IEEE Transactions on Consumer Electronics最新文献_第5页

A Lightweight Cloud-Edge Collaborative Intelligence Inference Framework With Runtime Dynamic Optimization for Resource-Constrained Consumer Electronics 面向资源受限消费类电子产品的轻量级云边缘协同智能推理框架及运行时动态优化

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3564777

Chenlu Wang;Yuhuai Peng;Dawei Zhang;Ryan Alturki;Bandar Alshawi;Majid Alotaibi

{"title":"A Lightweight Cloud-Edge Collaborative Intelligence Inference Framework With Runtime Dynamic Optimization for Resource-Constrained Consumer Electronics","authors":"Chenlu Wang;Yuhuai Peng;Dawei Zhang;Ryan Alturki;Bandar Alshawi;Majid Alotaibi","doi":"10.1109/TCE.2025.3564777","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564777","url":null,"abstract":"The proliferation of Internet of Things (IoT) and embedded computing has led to widespread deployment of smart consumer electronics requiring edge-based Artificial Intelligence (AI) capabilities. However, the heterogeneous nature of sensing data and dynamic edge environments poses significant challenges for efficient model inference on resource-constrained devices. To address these challenges, this paper presents a lightweight collaborative inference framework designed for consumer electronics. First, we formulate the inference optimization problem as a Mixed-Integer Nonlinear Programming (MINLP) problem, considering channel pruning, early exit and cloud offloading decisions to optimize the trade-off between accuracy and computational cost. Second, we propose a selective model activation mechanism based on Markov Decision Process (MDP), which employs a recursive self-attention mechanism to dynamically track inference budgets and guide decision-making through encoder-decoder architectures. The mechanism integrates entropy regularization during training to ensure robust and diverse execution paths. Comprehensive experiments demonstrate that our framework achieves 65.50% reduction in model parameters and 80.68% reduction in inference Floating Point Operations (FLOPs) while maintaining accuracy loss within 0.81% of the original model, making it suitable for real-time AI applications on resource-constrained consumer electronics.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6041-6054"},"PeriodicalIF":10.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud-Edge Collaborative Scalable Tucker-Based Tensor Computations for Ubiquitous Consumer Electronics Data 无所不在的消费电子数据的云边缘协作可扩展的基于tucker的张量计算

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3563698

Huazhong Liu;Weiyuan Zhang;Ren Li;Yunfan Zhang;Jihong Ding;Guangshun Zhang;Hanning Zhang;Laurence T. Yang

{"title":"Cloud-Edge Collaborative Scalable Tucker-Based Tensor Computations for Ubiquitous Consumer Electronics Data","authors":"Huazhong Liu;Weiyuan Zhang;Ren Li;Yunfan Zhang;Jihong Ding;Guangshun Zhang;Hanning Zhang;Laurence T. Yang","doi":"10.1109/TCE.2025.3563698","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563698","url":null,"abstract":"With the continuous expansion of consumer electronics applications, various data generated from ubiquitous consumer electronics devices are experiencing exponential growth. By leveraging the significant advantages of multidimensional association analysis, tensor-based big data technology has proven effective in uncovering hidden patterns within these data. However, the curse of dimensionality severely restricts the widespread exploitation of tensors, particularly on edge devices with limited computing and storage capabilities under cloud-edge computing environments. To address this challenge, we propose a series of cloud-edge collaborative scalable Tucker-based tensor computations to effectively analyze these ubiquitous data. First, we present a set of Tucker-based tensor operations that transform high-order and large-scale tensor operations into multiple low-order and small-scale operations while preserving the equivalence of their results. Then, we present a scalable Tucker-based computation architecture to adapt to the cloud-edge computing paradigm, including scalable inter-Tuckercore and intra-Tuckercore models. Furthermore, we implement some typical Tucker-based tensor computations based on various scalable models and analyze their complexity in detail. Finally, extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed scalable Tucker-based tensor computation method significantly improves computational efficiency, achieving an average efficiency improvement of 2 to 5 times compared to serial computation. These results confirm its suitability for cloud-edge collaboration to process ubiquitous consumer electronics data.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"4026-4038"},"PeriodicalIF":10.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 3 Kbits of Low-Cost, Low-Power EEPROM Integrated Into RFID Tag Integrated Circuits Available for Bio-Consumer Electronics 一种3kbit低成本、低功耗EEPROM集成到RFID标签集成电路中，可用于生物消费电子产品

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3564645

De-Ming Wang;Jian-Hao Cai;Jing Wu;De-Zhi Li;Jian-Guo Hu;Qing-Hua Zhong

{"title":"A 3 Kbits of Low-Cost, Low-Power EEPROM Integrated Into RFID Tag Integrated Circuits Available for Bio-Consumer Electronics","authors":"De-Ming Wang;Jian-Hao Cai;Jing Wu;De-Zhi Li;Jian-Guo Hu;Qing-Hua Zhong","doi":"10.1109/TCE.2025.3564645","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564645","url":null,"abstract":"RFID has great potential for applications in integrated bio-consumer electronics such as implantable medical sensors and wearable medical devices as a bridge between bio-signal acquisition and data storage. EEPROM, an important tool for storing biometric information, has been neglected in research in related fields. This paper presents a 3 Kbits EEPROM that could be utilized for passive RFID tag ICs, developed in SMIC <inline-formula> <tex-math>$0.13mu $ </tex-math></inline-formula>m EEPROM 2P6M CMOS process, boasting a chip area of <inline-formula> <tex-math>$338.28~mu $ </tex-math></inline-formula>m <inline-formula> <tex-math>${times } 310.78~mu $ </tex-math></inline-formula>m. The average power consumption for read and write operations was 560 nA and <inline-formula> <tex-math>$31~mu $ </tex-math></inline-formula>A respectively, which is 62% and 6.5% lower than the current state-of-the-art literature. This paper proposes a novel reading circuit that reads data using only one simple inverter, greatly saving power consumption and area compared with the traditional readout method using operational amplifiers. Furthermore, this paper proposes a power-optimized charge pump that switches the frequency divider generated by the low-frequency clock when the voltage rises to a high voltage to reduce power consumption. To fulfill the low-cost advantage, this paper places the device under the MIM capacitor using the MIM (shd) and M4 layers to separate. Thus, it is more suitable for bio-consumer electronics from a commercial perspective.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5437-5445"},"PeriodicalIF":10.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NFT-Based Digital Identity Authentication Framework for the Metaverse Environments 面向虚拟世界环境的基于nft的数字身份认证框架

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3564849

Wazir Zada Khan;Ayesha Siddiqa;Faisal Alanazi;Muhammad Khurram Khan

{"title":"NFT-Based Digital Identity Authentication Framework for the Metaverse Environments","authors":"Wazir Zada Khan;Ayesha Siddiqa;Faisal Alanazi;Muhammad Khurram Khan","doi":"10.1109/TCE.2025.3564849","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564849","url":null,"abstract":"The Metaverse creates a 3D virtual environment similar to the real world, enabling immersive interactions across diverse fields such as education, healthcare, and gaming. A critical aspect of these interactions is digital identity authentication, which ensures secure and trustworthy user experiences. This paper proposes a novel Non-Fungible Token (NFT) based digital identity authentication framework for the Metaverse, leveraging blockchain technology and Elliptic Curve Cryptography (ECC) to enhance security and user trust. The framework is tested using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, demonstrating its resilience against replay and man-in-the-middle (MITM) attacks. Our contributions include: <xref>(1)</xref> a secure NFT-based authentication mechanism, <xref>(2)</xref> a formal security analysis validating the framework’s robustness, and <xref>(3)</xref> a comprehensive discussion of practical implications. The proposed framework addresses key gaps in existing methods, offering a scalable and user-friendly solution for digital identity authentication in the Metaverse.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5699-5707"},"PeriodicalIF":10.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MTCF-Net: Leveraging Large Models for Multimodal Time Series Analysis in Sports and Fitness Consumer Electronics MTCF-Net：利用大型模型进行运动和健身消费电子产品的多模态时间序列分析

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3564731

Mingxu Lu;Te Qi;Chunlei Ci;Zhe Ren;Shuo Zhang;Yanfei Lv

{"title":"MTCF-Net: Leveraging Large Models for Multimodal Time Series Analysis in Sports and Fitness Consumer Electronics","authors":"Mingxu Lu;Te Qi;Chunlei Ci;Zhe Ren;Shuo Zhang;Yanfei Lv","doi":"10.1109/TCE.2025.3564731","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564731","url":null,"abstract":"The convergence of consumer electronics and sports has revolutionized how physical activities are monitored and analyzed. Devices such as smartwatches and fitness trackers collect extensive time series data for applications including activity recognition and personalized training. Large Models have emerged as powerful tools for processing such complex data. However, effectively applying these models to the temporal complexity and multimodal heterogeneity of sports data remains challenging. This paper introduces the Multi-Scale Temporal Dependency and Cooperative Feature Fusion Network (MTCF-Net), a framework leveraging the capabilities of Large Models to process multimodal time series data in sports and fitness consumer electronics. MTCF-Net integrates key components: Temporal Shift Module (TSM) and Temporal Dependency Modeling (TDM) for capturing short- and long-term dependencies, Multimodal Cooperative Feature Interaction (MCFI) for dynamic cross-modal integration, and Adaptive Feature Fusion (ADF) to prioritize task-relevant features dynamically. Extensive evaluations on the UCI-HAR and PAMAP2 datasets demonstrate MTCF-Net’s state-of-the-art performance, achieving accuracy scores of 96.44% and 98.31%, respectively. Ablation studies validate its modular design, showcasing how Large Models can enhance consumer electronics for smarter and more efficient sports applications. The model’s improved accuracy and ability enable more precise performance analysis, real-time feedback, and personalized training, thereby providing tangible benefits for both athletes and fitness enthusiasts in real-world scenarios.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"7304-7316"},"PeriodicalIF":10.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Vehicle Forensics: Investigating Data Streams for Traffic Prediction and Incident Mitigation 自动驾驶车辆取证：调查交通预测和事故缓解的数据流

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-28 DOI: 10.1109/TCE.2025.3564924

Vivek Srivastava;Sumita Mishra;Nishu Gupta;Eid Albalawi;Shakila Basheer

引用次数: 0

Efficient Path-Following for Urban Logistics: A Fuzzy Control Strategy for Consumer UAVs Under Disturbance Constraints 城市物流的高效路径跟踪：干扰约束下消费类无人机的模糊控制策略

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-25 DOI: 10.1109/TCE.2025.3564412

Xingling Shao;Jun Du;Yi Xia;Zekai Zhang;Xiangwang Hou;Mérouane Debbah

{"title":"Efficient Path-Following for Urban Logistics: A Fuzzy Control Strategy for Consumer UAVs Under Disturbance Constraints","authors":"Xingling Shao;Jun Du;Yi Xia;Zekai Zhang;Xiangwang Hou;Mérouane Debbah","doi":"10.1109/TCE.2025.3564412","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564412","url":null,"abstract":"The adoption of consumer unmanned aerial vehicles (UAVs) for logistics transportation is a key aspect of the emerging low-altitude economy, yet it faces significant challenges. Urban environments introduce complex obstacles, including dense buildings and unpredictable wind disturbances, while existing control methods struggle to balance path-following accuracy, disturbance rejection, and communication efficiency. To meet these demands, this paper proposes a quantized fuzzy learning path-following control for consumer UAVs. Firstly, a hysteresis quantized fuzzy disturbance observer (HQFDO) is proposed where the disturbances are approximately estimated by a neural network. Notably, a hysteresis quantizer is employed to reduce the communication bandwidth occupation by discretizing disturbance observations. Subsequently, a distributed velocity controller and a heading angle controller are designed to tackle the geometric and dynamic tasks separately. Specifically, the velocity controller introduces a projective arc length error to mitigate inefficiencies and safety risks associated with frequent acceleration and deceleration switches. Compared to conventional techniques, the proposed approach improves transient performance, enhances path attractivity, and optimizes communication resource utilization. Theoretical stability analysis is provided, and simulations validate the effectiveness of the proposed control strategy.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"7117-7128"},"PeriodicalIF":10.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Portable Physiotherapy Instrument With Stable Stimulation Current for Assisting in Tuina 辅助推拿的稳定刺激电流便携式物理治疗仪的设计

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-24 DOI: 10.1109/TCE.2025.3564176

Jinxi He;Yong Luo;Yapeng Dong;Chengxiang Wang;Bingjie Chen;Bo Yang;Qingsong Liu;Zechen Li

{"title":"Design of Portable Physiotherapy Instrument With Stable Stimulation Current for Assisting in Tuina","authors":"Jinxi He;Yong Luo;Yapeng Dong;Chengxiang Wang;Bingjie Chen;Bo Yang;Qingsong Liu;Zechen Li","doi":"10.1109/TCE.2025.3564176","DOIUrl":"https://doi.org/10.1109/TCE.2025.3564176","url":null,"abstract":"Chinese Tuina, a manual massage therapy in traditional Chinese medicine, can be further enhanced with appropriate electrical stimulation to achieve improved physical therapy effects. However, as the several key parameters (i.e., duration, contact area and force) in Tuina manipulation treatment change, the stimulation current may become unstable and result in discomfort to the patient. We developed a portable physiotherapy instrument to assist in Tuina physiotherapy. In our design, multi-electrodes are introduced to expand depth and range of stimulation. Meanwhile, a current control method is proposed to stabilize the stimulation current during the variation of the Tuina physiotherapist’s manipulation. The Euclidean Distance about average peak-to-peak value of the ideal output and our method output voltage in a single cycle is only 1.5899, this value represents the voltage data in volts (V) after being amplified by a factor of 10 by the oscilloscope. Compared with other control algorithms, the proposed method reduces the Euclidean distance by at least 38.960%. Furthermore, the proposed method can effectively decrease the current change caused by the changes of the physiotherapist’s manipulation, thus reducing the patient’s discomfort during treatment.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5425-5436"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Mixup-Based Contrast Learning for Data-Driven Predictive Maintenance in Long-Tailed Recognition 基于对抗性混合的长尾识别中数据驱动的预测性维护对比学习

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-24 DOI: 10.1109/TCE.2025.3563895

Ru Peng;Xingyu Chen;Xuguang Lan

{"title":"Adversarial Mixup-Based Contrast Learning for Data-Driven Predictive Maintenance in Long-Tailed Recognition","authors":"Ru Peng;Xingyu Chen;Xuguang Lan","doi":"10.1109/TCE.2025.3563895","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563895","url":null,"abstract":"Deep neural networks have achieved remarkable success in various computer vision tasks. However, in real-world applications, such as the Internet of Things (IoT), these models often struggle due to the long-tailed data distributions. For instance, in scenarios such as Holographic Counterpart Integration in IoT-based predictive maintenance for home systems or smart repair services, common operational states are prevalent in the dataset. In contrast, rare failures, such as hardware malfunctions or system breakdowns, are represented by only a few samples. This imbalance severely impacts models, making it difficult to accurately predict rare failures, leading to costly downtime or unanticipated equipment failure. Current contrastive learning-based methods are effective at optimizing feature distributions but often overlook inter-class relationships and are highly sensitive to class imbalance, which limits their generalization ability. To address these challenges, we propose the Adversarial Mixup-based supervised contrast learning (AMCL) framework, which integrates Mixup-based data augmentation with contrastive learning and incorporates an adversarial-inspired sample policy generator. AMCL generates boundary samples via a dynamically optimized Mixup strategy to enhance inter-class relationship modeling and improve predictions on ambiguous boundaries. Furthermore, we introduce a new MixCo loss function to account for the non-one-hot distribution of Mixup-generated targets, ensuring better alignment with augmented data and improving optimization efficiency. AMCL is easy to implement and achieves a performance superior to recent approaches for long-tailed recognition across various datasets such as ImageNet-LT, iNaturalist18, CIFAR-10-LT, and CIFAR-100-LT.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"5249-5258"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TBformer: Multi-Scale Transformer With Time-Behavior Attention for Multi-Modal Customer Churn Prediction TBformer：多模态客户流失预测的多尺度时间行为关注变压器

IF 10.9 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-04-24 DOI: 10.1109/TCE.2025.3563905

Yushi Li;Yunfei Tao;Ming Zhu;Ziwen Chen;Zhenyu Wen;Bideng Zhu

{"title":"TBformer: Multi-Scale Transformer With Time-Behavior Attention for Multi-Modal Customer Churn Prediction","authors":"Yushi Li;Yunfei Tao;Ming Zhu;Ziwen Chen;Zhenyu Wen;Bideng Zhu","doi":"10.1109/TCE.2025.3563905","DOIUrl":"https://doi.org/10.1109/TCE.2025.3563905","url":null,"abstract":"In highly competitive market of Internet service platforms, identifying and retaining potential churners through customer churn prediction techniques is crucial for maintaining platform vitality. The sequences of interaction behaviors between customers and platforms are closely related to churn prediction results. However, existing methods focus only on capturing the temporal dependencies in dynamic behavior sequences while ignoring the correlations between different behaviors. Moreover, classical methods apply only to static data, while deep learning-based methods focus on dynamic data, neither leveraging the complementary information between static and dynamic data. To address these issues, we propose a multi-modal customer churn prediction model based on Transformer with multi-scale Time-Behavior attention, TBformer, which adaptively fuses static and dynamic data. Time-Behavior module can capture multi-scale temporal dependencies and behavioral correlations in behavioral time series across time and behavior dimensions. We perform behavior-independent multi-scale dynamic feature fusion through bidirectional connection paths. Furthermore, the multi-modal fusion module based on the attention mechanism adaptively controls the fusion weights of static and dynamic features to improve performance. Extensive experiments on two publicly available datasets, KKBox and KDD, and a private dataset, HOF, demonstrate that our TBformer achieves an average AUC of 91.2% (+2.47%), outperforming the state-of-the-art customer churn prediction methods.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"3192-3203"},"PeriodicalIF":10.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0