Pub Date : 2024-08-13DOI: 10.1109/TAI.2024.3442153
Jun Fu;Yan Wang
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 $mathbf{x}$ 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.
社群检测是网络科学中的一个基础领域，也是一个被广泛研究的领域。为了进行社群检测，人们提出了各种有竞争力的多目标进化算法（MOEAs）。值得注意的是，最新的连续编码（CE）方法将原来的离散问题转化为连续问题，可以实现更好的社区划分。但是，原有的 CE 忽略了节点的重要结构特征，如聚类系数（CC），导致初始解不理想，降低了社区检测的性能。因此，我们提出了一种简单的方案，有效利用节点结构特征向量来增强社群检测。具体来说，我们提出了一种基于 CE 和 CC（CE-CC）的 MOEA，称为 CECC-Net。在 CECC-Net 中，CC 向量与连续向量（即与边缘相关的连续变量 $/mathbf{x}$）进行哈达玛乘积，从而得到一个改进的初始个体。然后，将非线性变换应用于连续值个体，就能得到离散值群体分组解决方案。此外，还设计了一个相应的自适应算子，作为该方案的重要组成部分，以减轻特征向量对群体多样性的负面影响。通过消融和对比实验，验证了所提方案的有效性。在合成网络和真实世界网络上的实验结果表明，与几种最先进的基于 EA 的群落检测算法相比，所提出的算法具有很强的竞争力。
{"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}
This research intends to address the critical need for precise brain tumor prediction through the development of an automated method that entwines the Firefly (FF) algorithm and the interval type-II fuzzy (IT2FLS) technique. The proposed method improves tumor delineation in complex brain tissue by using the FF algorithm to find possible cluster positions and the IT2FLS system for final clustering. This algorithm demonstrates its versatility by processing diverse image sequences from BRATS challenge datasets (2017, 2018, and 2020), which encompass varying levels of complexity. Through comprehensive evaluation metrics such as sensitivity, specificity, and dice-overlap index (DOI), the proposed algorithm consistently yields improved segmentation results. Ultimately, this research aims to augment oncologists' perceptual acumen, facilitating enhanced intuition and comprehension of patients' conditions, thereby advancing decision-making capabilities in medical research.
{"title":"Intelligent Multigrade Brain Tumor Identification in MRI: A Metaheuristic-Based Uncertain Set Framework","authors":"Saravanan Alagarsamy;Vishnuvarthanan Govindaraj;A. Shahina;D. Nagarajan","doi":"10.1109/TAI.2024.3441520","DOIUrl":"https://doi.org/10.1109/TAI.2024.3441520","url":null,"abstract":"This research intends to address the critical need for precise brain tumor prediction through the development of an automated method that entwines the Firefly (FF) algorithm and the interval type-II fuzzy (IT2FLS) technique. The proposed method improves tumor delineation in complex brain tissue by using the FF algorithm to find possible cluster positions and the IT2FLS system for final clustering. This algorithm demonstrates its versatility by processing diverse image sequences from BRATS challenge datasets (2017, 2018, and 2020), which encompass varying levels of complexity. Through comprehensive evaluation metrics such as sensitivity, specificity, and dice-overlap index (DOI), the proposed algorithm consistently yields improved segmentation results. Ultimately, this research aims to augment oncologists' perceptual acumen, facilitating enhanced intuition and comprehension of patients' conditions, thereby advancing decision-making capabilities in medical research.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5381-5391"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600363","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}
Pub Date : 2024-08-08DOI: 10.1109/TAI.2024.3440223
Jiaxu Liu;Xinping Yi;Xiaowei Huang
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.
{"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}
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.
{"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}
Pub Date : 2024-08-08DOI: 10.1109/TAI.2024.3440220
Linfang Yu;Zhen Qin;Liqun Xu;Zhiguang Qin;Kim-Kwang Raymond Choo
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.
人体姿态估计（HPE）依赖于不同身体部位之间的解剖关系来定位关键点。尽管基于卷积神经网络（CNN）的模型在 HPE 方面取得了重大进展，但它们通常无法明确学习不同身体部位之间的全局依赖关系。为了克服这一局限，我们提出了一种名为 SSpose 的空间感知 HPE 模型，它能明确捕捉图像中特定关键点与不同位置之间的空间依赖关系。所提出的 SSpose 模型采用混合 CNN 变换器编码器，可同时捕捉局部特征和全局依赖关系。为了更好地保留图像细节，还引入了多尺度融合模块来整合粗粒度和细粒度图像信息。通过与激活最大化（AM）原理建立联系，变换器的最终注意力层汇总了来自所有图像位置的贡献（即注意力分数），并形成热图中的最大位置，从而实现头部结构中的关键点定位。此外，为了解决卷积重建中的可见信息泄漏问题，我们还为 SSpose 模型设计了一个自监督训练框架。该框架利用掩码卷积和分层掩码策略，将掩码自动编码器（MAE）技术融入 SSpose 模型，从而促进了高效的自我监督学习。大量实验证明，SSpose 在姿态估计任务中表现优异。在 COCO val 集上，它的 AP 和 AR 分别达到 77.3% 和 82.1%，而在 COCO test-dev 集上，AP 和 AR 分别为 76.4% 和 81.5%。此外，该模型在 MPII 上也表现出很强的泛化能力。
{"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}
Pub Date : 2024-08-08DOI: 10.1109/TAI.2024.3440219
Padmaja Jonnalagedda;Brent Weinberg;Taejin L. Min;Shiv Bhanu;Bir Bhanu
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.
{"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}
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 $boldsymbol{sim}$ 91.30%. 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.
{"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}
Pub Date : 2024-08-08DOI: 10.1109/TAI.2024.3440225
Jino Jayan;Lal Priya P.S.;Hari Kumar R.
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.
{"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}
Pub Date : 2024-08-05DOI: 10.1109/TAI.2024.3438094
Lokesh Kumar;Arup Kumar Sadhu;Ranjan Dasgupta
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 priori ; 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.
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