Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models","authors":"Alanod AlMasaud,&nbsp;Heyam H. Al-Baity","doi":"10.1016/j.jksuci.2024.102264","DOIUrl":"10.1016/j.jksuci.2024.102264","url":null,"abstract":"<div><div>In the era of rapid technological advancement, users generate an overwhelming volume of data on social media networks and e-commerce platforms daily. This data, rich in opinions, sentiments, values, and habits, holds immense value for both consumers and businesses. Leveraging this unstructured data manually is error-prone and time-consuming. The field of Sentiment Analysis automates the process of analyzing human opinions from this data. Sentiment Analysis classifies text into positive, negative, or neutral sentiments. However, it confines text classification to a single sentiment polarity, providing a broad overview without accounting for specific aspects. With the growing demand for data analysis, this standard sentiment polarity classification is no longer sufficient. Aspect-Based Sentiment Analysis has emerged to dig deeper into the text, uncovering perspectives and points of view. It can identify multiple aspects in text with corresponding sentiment polarity. Therefore, interest in this field has increased and many research efforts have been devoted recently to tackle this problem for the English language. Unfortunately, there is a scarcity of Arabic research in this field. This study will address the aforementioned deficiency by investigating the potential of four transformer models namely, AraBERT v2.0, ArBERT, MARBERT, and Multilingual BERT in enhancing the accuracy of Aspect-Based Sentiment Analysis for Arabic texts using two dedicated corpora (AraMA and AraMAMS). The extensive experiments revealed that the proposed approach achieved its expected effect surpassing the results of previous studies in the field. The best results of Aspect Category Detection and Aspect Sentiment Classification tasks in AraMA corpus were obtained by using AraBERT v2.0 with F1-Measure result equals to 95.75% and 92.83% respectively. In addition, the best result of Aspect Category Detection and Aspect Sentiment Classification tasks in AraMAMS corpus were achieved by using AraBERT v2.0 with F1-Measure result equals to 95.54% and 89.52% respectively.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102264"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180412","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}

{"title":"Unmanned combat aerial vehicle path planning in complex environment using multi-strategy sparrow search algorithm with double-layer coding","authors":"Liangdong Qu ,&nbsp;Jingkun Fan","doi":"10.1016/j.jksuci.2024.102255","DOIUrl":"10.1016/j.jksuci.2024.102255","url":null,"abstract":"<div><div>Unmanned combat aerial vehicles (UCAV) path planning in complex environments demands a substantial number of path points to determine feasible paths. Establishing an effective flight path for UCAVs requires numerous path points to account for fuel constraints, artillery threats, and radar avoidance. This increase in path points raises the dimensionality of the problem, which in turn degrades algorithm performance. To mitigate this issue, a double-layer coding (DLC) model is utilized to remove redundant path points, consequently lowering computational complexity and operational difficulties. Meanwhile, this paper introduces a novel enhanced sparrow search algorithm (MESSA) based on multi-strategy for UCAV path planning. The MESSA incorporates a novel dynamic fitness regulation learning strategy (DFRL), a random differential learning strategy (RDL), an elite example equilibrium learning strategy (EEEL), a dynamic elimination and regeneration strategy based on the elite example (DERE), and quadratic interpolation (QI). Furthermore, MESSA is compared against 11 state-of-the-art algorithms, demonstrating exceptional optimization performance and robustness. Additionally, the combination of MESSA with the DLC model (DLC-MESSA) is applied to solve the UCAV path planning problem. The experimental results from five complex environments indicate that DLC-MESSA outperforms other algorithms in 80% of the cases by achieving the lowest average cost, thereby demonstrating its superior robustness and computational efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102255"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744902","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}

{"title":"Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification","authors":"Yujiang Wang ,&nbsp;Marshima Mohd Rosli ,&nbsp;Norzilah Musa ,&nbsp;Lei Wang","doi":"10.1016/j.jksuci.2024.102253","DOIUrl":"10.1016/j.jksuci.2024.102253","url":null,"abstract":"<div><div>Class imbalance is one of the most significant difficulties in modern machine learning. This is because of the inherent bias of standard classifiers toward favoring majority instances while often ignoring minority instances. Interpolation-based oversampling techniques are among the most popular solutions for generating synthetic minority samples to correct imbalanced class distributions. However, synthetic minority samples have a risk of overlapping with the majority-class samples. Inappropriate interpolation of minority samples during oversampling can also result in over generalization. To overcome these drawbacks, we propose a Clustering-based and Adaptive Position-aware Interpolation Oversampling algorithm (CAPAIO) for imbalanced binary dataset classification. CAPAIO initially employs an improved density-based clustering algorithm to group minority instances into inland, borderline, and trapped samples. It then adaptively determines the size of each subcluster and allocates weights to minority samples, guiding the synthesis of minority samples based on these weights. Finally, distinct interpolation oversampling algorithms are individually performed on these three categories of minority samples. The experimental results demonstrate the effectiveness of the proposed CAPAIO in most datasets compared with eleven other oversampling algorithms.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102253"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180408","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}

{"title":"Picking point identification and localization method based on swin-transformer for high-quality tea","authors":"Zhiyao Pan,&nbsp;Jinan Gu,&nbsp;Wenbo Wang,&nbsp;Xinling Fang,&nbsp;Zilin Xia,&nbsp;Qihang Wang,&nbsp;Mengni Wang","doi":"10.1016/j.jksuci.2024.102262","DOIUrl":"10.1016/j.jksuci.2024.102262","url":null,"abstract":"<div><div>In the nature scene, because of the high degree of similarity between the background and the tea buds, as well as the different growth postures of the tea buds, finding and precisely identifying the picking point is challenging. To solve these issues, this paper proposes a precise way to find the best picking point for tea buds by combining traditional algorithms with Swin-Transformer-based target detection and semantic segmentation algorithms, namely SORC-SFT. Firstly, an improved target detection algorithm, Swin-Oriented R-CNN (SORC), is used to realize the recognition of four types of high-quality tea. The mean Average Precision (mAP) of the four categories was 82.3% after replacing the feature fusion network FPN with PAFPN and adding the Coordinate Attention (CA) mechanism. Secondly, the corresponding segmentation mask of the four recognized categories is obtained by adding Semask, Feature Alignment Module (FAM), and Feature Selection Module (FSM) to the improved semantic segmentation algorithm Semask-Fa-Transformer (SFT). The mean Intersection over Union (mIoU) of the semantic segmentation algorithm for each category is 89.83%, 91.97%, 88.85%, and 89.68%, respectively. Finally, the morphology of different categories of tea buds is analyzed, and the traditional algorithm is used to realize the accurate localization of the identified tea buds. For the four tested categories, the proportion of correct samples in locating picking points is 96.18%, 91.28%, 93.85%, and 90.58%, respectively. The experimental results show that, out of all the algorithms, the proposed picking point identification and localization approach has the best performance and will make a strong contribution to the accurate identification of tea leaves during the intelligent picking process.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102262"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180405","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}

{"title":"Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach","authors":"Qiang Zhao ,&nbsp;Moyan Zhang ,&nbsp;Hongjuan Li ,&nbsp;Baozhen Song ,&nbsp;Yujun Li","doi":"10.1016/j.jksuci.2024.102266","DOIUrl":"10.1016/j.jksuci.2024.102266","url":null,"abstract":"<div><div>Skeleton-based action recognition, as a crucial research direction in computer vision, confronts numerous issues and challenges. Most existing research methods rely heavily on extensive labeled data for training, which significantly constraints their training effectiveness and generalization capability when labeled data is scarce. Consequently, how to integrate labeled and unlabeled data to overcome the limitations imposed by label scarcity has emerged as a pivotal research focus in skeleton-based action recognition. Targeting this label scarcity problem, this paper introduces a semi-supervised skeleton-based action recognition approach leveraging multi-dimensional feature-based graph contrastive learning. Firstly, three feature extractors are devised to extract and exploit the available informative cues from limited data thoroughly. The holistic feature extractor comprises five spatio-temporal graph convolutional blocks and a global average pooling layer. The detailed feature extractor is constructed by stacking the same spatio-temporal graph convolutional blocks, while the relational feature extractor primarily integrates stacked attention graph convolutional blocks and a global average pooling layer. Secondly, the sample relationship construction mechanism in graph contrastive learning is enhanced. A clustering process is employed to formulate soft positive/negative sample pairs based on sample similarity, and a sample connectivity matrix further weights the distances between these pairs, thereby enhancing classification accuracy. Furthermore, a novel loss function grounded in the information bottleneck theory is formulated to guide the model towards learning more robust and efficient skeleton action representations. Experimental evaluations demonstrate the superiority of our proposed method (MDKS) on two datasets, NTU60 and NW-UCLA. Specifically, on the NTU60 dataset, MDKS achieves classification accuracy improvements of 4.7% and 1.9% under the X-sub and X-view evaluation protocols, respectively, compared to the benchmark MAC-Learning method. On the NW-UCLA dataset, MDKS outperforms MAC-Learning by 1.4%, 1.2%, 1.9%, and 1.4% in classification accuracy under different labeled data ratios ranging from 5% to 40%. This work offers novel insights and methodologies for advancing skeleton-based action recognition. Future research will delve into label imbalance, label noise, multi-modal information fusion, and cross-scene generalization capabilities.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102266"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180411","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}

{"title":"Enhancing stock market predictions via hybrid external trend and internal components analysis and long short term memory model","authors":"Fatene Dioubi ,&nbsp;Negalign Wake Hundera ,&nbsp;Huiying Xu ,&nbsp;Xinzhong Zhu","doi":"10.1016/j.jksuci.2024.102252","DOIUrl":"10.1016/j.jksuci.2024.102252","url":null,"abstract":"<div><div>When it comes to financial decision-making, stock market predictability is extremely important since it offers valuable information that may guide investment strategies, risk management, and portfolio allocation overall. Traditional methods often fail to accurately predict stock prices due to their complexity and inability to handle non-linear and non-stationary patterns in market data. To address these issues, this study introduces an innovative model that combines the External Trend and Internal Components Analysis decomposition method (ETICA) with the Long Short-Term Memory (LSTM) model, aiming to enhance stock market predictions for S&amp;P 500, NASDAQ, Dow Jones, SSE and SZSE indices. Through rigorous testing across various training data proportions and epoch settings, our findings reveal that the proposed hybrid model outperforms the single LSTM model, delivering significantly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This enhanced precision reduces prediction errors, underscoring the model’s robustness and reliability. The superior performance of the ETICA-LSTM model highlights its potential as a powerful financial forecasting tool, promising to transform investment strategies, optimize risk management, and enhance portfolio performance.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102252"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180023","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}

{"title":"A secure, privacy-preserving, and cost-efficient decentralized cloud storage framework using blockchain","authors":"Swatisipra Das ,&nbsp;Minati Mishra ,&nbsp;Rojalina Priyadarshini ,&nbsp;Rabindra Kumar Barik ,&nbsp;Manob Jyoti Saikia","doi":"10.1016/j.jksuci.2024.102260","DOIUrl":"10.1016/j.jksuci.2024.102260","url":null,"abstract":"<div><div>Cloud services benefit countless users worldwide due to notable features, such as on-demand self-service, scalability, easy maintenance, etc. Secure storage and access to data in the cloud is critical. Cloud Identity and Access Management (IAM) service, which acts in a centralized way to provide access requests to the authenticated users. Controlled access sometimes fails to preserve the privacy of the sensitive information stored in the cloud due to several reasons, such as insider attacks, breaches of data security, or any other types of unauthorized access. This paper suggests a blockchain-assisted secure storage and access mechanism to secure sensitive data. Here blockchain is used as a trust management entity that verifies the identity of the user. Along with this it issues the Access Control Lists (ACLs) and identity token, and at the same time, it records all the interactions between the users and service providers. Data transmission is transparent since transactions are recorded. Importance is given to user privacy and decryption keys security. Linear(t,n) secret sharing scheme is used for key share generation and distribution. For experimentation, in MetaMask cryptocurrency wallet Goerli test network is used. Results reveal that our model consumes less cost to execute than other existing works. The total execution cost to upload and download a data file is 0.00281392 and 0.02455307 GoerliETH. Where the all verification operations such as identity token, ACL, access_log, and data integrity are executed in Zero gas value. The proposed model maintains a constant gas cost regardless of transaction volume, with costs of 33.04 ETH and 32.24 ETH for data upload and download. Moreover, we present a comparison of execution time performance in three different system configurations.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102260"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180403","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}

{"title":"A hierarchical and secure approach for automotive firmware upgrades","authors":"Feng Luo ,&nbsp;Zhihao Li ,&nbsp;Jiajia Wang ,&nbsp;Cheng Luo ,&nbsp;Hongqian Liu ,&nbsp;Dengcheng Liu","doi":"10.1016/j.jksuci.2024.102258","DOIUrl":"10.1016/j.jksuci.2024.102258","url":null,"abstract":"<div><div>With the development of intelligent and connected vehicles, the expansion of software necessitates an increased significance and frequency of automotive firmware upgrades. The abundance of potential attack vectors and valuable data renders these upgrades enticing targets for attackers. However, the prevailing security services used for automotive firmware upgrades are no longer sufficient to meet security requirements. Hence, this paper proposes a Secure Automotive Firmware Upgrade Approach (SAFUA), aimed at enhancing authentication and communication security during automotive firmware upgrades. To address the heterogeneous performance of in-vehicle nodes and diverse application contexts, this approach introduces multiple authentication modes tailored to various upgrade scenarios. Moreover, hierarchical authentication and secure communication strategies are designed to achieve a balance between security and efficiency requirements. Consolidating these methodologies, a standardized automotive firmware upgrade process is delineated. Formal and informal verification of the proposed approach is conducted to attest its security efficacy. Furthermore, a simulated vehicular environment is constructed to evaluate the temporal and spatial efficiency of the approach across diverse bus and device configurations. The results confirm the adaptability of the secure upgrade approach outlined herein to the automotive firmware upgrade landscape, offering robust security alongside enhanced upgrade efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102258"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179572","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}

{"title":"T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection","authors":"Pon Abisheka ,&nbsp;C. Deisy ,&nbsp;P. Sharmila","doi":"10.1016/j.jksuci.2024.102257","DOIUrl":"10.1016/j.jksuci.2024.102257","url":null,"abstract":"<div><div>Plagiarism has become a pervasive issue in academics and professionals to safeguard academic integrity and intellectual property rights. The escalating sophistication of plagiarized content through semantic manipulation and structural reorganization poses significant challenges to existing detection systems that rely primarily on lexical similarity measures. The proposed T-SRE (Transformer-based Semantic Relation Extraction), a novel framework addresses the limitations of traditional n-gram and string-matching approaches by leveraging deep semantic analysis. The proposed framework combines Dependency Parsing (DP) for syntactic relationship mapping and Named Entity Recognition (NER) for contextual entity identification, augmented by a transformer-based neural network that captures long-range contextual dependencies. This learning methodology incorporates three key components: a position-aware word reordering algorithm, Levenshtein distance metric for structural similarity, and contextual word embeddings for semantic preservation detection. The proposed T-SRE enhances text structure recognition by combining position-aware reordering with semantic preservation through ensemble learning. The system implements a hierarchical classification scheme that quantifies plagiarism severity through a four-tier taxonomy: heavy, low, non-plagiarized and verbatim copy. The Udacity benchmark dataset showcases the model’s superior detection capabilities, achieving 92% precision, 89% recall, and an F1-score of 90.5%, particularly in lightweight textual modifications.The framework achieves a granularity score of 1.28, outperforming existing approaches.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102257"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744903","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}

{"title":"Audio analysis with convolutional neural networks and boosting algorithms tuned by metaheuristics for respiratory condition classification","authors":"Safet Purkovic ,&nbsp;Luka Jovanovic ,&nbsp;Miodrag Zivkovic ,&nbsp;Milos Antonijevic ,&nbsp;Edin Dolicanin ,&nbsp;Eva Tuba ,&nbsp;Milan Tuba ,&nbsp;Nebojsa Bacanin ,&nbsp;Petar Spalevic","doi":"10.1016/j.jksuci.2024.102261","DOIUrl":"10.1016/j.jksuci.2024.102261","url":null,"abstract":"<div><div>In contemporary medical research, respiratory disorders have become a primary focus. Improving patient outcomes for any medical condition largely depends on early identification and prompt treatment. Traditionally, medical professionals diagnose respiratory diseases by auscultating a patient’s breathing. However, this method has inherent limitations, as it may not enable physicians to accurately identify every respiratory condition. This study explores the potential of using convolutional neural networks (CNNs) in conjunction with audio analysis for the identification of respiratory problems. This work proses a novel two-tier framework that integrates CNNs with extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to classify respiratory conditions. Additionally, modern optimization techniques are employed to enhance classification efficiency, recognizing the significant impact that appropriate hyperparameter tuning has on machine learning (ML) and deep learning (DL) performance. This research introduces a modified version of particle swarm optimization (PSO) tailored to meet the specific needs of ML and DL tuning. The proposed approach is validated using a real-world clinical dataset. Two studies, both based on mel spectrograms of patient breathing patterns, were conducted: the first aimed at determining whether patients have respiratory conditions (binary classification), while the second employed the same data structure for multi-class classification. In both scenarios, advanced optimizers were utilized to optimize model architecture and training settings. Under identical testing conditions, the proposed PSO metaheuristic achieved an accuracy of 98.14<span><math><mtext>%</mtext></math></span> for respiratory condition detection in binary classification and a slightly lower accuracy of 81.25<span><math><mtext>%</mtext></math></span> for specific condition identification in multi-class classification.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102261"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180402","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}