Expert Systems最新文献

{"title":"Optimal Task Allocation and Sequencing for Flight Test Based on a Memetic Algorithm With Lexicographic Optimisation","authors":"Bei Tian,&nbsp;Gang Xiao,&nbsp;Yu Shen,&nbsp;Xingwei Jiang","doi":"10.1111/exsy.13800","DOIUrl":"https://doi.org/10.1111/exsy.13800","url":null,"abstract":"<div>\u0000 \u0000 <p>The flight test plays an important role in the development of an aircraft. Currently, with the increasing complexity and higher validation requirements for aircraft, there is a crucial need to generate high-quality flight test task schedules in an efficient way. This paper proposes a flight test task scheduling problem (FTTSP), which involves assigning suitable aircraft and executing the flight test tasks in a given order. Generally, the flight test duration (FTD) is the primary optimisation objective for the flight test task schedule, as it has a direct impact on aircraft development costs and the time to enter the market. In this study, the FTTSP not only considers FTD but also takes into account task transfer consumption (TTC). A mixed-integer linear programming mathematical model is first formulated to describe the FTTSP characteristics with the optimisation of the FTD and the TTC in a sequential manner. Then, a memetic algorithm with lexicographic optimisation (MALO) is proposed, which can efficiently obtain a high-quality solution and ensure that the most critical metric can be fully optimised. In MALO, a two-vector encoding and a task logic relationship repair mechanism based on the binary tree are established. An idle time insertion decoding method is designed to improve the aircraft utilisation rate. In addition to the selection, crossover and mutation operators, a local search operator is designed to enhance the solution quality. Finally, the full-scale test instances are generated for the FTTSP to evaluate the algorithm's performance. The numerical results demonstrate the effectiveness and competitiveness of the MALO in generating a high-quality schedule for flight test tasks.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Security and Performance in Live VM Migration: A Machine Learning-Driven Framework With Selective Encryption for Enhanced Security and Performance in Cloud Computing Environments","authors":"Raseena M. Haris,&nbsp;Mahmoud Barhamgi,&nbsp;Ahmed Badawy,&nbsp;Armstrong Nhlabatsi,&nbsp;Khaled M. Khan","doi":"10.1111/exsy.13823","DOIUrl":"https://doi.org/10.1111/exsy.13823","url":null,"abstract":"<p>Live virtual machine (LVM) migration is pivotal in cloud computing for its ability to seamlessly transfer virtual machines (VMs) between physical hosts, optimise resource utilisation, and enable uninterrupted service. However, concerns persist regarding safeguarding sensitive data during migration, particularly in critical sectors like healthcare, banking and military operations. Existing migration methods often compromise between performance and data security, prompting the need for a balanced solution. To address this, we propose a novel framework merging machine learning with selective encryption to fortify the pre-copy live migration process. Our approach intelligently predicts optimal migration times while selectively encrypting sensitive data, ensuring confidentiality and integrity without compromising performance. Rigorous experiments demonstrate its effectiveness, showcasing an average 51.82% reduction in downtime and an average 72.73% decrease in total migration time across diverse workloads. This integration of selective encryption not only bolsters security but also optimises migration metrics, presenting a robust solution for uninterrupted service delivery in critical cloud computing domains.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113364","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 Novel Reciprocal Domain Adaptation Neural Network for Enhanced Diagnosis of Chronic Kidney Disease","authors":"Saeed Iqbal,&nbsp;Adnan N. Qureshi,&nbsp;Musaed Alhussein,&nbsp;Khursheed Aurangzeb,&nbsp;Muhammad Zubair,&nbsp;Amir Hussain","doi":"10.1111/exsy.13825","DOIUrl":"https://doi.org/10.1111/exsy.13825","url":null,"abstract":"<div>\u0000 \u0000 <p>Chronic kidney disease (CKD) is a major global health concern caused mostly by high blood pressure and glucose levels. Detecting CKD early is critical for reducing its negative consequences since it can lead to increased mortality rates. With CKD's rising incidence expected to make it the fifth biggest cause of death by 2040, rapid advances in diagnostic approaches are required. This study presents the Reciprocal Domain Adaptation Network (RDAN) as a potential approach to the various issues of CKD diagnosis. RDAN is a neural network model that will help to traverse the complexity of CKD diagnosis by smoothly combining diverse data sets. RDAN consists of two critical units at its foundation: Mutual Model Adaptation (MMA) and Domain Model Learning. The MMA unit uses a powerful Global and Local Pyramid Pooling technique to extract rich features from a variety of data domains. Meanwhile, the DML unit uses semi-supervised domain-independent features combined with MMA features to improve representation learning. RDAN includes a reciprocal regularizer to promote cross-domain knowledge transfer, maximising feature representation for accurate CKD identification. An analysis of RDAN's performance on a variety of real-world datasets showed remarkable results in terms of accuracy (96.94%), precision (98.81%), recall (98.73%), F1-Score (98.88%), and area under the curve (AUC—99.35%). These results highlight the unmatched expertise of RDAN in managing data bias, domain changes, and privacy issues related to CKD diagnosis. Beyond statistical measures, RDAN's implications promise revolutionary breakthroughs in early CKD identification and subsequent therapeutic therapies. RDAN stands out as a groundbreaking method for diagnosing CKD. It delivers exceptional accuracy and can be seamlessly applied in various clinical environments.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Algorithms to Address the Polarity and Stigma of Mental Health Disclosures on Instagram","authors":"Noemí Merayo,&nbsp;Alba Ayuso-Lanchares,&nbsp;Clara González-Sanguino","doi":"10.1111/exsy.13832","DOIUrl":"https://doi.org/10.1111/exsy.13832","url":null,"abstract":"<p>This research explores the social response to disclosures and conversations about mental health on social media, which is a pioneering and innovative approach. Unlike previous studies, which focused predominantly on psychopathological aspects, this study explores how communities react to conversations about mental health on Instagram, one of the favourite social media platforms among young people, breaking new ground not only in the Spanish context, but also on a global scale, filling a gap in international research. The study created a novel corpus by collecting and labelling comments on Instagram posts related to celebrity mental health disclosures, categorising them by polarity (positive, negative, neutral) and stigma. Additionally, the research implements machine learning algorithms to detect stigma and polarity in mental health disclosures on Instagram. While traditional techniques like Support Vector Machine (SVM) and RF (Random Forest) displayed decent performance with lower computational loads, advanced deep learning and BERT (Bidirectional Encoder Representation from Transformers) algorithms achieved outstanding results. In fact, BERT models achieve around 96% accuracy in polarity and stigma detection, while deep learning models achieve 80% for polarity and 87% for stigma, very high accuracy metrics. This research contributes significantly to understanding the impact of mental health discussions on social media, offering insights that can reduce stigma and raise awareness. Artificial intelligence can be used for more responsible use of social media and effective management of mental health problems in digital environments.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113886","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":"Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting","authors":"Álvaro Abad-Santjago,&nbsp;C. Peláez-Rodríguez,&nbsp;J. Pérez-Aracil,&nbsp;J. Sanz-Justo,&nbsp;C. Casanova-Mateo,&nbsp;S. Salcedo-Sanz","doi":"10.1111/exsy.13830","DOIUrl":"https://doi.org/10.1111/exsy.13830","url":null,"abstract":"<p>An accurate prediction of wind power generation is crucial for optimizing the integration of wind energy into the power grid, ensuring energy reliability. This research focuses on enhancing the accuracy of wind power generation forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized <i>WRF</i> forecast data alongside <i>ERA5</i> reanalysis data to estimate wind power generation for a wind farm located at Valladolid, Spain. The study evaluated the performance of ML models based on <i>WRF</i> and <i>ERA5</i> data individually, as well as a combined model using inputs from both datasets. The hybrid model combining WRF and ERA5 data with ML resulted in a 15% improvement in root mean square error (RMSE) and a 10% increase in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math> compared with standalone models, providing a more reliable 1-h forecast of wind power generation. Additionally, the availability of data over time was addressed: <i>WRF</i> provides the advantage of projecting data into the future, whereas <i>ERA5</i> offers retrospective data.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113060","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":"Automatic Code Summarization Using Abbreviation Expansion and Subword Segmentation","authors":"Yu-Guo Liang,&nbsp;Gui-Sheng Fan,&nbsp;Hui-Qun Yu,&nbsp;Ming-Chen Li,&nbsp;Zi-Jie Huang","doi":"10.1111/exsy.13835","DOIUrl":"https://doi.org/10.1111/exsy.13835","url":null,"abstract":"<div>\u0000 \u0000 <p>Automatic code summarization refers to generating concise natural language descriptions for code snippets. It is vital for improving the efficiency of program understanding among software developers and maintainers. Despite the impressive strides made by deep learning-based methods, limitations still exist in their ability to understand and model semantic information due to the unique nature of programming languages. We propose two methods to boost code summarization models: context-based abbreviation expansion and unigram language model-based subword segmentation. We use heuristics to expand abbreviations within identifiers, reducing semantic ambiguity and improving the language alignment of code summarization models. Furthermore, we leverage subword segmentation to tokenize code into finer subword sequences, providing more semantic information during training and inference, thereby enhancing program understanding. These methods are model-agnostic and can be readily integrated into existing automatic code summarization approaches. Experiments conducted on two widely used Java code summarization datasets demonstrated the effectiveness of our approach. Specifically, by fusing original and modified code representations into the Transformer model, our Semantic Enhanced Transformer for Code Summarizsation (SETCS) serves as a robust semantic-level baseline. By simply modifying the datasets, our methods achieved performance improvements of up to 7.3%, 10.0%, 6.7%, and 3.2% for representative code summarization models in terms of <i>BLEU-4</i>, <i>METEOR</i>, <i>ROUGE-L</i> and <i>SIDE</i>, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-LORA: A Vision-Language Approach for Synthetic Image Detection","authors":"Mamadou Keita,&nbsp;Wassim Hamidouche,&nbsp;Hessen Bougueffa Eutamene,&nbsp;Abdelmalik Taleb-Ahmed,&nbsp;David Camacho,&nbsp;Abdenour Hadid","doi":"10.1111/exsy.13829","DOIUrl":"https://doi.org/10.1111/exsy.13829","url":null,"abstract":"<div>\u0000 \u0000 <p>Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant interest, it has also raised concerns about the high challenge in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs). We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting-edge VLM, notably bootstrapping language image pre-training (BLIP)2. Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion-generated images from unknown diffusion-based generative models during training, showcasing robustness to noise, and demonstrating generalisation capabilities to GANs. The experiments show that Bi-LORA outperforms state of the art models in cross-generator tasks because it leverages multi-modal learning, open-world visual knowledge, and benefits from robust, high-level semantic understanding. By combining visual and textual knowledge, it can handle variations in the data distribution (such as those caused by different generators) and maintain strong performance across different domains. Its ability to transfer knowledge, robustly extract features and perform zero-shot learning also contributes to its generalisation capabilities, making it more adaptable to new generators. The experimental results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. The code and models associated with this research can be publicly accessed at https://github.com/Mamadou-Keita/VLM-DETECT.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Hyper-Tuned Multilayer Perceptron With Effective Stochastic Learning Strategies for Missing Values Imputation","authors":"Muhammad Hameed Siddiqi,&nbsp;Madallah Alruwaili,&nbsp;Yousef Alhwaiti,&nbsp;Saad Alanazi,&nbsp;Faheem Khan","doi":"10.1111/exsy.13828","DOIUrl":"https://doi.org/10.1111/exsy.13828","url":null,"abstract":"<div>\u0000 \u0000 <p>A vast amount of data in many different formats is produced and stored daily, offering machine learning a valuable resource to enhance its predictive capabilities. However, the pervasiveness of inaccuracies in real-world data presents a significant barrier that can seriously limit the effectiveness of learning algorithms. The ensemble models and hyper-tuned multi-layer perceptron (MLP) with need-based hidden neuron layers are effective frameworks for data imputation. Addressing the issue of missing data is a complex and demanding task, and much remains to be explored in developing effective and precise methods for predicting and imputing missing values across different datasets. The study offers important perspectives on using algorithms in machine learning to predict and impute gaps in data in recently updated datasets. The findings indicate that finely tuned MLP classifiers notably improve prediction accuracy and dependability compared to models with a static or reduced number of neurons. Furthermore, the study highlights the promising potential of ensemble models within the error-correcting output code (ECOC) framework as an effective approach for this task. It also suggests future research directions to refine further and strengthen machine learning-based imputation methods regarding precision and stability. ECOC framework includes all kinds of MLP classifiers and regressors such as binary classifiers, multi-class classifiers, or regression models. MLP models predict complex relationships in modern datasets. Hugging Face, COSMIC, SKlearn, and Kaggle have relevant and updated datasets. The weighted average recognition (96%) shows that the proposed MLP-based stochastic learning strategies achieved better results.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Transfer Learning Domain Adaptation Model With Federated Learning to Revolutionise Healthcare","authors":"Priyanka Verma,&nbsp;Nitesh Bharot,&nbsp;John G. Breslin,&nbsp;Donna O'Shea,&nbsp;Anand Kumar Mishra,&nbsp;Ankit Vidyarthi,&nbsp;Deepak Gupta","doi":"10.1111/exsy.13827","DOIUrl":"https://doi.org/10.1111/exsy.13827","url":null,"abstract":"<div>\u0000 \u0000 <p>The application of artificial intelligence (AI) in healthcare has been witnessing an increasing interest. Particularly, federated learning (FL) has become favourable due to its potential for enhancing model quality whilst maintaining data privacy and security. However, the effectiveness of present FL methodologies could underperform under non-IID conditions, characterised by divergent data distributions across clients. The globally constructed FL model may suffer potent issues by allowing the least-performing models to equal participation. Thus, we propose a new accuracy-based FL approach (FedAcc) which only takes into account the clients' validation accuracy to consider their participation during global aggregation, also called Smart Healthcare Amplified (SHA). However, with limited supervised data it is challenging to increase the model performance thus concept of transfer learning (TL) is used. TL enables the global model to integrate knowledge from precomputed systems, resulting in an efficient model. However, the complexity of the global system is amplified by these TL models, leading to challenges related to vanishing gradients, particularly when dealing with a substantial number of layers. To mitigate this, we present a Transfer Learning Domain Adaptation Model (TLDAM). TLDAM employs a two-layered sequentially trained TL model, which contains approximately 50% fewer layers compared to traditional TL models. TLDAM is trained on multiple datasets such as MNIST and CIFAR10, to enhance its knowledge and make it domain-adaptive. Moreover, experimental results conducted on the UCI-HAR dataset reveal the supremacy of our proposed framework with an accuracy of 94.2990%, F-score of 94.2820%, precision of 94.3058%, and recall of 94.2993% over traditional FL techniques and state-of-the-art techniques.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point Class-Adaptive Transformer (PCaT): A Novel Approach for Efficient Point Cloud Classification and Segmentation","authors":"Husnain Mushtaq,&nbsp;Xiaoheng Deng,&nbsp;Ping Jinag,&nbsp;Shaohua Wan,&nbsp;Rawal Javed,&nbsp;Irshad Ullah","doi":"10.1111/exsy.13831","DOIUrl":"https://doi.org/10.1111/exsy.13831","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent 3D point cloud classification has predominantly focused on local spatial attention, neglecting distant contextual relationships due to the inherent sparsity of LiDAR-generated data over longer distances. Existing 3D object detection methods prioritize local features, hindering the extraction of semantic information. Despite attempts with transformers, methods often reduce computations through local spatial attention, neglecting content class and scarcely establishing connections among distant global points. Our proposed point class-adaptive transformer (PCaT) addresses these limitations by establishing long-range feature dependencies while significantly reducing computations. PCaT includes three key modules: the class-adaptive transformer (CaT), which utilizes local self-attention and global self-attention based on class similarity to facilitate an efficient trade-off between capturing extended-global dependencies and managing computational challenges; nested binary clustering (NbC), which dynamically partitions queries into multiple clusters based on content features in each Transformer block; and the AfA, which aggregates high-dimensional features using max-pooling alongside a residual MLP component and low-dimensional features using average pooling and a CaT block. Additionally, PCaT incorporates point cloud segmentation via local–global feature aggregation (PcSeg) to facilitate effective point cloud segmentation. Extensive experimentation on the ModelNet40, ScanObjectNN, and S3DIS datasets demonstrates the superior performance and reasonable stability of PCaT compared with existing methods. PCaT achieves 94.2% overall accuracy (OA) and mIoU scores of 89.2% and 86.2% for the ScanObjectNN and S3DIS datasets, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}