Future Generation Computer Systems-The International Journal of Escience最新文献_第6页

Hybrid deep learning-based cyberthreat detection and IoMT data authentication model in smart healthcare 智能医疗中基于深度学习的混合网络威胁检测和IoMT数据认证模型

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-13 DOI: 10.1016/j.future.2025.107711

Manish Kumar , Sushil Kumar Singh , Sunggon Kim

{"title":"Hybrid deep learning-based cyberthreat detection and IoMT data authentication model in smart healthcare","authors":"Manish Kumar , Sushil Kumar Singh , Sunggon Kim","doi":"10.1016/j.future.2025.107711","DOIUrl":"10.1016/j.future.2025.107711","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT)-based medical devices and sensors play a significant role in healthcare applications, enabling on-site and remote monitoring of vital parameters in patients and alerting medical personnel in critical situations. However, these networks are vulnerable to cybersecurity threats, resulting in issues such as patient safety concerns, data breaches, ransom demands, and device tampering. Detecting cyberthreats efficiently is challenging because IoMT generates large temporal data. Furthermore, cyberattacks typically involve imbalanced classification, where classes are not equally represented. The absence of data authentication can lead to severe consequences, including threats to patient privacy and financial ramifications, ultimately eroding trust in the healthcare system.</div><div>This paper proposes an improved deep learning-based model for cyberthreat detection and IoMT data authentication in smart healthcare. First, it introduces an embedded Ensemble Learning (EL) technique to select important features of IoMT, which trims unnecessary features and reduces the possibility of overfitting by classifiers. These scaled inputs are fed into the proposed One-Dimensional Convolution Long Short-Term Memory (1D-CLSTM) Neural Network to classify cyberthreats. The random undersampling boosting technique has been applied to address issues like imbalance classification. The PoAh consensus algorithm is applied in the fog layer for data authentication. The proposed model is evaluated based on various performance metrics and compared to state-of-the-art techniques such as 1D-CNN, LSTM, and GRU. Evaluation results show that the proposed 1D-CLSTM achieves 100% accuracy with the WUSTL-EHMS-2020 and 98.55% test accuracy with the ECU-IoHT datasets. The PoAh-based authentication takes 3.47 s at average 9th iteration.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107711"},"PeriodicalIF":6.2,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990517","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

Multilayer multivariate forecasting network for precise resource utilization prediction in edge data centers 面向边缘数据中心资源利用精确预测的多层多元预测网络

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-10 DOI: 10.1016/j.future.2024.107692

Shivani Tripathi , Priyadarshni , Rajiv Misra , T.N. Singh

{"title":"Multilayer multivariate forecasting network for precise resource utilization prediction in edge data centers","authors":"Shivani Tripathi , Priyadarshni , Rajiv Misra , T.N. Singh","doi":"10.1016/j.future.2024.107692","DOIUrl":"10.1016/j.future.2024.107692","url":null,"abstract":"<div><div>Efficient resource management and accurate prediction of cloud workloads are vital in modern cloud computing environments, where dynamic and volatile workloads present significant challenges. Traditional forecasting models often fail to fully capture the intricate temporal dependencies and non-linear patterns inherent in cloud data, leading to inefficiencies in resource utilization. To overcome these limitations, this research introduces the MultiLayer Multivariate Resource Predictor (MMRP), a novel deep learning architecture that seamlessly integrates a Multi-Head Attention Transformer model with Convolutional Neural Networks and Bidirectional Long Short-Term Memory units. The proposed model is designed to excel in capturing long-range dependencies and complex patterns, thereby significantly enhancing the accuracy of workload predictions. Extensive, rigorous experimentation using real-world Alibaba and Google cluster traces reveals that the proposed model consistently outperforms existing state-of-the-art models and related cloud resource utilization prediction in both univariate and multivariate time series forecasting tasks. The model demonstrates a remarkable improvement in prediction performance, with an average R squared increase of 5.76% and a Mean Absolute Percentage Error reduction of 84.9% compared to the best-performing baseline models. Furthermore, our model achieves a significant reduction in Root Mean Square Error by approximately 35.34% and decreases Mean Absolute Error by about 39.49% on average. Its scalability and adaptability across various cloud environments underscore the proposed model’s potential to optimize resource allocation, paving the way for more efficient and reliable cloud-based systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107692"},"PeriodicalIF":6.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990518","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

FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-10 DOI: 10.1016/j.future.2025.107706

Aiting Yao , Shantanu Pal , Gang Li , Xuejun Li , Zheng Zhang , Frank Jiang , Chengzu Dong , Jia Xu , Xiao Liu

{"title":"FedShufde: A privacy preserving framework of federated learning for edge-based smart UAV delivery system","authors":"Aiting Yao , Shantanu Pal , Gang Li , Xuejun Li , Zheng Zhang , Frank Jiang , Chengzu Dong , Jia Xu , Xiao Liu","doi":"10.1016/j.future.2025.107706","DOIUrl":"10.1016/j.future.2025.107706","url":null,"abstract":"<div><div>In recent years, there has been a rapid increase in the integration of Internet of Things (IoT) systems into edge computing. This integration offers several advantages over traditional cloud computing, including lower latency and reduced network traffic. In addition, edge computing facilitates the protection of users’ sensitive data by processing it at the edge before transmitting it to the cloud using techniques such as Federated Learning (FL) and Differential Privacy (DP). However, these techniques have limitations, such as the risk of user information being obtained by attackers through the uploaded weights/model parameters in FL and the randomness of DP, which limits data availability. To address these issues, this paper proposes a framework called FedShufde (<strong><u>Fed</u></strong>erated Learning with a <strong><u>Shuf</u></strong>fle Model and <strong><u>D</u></strong>ifferential Privacy in <strong><u>E</u></strong>dge Computing Environments) to protect user privacy in edge computing-based IoT systems, using an Unmanned Aerial Vehicle (UAV) delivery system as an example. FedShufde uses local differential privacy and the shuffle model to prevent attackers from inferring user privacy from information such as UAV’s location, flight conditions, or delivery address. In addition, the network connection between the UAV and the edge server cannot be obtained by the cloud aggregator, and the shuffle model reduces the communication cost between the edge server and the cloud aggregator. Our experiments on a real-world edge-based smart UAV delivery system using public datasets demonstrate the significant advantages of our proposed framework over baseline strategies.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107706"},"PeriodicalIF":6.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

UD-LDP: A Technique for optimally catalyzing user driven Local Differential Privacy UD-LDP：一种最佳催化用户驱动的本地差分隐私的技术

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-09 DOI: 10.1016/j.future.2025.107712

Gnanakumar Thedchanamoorthy , Michael Bewong , Meisam Mohammady , Tanveer Zia , Md Zahidul Islam

引用次数: 0

Task replication based energy management using random-weighted privacy-preserving distributed algorithm for real-time embedded system

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-06 DOI: 10.1016/j.future.2025.107708

Dr． A. Velliangiri , Dr. Jayaraj Velusamy , Dr. Maheswari M , Dr. R．Leena Rose

{"title":"Task replication based energy management using random-weighted privacy-preserving distributed algorithm for real-time embedded system","authors":"Dr． A. Velliangiri , Dr. Jayaraj Velusamy , Dr. Maheswari M , Dr. R．Leena Rose","doi":"10.1016/j.future.2025.107708","DOIUrl":"10.1016/j.future.2025.107708","url":null,"abstract":"<div><div>Efficient energy management in real-time embedded systems is increasingly challenging due to the growing complexity of distributed tasks and the need for robust privacy preservation. Conventional task mapping and repartitioning techniques have focused on increasing the system reliability, efficiency, and lifespan, but typically incurred a high peak power generation because of Thermal Design Power (TDP) limitations which confines the scalability and applicability. To overcome these problems, the Task Replication-based Energy Management using Random-weighted Privacy-preserving Distributed Algorithm (TR-EM-R-RWPPDA-RTES) is proposed as a new scheme for real-time embedded systems. This architecture integrates Hotspot-Aware Task Mapping (HATM) to optimally load tasks across cores, Dynamic Heterogeneous Earliest Finish Time (DHEFT) scheduling to improve execution timing, and a Reliability-based Random-Weighted Privacy-Preserving Distributed Algorithm (R-RWPPDA) to optimize power consumption. Using these elements, the proposed approach reduces both system energy consumption and system trustworthiness. Comprehensive simulations based on the MiBench benchmark suite, as well as gem5 and McPAT simulators on ARM multicore processors (4, 8, and 16 cores), are also shown to validate the robustness of the proposed method. TR-EM-R-RWPPDA-RTES yields 23.73 %, 36.33 %, and37.84 % peak power consumption reduction with respect to the state-of-the-art solutions, thus providing a robust solution for energy-efficient, robust and reliable real-time embedded systems.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"167 ","pages":"Article 107708"},"PeriodicalIF":6.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143321949","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

Remote sensing revolutionizing agriculture: Toward a new frontier 遥感技术革新农业：迈向新前沿

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-06 DOI: 10.1016/j.future.2024.107691

Xiaoding Wang , Haitao Zeng , Xu Yang , Jiwu Shu , Qibin Wu , Youxiong Que , Xuechao Yang , Xun Yi , Ibrahim Khalil , Albert Y. Zomaya

{"title":"Remote sensing revolutionizing agriculture: Toward a new frontier","authors":"Xiaoding Wang , Haitao Zeng , Xu Yang , Jiwu Shu , Qibin Wu , Youxiong Que , Xuechao Yang , Xun Yi , Ibrahim Khalil , Albert Y. Zomaya","doi":"10.1016/j.future.2024.107691","DOIUrl":"10.1016/j.future.2024.107691","url":null,"abstract":"<div><div>Remote sensing-empowered agriculture is a significant approach that utilizes remote sensing (RS) to improve agricultural production and crop management. In the agricultural sector, RS allows the retrieval of extensive data related to land, vegetation, and crops, providing crucial information for farmers and decision-makers to enhance precision and efficiency in crop cultivation and management. The combination of RS and artificial intelligence (AI) holds tremendous potential for agricultural production. With the integration of AI, remote sensing-empowered agriculture has expanded, and its impact has become increasingly prominent. It is expected to have far-reaching effects on global agriculture, fostering the more efficient, sustainable, and intelligent development. In the agricultural field, this review presents a concise exploration of the principles and usage of RS. It also examines the role of AI in facilitating agricultural RS, summarizes the application of the combination of RS and AI in the field of agriculture, and discusses its effects. Opportunities and challenges arising from the integration of AI and AI in agriculture are also discussed. This review aims to accelerate the entry into a new era for agriculture empowered by RS.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107691"},"PeriodicalIF":6.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968055","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

RADiCe: A Risk Analysis Framework for Data Centers RADiCe：数据中心的风险分析框架

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-04 DOI: 10.1016/j.future.2024.107702

Fabian Mastenbroek , Tiziano De Matteis , Vincent van Beek , Alexandru Iosup

{"title":"RADiCe: A Risk Analysis Framework for Data Centers","authors":"Fabian Mastenbroek , Tiziano De Matteis , Vincent van Beek , Alexandru Iosup","doi":"10.1016/j.future.2024.107702","DOIUrl":"10.1016/j.future.2024.107702","url":null,"abstract":"<div><div>Datacenter service providers face engineering and operational challenges involving numerous risk aspects. Bad decisions can result in financial penalties, competitive disadvantage, and unsustainable environmental impact. Risk management is an integral aspect of the design and operation of modern datacenters, but frameworks that allow users to consider various risk trade-offs conveniently are missing. We propose <span>RADiCe</span>, an open-source framework that enables data-driven analysis of IT-related operational risks in sustainable datacenters. <span>RADiCe</span> uses monitoring and environmental data and, via discrete event simulation, assists datacenter experts through systematic evaluation of risk scenarios, visualization, and optimization of risks. Our analyses highlight the increasing risk datacenter operators face due to price surges in electricity and sustainability and demonstrate how <span>RADiCe</span> can evaluate and control such risks by optimizing the topology and operational settings of the datacenter. Eventually, <span>RADiCe</span> can evaluate risk scenarios by a factor 70x–330x faster than others, opening possibilities for interactive risk exploration.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107702"},"PeriodicalIF":6.2,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Forward-Secure multi-user and verifiable dynamic searchable encryption scheme within a zero-trust environment 零信任环境下的前向安全多用户可验证动态搜索加密方案

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-02 DOI: 10.1016/j.future.2024.107701

Zhihao Xu , Chengliang Tian , Guoyan Zhang , Weizhong Tian , Lidong Han

{"title":"Forward-Secure multi-user and verifiable dynamic searchable encryption scheme within a zero-trust environment","authors":"Zhihao Xu , Chengliang Tian , Guoyan Zhang , Weizhong Tian , Lidong Han","doi":"10.1016/j.future.2024.107701","DOIUrl":"10.1016/j.future.2024.107701","url":null,"abstract":"<div><div>Privacy-preserving searchable encryption can allow clients to encrypt the data for secure cloud storage, enabling subsequent data retrieval while preserving the privacy of data. In this paper, we initialize the study of constructing a secure dynamic searchable symmetric encryption (DSSE) scheme in a zero-trust environment characterized by the threat model of <em>honest-but-curious data owner (DO)</em> + <em>honest-but-curious data user (DU)</em> + <em>fully malicious cloud server (CS)</em>. To tackle these challenges, we introduce a multi-user DSSE scheme that emphasizes verifiability and privacy while integrating forward security. Our contributions include: Employing the oblivious pseudo-random function (OPRF) protocol for secure <em>DO</em>-<em>DU</em> interactions, ensuring the privacy of <em>DO</em>’s keys and <em>DU</em>’s queried keywords from each other, And maintaining the secure separation of data ownership and usage, Utilizing a multiset hash function-based state chain to achieve forward privacy and support <em>DO</em> updates of encrypted cloud data with verifiable query results Proposing a novel hash-based file encryption and authentication approach to protect file privacy and verify query results. additionally, We provide a comprehensive security analysis and experimental evaluation demonstrating the efficacy and efficiency of our approach. these advancements enhance DSSE schemes under a zero-trust environment, Addressing critical challenges of privacy, Verifiability, And operational efficiency</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107701"},"PeriodicalIF":6.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968057","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

Secure blockchain-based reputation system for IIoT-enabled retail industry with resistance to sybil attack

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-02 DOI: 10.1016/j.future.2024.107705

Wenjia Zhao , Xu Yang , Saiyu Qi , Junzhe Wei , Xinpei Dong , Xu Yang , Yong Qi

{"title":"Secure blockchain-based reputation system for IIoT-enabled retail industry with resistance to sybil attack","authors":"Wenjia Zhao , Xu Yang , Saiyu Qi , Junzhe Wei , Xinpei Dong , Xu Yang , Yong Qi","doi":"10.1016/j.future.2024.107705","DOIUrl":"10.1016/j.future.2024.107705","url":null,"abstract":"<div><div>Leveraging the recent surge in the electronic retail industry, retailer reputation has emerged with increasing significance in shaping consumer purchasing decisions. Despite this, the existing reputation platforms remain largely centralized, thereby enabling retailers to exert total control over reputation services, a reality that compromises the authentic portrayal of retailers. In response, we introduce a secure blockchain-based reputation system, named BlockRep, designed explicitly for the Industrial Internet of Things (IIoT) enabled retail industry. By eliminating dependency on trust inherently foundation in established E-retail platforms, BlockRep effectively resists sybil attack while ensuring review anonymity and authenticity, both critical security requirements of reputation systems. Initially, we champion a hybrid framework designed to enhance user interaction with our system. This approach leverages the centralized E-retail platform to facilitate trade services, whilst unfolding upon a blockchain platform that firmly authenticates the legitimacy of individual reviews. The authentication process is thus anchored to the correctness of cryptographic tokens, which are subsequently deposited on the blockchain. Additionally, we introduce a novel concept, ‘tax-endorsed reviews,’ devised to resist sybil attacks, such as injecting fake positive reviews for itself. Consequently, this necessitates the implementation of a four-party collaboration protocol. Finally, the security analysis complemented with our experimental results, definitively showcase the security and efficiency of BlockRep.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107705"},"PeriodicalIF":6.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167347","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 Kubernetes-based scheme for efficient resource allocation in containerized workflow 基于kubernetes的容器化工作流资源高效分配方案

IF 6.2 2区计算机科学

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-01-02 DOI: 10.1016/j.future.2024.107699

Danyang Liu , Yuanqing Xia , Chenggang Shan , Ke Tian , Yufeng Zhan

{"title":"A Kubernetes-based scheme for efficient resource allocation in containerized workflow","authors":"Danyang Liu , Yuanqing Xia , Chenggang Shan , Ke Tian , Yufeng Zhan","doi":"10.1016/j.future.2024.107699","DOIUrl":"10.1016/j.future.2024.107699","url":null,"abstract":"<div><div>In the cloud-native era, Kubernetes-based workflow engines simplify the execution of containerized workflows. However, these engines face challenges in dynamic environments with continuous workflow requests and unpredictable resource demand peaks. The traditional resource allocation approach, which relies merely on current workflow load data, also lacks flexibility and foresight, often leading to resource over-allocation or scarcity. To tackle these issues, we present a containerized workflow resource allocation (CWRA) scheme designed specifically for Kubernetes workflow engines. CWRA predicts future workflow tasks during the current task pod’s lifecycle and employs a dynamic resource scaling strategy to manage high concurrency scenarios effectively. This scheme includes resource discovery and allocation algorithm, which are essential components of our containerized workflow engine (CWE). Our experimental results, across various workflow arrival patterns, indicate significant improvements when compared to the Argo workflow engine. CWRA achieves a reduction in total workflow duration by 0.9% to 11.4%, decreases average workflow duration by a maximum of 21.5%, and increases CPU and memory utilization by 2.07% to 16.95%.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107699"},"PeriodicalIF":6.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968058","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