IEEE Transactions on Consumer Electronics最新文献_第7页

Toward Large-Scale Non-Motorized Vehicle Helmet Wearing Detection: A New Benchmark and Beyond 走向大规模非机动车辆头盔磨损检测：一个新的标杆和超越

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-09 DOI: 10.1109/TCE.2025.3527678

Weiyi Jing;Zhongjie Zhu;Hangwei Chen;Huizhi Wang;Feng Shao

{"title":"Toward Large-Scale Non-Motorized Vehicle Helmet Wearing Detection: A New Benchmark and Beyond","authors":"Weiyi Jing;Zhongjie Zhu;Hangwei Chen;Huizhi Wang;Feng Shao","doi":"10.1109/TCE.2025.3527678","DOIUrl":"https://doi.org/10.1109/TCE.2025.3527678","url":null,"abstract":"The challenge of detecting helmet-wearing on non-motorized vehicles within road traffic scenarios has long been beset by issues like inadequate feature extraction and background noise interference. To address these challenges, an algorithm tailored for detecting helmet-wearing on non-motorized vehicles amidst complex road traffic environments was proposed in this paper. This algorithm employs feature enhancement techniques and context-aware fusion strategies to effectively address the considerable challenges presented by the vast quantity of non-motorized vehicles, small target dimensions, and the need for accurate violation monitoring in real-world settings. Specifically, the algorithm integrates a hybrid heuristic attention mechanism to refine local feature extraction capabilities. By amalgamating global and local features, it enhances the detection and recognition capabilities for diminutive targets. Furthermore, with the adaptive Transformer module addressing the challenges of target localization and occlusion in dense scenes, we propose an object detection network YOLO-HD for Non-Motorized Vehicle Helmet Wearing Detection. Moreover, to mitigate the issue of scarce data in real-world non-motorized vehicle datasets, a large dataset called NVHD-20K is carefully created to detect non-motorized bicycle helmets. A novel annotation methodology is employed to discern between stationary and moving non-motorized vehicles, thereby reducing false positives. Experimental results substantiate the efficacy of the YOLO-HD, attaining a commendable detection accuracy of 94.2% for diminutive targets like helmets. This surpasses the performance of contemporary state-of-the-art algorithms, thus underscoring its significant practical utility.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"594-607"},"PeriodicalIF":4.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314764","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

Sensitivity Analysis-Based Explainable Diffusion Transformers for Anomaly Detection in Consumer Electronics Manufacturing 基于灵敏度分析的可解释扩散变压器在消费电子制造中的异常检测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-09 DOI: 10.1109/TCE.2025.3527809

Ling Yi;Shiyu Liu;Li Zhou;Zhaolong Ning;Jiajie Song;Qingda Chen;Ke Zhang;Jinliang Ding

{"title":"Sensitivity Analysis-Based Explainable Diffusion Transformers for Anomaly Detection in Consumer Electronics Manufacturing","authors":"Ling Yi;Shiyu Liu;Li Zhou;Zhaolong Ning;Jiajie Song;Qingda Chen;Ke Zhang;Jinliang Ding","doi":"10.1109/TCE.2025.3527809","DOIUrl":"https://doi.org/10.1109/TCE.2025.3527809","url":null,"abstract":"Consumer electronics play a crucial role in the artificial intelligence Internet of Things (AIoT), with anomaly detection (AD) being particularly critical for the consumer product manufacturing industry. However, existing AD methods suffer from limitations such as poor detection accuracy and lack of explainability, hindering their widespread adoption in industrial manufacturing. To address these issues, we propose SADiTAD, a Sensitivity Analysis-based Diffusion Transformer for Anomaly Detection. This model comprises a diffusion transformer (DiT)-based reconstruction enhancement sub-network and a vision transformer (ViT)-based detection sub-network. In the DiT sub-network, we introduce a structural similarity index measure (SSIM)-guided one-step denoising method to expedite the denoising process. Additionally, to enhance the model’s explainability, we develop a sensitivity analysis-based ViT (SA-ViT) model, which evaluates the sensitivity of input embeddings to various image regions to determine if the fault region is being accurately identified during anomaly detection. The proposed SADiTAD model has been evaluated on public datasets MVTec AD and VisA, demonstrating superior performance over existing state-of-the-art anomaly detection methods and providing enhanced explainability.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2039-2050"},"PeriodicalIF":4.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308352","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 Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing 消费类电子产品在车载边缘计算中的多层卸载优化策略

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-08 DOI: 10.1109/TCE.2025.3527043

Haiyang Lin;Bo Xiao;Xiaokang Zhou;Yonghong Zhang;Xiaodong Liu

{"title":"A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing","authors":"Haiyang Lin;Bo Xiao;Xiaokang Zhou;Yonghong Zhang;Xiaodong Liu","doi":"10.1109/TCE.2025.3527043","DOIUrl":"https://doi.org/10.1109/TCE.2025.3527043","url":null,"abstract":"In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2118-2130"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308339","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

Revisiting Alignment and Uniformity for Recommendation via Discrimination and Reliable Assessment 通过鉴别和可靠评估重新审视推荐的一致性和统一性

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-08 DOI: 10.1109/TCE.2025.3527007

Xinzhe Jiang;Lei Sang;Shun Lian;Yi Zhang;Yiwen Zhang

{"title":"Revisiting Alignment and Uniformity for Recommendation via Discrimination and Reliable Assessment","authors":"Xinzhe Jiang;Lei Sang;Shun Lian;Yi Zhang;Yiwen Zhang","doi":"10.1109/TCE.2025.3527007","DOIUrl":"https://doi.org/10.1109/TCE.2025.3527007","url":null,"abstract":"Utilizing alignment and uniformity for recommendation has shown success in considering similarities between users and items. Despite this effectiveness, we argue that they suffer from two limitations: (1) alignment loss as a measure of model quality fluctuates significantly during adjustment, leading to inaccurate assessments. (2) Current methods ignore potential connections for user-user and item-item, resulting in incomplete understanding of user preferences and item characteristics.To address these issues, we propose using the trace of user and item correlation matrices as a new assessment metric to replace traditional alignment for the first time. This design reduces the impact of hyperparameters on model assessment, ensuring that trace and model quality are optimized simultaneously, thereby improving recommendation accuracy. Based on this, we introduce a new model Alignment and Uniformity with Discrimination, which additionally considers the similarities for user-user and item-item. Specifically, DiscrimAU calculates the Euclidean distance between the user (item) relevance matrix and its fully aligned matrix, distinguishing the relevance levels among different users (items). This process ensures that highly relevant users and items are more closely aligned, capturing more information. Extensive experiments on three datasets show that the proposed model achieves a maximum improvement of 6.29%, clearly demonstrating its effectiveness.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"997-1007"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314834","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

SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images SwinBTC：将学习转移到使用增强磁共振图像的医疗电子脑肿瘤分类

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-08 DOI: 10.1109/TCE.2025.3527061

Honghao Gao;Yaping Wan;Hongxia Xu;Lingchao Chen;Junsheng Xiao;Qionghuizi Ran

{"title":"SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images","authors":"Honghao Gao;Yaping Wan;Hongxia Xu;Lingchao Chen;Junsheng Xiao;Qionghuizi Ran","doi":"10.1109/TCE.2025.3527061","DOIUrl":"https://doi.org/10.1109/TCE.2025.3527061","url":null,"abstract":"Brain tumors require AI-assisted, precise treatments. Methods based on healthcare electronics, such as magnetic resonance imaging (MRI), computed tomography (CT), and gastrointestinal endoscopy, are widely used in hospitals. Maximizing the functionality of these electronic devices is crucial for improving tumor lesion detection and is a challenge in AI-assisted medical applications. Precise classification is vital for effectively planning brain tumor treatments, and accurate classification results offer crucial insights that enable physicians to devise optimal treatment strategies. Therefore, this paper proposes a novel brain tumor classification method named SwinBTC, which is based on the healthcare Internet of Things (HIoT) and integrates a pretrained and fine-tuned Swin transformer model to improve the performance of healthcare electronics. First, a transfer learning (TL)-based brain tumor classification model called SwinBTC is proposed. The general features in the images are used to improve the classification ability of the brain tumor MRI results, further accelerate the model training speed, and avoid overfitting problems. Second, clinical magnetic resonance images obtained from HIoT nodes are used to enrich the dataset, and online and offline data augmentation techniques are used to expand the utilized dataset and increase data diversity, improving the generalizability and reliability of the developed model. Finally, the performance of the proposed approach is evaluated on the CE-MRI and TT-MRI datasets via various classification metrics, and the experimental results reveal that our method outperforms other baselines.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2297-2308"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308202","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 Dual-Frequency Hybrid Wireless Charging System With Anti-Offset Integrated Receiver for Mobile Desktop Charging 一种用于移动桌面充电的抗偏移集成接收器双频混合无线充电系统

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-08 DOI: 10.1109/TCE.2025.3526985

Ronghuan Xie;Xingpeng Yu;Qiliang Liu;Xiaoying Chen;Xingkui Mao;Xiangpeng Cheng;Yiming Zhang

{"title":"A Dual-Frequency Hybrid Wireless Charging System With Anti-Offset Integrated Receiver for Mobile Desktop Charging","authors":"Ronghuan Xie;Xingpeng Yu;Qiliang Liu;Xiaoying Chen;Xingkui Mao;Xiangpeng Cheng;Yiming Zhang","doi":"10.1109/TCE.2025.3526985","DOIUrl":"https://doi.org/10.1109/TCE.2025.3526985","url":null,"abstract":"In wireless charging for consumer electronics, enhancing misalignment tolerance ability can improve user experience. To achieve this goal, this paper proposes a novel dual-frequency hybrid wireless charging system, which combines the series-series (S-S) and double-sided inductor capacitor capacitor (LCC-LCC) topologies at two different frequencies. The S-S and LCC-LCC topologies have opposite trends against misalignment and combining them can help achieve misalignment-tolerance characteristics. The S and LCC compensations are paralleled at the primary side and integrated at the secondary side. Thus, there are two transmitting coils, which are stacked for cross-coupling elimination, and only one receiving coil to reduce the size and weight of consumer electronics. The mathematical models are built and the misalignment tolerance mechanism is analyzed. The magnetic coupler and the design flow chart are presented. Finally, an experimental prototype is established to verify the feasibility of the proposed system. Both the misalignment-tolerance performance and the constant-current (CC) output are tested. The experimental results are well aligned with calculated ones, indicating that the proposed scheme has good misalignment tolerance performance and CC output, which is suitable for mobile desktop charging.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"571-578"},"PeriodicalIF":4.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308369","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

SJ-PVC: An Efficient Perceptual Video Compression Scheme Based on Adaptive QP and Rate-Distortion Optimization 基于自适应QP和率失真优化的高效感知视频压缩方案

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-06 DOI: 10.1109/TCE.2025.3526479

Yunzuo Zhang;Tong Wang;Yaoge Xiao;Tian Zhang;Yuekui Zhang;Ran Tao

{"title":"SJ-PVC: An Efficient Perceptual Video Compression Scheme Based on Adaptive QP and Rate-Distortion Optimization","authors":"Yunzuo Zhang;Tong Wang;Yaoge Xiao;Tian Zhang;Yuekui Zhang;Ran Tao","doi":"10.1109/TCE.2025.3526479","DOIUrl":"https://doi.org/10.1109/TCE.2025.3526479","url":null,"abstract":"Perceptual Video Compression (PVC) is a promising approach to enhancing compression efficiency. The Human Visual System (HVS) possesses many important perceptual characteristics, which can be utilized to further enhance encoding efficiency without significantly degrading perceptual quality. This paper addresses the issue that existing video compression methods have not fully leveraged HVS characteristics by proposing a video compression scheme, SJ-PVC, that uses a Just Noticeable Distortion (JND) estimation model based on HVS characteristics. Specifically, we design a structurally simplified network to address the structural redundancy in existing multi-scale feature-based Video Saliency Prediction (VSP) models. This network simplifies the model structure while maintaining high accuracy. Furthermore, we propose an adaptive Quantization Parameter (QP) selection algorithm that classifies each CU based on JND characteristics and saliency maps, allowing for more precise control of QP values, which significantly enhances the overall visual quality of the video. Finally, we introduce a Rate-Distortion Optimization algorithm based on HVS characteristics, which considers visual masking effects and saliency information during the encoding process to select the optimal encoding scheme. Experimental results demonstrate that SJ-PVC improves subjective video quality, significantly reduces bitrate, and shortens encoding time.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"706-719"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314717","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

CLIP-Based Natural Language-Guided Low-Redundancy Fusion of Infrared and Visible Images 基于自然语言引导的红外与可见光图像低冗余融合

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-06 DOI: 10.1109/TCE.2025.3526792

Jundong Zhang;Kangjian He;Dan Xu;Hongzhen Shi

{"title":"CLIP-Based Natural Language-Guided Low-Redundancy Fusion of Infrared and Visible Images","authors":"Jundong Zhang;Kangjian He;Dan Xu;Hongzhen Shi","doi":"10.1109/TCE.2025.3526792","DOIUrl":"https://doi.org/10.1109/TCE.2025.3526792","url":null,"abstract":"The objective of infrared and visible image fusion is to produce a fused image that encompasses significant objects and intricate textures. However, existing methods frequently prioritize the extraction of complementary information, often overlooking the detrimental effects of redundant features. Moreover, due to the absence of authentic fused images, traditional mathematically defined loss functions face challenges in accurately modeling the characteristics of fused images. To address these challenges, this paper utilizes CLIP to design a natural language-guided, low-redundancy feature infrared and visible image fusion network. On one hand, we designed a Partial Feature Extraction(PFE) block and a Spatial-Channel Reconstruction Screening(SCRS) block to effectively reduce redundant features and enhance the focus on critical features. Additionally, we leveraged the CLIP model to bridge the gap between images and natural language, innovatively crafting a language-driven loss function to guide the fusion process through linguistic expressions. Extensive experiments conducted on multiple public datasets demonstrate that this method outperforms existing advanced techniques in both visual quality and quantitative assessment. Moreover, it achieves superior detection accuracy compared to current methods, reaching an advanced level of performance. The source code will be released at <uri>https://github.com/VCMHE/CNLFusion</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"931-944"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314734","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

Dual Encoder Cross-Shape Transformer Network for Medical Image Segmentation in Internet of Medical Things for Consumer Health 面向消费者健康的医疗物联网医疗图像分割双编码器交叉变形网络

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-06 DOI: 10.1109/TCE.2025.3526801

Yawu Zhao;Shudong Wang;Yulin Zhang;Yande Ren;Yuanyuan Zhang;Shanchen Pang

{"title":"Dual Encoder Cross-Shape Transformer Network for Medical Image Segmentation in Internet of Medical Things for Consumer Health","authors":"Yawu Zhao;Shudong Wang;Yulin Zhang;Yande Ren;Yuanyuan Zhang;Shanchen Pang","doi":"10.1109/TCE.2025.3526801","DOIUrl":"https://doi.org/10.1109/TCE.2025.3526801","url":null,"abstract":"In emerging consumer healthcare, high-performance and robust medical image segmentation methods are essential for personalized diagnosis and treatment. Thus, early screening of aneurysms reduces the risk of aneurysm rupture and provides therapeutic and preventive measures. However, uncontrollable factors such as uncertainty in the size and location shape of tumors in medical images a significant challenge to medical image segmentation. These factors make extracting high-quality features from aneurysm images difficult, resulting in poor segmentation. Then, we designed a dual encoder cross-shape transform network (DECSTNet) to capture aneurysm feature information. The dual encoder structure can extract aneurysm feature information at different scales, the adaptive dynamic feature fusion module can fuse features at different scales between the encoders, and the cross-shape window transform layer can compute the width and height of the image in parallel for local self-attention, which enhances the interactive capability of the telematic information while realizing the complementarity of the local information. Through extensive experiments, we demonstrate the excellent segmentation performance of DECSTNet on the private and three public datasets. Noteworthy our method has significant segmentation performance and fewer parameters, making it well-suited to be deployed on IoMT diagnostic platforms for medical image segmentation to promote healthy patient consumption.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"538-549"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308442","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

MWMF-GLRW: Using Smart Model to Accurately Predict Non-Coding RNA Interactions for Healthy Consumption MWMF-GLRW：使用智能模型准确预测健康消费的非编码RNA相互作用

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-01-06 DOI: 10.1109/TCE.2025.3526783

Tiyao Liu;Shudong Wang;Yawu Zhao;Xiaodong Tan;Shanchen Pang

{"title":"MWMF-GLRW: Using Smart Model to Accurately Predict Non-Coding RNA Interactions for Healthy Consumption","authors":"Tiyao Liu;Shudong Wang;Yawu Zhao;Xiaodong Tan;Shanchen Pang","doi":"10.1109/TCE.2025.3526783","DOIUrl":"https://doi.org/10.1109/TCE.2025.3526783","url":null,"abstract":"In the rapidly evolving field of consumer healthcare, the exploration of non-coding RNA interactions is crucial for drug development and personalized therapy. However, through traditional experimental validation methods, it is usually costly in terms of labor and money. In this article, we propose a efficient smart model utilizing multi-perspective weighted matrix factorization with global and local interactive-based random walk (MWMF-GLRW) to assist personalized treatment. First, only known interaction information was used to compute noncoding RNA similarities and perform fusions to ensure simplicity and generalizability of the model. Second, we innovatively develop a multi-perspective weighted matrix factorization technique. This method extracts key features while preserving the matrix structure, effectively enhancing the robustness and formative nature of miRNA-lncRNA edges between miRNA and lncRNA networks. Third, we introduce a new random walk method that considers both global information and local details of heterogeneous networks. This iterative interaction mechanism dynamically adjusts the model, enhancing its robustness and accuracy. Experiments show that MWMF-GLRW surpasses the state-of-the-art model on three datasets using only known interaction information. Notably, the simplicity of our methodology, combined with its high predictive efficiency, makes it well-suited for application in medical electronic devices aimed at promoting healthy patient consumption.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1079-1091"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314819","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