Computational Intelligence最新文献

{"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}

{"title":"Retraction","authors":"","doi":"10.1111/coin.70116","DOIUrl":"https://doi.org/10.1111/coin.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>N. Dhanachandra</span>, <span>Y.J. Chanu</span>, and <span>K.M. Singh</span>, “ <span>A New Hybrid Image Segmentation Approach Using Clustering and Black Hole Algorithm</span>,” <i>Computational Intelligence</i> <span>39</span> no. <span>2</span> (<span>2023</span>): <span>194</span>–<span>213</span>, \u0000https://doi.org/10.1111/coin.12297.</p>\u0000 <p>The above article, published online on 01 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.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915100","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.70118","DOIUrl":"https://doi.org/10.1111/coin.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>P. Deepika</span>, <span>R.M. Suresh</span>, and <span>P. Pabitha</span>, “ <span>Defending Against Child Death: Deep Learning-based Diagnosis Method for Abnormal Identification of Fetus Ultrasound Images</span>,” <i>Computational Intelligence</i> <span>37</span> no. <span>1</span> (<span>2021</span>): <span>128</span>–<span>154</span>, \u0000https://doi.org/10.1111/coin.12394.</p>\u0000 <p>The above article, published online on 07 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 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.70118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915136","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.70120","DOIUrl":"https://doi.org/10.1111/coin.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>A. A. Babu</span>, <span>V. M. A. Rajam</span>, “ <span>Water-body Segmentation from Satellite Images using Kapur's Entropy-based Thresholding Method</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>3</span> (<span>2020</span>): <span>1242</span>–<span>1260</span>, \u0000https://doi.org/10.1111/coin.12339.</p>\u0000 <p>The above article, published online on 14 June 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 do not agree with 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.70120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915096","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.70117","DOIUrl":"https://doi.org/10.1111/coin.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>J.D. Kharibam</span>, <span>T. Khelchandra</span>, “ <span>Automatic Speaker Recognition from Speech Signal Using Bidirectional Long Short-term Memory Recurrent Neural Network</span>,” <i>Computational Intelligence</i> <span>39</span> no. <span>2</span> (<span>2023</span>): <span>170</span>–<span>193</span>, \u0000https://doi.org/10.1111/coin.12278.</p>\u0000 <p>The above article, published online on 23 January 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.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915099","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.70119","DOIUrl":"https://doi.org/10.1111/coin.70119","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>G. Premalatha</span>, <span>P. V. Chandramani</span>, “ <span>Improved Gait Recognition through Gait Energy mage Partitioning</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>3</span> (<span>2020</span>): <span>1261</span>–<span>1274</span>, \u0000https://doi.org/10.1111/coin.12340.</p>\u0000 <p>The above article, published online on 22 June 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.70119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915095","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.70121","DOIUrl":"https://doi.org/10.1111/coin.70121","url":null,"abstract":"<div>\u0000 \u0000 <p>\u0000 <b>RETRACTION</b>: <span>S. Narasimhan</span>, <span>M. Arunachalam</span>, “ <span>Bio-PUF-MAC Authenticated Encryption for Iris Biometrics</span>,” <i>Computational Intelligence</i> <span>36</span> no. <span>3</span> (<span>2020</span>): <span>1221</span>–<span>124</span>, \u0000https://doi.org/10.1111/coin.12332.</p>\u0000 <p>The above article, published online on 27 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 do not agree with 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.70121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915097","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":"Enhanced Deep Learning Framework for Precise MRI-Based Alzheimer's Disease Stage Classification","authors":"Saravanan Chandrasekaran,&nbsp;Surbhi Bhatia Khan,&nbsp;Muskan Gupta,&nbsp;T. R. Mahesh,&nbsp;Abdulmajeed Alqhatani,&nbsp;Ahlam Almusharraf","doi":"10.1111/coin.70123","DOIUrl":"https://doi.org/10.1111/coin.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in-depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in-depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine-tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher-order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high-accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861645","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 Machine Learning Approach of Text Classification for High- and Low-Resource Languages","authors":"Muhammad Owais Raza,&nbsp;Naeem Ahmed Mahoto,&nbsp;Asadullah Shaikh,&nbsp;Nazia Pathan,&nbsp;Hani Alshahrani,&nbsp;M. A. Elmagzoub","doi":"10.1111/coin.70114","DOIUrl":"https://doi.org/10.1111/coin.70114","url":null,"abstract":"<div>\u0000 \u0000 <p>A large amount of data have been published online in textual format for the last decade because of the advancement of information and communication technologies. This is an open challenge to organize and classify large amounts of textual data automatically, especially for a language that has limited resources available online. In this study, two types of approaches are adopted for experiments. First one is a traditional strategy that uses six (06) classical state-of-the-art classification models (1. decision tree (DT), 2. logistic regression (LR), 3. support vector machine (SVM), 4. k-nearest neighbour (k-NN), 5. Naive Bayes (NB), and 6. random forest (RF)) along with two (02) ensemble methods (1. Adaboost and 2. gradient boosting (GB)) and second modeling technique is our proposed voting based ensembling scheme. Models are trained on a 75-25 split where 75% of data is used for training and 25% for testing. The evaluation of the classification models is carried out based on accuracy, precision, recall, and F1-score indexes. The experimental outcomes witnessed that for the traditional approach, gradient boosting outperformed for the limited resource language with 98.08% F1-score, while SVM performed better (97.34% F1-score) for the resource-rich language.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853811","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}