employ a federated learning (FL) framework that ensures scalable training in an industrial IoT setting while protecting patient data. Our global FL model achieves around 96.25% accuracy on a validation dataset, outperforming individual clients by 3.42%. By eliminating the need for sharing raw data, patient privacy is preserved, and the system offers improved flexibility and scalability for medical diagnosis.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472967","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":"A Framework for Integrated Bank ATM Transactions Security Based on Multi-Modal Biometric Authentication Using Adaptive and Attentive Assisted Mobilenet-V3","authors":"M. Ravi Prasad,&nbsp;N. Thillaiarasu","doi":"10.1111/coin.70083","DOIUrl":"https://doi.org/10.1111/coin.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent times, the Automated Teller Machine (ATM) scam has increased widely in this society. The technology has emerged to steal money or hack the service during the ATM transaction. Many thieves are using the strategy of skimming and trapping to steal money from ATM devices. In order to secure the transaction in ATMs, several authentication frameworks with recognition have been implemented. This safety work could be dealt with through biometric identification such as face, retina, and fingerprint recognition of the user. Due to high demand for security and reliable authentication schemes, the multimodal biometric system has emerged. The multimodal biometric system needs more than one biometric trait of an individual for identification and security purposes. Therefore, an integrated bank ATM transactions security model using a deep learning-based multi-modal biometric authentication system is developed to provide higher security during ATM transactions. Initially, the ATM card with Personal Identification Number (PIN) is given as the input for making ATM transactions in integrated banks. If the PIN is verified, then the appropriate person is identified through the biometric information of the user. The biometric information of the users includes Fingerprint, Face recognition, Retina, and speech. The speech information is in the format of signal, and hence the de-noising is performed to eliminate the noises from the input signal. The de-noised signal is given to Short Time Fourier Transform (STFT) to perform a signal transformation. After applying STFT, the spectrogram of images is attained. Finally, both the fingerprint, face, Retina, and spectrogram images are combined and given as the input for the recognition stage. Here, the Adaptive and Attentive-based Mobilenet-v3 (AAMNet) network is used for the recognition of input images, where the parameters from the Mobilenet-v3 are optimized using the Enhanced Archerfish Hunting Optimizer (EAHO) to improve the recognition performance. After recognizing the biometric information of the users, the money transactions in ATMs are completed. Therefore, the security of the integrated banking ATM transaction is highly improved, and the illegal transaction is avoided. The experimental result of the developed security model in ATM transactions is validated with the traditional models to ensure the effectiveness of the developed system. Hence, the effective results of the proposed model attain nearly 93% for accuracy, sensitivity, specificity, and also nearly 6% for FPR and FNR, respectively. This result could be used for practical applications like banking and the financial sector, money transactions, funding management, and so forth.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472829","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.70089","DOIUrl":"https://doi.org/10.1111/coin.70089","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>D. Guo</span>, <span>C. Wang</span>, “ <span>Sequence Recommendation Based on Deep Learning</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>4</span> (<span>2020</span>): <span>1704</span>–<span>1722</span>, \u0000https://doi.org/10.1111/coin.12307.</p>\u0000 <p>The above article, published online on 11 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. The authors have been informed of the retraction.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323512","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.70091","DOIUrl":"https://doi.org/10.1111/coin.70091","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>A. Shanmugam</span>, <span>J. Paramasivam</span>, “ <span>A Two-Level Authentication Scheme for Clone ode Detection in Smart Cities Using Internet of Things</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>3</span> (<span>2000</span>): <span>1200</span>–<span>1220</span>, \u0000https://doi.org/10.1111/coin.12330.</p>\u0000 <p>The above article, published online on 17 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. The authors have been informed of the retraction.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323476","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.70085","DOIUrl":"https://doi.org/10.1111/coin.70085","url":null,"abstract":"<p><b>RETRACTION</b>: <span>N. Zhang</span>, <span>Y. Han</span>, <span>R. G. Crespo</span>, and <span>O. S. Martinez</span>, “ <span>Physical Education Teaching for Saving Energy in Basketball Sports Athletics Using Hidden Markov and Motion Model</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>3</span> (<span>2021</span>): <span>1125</span>–<span>1140</span>, https://doi.org/10.1111/coin.12343.</p><p>The above article, published online on 08 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323361","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}