Pub Date : 2025-01-23DOI: 10.1109/TETCI.2025.3529608
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2025.3529608","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529608","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850899","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-01-23DOI: 10.1109/TETCI.2025.3529610
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2025.3529610","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529610","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-01-23DOI: 10.1109/TETCI.2025.3529606
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2025.3529606","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529606","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"C2-C2"},"PeriodicalIF":5.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-01-01DOI: 10.1109/TETCI.2024.3485677
Behnam Zeinali;Di Zhuang;J. Morris Chang
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.
{"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}
Pub Date : 2024-12-26DOI: 10.1109/TETCI.2024.3518613
Geng Han;Jiachen Zhao;Lele Zhang;Fang Deng
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.
{"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}
Pub Date : 2024-12-11DOI: 10.1109/TETCI.2024.3499997
Gan Ruan;Leandro L. Minku;Zhao Xu;Xin Yao
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.
{"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}
Pub Date : 2024-12-02DOI: 10.1109/TETCI.2024.3502355
Asit Barman;Swalpa Kumar Roy;Swagatam Das;Paramartha Dutta
In the vast landscape of machine learning, meta-learning stands out as a challenging and dynamic area of exploration. While traditional machine learning models rely on standard algorithms to learn from data, meta-learning elevates this process by leveraging prior knowledge to adapt and improve learning experiences, mimicking the adaptive nature of human learning. This paradigm offers promising avenues for addressing the limitations of conventional deep learning approaches, such as data and computational constraints, as well as issues related to generalization. In this comprehensive survey, we delve into the intricacies of meta-learning methodologies. Beginning with a foundational overview of meta-learning and its associated fields, we present a detailed methodology elucidating the workings of meta-learning. Recognizing the importance of rigorous evaluation, we also furnish a comprehensive summary of prevalent benchmark datasets and recent advancements in meta-learning techniques applied to these datasets. Additionally, we explore meta-learning's diverse applications and achievements in domains like reinforcement learning and few-shot learning. Lastly, we examine practical hurdles and potential research directions, providing insights for future endeavors in this burgeoning field.
{"title":"Exploring the Horizons of Meta-Learning in Neural Networks: A Survey of the State-of-the-Art","authors":"Asit Barman;Swalpa Kumar Roy;Swagatam Das;Paramartha Dutta","doi":"10.1109/TETCI.2024.3502355","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3502355","url":null,"abstract":"In the vast landscape of machine learning, meta-learning stands out as a challenging and dynamic area of exploration. While traditional machine learning models rely on standard algorithms to learn from data, meta-learning elevates this process by leveraging prior knowledge to adapt and improve learning experiences, mimicking the adaptive nature of human learning. This paradigm offers promising avenues for addressing the limitations of conventional deep learning approaches, such as data and computational constraints, as well as issues related to generalization. In this comprehensive survey, we delve into the intricacies of meta-learning methodologies. Beginning with a foundational overview of meta-learning and its associated fields, we present a detailed methodology elucidating the workings of meta-learning. Recognizing the importance of rigorous evaluation, we also furnish a comprehensive summary of prevalent benchmark datasets and recent advancements in meta-learning techniques applied to these datasets. Additionally, we explore meta-learning's diverse applications and achievements in domains like reinforcement learning and few-shot learning. Lastly, we examine practical hurdles and potential research directions, providing insights for future endeavors in this burgeoning field.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"27-42"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106793","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}
Pub Date : 2024-12-02DOI: 10.1109/TETCI.2024.3508953
{"title":"2024 Index IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 8","authors":"","doi":"10.1109/TETCI.2024.3508953","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3508953","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4261-4326"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2024-12-02DOI: 10.1109/TETCI.2024.3502414
Lin Guo;Xiujuan Lei;Lian Liu;Ming Chen;Yi Pan
Drug-Drug Interaction is characterized by a modification in the action of one drug due to its concurrent use with another. It involves the safety and universality of drugs, and is one of the most meaningful issues in clinical drug combination therapy and drug development. We prefer to use computational methods to achieve DDI prediction in order to achieve large-scale prediction. This article designs a DDI prediction model DualC based on the layer attention mechanism of Graph Convolutional Network and 1 Dimensional-Convolutional Neural Network to extract topological and structural information of drugs. First, the DDI network is obtained from the drug relationship data in the database and the drug similarity network is calculated with the help of drug target features, then they are constructed into a heterogeneous network. Next, the layer attention mechanism and Graph Convolutional Network are used to learn the topological information. Subsequently, the structural information is acquired from the chemical substructure similarity matrix utilizing 1 Dimensional-Convolutional Neural Network. Finally, use the sigmoid function for DDI prediction. The experimental results show advantages of DualC which AUC reaches 0.965 and ACC reaches 0.973. The case study further proves DualC has certain practical significance.
{"title":"DualC: Drug-Drug Interaction Prediction Based on Dual Latent Feature Extractions","authors":"Lin Guo;Xiujuan Lei;Lian Liu;Ming Chen;Yi Pan","doi":"10.1109/TETCI.2024.3502414","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3502414","url":null,"abstract":"Drug-Drug Interaction is characterized by a modification in the action of one drug due to its concurrent use with another. It involves the safety and universality of drugs, and is one of the most meaningful issues in clinical drug combination therapy and drug development. We prefer to use computational methods to achieve DDI prediction in order to achieve large-scale prediction. This article designs a DDI prediction model DualC based on the layer attention mechanism of Graph Convolutional Network and 1 Dimensional-Convolutional Neural Network to extract topological and structural information of drugs. First, the DDI network is obtained from the drug relationship data in the database and the drug similarity network is calculated with the help of drug target features, then they are constructed into a heterogeneous network. Next, the layer attention mechanism and Graph Convolutional Network are used to learn the topological information. Subsequently, the structural information is acquired from the chemical substructure similarity matrix utilizing 1 Dimensional-Convolutional Neural Network. Finally, use the sigmoid function for DDI prediction. The experimental results show advantages of DualC which AUC reaches 0.965 and ACC reaches 0.973. The case study further proves DualC has certain practical significance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"946-960"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106794","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}
Pub Date : 2024-11-22DOI: 10.1109/TETCI.2024.3501719
{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3501719","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3501719","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10765922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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