IET Software最新文献

{"title":"Developing a User-Centric Quality Model for Gaming as a Service (GaaS): Enhancing User Satisfaction Through Key Quality Factors","authors":"Ameen Shaheen,&nbsp;Ahmad Alkhatib,&nbsp;Mahmoud Farfoura,&nbsp;Rand Albustanji","doi":"10.1049/sfw2/6662968","DOIUrl":"https://doi.org/10.1049/sfw2/6662968","url":null,"abstract":"<p>This study presents a comprehensive and user-centric quality model for gaming as a service (GaaS), grounded in a multistage survey methodology involving pretest, postgame, and posttest evaluations. The research identifies and empirically validates key quality attributes that influence user satisfaction, including controllability, responsiveness, accessibility, cost transparency, security, and social features. Data from 62 cloud gamers, analyzed through ANOVA and regression techniques, reveal that users prioritize high-resolution graphics, diverse game libraries, intuitive controls (ICs), and seamless audio–visual performance. The findings highlight a strong alignment between user expectations and the proposed quality model. Practical recommendations are offered for GaaS providers, focusing on improved user onboarding, transparent system requirements, enhanced social features, and robust security protocols. The study also discusses emerging technologies such as AI-driven personalization and adaptive streaming, which hold promise for enhancing quality of experience (QoE) in dynamic network conditions. Future research should include larger and more diverse user samples, longitudinal analysis, and cross-cultural perspectives to further validate and refine the model.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6662968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144927300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Elevating Cloud Security With Advanced Trust Evaluation and Optimization of Hybrid Fireberg Technique","authors":"Himani Saini,&nbsp;Gopal Singh,&nbsp;Amrinder Kaur,&nbsp;Sunil Saini,&nbsp;Niyaz Ahmad Wani,&nbsp;Vikram Chopra,&nbsp;Rashiq Rafiq Marie,&nbsp;Tehseen Mazhar,&nbsp;Mamoon M. Saeed","doi":"10.1049/sfw2/3296533","DOIUrl":"https://doi.org/10.1049/sfw2/3296533","url":null,"abstract":"<p>The rapid expansion of the cloud service industry has raised the critical challenge of ensuring efficient job allocation and trust within a backdrop of heightened privacy concerns. Existing models often struggle to achieve an optimal balance between these factors, particularly in dynamic cloud environments. This research introduces a comprehensive approach that optimizes trust-based job allocation in cloud services while addressing privacy issues. Our proposed hybrid model integrates k-anonymity techniques for privacy preservation, coupled with a firefly-Levenberg (Fireberg) optimization to bolster trust generation. It also employs the time-aware modified best fit decreasing (T-MBFD) allocation policy to make resource allocation time-sensitive. This strategic allocation approach enhances cloud computing system performance and scalability. Simulations using a dataset of 95,000 records demonstrate that our model achieves an impressive 96% accuracy, surpassing existing literature by 5%–14%. The results highlight the model’s ability to provide robust privacy protection while ensuring efficient resource allocation. The proposed hybrid model promises cloud service users high-quality, secure, and efficient job allocations, thereby improving customer satisfaction and trust. This research makes significant contributions to fortifying the reliability and appeal of cloud services in an evolving digital landscape.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/3296533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Real Time Cardiomyopathy Detection Tool Using Ml Ensemble Models","authors":"Salvador de Haro,&nbsp;Esteban Becerra,&nbsp;Pilar González-Férez,&nbsp;José M. García,&nbsp;Gregorio Bernabé","doi":"10.1049/sfw2/4518420","DOIUrl":"https://doi.org/10.1049/sfw2/4518420","url":null,"abstract":"<div>\u0000 <p>Left Ventricular noncompaction (LVNC) is a recently classified form of cardiomyopathy. Although various methods have been proposed for accurately quantifying trabeculae in the left ventricle (LV), consensus on the optimal approach remains elusive. Previous research introduced DL-LVTQ, a deep learning solution for trabecular quantification based on a UNet 2D convolutional neural network (CNN) architecture and a graphical user interface (GUI) to streamline its use in clinical workflows. Building on this foundation, this work presents LVNC detector, an enhanced application designed to support cardiologists in the automated diagnosis of LVNC. The application integrates two segmentation models: DL-LVTQ and ViTUNet, the latter inspired by modern hybrid architectures combining convolutional neural networks (CNNs) and transformer-based designs. These models, implemented within an ensemble framework, leverage advancements in deep learning to improve the accuracy and robustness of magnetic resonance imaging (MRI) segmentation. Key innovations include multithreading to optimize model loading times and ensemble methods to enhance segmentation consistency across MRI slices. Additionally, the platform-independent design ensures compatibility with Windows and Linux, eliminating complex setup requirements. The LVNC detector delivers an efficient and user-friendly solution for LVNC diagnosis. It enables real-time performance and allows cardiologists to select and compare segmentation models for improved diagnostic outcomes. This work demonstrates how state-of-the-art machine learning techniques can seamlessly integrate into clinical practice to reduce human error and expedite diagnostic processes.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4518420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"DAA-UNet: A Dense Connectivity and Atrous Spatial Pyramid Pooling Attention UNet Model for Retinal Optical Coherence Tomography Fluid Segmentation","authors":"Tianhan Hu,&nbsp;Jiao Ding,&nbsp;Yuting Liu,&nbsp;Yantao Zhang,&nbsp;Li Yang","doi":"10.1049/sfw2/6006074","DOIUrl":"https://doi.org/10.1049/sfw2/6006074","url":null,"abstract":"<div>\u0000 <p>Retinal optical coherence tomography (OCT) fluid segmentation is a vital tool for diagnosing and treating various ophthalmic diseases. Based on clinical manifestations, retinal fluid accumulation is classified into three categories: intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). PED is primarily associated with diabetic macular edema (DME). In contrast, IRF and SRF play critical roles in diagnosing age-related macular degeneration (AMD) and retinal vein occlusion (RVO). To address challenges posed by variations in OCT imaging devices, as well as the varying sizes, irregular shapes, and blurred boundaries of fluid accumulation areas, this study proposes DAA-UNet, an enhanced UNet architecture. The proposed model incorporates dense connectivity, Atrous Spatial Pyramid Pooling (ASPP), and attention gate (AG) in the paths of UNet. Dense connectivity expands the model’s depth, whereas ASPP facilitates the extraction of multiscale image features. The AG emphasize critical spatial location information, improving the model’s ability to distinguish different fluid accumulation types. Experimental results on the MICCAI 2017 RETOUCH challenge dataset showed that DAA-UNet demonstrates superior performance, with a mean Dice Similarity Coefficient (<i>mDSC</i>) of 90.2%, 91.6%, and 90.5% on cirrus, spectralis, and topcon devices, respectively. These results outperform existing models, including UNet, SFU, Attention-UNet, Deeplabv3+, nnUNet RASPP, and MsTGANet.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6006074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Commit Classification Framework Incorporated With Prompt Tuning and External Knowledge","authors":"Jiajun Tong,&nbsp;Xiaobin Rui","doi":"10.1049/sfw2/5566134","DOIUrl":"https://doi.org/10.1049/sfw2/5566134","url":null,"abstract":"<div>\u0000 <p>Commit classification is an important task in software maintenance, since it helps software developers classify code changes into different types according to their nature and purpose. This allows them to better understand how their development efforts are progressing, identify areas where they need improvement, and make informed decisions about when and how to release new versions of their software. However, existing methods are all discriminative models, usually with complex architectures that require additional output layers to produce class label probabilities, making them task-specific and unable to learn features across different tasks. Moreover, they require a large amount of labeled data for fine tuning, and it is difficult to learn effective classification boundaries in the case of limited labeled data. To solve the above problems, we propose a generative framework that incorporates prompt tuning for commit classification with external knowledge (IPCK), which simplifies the model structure and learns features across different tasks, only based on the commit message information as the input. First, we proposed a generative framework based on T5 (text-to-text transfer transformer). This encoder–decoder construction method unifies different commit classification tasks into a text-to-text problem, simplifying the model’s structure by not requiring an extra output layer. Second, instead of fine tuning, we design a prompt tuning solution that can be adopted in few-shot scenarios with only limited samples. Furthermore, we incorporate external knowledge via an external knowledge graph to map the probabilities of words into the final labels in the speech machine step to improve performance in few-shot scenarios. Extensive experiments on two open available datasets demonstrate that our framework can solve the commit classification problem simply but effectively for both single-label binary classification and single-label multiclass classification purposes with 90% and 83% accuracy. Further, in the few-shot scenarios, our method improves the adaptability of the model without requiring a large number of training samples for fine tuning.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5566134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Systematic Literature Review on Graphical User Interface Testing Through Software Patterns","authors":"Ambreen Kousar,&nbsp;Saif Ur Rehman Khan,&nbsp;Atif Mashkoor,&nbsp;Javed Iqbal","doi":"10.1049/sfw2/9140693","DOIUrl":"https://doi.org/10.1049/sfw2/9140693","url":null,"abstract":"<div>\u0000 <p><b>Context:</b> Graphical user interface (GUI) testing of mobile applications (apps) is significant from a user perspective to ensure that the apps are visually appealing and user-friendly. Pattern-based GUI testing (PBGT) is an innovative model-based testing (MBT) approach designed to enhance user satisfaction and reusability while minimizing the effort required to model and test UIs of mobile apps. In the literature, several primary studies have been conducted in the domain of PBGT.</p>\u0000 <p><b>Problem:</b> The current state-of-the-art lacks comprehensive secondary studies within the PBGT domain. To our knowledge, this area has insufficient focus on in-depth research. Consequently, numerous challenges and limitations persist in the existing literature.</p>\u0000 <p><b>Objective:</b> This study aims to fill the gaps mentioned above in the existing body of knowledge. We highlight popular research topics and analyze their relationships. We explore current state-of-the-art approaches and techniques, a taxonomy of tools and modeling languages, a list of reported UI test patterns (UITPs), and a taxonomy of writing UITPs. We also highlight practical challenges, limitations, and gaps in the targeted research area. Furthermore, the current study intends to highlight future research directions in this domain.</p>\u0000 <p><b>Method:</b> We conducted a systematic literature review (SLR) on PBGT in the context of Android and web apps. A hybrid methodology that combines the Kitchenham and PRISMA guidelines is adopted to achieve the targeted research objectives (ROs). We perform a keyword-based search on well-known databases and select 30 (out of 557) studies.</p>\u0000 <p><b>Results:</b> The current study identifies 11 tools used in PBGT and devises a taxonomy to categorize these tools. A taxonomy for writing UITPs has also been developed. In addition, we outline the limitations of the targeted research domain and future directions.</p>\u0000 <p><b>Conclusion:</b> This study benefits the community and readers by better understanding the targeted research area. A comprehensive knowledge of existing tools, techniques, and methodologies is helpful for practitioners. Moreover, the identified limitations, gaps, emerging trends, and future research directions will benefit researchers who intend to work further in future research.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9140693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Automated Hybrid Methodology for Software Architecture Style Selection Using Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process","authors":"Muna Alrazgan,&nbsp;Ahmed Ghoneim,&nbsp;Luluah Albesher,&nbsp;Razan Aldossari,&nbsp;Shahad Alotaibi,&nbsp;Lama Alsaykhan,&nbsp;Norah Alshahrani,&nbsp;Maha Alshammari","doi":"10.1049/sfw2/9943825","DOIUrl":"https://doi.org/10.1049/sfw2/9943825","url":null,"abstract":"<div>\u0000 <p>In software engineering, selecting the appropriate architectural style for software systems is risky and sensitive. The selection process is a multicriteria decision-making (MCDM) problem. Consequently, selecting a suitable architecture is a key challenge in software development. This study presents an automated hybrid methodology based on the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) to evaluate and suggest multiple architectural styles based on quality attributes (QAs) alone rather than relying on expert opinions. A Tera-PROMISE dataset is presented to illustrate the proposed methodology and then compare the result of the methodology with expert judgments. Moreover, to support the proposed methodology, a case study is carried out to compare the proposed method to previous studies.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/9943825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Blockchain Consensus Scheme Based on the Proof of Distributed Deep Learning Work","authors":"Hui Zhi,&nbsp;HongCheng Wu,&nbsp;Yu Huang,&nbsp;ChangLin Tian,&nbsp;SuZhen Wang","doi":"10.1049/sfw2/3378383","DOIUrl":"https://doi.org/10.1049/sfw2/3378383","url":null,"abstract":"<div>\u0000 <p>With the development of artificial intelligence and blockchain technology, the training of deep learning models needs large computing resources. Meanwhile, the Proof of Work (PoW) consensus mechanism in blockchain systems often leads to the wastage of computing resources. This article combines distributed deep learning (DDL) with blockchain technology and proposes a blockchain consensus scheme based on the proof of distributed deep learning work (BCDDL) to reduce the waste of computing resources in blockchain. BCDDL treats DDL training as a mining task and allocates different training data to different nodes based on their computing power to improve the utilization rate of computing resources. In order to balance the demand and supply of computing resources and incentivize nodes to participate in training tasks and consensus, a dynamic incentive mechanism based on task size and computing resources (DIM-TSCR) is proposed. In addition, in order to reduce the impact of malicious nodes on the accuracy of the global model, a model aggregation algorithm based on training data size and model accuracy (MAA-TM) is designed. Experiments demonstrate that BCDDL can significantly increase the utilization rate of computing resources and diminish the impact of malicious nodes on the accuracy of the global model.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/3378383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Code Parameter Summarization Based on Transformer and Fusion Strategy","authors":"Fanlong Zhang,&nbsp;Jiancheng Fan,&nbsp;Weiqi Li,&nbsp;Siau-cheng Khoo","doi":"10.1049/sfw2/3706673","DOIUrl":"https://doi.org/10.1049/sfw2/3706673","url":null,"abstract":"<div>\u0000 <p><b>Context:</b> As more time has been spent on code comprehension activities during software development, automatic code summarization has received much attention in software engineering research, with the goal of enhancing software comprehensibility. In the meantime, it is prevalently known that a good knowledge about the declaration and the use of method parameters can effectively enhance the understanding of the associated methods. A traditional approach used in software development is to declare the types of method parameters.</p>\u0000 <p><b>Objective:</b> In this work, we advocate parameter-level code summarization and propose a novel approach to automatically generate parameter summaries of a given method. Parameter summarization is considerably challenging, as neither do we know the kind of information of the parameters that can be employed for summarization nor do we know the methods for retrieving such information.</p>\u0000 <p><b>Method:</b> We present paramTrans, which is a novel approach for parameter summarization. paramTrans characterizes the semantic features from parameter-related information based on transformer; it also explores three fusion strategies for absorbing the method-level information to enhance the performance. Moreover, to retrieve parameter-related information, a parameter slicing algorithm (named paramSlice) is proposed, which slices the parameter-related node from the abstract syntax tree (AST) at the statement level.</p>\u0000 <p><b>Results:</b> We conducted experiments to verify the effectiveness of our approach. Experimental results show that our approach possesses an effective ability in summarizing parameters; such ability can be further enhanced by understanding the available summaries about individual methods, through the introduction of three fusion strategies.</p>\u0000 <p><b>Conclusion:</b> We recommend developers employ our approach as well as the fusion strategies to produce parameter summaries to enhance the comprehensibility of code.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/3706673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Software Defect Prediction Method Based on Clustering Ensemble Learning","authors":"Hongwei Tao,&nbsp;Qiaoling Cao,&nbsp;Haoran Chen,&nbsp;Yanting Li,&nbsp;Xiaoxu Niu,&nbsp;Tao Wang,&nbsp;Zhenhao Geng,&nbsp;Songtao Shang","doi":"10.1049/2024/6294422","DOIUrl":"https://doi.org/10.1049/2024/6294422","url":null,"abstract":"<div>\u0000 <p>The technique of software defect prediction aims to assess and predict potential defects in software projects and has made significant progress in recent years within software development. In previous studies, this technique largely relied on supervised learning methods, requiring a substantial amount of labeled historical defect data to train the models. However, obtaining these labeled data often demands significant time and resources. In contrast, software defect prediction based on unsupervised learning does not depend on known labeled data, eliminating the need for large-scale data labeling, thereby saving considerable time and resources while providing a more flexible solution for ensuring software quality. This paper conducts software defect prediction using unsupervised learning methods on data from 16 projects across two public datasets (PROMISE and NASA). During the feature selection step, a chi-squared sparse feature selection method is proposed. This feature selection strategy combines chi-squared tests with sparse principal component analysis (SPCA). Specifically, the chi-squared test is first used to filter out the most statistically significant features, and then the SPCA is applied to reduce the dimensionality of these significant features. In the clustering step, the dot product matrix and Pearson correlation coefficient (PCC) matrix are used to construct weighted adjacency matrices, and a clustering overlap method is proposed. This method integrates spectral clustering, Newman clustering, fluid clustering, and Clauset–Newman–Moore (CNM) clustering through ensemble learning. Experimental results indicate that, in the absence of labeled data, using the chi-squared sparse method for feature selection demonstrates superior performance, and the proposed clustering overlap method outperforms or is comparable to the effectiveness of the four baseline clustering methods.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6294422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}