IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第3页

IEEE Computational Intelligence Society Information

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

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

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

Alzheimer's Disease Diagnosis Using Ensemble of Random Weighted Features and Fuzzy Least Square Twin Support Vector Machine

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

A Novel Immune Algorithm for Multiparty Multiobjective Optimization

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-06 DOI: 10.1109/TETCI.2024.3515013

Kesheng Chen;Wenjian Luo;Qi Zhou;Yujiang liu;Peilan Xu;Yuhui Shi

{"title":"A Novel Immune Algorithm for Multiparty Multiobjective Optimization","authors":"Kesheng Chen;Wenjian Luo;Qi Zhou;Yujiang liu;Peilan Xu;Yuhui Shi","doi":"10.1109/TETCI.2024.3515013","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3515013","url":null,"abstract":"Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1238-1252"},"PeriodicalIF":5.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716517","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

ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-01-01 DOI: 10.1109/TETCI.2024.3485677

Behnam Zeinali;Di Zhuang;J. Morris Chang

{"title":"ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search","authors":"Behnam Zeinali;Di Zhuang;J. Morris Chang","doi":"10.1109/TETCI.2024.3485677","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3485677","url":null,"abstract":"Deep neural networks have demonstrated superior performance in various computer vision tasks compared to traditional machine learning algorithms. However, deploying these models on resource-constrained mobile and IoT devices poses computational challenges. Many devices resort to cloud computing, where complex deep learning models analyze data on servers. This approach increases communication costs and hampers system efficiency in the absence of a network connection. In this paper, we introduce a novel framework for deploying deep neural networks on IoT devices. This framework leverages both cloud and on-device models by extracting meta-information from each sample's classification result. It assesses the classification's performance to determine whether sending the sample to the server is necessary. Extensive experiments on CIFAR10 and CINIC10 datasets reveal that only 45% of CIFAR10 and 60% of CINIC10 test data need to be transmitted to the server using this technique. The overall accuracy of the framework is 94% and 89%, respectively, enhancing the accuracy of both client and server models.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"961-971"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106792","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

A Survey of Human-Object Interaction Detection With Deep Learning

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-12-26 DOI: 10.1109/TETCI.2024.3518613

Geng Han;Jiachen Zhao;Lele Zhang;Fang Deng

{"title":"A Survey of Human-Object Interaction Detection With Deep Learning","authors":"Geng Han;Jiachen Zhao;Lele Zhang;Fang Deng","doi":"10.1109/TETCI.2024.3518613","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3518613","url":null,"abstract":"Human-object interaction (HOI) detection has attracted significant attention due to its wide applications, including human-robot interactions, security monitoring, automatic sports commentary, etc. HOI detection aims to detect humans, objects, and their interactions in a given image or video, so it needs a higher-level semantic understanding of the image than regular object recognition or detection tasks. It is also more challenging technically because of some unique difficulties, such as multi-object interactions, long-tail distribution of interaction categories, etc. Currently, deep learning methods have achieved great performance in HOI detection, but there are few reviews describing the recent advance of deep learning-based HOI detection. Moreover, the current stage-based category of HOI detection methods is causing confusion in community discussion and beginner learning. To fill this gap, this paper summarizes, categorizes, and compares methods using deep learning for HOI detection over the last nine years. Firstly, we summarize the pipeline of HOI detection methods. Then, we divide existing methods into three categories (two-stage, one-stage, and transformer-based), distinguish them in formulas and schematics, and qualitatively compare their advantages and disadvantages. After that, we review each category of methods in detail, focusing on HOI detection methods for images. Moreover, we explore the development process of using foundation models for HOI detection. We also quantitatively compare the performance of existing methods on public HOI datasets. At last, we point out the future research direction of HOI detection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"3-26"},"PeriodicalIF":5.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106791","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

Adaptive Non-Homogeneous Granulation-Aided Density-Based Deep Feature Clustering for Far Infrared Sign Language Images

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-12-11 DOI: 10.1109/TETCI.2024.3510292

Pritam Paral;Saibal Ghosh;Sankar K. Pal;Amitava Chatterjee

{"title":"Adaptive Non-Homogeneous Granulation-Aided Density-Based Deep Feature Clustering for Far Infrared Sign Language Images","authors":"Pritam Paral;Saibal Ghosh;Sankar K. Pal;Amitava Chatterjee","doi":"10.1109/TETCI.2024.3510292","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3510292","url":null,"abstract":"In image clustering applications, deep feature clustering has recently demonstrated impressive performance, which employs deep neural networks for feature learning that favors clustering exercises. In this context, density-based methods have emerged as the preferred choice for the clustering mechanism within the framework of deep feature clustering. However, as the performance of these clustering algorithms is primarily effective on the low-dimensional feature data, deep feature learning models play a crucial role here. With far infrared (FIR) thermal imaging systems working in real-world scenarios, the images captured are largely affected by blurred edges, background noise, thermal irregularities, few details, etc. In this work, we demonstrate the effectiveness of granular computing-based techniques in such scenarios, where the input data contains indiscernible image regions and vague boundary regions. We propose a novel adaptive non-homogeneous granulation (ANHG) technique here that can adaptively select the smallest possible size of granules within a purview of unequally-sized granulation, based on a segmentation assessment index. Proposed ANHG in combination with deep feature learning helps in extracting complex, indiscernible information from the image data and capturing the local intensity variation of the data. Experimental results show significant performance improvement of the density-based deep feature clustering method after the incorporation of the proposed granulation scheme.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1269-1280"},"PeriodicalIF":5.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716356","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

Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-12-11 DOI: 10.1109/TETCI.2024.3499997

Gan Ruan;Leandro L. Minku;Zhao Xu;Xin Yao

{"title":"Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network","authors":"Gan Ruan;Leandro L. Minku;Zhao Xu;Xin Yao","doi":"10.1109/TETCI.2024.3499997","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3499997","url":null,"abstract":"In Open Radio Access Network (O-RAN), intelligent techniques are urged to achieve the automation of the computing resource allocation, so as to save computing resources and increase their utilization rate, as well as decrease the network delay. However, the existing formulation of this problem as an optimization problem defines the capacity utility of resource in an inappropriate way and it tends to cause much delay. Moreover, the only algorithm proposed to solve this problem is a greedy search algorithm, which is not ideal as it could get stuck into local optima. To overcome these issues, a new formulation that better describes the problem is proposed. In addition, an evolutionary algorithm (EA) is designed to find a resource allocation scheme to proactively and dynamically deploy the computing resource for processing upcoming traffic data. A multivariate long short-term memory model is used in the proposed EA to predict future traffic data for the production of deployment scheme. As a global search approach, the EA is less likely to get stuck in local optima than greed search, leading to better solutions. Experimental studies carried out on real-world datasets and artificially generated datasets with different scenarios and properties have demonstrated the significant superiority of our proposed EA over a baseline greedy algorithm under all parameter settings. Moreover, experimental studies with all afore-mentioned datasets are performed to compare the proposed EA and two variants under different parameter settings, to demonstrate the impact of different algorithm choices.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"1001-1018"},"PeriodicalIF":5.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361047","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

PETformer: Long-Term Time Series Forecasting via Placeholder-Enhanced Transformer

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-12-02 DOI: 10.1109/TETCI.2024.3502437

Shengsheng Lin;Weiwei Lin;Wentai Wu;Songbo Wang;Yongxiang Wang

{"title":"PETformer: Long-Term Time Series Forecasting via Placeholder-Enhanced Transformer","authors":"Shengsheng Lin;Weiwei Lin;Wentai Wu;Songbo Wang;Yongxiang Wang","doi":"10.1109/TETCI.2024.3502437","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3502437","url":null,"abstract":"Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches. This evidence suggests that a notable gap remains in fully leveraging the potential of Transformer in LTSF tasks. Therefore, this study investigates key issues when applying Transformer to LTSF, encompassing aspects of temporal continuity, information density, and multi-channel relationships. We introduce the Placeholder-enhanced Technique (PET) to enhance the computational efficiency and predictive accuracy of Transformer in LTSF tasks. Furthermore, we delve into the impact of larger patch strategies and channel interaction strategies on Transformer's performance, specifically Long Sub-sequence Division (LSD) and Multi-channel Separation and Interaction (MSI). These strategies collectively constitute a novel model termed PETformer. Extensive experiments have demonstrated that PETformer achieves state-of-the-art performance on eight commonly used public datasets for LTSF, surpassing all existing models. The insights and enhancement methodologies presented in this paper serve as valuable reference points and sources of inspiration for future research endeavors.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1189-1201"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716519","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