IEEE transactions on artificial intelligence最新文献_第7页

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}

引用次数: 0

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

引用次数: 0

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

引用次数: 0

DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3440223

Jiaxu Liu;Xinping Yi;Xiaowei Huang

{"title":"DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks","authors":"Jiaxu Liu;Xinping Yi;Xiaowei Huang","doi":"10.1109/TAI.2024.3440223","DOIUrl":"https://doi.org/10.1109/TAI.2024.3440223","url":null,"abstract":"Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to shallow architectures due to the computational expense of hyperbolic operations and the issue of oversmoothing as depth increases. Although treatments have been applied to alleviate oversmoothing in graph convolutional networks (GCNs), developing a hyperbolic solution presents distinct challenges since operations must be carefully designed to fit the hyperbolic nature. Addressing these challenges, we propose DeepHGCN, the first deep multilayer HGCN architecture with dramatically improved computational efficiency and substantially reduced oversmoothing. DeepHGCN features two key innovations: 1) a novel hyperbolic feature transformation layer that enables fast and accurate linear mappings; and 2) techniques such as hyperbolic residual connections and regularization for both weights and features, facilitated by an efficient hyperbolic midpoint method. Extensive experiments demonstrate that DeepHGCN achieves significant improvements in link prediction (LP) and node classification (NC) tasks compared to both Euclidean and shallow hyperbolic GCN variants.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6172-6185"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810282","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}

引用次数: 0

Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3440248

Raghvendra Kumar;Bhargav Goddu;Sriparna Saha;Adam Jatowt

{"title":"Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?","authors":"Raghvendra Kumar;Bhargav Goddu;Sriparna Saha;Adam Jatowt","doi":"10.1109/TAI.2024.3440248","DOIUrl":"https://doi.org/10.1109/TAI.2024.3440248","url":null,"abstract":"In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large language models (LLMs) for detecting misinformation. Our study employs a versatile approach, covering multiple LLMs with few- and zero-shot prompting. These models are rigorously evaluated across various fake news and rumor detection datasets. Introducing a novel dimension, we additionally incorporate sentiment and emotion annotations to understand the emotional influence on misinformation detection using LLMs. Moreover, to extend our inquiry, we employ ChatGPT to intentionally distort authentic news as well as human-written fake news, utilizing zero-shot and iterative prompts. This deliberate corruption allows for a detailed examination of various parameters such as abstractness, concreteness, and named entity density, providing insights into differentiating between unaltered news, human-written fake news, and its LLM-corrupted counterpart. Our findings aspire to furnish a refined framework for discerning authentic news, human-generated misinformation, and LLM-induced distortions. This multifaceted approach, utilizing various prompt techniques, contributes to a comprehensive understanding of the subtle variations shaping misinformation sources.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"14-24"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975893","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}

引用次数: 0

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}

引用次数: 0

A Comprehensive Radiogenomic Feature Characterization of 19/20 Co-gain in Glioblastoma

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3440219

Padmaja Jonnalagedda;Brent Weinberg;Taejin L. Min;Shiv Bhanu;Bir Bhanu

{"title":"A Comprehensive Radiogenomic Feature Characterization of 19/20 Co-gain in Glioblastoma","authors":"Padmaja Jonnalagedda;Brent Weinberg;Taejin L. Min;Shiv Bhanu;Bir Bhanu","doi":"10.1109/TAI.2024.3440219","DOIUrl":"https://doi.org/10.1109/TAI.2024.3440219","url":null,"abstract":"The prognosis and treatment planning of glioblastoma multiforme (GBM) involves a holistic analysis of imaging, clinical, and molecular data. The correlation of imaging and molecular features has garnered much interest due to its potential to reduce the number of invasive procedures on a patient and resource utilization of the overall prognostic and treatment planning process. This article detects and characterizes the impact of tumor biomarkers (such as shape, texture, location, and the tissue surrounding the tumor) in detecting a prognostic mutation – the concurrent gain of 19 and 20 chromosomes, and proposes two novel ideas for this analysis. First, to address the challenges associated with the limited, diverse, and complex nature of medical data, this article proposes a novel generative model – the realistic radiogenomic design using disentanglement in generative adversarial networks (R2D2-GAN), designed to recreate highly subtle, unapparent manifestations of mutations in magnetic resonance imaging. It generates high-resolution, diverse data that captures the discriminatory visual features of the molecular markers while tackling the high diversity, unbalanced, and limited GBM data with rare mutations correlating with patient survival such as 19/20 co-gain. Second, this study proposes a quantitative metric called the synthetic image fidelity (SIF) score to evaluate the performance of GANs in learning visually unapparent prognostic features through the use of gradient-based model explanations. Results are compared with current methods.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6442-6456"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810724","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}

引用次数: 0

Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification 人工智能驱动的声学和语言行为预测分析用于 ASD 识别

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3439288

Ashwini B.;Deeptanshu;Sheffali Gulati;Jainendra Shukla

{"title":"Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification","authors":"Ashwini B.;Deeptanshu;Sheffali Gulati;Jainendra Shukla","doi":"10.1109/TAI.2024.3439288","DOIUrl":"https://doi.org/10.1109/TAI.2024.3439288","url":null,"abstract":"The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately \u0000<inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>\u000091.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5709-5719"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645491","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}

引用次数: 0

Tailor-Made Reinforcement Learning Approach With Advanced Noise Optimization for Soft Continuum Robots 针对软连续机器人的定制强化学习方法与高级噪声优化

IEEE transactions on artificial intelligence Pub Date : 2024-08-08 DOI: 10.1109/TAI.2024.3440225

Jino Jayan;Lal Priya P.S.;Hari Kumar R.

{"title":"Tailor-Made Reinforcement Learning Approach With Advanced Noise Optimization for Soft Continuum Robots","authors":"Jino Jayan;Lal Priya P.S.;Hari Kumar R.","doi":"10.1109/TAI.2024.3440225","DOIUrl":"https://doi.org/10.1109/TAI.2024.3440225","url":null,"abstract":"Advancements in the fusion of reinforcement learning (RL) and soft robotics are presented in this study, with a focus on refining training methodologies for soft planar continuum robots (SPCRs). The proposed modifications to the twin-delayed deep deterministic (TD3) policy gradient algorithm introduce the innovative dynamic harmonic noise (DHN) to enhance exploration adaptability. Additionally, a tailored adaptive task achievement reward (ATAR) is introduced to balance goal achievement, time efficiency, and trajectory smoothness, thereby improving precision in SPCR navigation. Evaluation metrics, including mean squared distance (MSD), mean error (ME), and mean episodic reward (MER), demonstrate robust generalization capabilities. Significant improvements in average reward, success rate, and convergence speed for the proposed modified TD3 algorithm over traditional TD3 are highlighted in the comparative analysis. Specifically, a 45.17% increase in success rate and a 4.92% increase in convergence speed over TD3 are demonstrated by the proposed TD3. Beyond insights into RL and soft robotics, potential applicability of RL in diverse scenarios is underscored, laying the foundation for future breakthroughs in real-world applications.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5509-5518"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600174","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}

引用次数: 0

Simultaneous Learning and Planning Within Sensing Range: An Approach for Local Path Planning

IEEE transactions on artificial intelligence Pub Date : 2024-08-05 DOI: 10.1109/TAI.2024.3438094

Lokesh Kumar;Arup Kumar Sadhu;Ranjan Dasgupta

{"title":"Simultaneous Learning and Planning Within Sensing Range: An Approach for Local Path Planning","authors":"Lokesh Kumar;Arup Kumar Sadhu;Ranjan Dasgupta","doi":"10.1109/TAI.2024.3438094","DOIUrl":"https://doi.org/10.1109/TAI.2024.3438094","url":null,"abstract":"This article proposes an approach for local path planning. Unlike traditional approaches, the proposed local path planner simultaneously learns and plans within the sensing range (SLPA-SR) during local path planning. SLPA-SR is the synergy between the local path planner, the dynamic window approach (DWA), the obstacle avoidance by velocity obstacle (VO) approach, and the proposed next-best reward learning (NBR) algorithms. In the proposed SLPA-SR, the DWA acts as an actuator and helps to balance exploration and exploitation in the proposed NBR. In the proposed NBR, dimensions of state and action do not need to be defined a \u0000<italic>priori</i>\u0000; rather, dimensions of state and action change dynamically. The proposed SLPA-SR is simulated and experimentally validated on the TurtleBot3 Waffle Pi. The performance of the proposed SLPA-SR is tested in several typical environments, both in simulation and hardware experiments. The proposed SLPA-SR outperforms the contender algorithms (i.e., DWA, DWA-RL, improved time elastic band, predictive artificial potential field, and artificial potential field) by a significant margin in terms of run-time, linear velocity, angular velocity, success rate, average trajectory length, and average velocity. The efficacy of the proposed NBR is established by analyzing the percentage of exploitation, average reward, and state-action pair count.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6399-6411"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810194","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}

引用次数: 0