IEEE transactions on artificial intelligence最新文献

IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-11-12 DOI: 10.1109/TAI.2024.3489337

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IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-10-16 DOI: 10.1109/TAI.2024.3470571

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Editorial: From Explainable Artificial Intelligence (xAI) to Understandable Artificial Intelligence (uAI) 社论：从可解释的人工智能（xAI）到可理解的人工智能（uAI）

IEEE transactions on artificial intelligence Pub Date : 2024-09-10 DOI: 10.1109/TAI.2024.3439048

Hussein Abbass;Keeley Crockett;Jonathan Garibaldi;Alexander Gegov;Uzay Kaymak;Joao Miguel C. Sousa

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IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-09-10 DOI: 10.1109/TAI.2024.3449732

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Heterogeneous Hypergraph Embedding for Node Classification in Dynamic Networks 用于动态网络节点分类的异构超图嵌入

IEEE transactions on artificial intelligence Pub Date : 2024-08-26 DOI: 10.1109/TAI.2024.3450658

Malik Khizar Hayat;Shan Xue;Jia Wu;Jian Yang

{"title":"Heterogeneous Hypergraph Embedding for Node Classification in Dynamic Networks","authors":"Malik Khizar Hayat;Shan Xue;Jia Wu;Jian Yang","doi":"10.1109/TAI.2024.3450658","DOIUrl":"https://doi.org/10.1109/TAI.2024.3450658","url":null,"abstract":"Graphs are a foundational way to represent scenarios where objects interact in pairs. Recently, graph neural networks (GNNs) have become widely used for modeling simple graph structures, either in homogeneous or heterogeneous graphs, where edges represent pairwise relationships between nodes. However, many real-world situations involve more complex interactions where multiple nodes interact simultaneously, as observed in contexts such as social groups and gene-gene interactions. Traditional graph embeddings often fail to capture these multifaceted nonpairwise dynamics. A hypergraph, which generalizes a simple graph by connecting two or more nodes via a single hyperedge, offers a more efficient way to represent these interactions. While most existing research focuses on homogeneous and static hypergraph embeddings, many real-world networks are inherently heterogeneous and dynamic. To address this gap, we propose a GNN-based embedding for dynamic heterogeneous hypergraphs, specifically designed to capture nonpairwise interactions and their evolution over time. Unlike traditional embedding methods that rely on distance or meta-path-based strategies for node neighborhood aggregation, a \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-hop neighborhood strategy is introduced to effectively encapsulate higher-order interactions in dynamic networks. Furthermore, the information aggregation process is enhanced by incorporating semantic hyperedges, further enriching hypergraph embeddings. Finally, embeddings learned from each timestamp are aggregated using a mean operation to derive the final node embeddings. Extensive experiments on five real-world datasets, along with comparisons against homogeneous, heterogeneous, and hypergraph-based baselines (both static and dynamic), demonstrate the robustness and superiority of our model.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5465-5477"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism 使用分流抑制机制的基于深度学习的人群计数方法

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443789

Fok Hing Chi Tivive;Abdesselam Bouzerdoum;Son Lam Phung;Hoang Thanh Le;Hamza Baali

{"title":"A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism","authors":"Fok Hing Chi Tivive;Abdesselam Bouzerdoum;Son Lam Phung;Hoang Thanh Le;Hamza Baali","doi":"10.1109/TAI.2024.3443789","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443789","url":null,"abstract":"Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at \u0000<uri>https://github.com/ftivive/SINet</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5733-5745"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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An Improved Continuous-Encoding-Based Multiobjective Evolutionary Algorithm for Community Detection in Complex Networks 用于复杂网络中社群检测的基于连续编码的改进型多目标进化算法

IEEE transactions on artificial intelligence Pub Date : 2024-08-13 DOI: 10.1109/TAI.2024.3442153

Jun Fu;Yan Wang

{"title":"An Improved Continuous-Encoding-Based Multiobjective Evolutionary Algorithm for Community Detection in Complex Networks","authors":"Jun Fu;Yan Wang","doi":"10.1109/TAI.2024.3442153","DOIUrl":"https://doi.org/10.1109/TAI.2024.3442153","url":null,"abstract":"Community detection is a fundamental and widely studied field in network science. To perform community detection, various competitive multiobjective evolutionary algorithms (MOEAs) have been proposed. It is worth noting that the latest continuous encoding (CE) method transforms the original discrete problem into a continuous one, which can achieve better community partitioning. However, the original CE ignored important structural features of nodes, such as the clustering coefficient (CC), resulting in poor initial solutions and reduced the performance of community detection. Therefore, we propose a simple scheme to effectively utilize node structure feature vectors to enhance community detection. Specifically, a CE and CC-based (CE-CC) MOEA called CECC-Net is proposed. In CECC-Net, the CC vector performs the Hadamard product with a continuous vector (i.e., a concatenation of the continuous variables \u0000<inline-formula><tex-math>$mathbf{x}$</tex-math></inline-formula>\u0000 associated with the edges), resulting in an improved initial individual. Then, applying the nonlinear transformation to the continuous-valued individual yields a discrete-valued community grouping solution. Furthermore, a corresponding adaptive operator is designed as an essential part of this scheme to mitigate the negative effects of feature vectors on population diversity. The effectiveness of the proposed scheme was validated through ablation and comparative experiments. Experimental results on synthetic and real-world networks demonstrate that the proposed algorithm has competitive performance in comparison with several state-of-the-art EA-based community detection algorithms.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5815-5827"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-08-13 DOI: 10.1109/TAI.2024.3436231

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Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework 磁共振成像中的智能多级脑肿瘤识别：基于元搜索的不确定集合框架

IEEE transactions on artificial intelligence Pub Date : 2024-08-12 DOI: 10.1109/TAI.2024.3441520

Saravanan Alagarsamy;Vishnuvarthanan Govindaraj;A. Shahina;D. Nagarajan

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SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation SSpose：用于人体姿态估计的自监督空间感知模型

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3440220

Linfang Yu;Zhen Qin;Liqun Xu;Zhiguang Qin;Kim-Kwang Raymond Choo

{"title":"SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation","authors":"Linfang Yu;Zhen Qin;Liqun Xu;Zhiguang Qin;Kim-Kwang Raymond Choo","doi":"10.1109/TAI.2024.3440220","DOIUrl":"https://doi.org/10.1109/TAI.2024.3440220","url":null,"abstract":"Human pose estimation (HPE) relies on the anatomical relationships among different body parts to locate keypoints. Despite the significant progress achieved by convolutional neural networks (CNN)-based models in HPE, they typically fail to explicitly learn the global dependencies among various body parts. To overcome this limitation, we propose a spatial-aware HPE model called SSpose that explicitly captures the spatial dependencies between specific key points and different locations in an image. The proposed SSpose model adopts a hybrid CNN-Transformer encoder to simultaneously capture local features and global dependencies. To better preserve image details, a multiscale fusion module is introduced to integrate coarse- and fine-grained image information. By establishing a connection with the activation maximization (AM) principle, the final attention layer of the Transformer aggregates contributions (i.e., attention scores) from all image positions and forms the maximum position in the heatmap, thereby achieving keypoint localization in the head structure. Additionally, to address the issue of visible information leakage in convolutional reconstruction, we have devised a self-supervised training framework for the SSpose model. This framework incorporates mask autoencoder (MAE) technology into SSpose models by utilizing masked convolution and hierarchical masking strategy, thereby facilitating efficient self-supervised learning. Extensive experiments demonstrate that SSpose performs exceptionally well in the pose estimation task. On the COCO val set, it achieves an AP and AR of 77.3% and 82.1%, respectively, while on the COCO test-dev set, the AP and AR are 76.4% and 81.5%. Moreover, the model exhibits strong generalization capabilities on MPII.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5403-5417"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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