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

Solving Orienteering Problems by Hybridizing Evolutionary Algorithm and Deep Reinforcement Learning 通过混合进化算法和深度强化学习解决定向问题

IEEE transactions on artificial intelligence Pub Date : 2024-06-04 DOI: 10.1109/TAI.2024.3409520

Rui Wang;Wei Liu;Kaiwen Li;Tao Zhang;Ling Wang;Xin Xu

{"title":"Solving Orienteering Problems by Hybridizing Evolutionary Algorithm and Deep Reinforcement Learning","authors":"Rui Wang;Wei Liu;Kaiwen Li;Tao Zhang;Ling Wang;Xin Xu","doi":"10.1109/TAI.2024.3409520","DOIUrl":"https://doi.org/10.1109/TAI.2024.3409520","url":null,"abstract":"The orienteering problem (OP) is widely applied in real life. However, as the scale of real-world problem scenarios grows quickly, traditional exact, heuristics, and learning-based methods have difficulty balancing optimization accuracy and efficiency. This study proposes a problem decomposition-based double-layer optimization framework named DEA-DYPN to solve OPs. Using a diversity evolutionary algorithm (DEA) as the external optimizer and a dynamic pointer network (DYPN) as the inner optimizer, we significantly reduce the difficulty of solving large-scale OPs. Several targeted optimization operators are innovatively designed for stronger search ability, including a greedy population initialization heuristic, an elite strategy, a population restart mechanism, and a fitness-sharing selection strategy. Moreover, a dynamic embedding mechanism is introduced to DYPN to improve its characteristic learning ability. Extensive comparative experiments on OP instances with sizes from 20 to 500 are conducted for algorithmic performance validation. More experiments and analyses, including the significance test, stability analysis, complexity analysis, sensitivity analysis, and ablation experiments, are also conducted for comprehensive algorithmic evaluation. Experimental results show that our proposed DEA-DYPN ranks first according to the Friedman test and outperforms the competitor algorithms by 69%.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5493-5508"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600096","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

Comparative Evaluation in the Wild: Systems for the Expressive Rendering of Music 野外比较评估：音乐表现力渲染系统

IEEE transactions on artificial intelligence Pub Date : 2024-06-04 DOI: 10.1109/TAI.2024.3408717

Kyle Worrall;Zongyu Yin;Tom Collins

{"title":"Comparative Evaluation in the Wild: Systems for the Expressive Rendering of Music","authors":"Kyle Worrall;Zongyu Yin;Tom Collins","doi":"10.1109/TAI.2024.3408717","DOIUrl":"https://doi.org/10.1109/TAI.2024.3408717","url":null,"abstract":"There have been many attempts to model the ability of human musicians to take a score and perform or render it expressively, by adding tempo, timing, loudness, and articulation changes to nonexpressive music data. While expressive rendering models exist in academic research, most of these are not open source or accessible, meaning they are difficult to evaluate empirically and have not been widely adopted in professional music software. Systematic comparative evaluation of such algorithms stopped after the last performance rendering contest (RENCON) in 2013, making it difficult to compare newer models to existing work in a fair and valid way. In this article, we introduce the first transformer-based model for expressive rendering, cue-free express + pedal (CFE + P), which predicts expressive attributes such as notewise dynamics and micro-timing adjustments, and beatwise tempo and sustain pedal use based only on the start and end times and pitches of notes (e.g., inexpressive musical instrument digital interface (MIDI) input). We perform two comparative evaluations on our model against a nonmachine learning baseline taken from professional music software and two open-source algorithms—a feedforward neural network (FFNN) and hierarchical recurrent neural network (HRNN). The results of two listening studies indicate that our model renders passages that outperform what can be done in professional music software such as Logic Pro and Ableton Live.\u0000<xref>1</xref>\u0000<fn><label>1</label>All data and preexisting hypotheses can be accessed via the Open Science Foundation: <uri>https://osf.io/6uwjk/</uri>.</fn>","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 10","pages":"5290-5303"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443129","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 Causality-Informed Graph Intervention Model for Pancreatic Cancer Early Diagnosis 胰腺癌早期诊断的因果关系图干预模型

IEEE transactions on artificial intelligence Pub Date : 2024-04-30 DOI: 10.1109/TAI.2024.3395586

Xinyue Li;Rui Guo;Hongzhang Zhu;Tao Chen;Xiaohua Qian

{"title":"A Causality-Informed Graph Intervention Model for Pancreatic Cancer Early Diagnosis","authors":"Xinyue Li;Rui Guo;Hongzhang Zhu;Tao Chen;Xiaohua Qian","doi":"10.1109/TAI.2024.3395586","DOIUrl":"https://doi.org/10.1109/TAI.2024.3395586","url":null,"abstract":"Pancreatic cancer is a highly fatal cancer type. Patients are typically in an advanced stage at their first diagnosis, mainly due to the absence of distinctive early stage symptoms and lack of effective early diagnostic methods. In this work, we propose an automated method for pancreatic cancer diagnosis using noncontrast computed tomography (CT), taking advantage of its widespread availability in clinic. Currently, a primary challenge limiting the clinical value of intelligent systems is low generalization, i.e., the difficulty of achieving stable performance across datasets from different medical sources. To address this challenge, a novel causality-informed graph intervention model is developed based on a multi-instance-learning framework integrated with graph neural network (GNN) for the extraction of local discriminative features. Within this model, we develop a graph causal intervention scheme with three levels of intervention for graph nodes, structures, and representations. This scheme systematically suppresses noncausal factors and thus lead to generalizable predictions. Specifically, first, a target node perturbation strategy is designed to capture target-region features. Second, a causal-structure separation module is developed to automatically identify the causal graph structures for obtaining stable representations of whole target regions. Third, a graph-level feature consistency mechanism is proposed to extract invariant features. Comprehensive experiments on large-scale datasets validated the promising early diagnosis performance of our proposed model. The model generalizability was confirmed on three independent datasets, where the classification accuracy reached 86.3%, 80.4%, and 82.2%, respectively. Overall, we provide a valuable potential tool for pancreatic cancer screening and early diagnosis.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4675-4685"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169722","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

SBP-GCA: Social Behavior Prediction via Graph Contrastive Learning With Attention SBP-GCA：通过图形对比学习进行注意力社会行为预测

IEEE transactions on artificial intelligence Pub Date : 2024-04-30 DOI: 10.1109/TAI.2024.3395574

Yufei Liu;Jia Wu;Jie Cao

{"title":"SBP-GCA: Social Behavior Prediction via Graph Contrastive Learning With Attention","authors":"Yufei Liu;Jia Wu;Jie Cao","doi":"10.1109/TAI.2024.3395574","DOIUrl":"https://doi.org/10.1109/TAI.2024.3395574","url":null,"abstract":"Social behavior prediction on social media is attracting significant attention from researchers. Social e-commerce focuses on engagement marketing, which emphasizes social behavior because it effectively increases brand recognition. Currently, existing works on social behavior prediction suffer from two main problems: 1) they assume that social influence probabilities can be learned independently of each other, and their calculations do not include any influence probability estimations based on friends’ behavior; and 2) negative sampling of subgraphs is usually ignored in social behavior prediction work. To the best of our knowledge, introducing graph contrastive learning (GCL) to social behavior prediction is novel and interesting. In this article, we propose a framework, social behavior prediction via graph contrastive learning with attention named SBP-GCA, to promote social behavior prediction. First, two methods are designed to extract subgraphs from the original graph, and their structural features are learned by GCL. Then, it models how a user's behavior is influenced by neighbors and learns influence features via graph attention networks (GATs). Furthermore, it combines structural features, influence features, and intrinsic features to predict social behavior. Extensive and systematic experiments on three datasets validate the superiority of the proposed SBP-GCA.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4708-4722"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169601","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 Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals 利用脑电信号检测重度抑郁症的鲁棒深度学习模型

IEEE transactions on artificial intelligence Pub Date : 2024-04-30 DOI: 10.1109/TAI.2024.3394792

Israq Ahmed Anik;A. H. M. Kamal;Muhammad Ashad Kabir;Shahadat Uddin;Mohammad Ali Moni

{"title":"A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals","authors":"Israq Ahmed Anik;A. H. M. Kamal;Muhammad Ashad Kabir;Shahadat Uddin;Mohammad Ali Moni","doi":"10.1109/TAI.2024.3394792","DOIUrl":"https://doi.org/10.1109/TAI.2024.3394792","url":null,"abstract":"Major depressive disorder (MDD), commonly called depression, is a prevalent psychiatric condition diagnosed via questionnaire-based mental status assessments. However, this method often yields inconsistent and inaccurate results. Furthermore, there is currently a lack of a comprehensive diagnostic framework for MDD that assesses various brainwaves (alpha, theta, gamma, etc.) of electroencephalogram (EEG) signals as potential biomarkers, aiming to identify the most effective one for achieving accurate and robust diagnostic outcomes. To address this issue, we propose an innovative approach employing a deep convolutional neural network (DCNN) for MDD diagnosis utilizing the brainwaves present in EEG signals. Our proposed model, an extended 11-layer 1-D convolutional neural network (Ex-1DCNN), is designed to automatically learn from input EEG signals, foregoing the need for manual feature selection. By harnessing intrinsic brainwave patterns, our model demonstrates adaptability in classifying EEG signals into depressive and healthy categories. We have conducted an extensive analysis to identify optimal brainwave features and epoch duration for accurate MDD diagnosis. Leveraging EEG data from 34 MDD patients and 30 healthy subjects, we have identified that the Gamma brainwave at a 15-s epoch duration is the most effective configuration, achieving an accuracy of 99.60%, sensitivity of 100%, specificity of 99.21%, and an F1 score of 99.60%. This study highlights the potential of deep-learning techniques in streamlining the diagnostic process for MDD and offering a reliable aid to clinicians in MDD diagnosis.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 10","pages":"4938-4947"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443088","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

Hedge-Embedded Linguistic Fuzzy Neural Networks for Systems Identification and Control 用于系统识别和控制的绿篱嵌入式语言模糊神经网络

IEEE transactions on artificial intelligence Pub Date : 2024-04-30 DOI: 10.1109/TAI.2024.3395416

Hamed Rafiei;Mohammad-R. Akbarzadeh-T.

{"title":"Hedge-Embedded Linguistic Fuzzy Neural Networks for Systems Identification and Control","authors":"Hamed Rafiei;Mohammad-R. Akbarzadeh-T.","doi":"10.1109/TAI.2024.3395416","DOIUrl":"https://doi.org/10.1109/TAI.2024.3395416","url":null,"abstract":"In the realm of natural language processing, hedge-embedded structures have contributed considerably by appreciating linguistic variables and distinguishing overlapped classes. This aspect of natural languages considerably affects the building of linguistically interpretable architectures for fuzzy neural networks (FNNs). Here, we propose extending the idea of hedge-embedded linguistic fuzzy neural networks (LiFNNs) to the systems identification and control paradigm. This perspective leads us to the universal approximation property for this mathematical construct using the Stone–Weierstrass theorem and the proof of stability for the resulting nonlinear system identification process using the Lyapunov function. Furthermore, the power activation functions in the membership degrees of the proposed network enable linguistic hedge interpretation and more precise learning. Finally, the proposed LiFNN, optimized using a backpropagation learning algorithm, is evaluated on several problems in function approximation (periodic functions and quadratic Hermite function), system identification (a nonlinear system), and direct adaptive control fields. Results show that memberships are more distinguishable in the proposed LiFNN, leading to \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000050% less error on the average and higher granulation and interpretability.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 10","pages":"4928-4937"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442980","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

Improving Code Summarization With Tree Transformer Enhanced by Position-Related Syntax Complement 用位置相关语法补全增强的树形变换器改进代码总结工作

IEEE transactions on artificial intelligence Pub Date : 2024-04-30 DOI: 10.1109/TAI.2024.3395231

Jie Song;Zexin Zhang;Zirui Tang;Shi Feng;Yu Gu

{"title":"Improving Code Summarization With Tree Transformer Enhanced by Position-Related Syntax Complement","authors":"Jie Song;Zexin Zhang;Zirui Tang;Shi Feng;Yu Gu","doi":"10.1109/TAI.2024.3395231","DOIUrl":"https://doi.org/10.1109/TAI.2024.3395231","url":null,"abstract":"Code summarization aims to generate natural language (NL) summaries automatically given the source code snippet, which aids developers in understanding source code faster and improves software maintenance. Recent approaches using NL techniques in code summarization fall short of adequately capturing the syntactic characteristics of programming languages (PLs), particularly the position-related syntax, from which the semantics of the source code can be extracted. In this article, we present Syntax transforMer (SyMer) based on the transformer architecture where we enhance it with position-related syntax complement (PSC) to better capture syntactic characteristics. PSC takes advantage of unambiguous relations among code tokens in abstract syntax tree (AST), as well as the gathered attention on crucial code tokens indicated by its syntactic structure. The experimental results demonstrate that SyMer outperforms state-of-the-art models by at least 2.4% bilingual evaluation understudy (BLEU), 1.0% metric for evaluation of translation with explicit ORdering (METEOR) on Java benchmark, and 4.8% (BLEU), 5.1% (METEOR), and 3.2% recall-oriented understudy for gisting evaluation - longest common subsequence (ROUGE-L) on Python benchmark.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4776-4786"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169695","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

Redefining Real-Time Road Quality Analysis With Vision Transformers on Edge Devices 利用边缘设备上的视觉转换器重新定义实时道路质量分析

IEEE transactions on artificial intelligence Pub Date : 2024-04-29 DOI: 10.1109/TAI.2024.3394797

Tasnim Ahmed;Naveed Ejaz;Salimur Choudhury

{"title":"Redefining Real-Time Road Quality Analysis With Vision Transformers on Edge Devices","authors":"Tasnim Ahmed;Naveed Ejaz;Salimur Choudhury","doi":"10.1109/TAI.2024.3394797","DOIUrl":"https://doi.org/10.1109/TAI.2024.3394797","url":null,"abstract":"Road infrastructure is essential for transportation safety and efficiency. However, the current methods for assessing road conditions, crucial for effective planning and maintenance, suffer from high costs, time-intensive procedures, infrequent data collection, and limited real-time capabilities. This article presents an efficient lightweight system to analyze road quality from video feeds in real time. The backbone of the system is EdgeFusionViT, a novel vision transformer (ViT)-based architecture that uses an attention-based late fusion mechanism. The proposed architecture outperforms lightweight convolutional neural network (CNN)-based and ViT-based models. Its practicality is demonstrated by its deployment on an edge device, the Nvidia Jetson Orin Nano, enabling real-time road analysis at 12 frames per second. EdgeFusionViT outperforms existing benchmarks, achieving an impressive accuracy of 89.76% on the road surface condition dataset (RSCD). Notably, the model maintains a commendable accuracy of 76.89% even when trained with only 2% of the dataset, demonstrating its robustness and efficiency. These findings highlight the system's potential in road infrastructure management. It aids in creating safer, more efficient transport systems through timely, accurate road condition assessments. The study sets a new benchmark and opens up possibilities for advanced machine learning in infrastructure management.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 10","pages":"4972-4983"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442988","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

Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection 用于无监督异常检测的四元神经元赋能异构自动编码器

IEEE transactions on artificial intelligence Pub Date : 2024-04-29 DOI: 10.1109/TAI.2024.3394795

Jing-Xiao Liao;Bo-Jian Hou;Hang-Cheng Dong;Hao Zhang;Xiaoge Zhang;Jinwei Sun;Shiping Zhang;Feng-Lei Fan

{"title":"Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection","authors":"Jing-Xiao Liao;Bo-Jian Hou;Hang-Cheng Dong;Hao Zhang;Xiaoge Zhang;Jinwei Sun;Shiping Zhang;Feng-Lei Fan","doi":"10.1109/TAI.2024.3394795","DOIUrl":"https://doi.org/10.1109/TAI.2024.3394795","url":null,"abstract":"Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function. Employing such a novel type of neurons offers a new perspective on developing deep learning. When analyzing quadratic neurons, we find that there exists a function such that a heterogeneous network can approximate it well with a polynomial number of neurons but a purely conventional or quadratic network needs an exponential number of neurons to achieve the same level of error. Encouraged by this inspiring theoretical result on heterogeneous networks, we directly integrate conventional and quadratic neurons in an autoencoder to make a new type of heterogeneous autoencoders. To our best knowledge, it is the first heterogeneous autoencoder that is made of different types of neurons. Next, we apply the proposed heterogeneous autoencoder to unsupervised anomaly detection (AD) for tabular data and bearing fault signals. The AD faces difficulties such as data unknownness, anomaly feature heterogeneity, and feature unnoticeability, which is suitable for the proposed heterogeneous autoencoder. Its high feature representation ability can characterize a variety of anomaly data (heterogeneity), discriminate the anomaly from the normal (unnoticeability), and accurately learn the distribution of normal samples (unknownness). Experiments show that heterogeneous autoencoders perform competitively compared with other state-of-the-art models.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4723-4737"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169602","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

Linear Regression-Based Autonomous Intelligent Optimization for Constrained Multiobjective Problems 基于线性回归的自主智能优化，解决受限多目标问题

IEEE transactions on artificial intelligence Pub Date : 2024-04-18 DOI: 10.1109/TAI.2024.3391230

Yan Wang;Xiaoyan Sun;Yong Zhang;Dunwei Gong;Hejuan Hu;Mingcheng Zuo

引用次数: 0