IEEE Access最新文献

{"title":"An Exploration of Controllability in Symbolic Music Infilling","authors":"Rui Guo;Dorien Herremans","doi":"10.1109/ACCESS.2025.3554648","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554648","url":null,"abstract":"This study uses a transformer model to enhance the controllability of generative symbolic music models, specifically related to the infilling task. We introduce a novel Symbolic Music representation with Explicit Rest notation (SMER) encoding incorporating five basic duration types and explicit rest note tokens similar to standard music notation. We compare this approach with another event-based symbolic music encoding called “REMI” (REvamped MIDI-derived events) regarding controllability over bar-level tension and track-level texture, which refers to how musical elements such as melody and harmony are combined in a musical composition. The SMER encoding is compared with another controllable infilling model, Multi-Track Music Machine (MMM), for track-level density controllability. The findings confirm that the proposed SMER demonstrates superior controllability and generates music stylistically more similar to the original music than that generated by MMM. We propose strategies to further enhance track-level texture control by training two models, controlling each bar’s texture (SMER BAR), and predicting each bar’s texture after each bar’s generation (SMER Pre). Those two models with bar-level texture control effectively increase track-level texture control. To explore the interaction of the controllability of different controls, we thoroughly analyzed the controllability of different types and levels of texture controls. Finally, we implemented an interactive interface to facilitate interactive music composition with AI to help bridge the gap between the AI model and musicians.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54873-54891"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary Algorithm for the Traveling Salesman Problem With Innovative Encoding on Hybrid Quantum-Classical Machines","authors":"Ravi Saini;Ashish Mani;M. S. Prasad;Siddhartha Bhattacharyya;Jan Platos","doi":"10.1109/ACCESS.2025.3554690","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554690","url":null,"abstract":"The Traveling Salesman Problem (TSP) is a widely studied NP-complete optimization challenge with significant theoretical and practical implications. This study proposes a hybrid quantum-classical framework using a Quantum-Inspired Evolutionary Algorithm (QEA) with Sort Gray Binary Encoding to solve the TSP. The proposed method guarantees the generation of valid TSP tours by eliminating invalid solutions. It employs quantum superposition with intrinsic randomness to enhance computational efficiency and scalability. The framework was implemented on cloud-based NISQ platforms, including IBM Quantum and AWS Braket, demonstrating its practicality and effectiveness. Experimental evaluations revealed that the proposed framework successfully solved TSP instances with up to 15 cities, achieving superior performance compared to classical methods and showcasing its ability to scale under NISQ constraints. The results also highlight the potential of hybrid quantum-classical approaches to overcome hardware limitations in current quantum systems. This study establishes a robust hybrid methodology for solving combinatorial optimization problems. It also sets a benchmark for leveraging the capabilities of NISQ-era quantum devices in real-world applications, thereby providing a foundation for future research in hybrid quantum-classical optimization techniques.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54223-54239"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EF-StrongSORT: An Enhanced Feature StrongSORT Model for Multi-Object Tracking","authors":"Miar Mamdouh Khalil;Sherine Nagy Saleh;Noha S. Tawfik;Mazen Nabil Elagamy","doi":"10.1109/ACCESS.2025.3554706","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554706","url":null,"abstract":"Multi-object tracking (MOT) faces persistent challenges owing to the complexities introduced by occlusions, dynamic appearance variations, and the rapid motion of objects within a scene. These issues are further complicated by the need for robust identity management and consistent object re-identification across frames. To improve the performance of multi-object tracking, this study introduces EF-StrongSORT, which extends the StrongSORT model, incorporating advanced object detection, efficient feature extraction, and identity management techniques. The EF-StrongSORT demonstrates an improvement over conventional tracking methods by achieving higher accuracy and robustness in challenging scenarios. The experimental results show that the EF-StrongSORT enhances the performance of multi-object tracking techniques and is better than existing approaches on the MOT17, MOT20 and DanceTrack benchmarks. On the MOT17 dataset, EF-StrongSORT outperformed StrongSORT with improvements of +5.5 in HOTA, +7.7 in MOTA, +4 in IDF1, and a decrease of 828 in IDS. On the MOT20 dataset, EF-StrongSORT showed improvements of +2 in HOTA, +6.3 in MOTA, +1.1 in IDF1, and a reduction of 16 in IDS compared to StrongSORT. On the DanceTrack dataset, EF-StrongSORT achieved improvements of +4.3 in HOTA, +2.2 in MOTA, and +1.4 in AssA compared to the latest state-of-the-art model, LQTTrack. These results emphasize the contributions of the proposed model to the improvement of quality and efficiency of multi-object tracking systems targeted for specific problems, including object appearance changes and occlusions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53608-53620"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Security and Privacy-Preserving Consortium Blockchain-Based Accessing Control in Mobile Crowdsensing","authors":"Abdulrahman Alamer","doi":"10.1109/ACCESS.2025.3554600","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554600","url":null,"abstract":"In current mobile crowdsensing (MCS) systems, there is limited attention given to the security threats associated with access to the profile records (PR) of participating mobile devices. For example, most existing studies consider stakeholders of MCS applications as fully trusted entities, which granting them unlimited authorization to access the Participated Mobile Devices’ PR for the purpose of collecting sensing data. From this point, hackers may exploit this trusted point to gain unlimited authorization access to a particular participated mobile devices. They can achieve this by launching attacks, such as creating counterfeit applications as a trusted MCS applications and then posing as legitimate stakeholders to request access to the targeted devices. Thus, the hacker will gain full authorized access to the participating mobile devices’ PR, in which will result in the disclosure of security and privacy-related information of their participated devices. Therefore, the blockchain paradigm is recommended as the optimal solution for ensuring data access, owing to its advantages of immutability. However, because the blockchain is a decentralized database, a malicious MCS-server will be able to disclose the privacy of participating mobile devices by linking multiple blocks generated for the same device while it performs different tasks. Based on the aforementioned issue, this work designs a consortium blockchain-based access control system to protect the privacy rights of participating mobile devices in MCS. Furthermore, an efficient searchable keyword encryption methodology is proposed to link between the consortium blockchain and the privacy blockchain, thereby enhancing system security and access control. Finally, a security analysis and performance evaluation are conducted to demonstrate the efficiency of the proposed protocol.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53815-53834"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fake News Detection Landscape: Datasets, Data Modalities, AI Approaches, Their Challenges, and Future Perspectives","authors":"Fiza Gulzar Hussain;Muhammad Wasim;Seemab Hameed;Abdur Rehman;Muhammad Nabeel Asim;Andreas Dengel","doi":"10.1109/ACCESS.2025.3553909","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553909","url":null,"abstract":"Social media platforms have transformed the world into a global village by providing a unique platform for unrestricted communication and opinion sharing. However, this freedom is used to spread misinformation and disrupt societal harmony. To combat misinformation and fake news on social media platforms, multifarious AI applications have been developed to detect such content in various languages and data modalities, including text, images, and videos. To establish a distinctive platform that fosters the rapid development of AI-driven fake news detectors, researchers have published several review articles in recent years. However, many of these articles are outdated, and lack comprehensiveness, particularly regarding recent trends, public datasets, representation learning methods, and classifiers details. The limited scope of articles hindered their ability to provide in-depth information on predictors’ language specificity, data modality focus, and state-of-the-art performance across diverse languages. This paper offers a unique platform with detailed information on these aspects to address this gap. It offers a detailed roadmap for understanding the scope, strengths, and limitations of existing review articles on the subject. To support the development of new benchmark datasets and more accurate predictors, it conducts an in-depth analysis of the definitions and perspectives of fake news within the context of existing literature. This analysis provides a more comprehensive definition and basic concepts of fake news. In addition, 310 fake news detection articles published in the last 8 years have been thoroughly investigated. Within the landscape of these articles, the study presents details of languages, datasets, data modalities, predictor architecture designs, and their performance metrics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54757-54778"},"PeriodicalIF":3.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XRGuard: A Model-Agnostic Approach to Ransomware Detection Using Dynamic Analysis and Explainable AI","authors":"M. Adnan Alvi;Zunera Jalil","doi":"10.1109/ACCESS.2025.3553562","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553562","url":null,"abstract":"Ransomware remains a persistent and evolving cybersecurity threat, demanding advanced and adaptable detection strategies. Traditional methods often fall short as signature-based systems are easily circumvented by emerging ransomware variants, while techniques like obfuscation and polymorphism add complexity to the detection process. Although machine learning and deep learning techniques present viable solutions, the opacity of complex black-box models can hinder their application in critical security environments. This paper introduces XRGuard, a novel ransomware detection framework that utilizes machine learning techniques to analyze Event Tracing for Windows (ETW) logs, identifying critical file I/O patterns indicative of ransomware attacks. By incorporating XAI techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), XRGuard bridges the trust gap associated with complex machine learning models by providing transparent and interpretable explanations for its decisions. Experimental results demonstrate that XRGuard achieves a 99.69% accuracy rate with an exceptionally low false positive rate of 0.5%. By enhancing detection accuracy and offering clear explanations of its operations, XRGuard not only improves security but also fosters trust and a deeper understanding of ransomware behaviors.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53159-53170"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Flaky Tests Through Linguistic Diversity: A Cross-Language and Comparative Machine Learning Study","authors":"Azeem Ahmad;Xin Sun;Muhammad Rashid Naeem;Yasir Javed;Mohammad Akour;Kristian Sandahl","doi":"10.1109/ACCESS.2025.3553626","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553626","url":null,"abstract":"Software development is significantly impeded by flaky tests, which intermittently pass or fail without requiring code modifications, resulting in a decline in confidence in automated testing frameworks. Code smells (i.e., test case or production code) are the primary cause of test flakiness. In order to ascertain the prevalence of test smells, researchers and practitioners have examined numerous programming languages. However, one isolated experiment was conducted, which focused solely on one programming language. Across a variety of programming languages, such as Java, Python, C++, Go, and JavaScript, this study examines the predictive accuracy of a variety of machine learning classifiers in identifying flaky tests. We compare the performance of classifiers such as Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, and Logistic Regression in both single-language and cross-language settings. In order to ascertain the impact of linguistic diversity on the flakiness of test cases, models were trained on a single language and subsequently tested on a variety of languages. The following key findings indicate that Random Forest and Logistic Regression consistently outperform other classifiers in terms of accuracy, adaptability, and generalizability, particularly in cross-language environments. Additionally, the investigation contrasts our findings with those of previous research, exhibiting enhanced precision and accuracy in the identification of flaky tests as a result of meticulous classifier selection. We conducted a thorough statistical analysis, which included t-tests, to assess the importance of classifier performance differences in terms of accuracy and F1-score across a variety of programming languages. This analysis emphasizes the substantial discrepancies between classifiers and their effectiveness in detecting flaky tests. The datasets and experiment code utilized in this study are accessible through an open source GitHub repository to facilitate reproducibility is available at: <uri>https://github.com/PELAB-LiU/FlakyCrossLanguage</uri>. Our results emphasize the effectiveness of probabilistic and ensemble classifiers in improving the reliability of automated testing, despite certain constraints, including the potential biases introduced by language-specific structures and dataset variability. This research provides developers and researchers with practical insights that can be applied to the mitigation of flaky tests in a variety of software environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54561-54584"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937160","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Classification of Algarrobo Trees in Seasonally Dry Forests of Peru Using Aerial Imagery","authors":"Wilson Castro;Himer Avila-George;William Nauray;Roenfi Guerra;Jorge Rodriguez;Jorge Castro;Miguel de-la-Torre","doi":"10.1109/ACCESS.2025.3553752","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553752","url":null,"abstract":"Seasonally dry forests require tailored strategies to address the challenges posed by deforestation and desertification, particularly for the conservation of protected tree species. Efforts to defend protected flora involve maintaining accurate records of superior specimens of seed-producer trees (plus trees), to support informed management decisions in designated areas. In this paper, a methodology is proposed for the automated classification of plus algarrobo trees (Neltuma pallida), based on RGB aerial imagery and deep learning classifiers. As a proof of concept, three state-of-the-art approaches were evaluated in selected zones of interest, and a new application-specific architecture called AlgarroboNet was proposed for efficient classification of plus algarrobo trees. Initially, a dataset containing the geographic and phenological characteristics of algarrobo trees was provided by the Peru National Forest Service. Subsequent in-situ evaluations retrieved detailed morphometric measurements of plus trees, along with aerial imagery for both plus and non-plus specimens. After correction and segmentation, a balanced database was prepared to evaluate the four deep classifiers. The performance of each approach was summarized using both accuracy and <inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>-measure, following a hold-out validation strategy with 30 trials. The results reveal that state-of-the-art approaches show a <inline-formula> <tex-math>$F_{1}$ </tex-math></inline-formula>-measure that ranges between 0.92 and 0.99 among the models, presenting a trade-off between computational resources and accuracy. Through a detailed analysis, the proposed methodology shows potential for large-scale monitoring and characterization of plus algarrobo trees, and suggest being suitable for implementation, aiming to maintain an up-to-date inventory and enforce reforestation programs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54960-54975"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based MRI Brain Tumor Segmentation With EfficientNet-Enhanced UNet","authors":"Pradeep Kumar Tiwary;Prashant Johri;Alok Katiyar;Mayur Kumar Chhipa","doi":"10.1109/ACCESS.2025.3554405","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554405","url":null,"abstract":"Medical image segmentation plays a critical role in the field of medical image processing. Precisely delineating brain tumor areas from multimodal MRI scans is crucial for clinical diagnosis and predicting patient outcomes. However, challenges arise from similar intensity patterns, varying tumor shapes, and indistinct boundaries, which complicate brain tumor segmentation. Traditional segmentation networks like UNet face difficulties in capturing comprehensive long-range dependencies within the feature space due to the limitations of CNN receptive fields. This limitation is particularly significant in tasks requiring detailed predictions such as brain tumor segmentation. Inspired by these constraints, this study suggests incorporating EfficientNet as an encoder within UNet, with a thorough reassessment of its fundamental components: the encoder, bottleneck, and skip connections. EfficientNet replaces UNet’s encoder, initially frozen to retain learned features from pre-trained weights, adept at extracting detailed features crucial for precise segmentation like brain tumors from MRI scans. Preserving UNet’s bottleneck compresses EfficientNet’s outputs, while skip connections maintain spatial integrity during decoder upsampling. The decoder reconstructs the original image size by merging encoder-decoder features, refining boundaries with convolutional layers for accurate clinical insights. The study conducted multiclass operations on the Brain-Tumor.npy dataset from Kaggle, consisting of 3064 T1-weighted contrast-enhanced images from 233 patients with meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices). Experimental findings in brain tumor segmentation tasks show that the proposed model achieves performance on par with or better than recent CNN or Transformer models. Specifically, the model achieves an accuracy of 0.9925 and a loss of 0.2991 on the dataset.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54920-54937"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational and Planning Perspectives on Battery Swapping and Wireless Charging Technologies: A Multidisciplinary Review","authors":"Sarah M. Kandil;Akmal Abdelfatah;Maher A. Azzouz","doi":"10.1109/ACCESS.2025.3554336","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554336","url":null,"abstract":"The electrification of the transportation system is considered one of the most viable solutions to address the pressing need to shift towards sustainable development. However, one of the major challenges to the rapid adoption of Electric Vehicles (EVs) is the lack of the right charging infrastructure where and when it is needed. Motivated by the necessity for a multidisciplinary approach, this study addresses the complex operational and planning challenges involved in integrating transportation and electrical networks for effective EV charging. Thus, this research aims to survey the literature to identify the adopted technologies, and the application and allocation of the right mix of the technologies to better serve the seamless adoption of EVs. That problem is multifaced where transportation network requirements and the electrical grid are important to support the charging loads at the needed time. The literature survey adopts the PRISMA methodology, contributing to the existing literature by highlighting the following shortcomings: 1) the problem of allocating the charging technology is either purely viewed from a transportation or an electrical perspective, 2) there is a gap in adopting both networks’ requirements comprehensively, 3) the literature predominately focuses on a single technology either battery swapping or wireless charging, 4) introducing new technologies highlights the impact of each on both networks in terms of return of investment, traffic flows, and power grids operational conditions, 5) research direction is focused more towards operation and routing while service allocation is relatively new and has not yet been extensively explored, and 6) transportation network research focuses on a static representation of the transportation network and EV demand. This study’s main contribution, besides identifying critical gaps in the existing literature and possible future research directions, is the proposal of a novel framework that integrates multiple charging technologies. This framework is designed to optimize infrastructure deployment, enhancing the efficiency and economic viability of EV charging systems across both transportation and electrical networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52775-52806"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}