Computational Intelligence最新文献

{"title":"Novel Nonlinearity Extracting Method of Diverse Music Signals Based on Chaotic Techniques for Musical Processing System","authors":"Xueqing Huang,&nbsp;Na Long,&nbsp;Xiaolei Yang","doi":"10.1111/coin.70138","DOIUrl":"https://doi.org/10.1111/coin.70138","url":null,"abstract":"<div>\u0000 \u0000 <p>Diverse musical styles are crucial ways for human beings to represent their emotions and interact with each other, whereas the essentials of musical signals are a time-lagged nonlinear dynamical system and their nonlinearity is difficult to analyze by conventional approaches. In this paper, the music is firstly framed depending on the subsections of its structure, then the Lyapunov exponent and the correlation dimension of the music signal are computationally analyzed, which reveals that the internal construction of the music signal is sophisticated with weak chaotic features. By retrieving the local characteristics of the music signal and extrapolating its holistic characteristics, the nonlinearity of the signal rendered by diverse musical styles also has a distinguishable difference. It is observed from the experiments that the maximum Lyapunov exponent of music characterized as “happy” and “relaxing” reaches 0.23, while the range of fluctuations in the correlation dimensions spans from 3.2 to 5.7. Furthermore, a discrepancy of 4.1 is noted in the correlation dimensions of music classified as “loud” and “uplifting,” indicative of the intricate nature of music signals' internal structures and the attenuation of chaotic characteristics. The M5 model exhibits an accuracy of 91.26% for classical music, representing a 2.9% enhancement over conventional methodologies. According to the aforementioned chaotic analysis, the originally designed nonlinearity extracting pattern for diverse music signals in the musical recognizing system demonstrates excellent performance.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272889","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":"Seasonality-Aware, Positional, and Topological-Guided GNN (SPT-GNN) for Movie Recommendation","authors":"Cevher Özden,&nbsp;Alper Özcan","doi":"10.1111/coin.70148","DOIUrl":"https://doi.org/10.1111/coin.70148","url":null,"abstract":"<div>\u0000 \u0000 <p>There has been an increasing interest in using GNNs to build recommender systems as they enable the representation of complex relationships between users and items through knowledge graph embeddings. However, most of the knowledge-graph-based systems focus only on ratings or reviews to build relationships. This prevents a comprehensive understanding of structural and positional information within graph data as well as user preferences that can change in time, as well. In order to address these issues, this paper aims to propose an advanced end-to-end Graph Neural Network architecture that significantly enhances recommendation system capabilities through the integration of state-of-the-art embedding techniques, knowledge graph frameworks, and transfer learning strategies. Incorporating positional encoding and topological feature extraction, the proposed model captures intricate user–item relationships and offers a robust representation that surpasses current approaches. A pretrained encoder facilitates knowledge transfer, effectively bridging domain gaps and amplifying prediction accuracy. Comprehensive evaluations against established baseline models reveal that our architecture has demonstrated enhanced accuracy, precision, and overall robustness. These results highlight the efficacy of combining knowledge graphs, sophisticated embedding strategies, and cross-domain transfer learning in building next-generation recommender systems, providing valuable insights for future advancements in the field.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272891","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":"Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images","authors":"Mohsen Ahmadi,&nbsp;Masoumeh Farhadi Nia,&nbsp;Sara Asgarian,&nbsp;Kasra Danesh,&nbsp;Elyas Irankhah,&nbsp;Ahmad Gholizadeh Lonbar,&nbsp;Abbas Sharifi","doi":"10.1111/coin.70145","DOIUrl":"https://doi.org/10.1111/coin.70145","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272890","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":"Graph Neural Network-Based Online Collaborative Filtering Using Transductive Node Embeddings","authors":"Gábor Szűcs,&nbsp;Richárd Kiss","doi":"10.1111/coin.70144","DOIUrl":"https://doi.org/10.1111/coin.70144","url":null,"abstract":"<p>The field of recommendation systems is a hot topic thanks to the increasing number of available digital products and services. In connection with this topic, the research of Graph Neural Network solutions has played a significant role in recent years. Research and development of an online recommendation system that also manages the challenges of a rapidly changing environment are important from a practical point of view as well. Our aim was to develop an approach that possesses scalable inference and adaptation and uses latent features. The main contribution of this paper is the development of a candidate generation process for online collaborative filtering on implicit feedback data that can scale to large user and item bases. We proposed multiple ways how embeddings can be obtained in a fast and scalable way, namely Lookup, Inductive neighbor aggregation, Neighbor aggregation with importance scores, and GraphSAGE-based Graph Neural Network (GraphSAGE+) method with continuous representation update for online learning. By combining these inductive and transductive methods for the embeddings, we developed a novel online Collaborative Filtering approach. We evaluated our approach on two e-commerce datasets and found that it outperformed traditional recommendation algorithms such as Matrix Factorization.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272814","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":"Focused Segmentation in Biomedical Imaging via Attention Driven GAN-UNet","authors":"Anamika Rangra,&nbsp;Chandan Kumar","doi":"10.1111/coin.70128","DOIUrl":"https://doi.org/10.1111/coin.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Brain tumor segmentation is critical for diagnosis, treatment planning, and evaluation. However, existing methods such as U-Net, FCN, and Mask R-CNN often struggle with capturing fine-grained tumor boundaries, handling complex tumor heterogeneity, and maintaining high sensitivity across different tumor subregions. To overcome these challenges, this study proposes an Attention-Driven GAN-UNet framework that integrates U-Net with Generative Adversarial Networks (GANs) and a Channel-Spatial Attention Module (CSAM). This innovative approach enhances segmentation accuracy and focus mapping by directing the network's attention to clinically relevant regions. Trained on the BraTS 2020 dataset, our method surpasses traditional techniques, achieving a Dice Similarity Coefficient (DSC) of 0.99. The proposed framework visualizes intricate tumor morphologies, reduces false positives, and offers robust computational efficiency, making AttnGAN-UNet a promising tool for clinical brain tumor segmentation and analysis.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223950","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":"Efficient Right-Decoupled Composite Manifold Optimization for Visual Inertial Odometry","authors":"Yangyang Ning","doi":"10.1111/coin.70127","DOIUrl":"https://doi.org/10.1111/coin.70127","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;A composite manifold is defined as a concatenation of noninteracting manifolds, which may experience some loss of accuracy and consistency when propagating IMU dynamics based on Lie theory. However, from the perspective of ordinary differential equation modeling in dynamics, they demonstrate similar convergence rates and reduced computational complexity in iterative manifold optimization. In this context, this paper proposes a right decoupled composite manifold &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;SO&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ leftlangle mathbf{SO}(3),{mathbb{R}}^3,{mathbb{R}}^3rightrangle $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for visual-inertial sliding-window iterative optimization compared with other manifolds including chained translation and rotation &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;SO&lt;/mi&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;mo&gt;×&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;ℝ&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ leftlangle mathbf{SO}(3)times {mathbb{R}}^3,{mathbb{R}}^3rightrangle $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, special Euclidean group &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 ","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223951","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":"SEDLF-LDD: A Stacking Ensemble-Based Deep Learning Framework for Lung Disease Diagnosis","authors":"Prashansa Taneja,&nbsp;Aman Sharma,&nbsp;Mrityunjay Singh","doi":"10.1111/coin.70126","DOIUrl":"https://doi.org/10.1111/coin.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>There is a growing need for accurate and swift diagnostic tools for lung disease diagnosis in healthcare. This work presents a Stacking Ensemble-based Deep Learning Framework for Enhanced Lung Disease Diagnosis (SEDLF-LDD). The stacking is a widely used ensemble learning technique that enhances the model's performance by combining the predictions from multiple base-learners using a meta-learner. The proposed framework selects the five best-performing pre-trained models, namely, ResNet50, MobileNetV2, VGG16, VGG19, and DenseNet201, as the base-learners and Multilayer Perceptron (MLP) as a meta-learner. To ensure broader applicability, we curated a dataset of chest X-ray images of Lung Disease. Initially, we choose the ten transfer learning models, fine-tune them to extract features relevant to respiratory diseases on the dataset, and select Top-5 best-performing models as base-learners. The effectiveness of the framework is determined by analysis of precision, recall, F1-score, or the area under the receiver operator characteristic (AUC-ROC) curve. The experimental results show an effective result with 97.65% accuracy.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101608","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 “Enhanced Deep Learning Framework for Precise MRI-Based Alzheimer's Disease Stage Classification”","authors":"","doi":"10.1111/coin.70125","DOIUrl":"https://doi.org/10.1111/coin.70125","url":null,"abstract":"<p>S. Chandrasekaran, S. B. Khan, M. Gupta, T. R. Mahesh, A. Alqhatani, and A. Almusharraf, “Enhanced Deep Learning Framework for Precise MRI-Based Alzheimer's Disease Stage Classification,” <i>Computational Intelligence</i> 41, no. 4 (2025): e70123, https://doi.org/10.1111/coin.70123.</p><p>In the published article, Affiliation 4 was incorrectly listed as:</p><p>4 Department of Information Systems, College of Computer Science and Information Systems, Nazran University, Najran, Saudi Arabia</p><p>The correct affiliation is:</p><p>4 Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia</p><p>We apologize for this error.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145101636","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":"Fairness Evaluation of Neural Networks Through Computational Profile Likelihood","authors":"Benjamin Djian,&nbsp;Ettore Merlo,&nbsp;Sébastien Gambs,&nbsp;Rosin Claude Ngueveu","doi":"10.1111/coin.70124","DOIUrl":"https://doi.org/10.1111/coin.70124","url":null,"abstract":"<p>Despite high predictive performance, machine learning models can be unfair towards specific demographic subgroups characterized by sensitive attributes such as gender or race. This paper presents a novel approach using Computational Profile Likelihood (CPL) to assess potential bias in neural network decisions with respect to sensitive attributes. CPL estimates the conditional probability of a network's internal neuron excitation levels during predictions. To assess the impact of sensitive attributes on predictions, the CPL distribution of individuals sharing a particular value of a sensitive attribute and a specific outcome (e.g., “women” and “high income”) is compared to a subgroup sharing another value of the sensitive attribute but with the same outcome (e.g., “men” and “high income”). The resulting disparities between distributions can be used to quantify the bias with respect to the sensitive attribute and the outcome class. We also assess the efficacy of bias reduction techniques through their influence on the resulting disparities. Experimental results on three widely used datasets indicate that the CPL of the trained models can be used to characterize significant differences between multiple protected groups, highlighting that these models display quantifiable biases. Furthermore, after applying bias mitigation methods, the gaps in CPL distributions are reduced, indicating a more similar internal representation for profiles of different protected groups.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037621","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.70115","DOIUrl":"https://doi.org/10.1111/coin.70115","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>P. Kirubanantham</span>, <span>G. Vijayakumar</span>, “ <span>Novel Recommendation System Based on Long-term Composition for Adaptive Web Services</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>3</span> (<span>2020</span>): <span>1063</span>–<span>1077</span>, \u0000https://doi.org/10.1111/coin.12309.</p>\u0000 <p>The above article, published online on 17 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 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915098","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}