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 : 2024-10-16 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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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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Toward Correlated Sequential Rules 实现相关的序列规则

IEEE transactions on artificial intelligence Pub Date : 2024-07-22 DOI: 10.1109/TAI.2024.3429306

Lili Chen;Wensheng Gan;Chien-Ming Chen

{"title":"Toward Correlated Sequential Rules","authors":"Lili Chen;Wensheng Gan;Chien-Ming Chen","doi":"10.1109/TAI.2024.3429306","DOIUrl":"https://doi.org/10.1109/TAI.2024.3429306","url":null,"abstract":"The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption. All codes are accessible on GitHub: \u0000<uri>https://github.com/DSI-Lab1/CoUSR</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442960","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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Observer-Based Adaptive Fuzzy Control for Singular Systems with Nonlinear Perturbation and Actuator Saturation 基于观测器的自适应模糊控制，用于具有非线性扰动和致动器饱和的奇异系统

IEEE transactions on artificial intelligence Pub Date : 2024-07-16 DOI: 10.1109/TAI.2024.3429052

Qingtan Meng;Qian Ma

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Artificial Intelligence-Driven Framework for Augmented Reality Markerless Navigation in Knee Surgery 人工智能驱动的膝关节手术增强现实无标记导航框架

IEEE transactions on artificial intelligence Pub Date : 2024-07-16 DOI: 10.1109/TAI.2024.3429048

Xue Hu;Fabrizio Cutolo;Hisham Iqbal;Johann Henckel;Ferdinando Rodriguez y Baena

{"title":"Artificial Intelligence-Driven Framework for Augmented Reality Markerless Navigation in Knee Surgery","authors":"Xue Hu;Fabrizio Cutolo;Hisham Iqbal;Johann Henckel;Ferdinando Rodriguez y Baena","doi":"10.1109/TAI.2024.3429048","DOIUrl":"https://doi.org/10.1109/TAI.2024.3429048","url":null,"abstract":"Conventional orthopedic navigation systems depend on marker-based tracking, which may introduce additional skin incisions, increase the risk and discomfort for the patient, and entail increased workflow complexity. The guidance is conveyed via 2-D monitors, which may distract the surgeon and increase the cognitive burden. This study presents an artificial intelligence (AI)—driven surgical navigation framework for knee replacement surgery. The system comprises an augmented reality (AR) interface that combines an occlusions-robust deep learning-based markerless bone tracking and registration algorithm with a commercial HoloLens 2 headset calibrated for the user's perspective on both eyes. The feasibility of such a system in navigating a bone drilling task is investigated with an experienced orthopedic surgeon on three cadaveric knees under realistic operating room (OR) conditions. After registering an implant model to computed tomography (CT) scans, the preoperative plans are determined based on the location of the fixation pins. Navigation accuracy is quantified using a highly accurate optical tracking system. The achieved drilling error is 7.88 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 2.41 mm in translation and 7.36 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 1.77\u0000<inline-formula><tex-math>${}^{boldsymbol{circ}}$</tex-math></inline-formula>\u0000 in orientation. The results demonstrate the viability of integrating AI and AR technology to navigate knee surgery.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation 用于分类和分段的全局注意力引导双域点云特征学习

IEEE transactions on artificial intelligence Pub Date : 2024-07-16 DOI: 10.1109/TAI.2024.3429050

Zihao Li;Pan Gao;Kang You;Chuan Yan;Manoranjan Paul

{"title":"Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation","authors":"Zihao Li;Pan Gao;Kang You;Chuan Yan;Manoranjan Paul","doi":"10.1109/TAI.2024.3429050","DOIUrl":"10.1109/TAI.2024.3429050","url":null,"abstract":"Previous studies have demonstrated the effectiveness of point-based neural models on the point cloud analysis task. However, there remains a crucial issue on producing the efficient input embedding for raw point coordinates. Moreover, another issue lies in the limited efficiency of neighboring aggregations, which is a critical component in the network stem. In this paper, we propose a global attention-guided dual-domain feature learning network (GAD) to address the above-mentioned issues. We first devise the contextual position-enhanced transformer (CPT) module, which is armed with an improved global attention mechanism, to produces a global-aware input embedding that serves as the guidance to subsequent aggregations. Then, the dual-domain K-nearest neighbor feature fusion (DKFF) is cascaded to conduct effective feature aggregation through novel dual-domain feature learning which appreciates both local geometric relations and long-distance semantic connections. Extensive experiments on multiple point cloud analysis tasks (e.g., classification, part segmentation, and scene semantic segmentation) demonstrate the superior performance of the proposed method and the efficacy of the devised modules.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655081","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

U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services U-Park：以用户为中心的电动共享微型交通服务智能停车推荐系统

IEEE transactions on artificial intelligence Pub Date : 2024-07-16 DOI: 10.1109/TAI.2024.3428513

Sen Yan;Noel E. O’Connor;Mingming Liu

{"title":"U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services","authors":"Sen Yan;Noel E. O’Connor;Mingming Liu","doi":"10.1109/TAI.2024.3428513","DOIUrl":"https://doi.org/10.1109/TAI.2024.3428513","url":null,"abstract":"Electric shared micromobility services (ESMSs) has become a vital element within the mobility as a service framework, contributing to sustainable transportation systems. However, existing ESMS face notable design challenges such as shortcomings in integration, transparency, and user-centered approaches, resulting in increased operational costs and decreased service quality. A key operational issue for ESMS revolves around parking, particularly ensuring the availability of parking spaces as users approach their destinations. For instance, a recent study illustrated that nearly 13% of shared e-bike users in Dublin, Ireland, encounter difficulties parking their e-bikes due to inadequate planning and guidance. In response, we introduce U-Park, a user-centric smart parking recommendation system designed for ESMS, providing tailored recommendations to users by analyzing their historical mobility data, trip trajectory, and parking space availability. We present the system architecture, implement it, and evaluate its performance using real-world data from an Irish-based shared e-bike provider, MOBY Bikes. Our results illustrate U-Park's ability to predict a user's destination within a shared e-bike system, achieving an approximate accuracy rate of over 97.60%, all without requiring direct user input. Experiments have proven that this predictive capability empowers U-Park to suggest the optimal parking station to users based on the availability of predicted parking spaces, improving the probability of obtaining a parking spot by 24.91% on average and 29.66% on maximum when parking availability is limited.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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 : 2024-07-16 DOI: 10.1109/TAI.2024.3422574

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