IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第8页

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529608

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors IEEE计算智能信息新主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529610

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IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-23 DOI: 10.1109/TETCI.2025.3529606

引用次数: 0

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach 增强不平衡学习：一种新的松弛因子模糊支持向量机方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-22 DOI: 10.1109/TETCI.2024.3524718

M. Tanveer;Anushka Tiwari;Mushir Akhtar;Chin-Teng Lin

{"title":"Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach","authors":"M. Tanveer;Anushka Tiwari;Mushir Akhtar;Chin-Teng Lin","doi":"10.1109/TETCI.2024.3524718","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3524718","url":null,"abstract":"In real-world applications, class-imbalanced datasets pose significant challenges for machine learning algorithms, such as support vector machines (SVMs), particularly in effectively managing imbalance, noise, and outliers. Fuzzy support vector machines (FSVMs) address class imbalance by assigning varying fuzzy memberships to samples; however, their sensitivity to imbalanced datasets can lead to inaccurate assessments. The recently developed slack-factor-based FSVM (SFFSVM) improves traditional FSVMs by using slack factors to adjust fuzzy memberships based on misclassification likelihood, thereby rectifying misclassifications induced by the hyperplane obtained via different error cost (DEC). Building on SFFSVM, we propose an improved slack-factor-based FSVM (ISFFSVM) that introduces a novel location parameter. This novel parameter significantly advances the model by constraining the DEC hyperplane's extension, thereby mitigating the risk of misclassifying minority class samples. It ensures that majority class samples with slack factor scores approaching the location threshold are assigned lower fuzzy memberships, which enhances the model's discrimination capability. Extensive experimentation on a diverse array of real-world KEEL datasets demonstrates that the proposed ISFFSVM consistently achieves higher F1-scores, Matthews correlation coefficients (MCC), and area under the precision-recall curve (AUC-PR) compared to baseline classifiers. Consequently, the introduction of the location parameter, coupled with the slack-factor-based fuzzy membership, enables ISFFSVM to outperform traditional approaches, particularly in scenarios characterized by severe class disparity.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3112-3121"},"PeriodicalIF":5.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

K-Core Structure Feature Encoding-Based Enhanced Federated Graph Learning Framework 基于k核结构特征编码的增强联邦图学习框架

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-20 DOI: 10.1109/TETCI.2025.3526278

Dongdong Li;Bo Liu;Chunqiao Yang;Fang Shi;Yunfei Peng;Weiwei Lin

{"title":"K-Core Structure Feature Encoding-Based Enhanced Federated Graph Learning Framework","authors":"Dongdong Li;Bo Liu;Chunqiao Yang;Fang Shi;Yunfei Peng;Weiwei Lin","doi":"10.1109/TETCI.2025.3526278","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526278","url":null,"abstract":"Federated Graph Learning (FGL) demonstrates tremendous potential in distributed graph data analysis and modeling. The rapid growth of graph data and the increasing awareness of privacy protection make FGL research highly valuable. However, its development faces two critical challenges: the non-IID problem in heterogeneous graphs and low communication efficiency. This study proposes an Enhanced FGL framework based on K-core Structure Feature Encoding (FedKcore) to utilize various heterogeneous graphs efficiently. The nested chain structure containing rich information and linear encoding time make K-core structural attributes highly suitable for graph enhancement and aggregate sharing on edge devices. Client personalization capabilities are enhanced by combining original features with K-core attributes for local training. To improve convergence speed and overcome the non-IID challenge, we aggregate and share only the learnable parameters related to K-core attributes. Upon this, the introduced Circle Loss function optimizes feature space and boundaries, enhancing the performance of K-core attributes. Extensive experiments on heterogeneous graphs show that, compared to the state-of-the-art FedStar, FedKcore improves accuracy by over 1.3% and speeds up convergence by 1.3 times.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3097-3111"},"PeriodicalIF":5.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PCR: A Parallel Convolution Residual Network for Traffic Flow Prediction PCR：一种用于交通流预测的并行卷积残差网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-16 DOI: 10.1109/TETCI.2025.3525656

Changqi Zuo;Xu Zhang;Gen Zhao;Liang Yan

{"title":"PCR: A Parallel Convolution Residual Network for Traffic Flow Prediction","authors":"Changqi Zuo;Xu Zhang;Gen Zhao;Liang Yan","doi":"10.1109/TETCI.2025.3525656","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3525656","url":null,"abstract":"Traffic flow prediction is crucial in smart cities and traffic management, yet it presents challenges due to intricate spatial-temporal dependencies and external factors. Most existing research relied on a traditional data selection approach to represent temporal dependence. However, only considering spatial dependence in adjacent or distant regions limits the performance. In this paper, we propose an end-to-end Parallel Convolution Residual network (PCR) for grid-based traffic flow prediction. First, we introduce a novel data selection strategy to capture more temporal dependence, and then we implement an early fusion strategy without any additional operations to obtain a lighter model. Second, we propose to extract external features with feature embedding matrix operations, which can represent the interrelationships between different kinds of external data. Finally, we build a parallel residual network with concatenated features as input, which is composed of a standard residual net (SRN) to extract short spatial dependence and a dilated residual net (DRN) to extract long spatial dependence. Experiments on three traffic flow datasets TaxiBJ, BikeNYC, and TaxiCQ exhibit that the proposed method outperforms the state-of-the-art models with the most minor parameters.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3072-3083"},"PeriodicalIF":5.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Sampling Approach to Offspring Generation for Evolutionary Large-Scale Constrained Multi-Objective Optimization 进化大规模约束多目标优化子代生成的有效抽样方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-16 DOI: 10.1109/TETCI.2025.3526268

Langchun Si;Xingyi Zhang;Yajie Zhang;Shangshang Yang;Ye Tian

{"title":"An Efficient Sampling Approach to Offspring Generation for Evolutionary Large-Scale Constrained Multi-Objective Optimization","authors":"Langchun Si;Xingyi Zhang;Yajie Zhang;Shangshang Yang;Ye Tian","doi":"10.1109/TETCI.2025.3526268","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526268","url":null,"abstract":"Constrained multi-objective evolutionary algorithms have been extensively used for solving real-world problems. However, most algorithms struggle to efficiently find feasible solutions when the problem involves massive decision variables and decision space constraints. To tackle this issue, an efficient sampling approach is suggested to guide the offspring generation, where three types of directions are utilized according to the status of the current population, including the feasibility-preferred direction, the convergence-preferred direction, and the diversity-preferred direction. Specifically, the proposed approach adopts the feasibility-preferred direction to guide solutions towards constraint satisfaction when most solutions are infeasible, whereas the convergence-preferred direction is utilized to guide solutions to approach the optimal set when most solutions are dominated, and the diversity-preferred direction is employed to spread solutions to cover the optimal set when most solutions are non-dominated. Besides, a reinforcement learning approach is proposed to automatically determine the constraint handling technique in each iteration. With the proposed approaches, a large-scale constrained multi-objective evolutionary algorithm is also developed. The experiment is conducted on 31 benchmark problems with 1000 dimensions and five real-world problems with dimensions varying from 1170 to 2610, and experimental results reveal the competitive effectiveness of the proposed algorithm.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2080-2092"},"PeriodicalIF":5.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-Sampled Adaptive Fuzzy Control of MASS via Intermittent Position Data 基于间歇位置数据的事件采样质量自适应模糊控制

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-16 DOI: 10.1109/TETCI.2025.3526331

Guibing Zhu;Yong Ma;Songlin Hu

{"title":"Event-Sampled Adaptive Fuzzy Control of MASS via Intermittent Position Data","authors":"Guibing Zhu;Yong Ma;Songlin Hu","doi":"10.1109/TETCI.2025.3526331","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3526331","url":null,"abstract":"In this article, a novel event-sampled adaptive fuzzy control solution is developed for the tracking issue of maritime autonomous surface ships (MASS) under a cyber environment. In the control design, the network resources constraint, internal/external uncertainties and input saturations are involved. To save the issue of network resources, only the intermittent position data is transmitted for the control design of MASS, and an event-sampled adaptive fuzzy state observer (ESAFSO) that only depends on the intermittent position data is designed to recover the untransmitted velocity. The designed ESAFSO is independent of the design of control law, which can coordinate the network resource-saving and uncertainty compensation. In addition, to accommodate the effect resulting from the event sample, which makes the signal discontinuous required by control design, a new design method is developed by using the backstepping design framework with a multivariable dynamic surface control technique. Furthermore, a dynamic event triggering mechanism is established in the controller-actuator (C-A) channel, and a novel adaptive fuzzy output feedback control solution is developed. Under Lyapunov theory, it is indicated that, under the proposed event-sampled adaptive fuzzy control solution for the MASS only intermittent position data, all signals of the closed-loop control system are proved to be bounded. The effectiveness of the developed control solution is illustrated by simulations.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3084-3096"},"PeriodicalIF":5.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking Medical LLMs on Anesthesiology: A Comprehensive Dataset in Chinese 麻醉学医学法学硕士标杆化：中文综合数据集

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-14 DOI: 10.1109/TETCI.2024.3502465

Bohao Zhou;Yibing Zhan;Zhonghai Wang;Yanhong Li;Chong Zhang;Baosheng Yu;Liang Ding;Hua Jin;Weifeng Liu;Xiongbin Wang;Dapeng Tao

{"title":"Benchmarking Medical LLMs on Anesthesiology: A Comprehensive Dataset in Chinese","authors":"Bohao Zhou;Yibing Zhan;Zhonghai Wang;Yanhong Li;Chong Zhang;Baosheng Yu;Liang Ding;Hua Jin;Weifeng Liu;Xiongbin Wang;Dapeng Tao","doi":"10.1109/TETCI.2024.3502465","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3502465","url":null,"abstract":"With the recent success of large language models (LLMs), interest in developing them for medical domains has increased. However, due to the lack of benchmark datasets, evaluating the capabilities of medical LLMs remains challenging, particularly in highly specialized fields such as anesthesiology. To address this gap, we introduce a comprehensive anesthesiology benchmark dataset in Chinese, known as the Chinese Anesthesiology Benchmark (CAB). This benchmark facilitates the evaluation of medical LLMs for anesthesiology across three crucial dimensions: knowledge, application, and safety. Specifically, the CAB provides more than 8 k questions collected from examinations and books for knowledge-level evaluation; more than 2 k questions collected from online anesthesia consultations and hospitals for application-level evaluation; and 136 tests from seven anesthesia medical care scenarios for safety-level evaluation. With the proposed CAB dataset, we conducted a thorough evaluation of six medical LLMs, such as Bianque-2 and HuatuoGPT-13B, and eleven general LLMs, such as Qwen-7B-Chat and GPT-4. The evaluation results revealed that there are still clear gaps in the capacities of medical LLMs for anesthesiology compared with those of medical students in the field of anesthesia. We hope that the proposed CAB dataset can facilitate the development of medical LLMs for anesthesiology.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3057-3071"},"PeriodicalIF":5.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alzheimer's Disease Diagnosis Using Ensemble of Random Weighted Features and Fuzzy Least Square Twin Support Vector Machine 基于随机加权特征集合和模糊最小二乘双支持向量机的阿尔茨海默病诊断

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-13 DOI: 10.1109/TETCI.2024.3523714

Rahul Sharma;Tripti Goel;M Tanveer;Mujahed Al-Dhaifallah

{"title":"Alzheimer's Disease Diagnosis Using Ensemble of Random Weighted Features and Fuzzy Least Square Twin Support Vector Machine","authors":"Rahul Sharma;Tripti Goel;M Tanveer;Mujahed Al-Dhaifallah","doi":"10.1109/TETCI.2024.3523714","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3523714","url":null,"abstract":"Alzheimer's disease (AD) is a devastating neurological condition affecting a significant portion of the world's aging population. Magnetic resonance imaging (MRI) has been widely adopted to visualize and analyze the structural atrophies and other brain deformities caused by AD. Due to the differences in brain anatomy, brain sub-regions such as grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) deteriorate. Research shows that changes in GM, WM, and CSF are among the earliest detectable AD markers, supporting their use in early diagnosis and monitoring disease progression. In this paper, GM, WM, and CSF have been extracted from the T1-weighted MRI scan acquired from the ADNI database. A fine-tuned DL model has been implemented for automated and all levels of feature extraction. For classification, the data points from the input space are explicitly translated into a randomized feature space using a neural network with randomly generated weights for the hidden layer. After feature projection, the extended features train classification models, where two non-parallel hyperplanes are optimized, with all associated parameters undergoing fuzzification to enhance model robustness. The proposed classifier, a randomized vectored fuzzy least square twin support vector machine, adeptly manages the challenges of uncertain, imbalanced, and nonlinear data commonly found in medical imaging. It integrates fuzzy membership functions to systematically address data uncertainty and employs a least squares formulation to optimize the model efficiently, ensuring high accuracy and scalability. The performance of the proposed model is tested and compared with popular state-of-the-art models, showing significant improvements in effectiveness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1281-1291"},"PeriodicalIF":5.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0