Egyptian Informatics Journal最新文献

{"title":"DeepSecure watermarking: Hybrid Attention on Attention Net and Deep Belief Net based robust video authentication using Quaternion Curvelet Transform domain","authors":"Satish D. Mali,&nbsp;Agilandeeswari Loganthan","doi":"10.1016/j.eij.2024.100514","DOIUrl":"10.1016/j.eij.2024.100514","url":null,"abstract":"<div><p>Digital videos have entered every facet of people’s lives because of the rise of live-streaming platforms and the Internet’s expansion &amp; popularity. Additionally, there are a tonne of pirated videos on the Internet that seriously violate the rights and interests of those who own copyrights to videos, hindering the growth of the video business. As a result, trustworthy video watermarking algorithms for copyright defense have emerged in response to consumer demand. To effectively watermark videos, this article proposes a robust feature extraction approach namely Attention on Attention Net (AoA Net). AoA Net extracts the robust features from the Deep Belief Network features of the cover video frames and then generates the score map that helps to identify the suitable location for embedding. The Golden Section Fibonacci Tree Optimization is used to identify the Key frames and then apply Quaternion Curvelet Transform (QCT) on those frames to obtain the QCT coefficients over which the watermark needs to be embedded. Thus, the embedding phase involves embedding the watermark on the obtained score map. Next, an Inverse QCT and the concatenation produce the watermarked video. The resultant video is now vulnerable to adversarial attacks when it is transferred over the Adversary Layer. Consequently, the embedded video is given to the decoder and the extraction phase, which performs key frame extraction and QCT. On the obtained QCT coefficients the similar AoA Net features are used to generate the score map and thus the watermark gets extracted. The performance of the devised technique is evaluated for various intentional and unintentional attacks, and it is assessed using PSNR, MSE, SSIM, BER, and NCC. Finally, the proposed method attains the enhanced visual quality outcome with an Average PSNR and SSIM of 64.33 and 0.9895 respectively. The robustness of the proposed AoADB_QCT attains an average NCC of 0.9999, and BER of 0.001251.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400077X/pdfft?md5=2bbcd2015292ce2edc29923fb90a845e&pid=1-s2.0-S111086652400077X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iLDA: A new dimensional reduction method for non-Gaussian and small sample size datasets","authors":"Usman Sudibyo ,&nbsp;Supriadi Rustad ,&nbsp;Pulung Nurtantio Andono ,&nbsp;Ahmad Zainul Fanani ,&nbsp;Catur Supriyanto","doi":"10.1016/j.eij.2024.100533","DOIUrl":"10.1016/j.eij.2024.100533","url":null,"abstract":"<div><p>High-dimensional non-Gaussian data is widely found in the real world, such as in face recognition, facial expressions, document recognition, and text processing. Linear discriminant analysis (LDA) as dimensionality reduction performs poorly on non-Gaussian data and fails on high-dimensional data when the number of features is greater than the number of instances, commonly referred to as a small sample size (SSS) problem. We proposed a new method to reduce the number of dimensions called iterative LDA (iLDA). This method will handle the iterative use of LDA by gradually extracting features until the best separability is reached. The proposed method produces better vector projections than LDA for Gaussian and non-Gaussian data and avoids the singularity problem in high-dimensional data. Running LDA does not necessarily increase the excessive computational cost caused by calculating eigenvectors since the eigenvectors are calculated from small-dimensional matrices. The experimental results show performance improvement on 8 out of 10 small-dimensional datasets, and the best improvement occurs on the ULC dataset, from 0.753 to 0.861. For image datasets, accuracy improved in all datasets, with the Chest CT-Scan dataset showing the greatest improvement, followed by Georgia Tech from 0.6044 to 0.8384 and 0.8883 to 0.9481, respectively.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000963/pdfft?md5=bda3e96355d0ec1968200790f98f4afd&pid=1-s2.0-S1110866524000963-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing medical image classification via federated learning and pre-trained model","authors":"Parvathaneni Naga Srinivasu ,&nbsp;G. Jaya Lakshmi ,&nbsp;Sujatha Canavoy Narahari ,&nbsp;Jana Shafi ,&nbsp;Jaeyoung Choi ,&nbsp;Muhammad Fazal Ijaz","doi":"10.1016/j.eij.2024.100530","DOIUrl":"10.1016/j.eij.2024.100530","url":null,"abstract":"<div><p>The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed across<!--> <!-->several assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000938/pdfft?md5=befbe156111530909638be74ef3d9cfa&pid=1-s2.0-S1110866524000938-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data fusion for driver drowsiness recognition: A multimodal perspective","authors":"S. Priyanka ,&nbsp;S. Shanthi ,&nbsp;A. Saran Kumar ,&nbsp;V. Praveen","doi":"10.1016/j.eij.2024.100529","DOIUrl":"10.1016/j.eij.2024.100529","url":null,"abstract":"<div><p>Drowsiness is characterized by decreased alertness and an increased inclination to fall asleep, typically from factors such as fatigue, sleep deprivation, or other related influences. In the context of driving, drowsiness poses substantial safety risks. The detection of driver drowsiness is of paramount importance in ensuring road safety, contributing to a significant number of accidents worldwide. Utilizing AI for drowsiness detection offers a potent solution to enhance road safety by identifying driver fatigue and preventing potential accidents. The proposed system addresses the challenge of detecting driver drowsiness using a WACHSens dataset collected from both manual and automated driving modes, encompassing rested and fatigued states. Various data sources, including vehicle-related information, facial expressions, and bio signals are employed to create a robust drowsiness detection system. A novel approach that leverages Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively detect drowsiness in drivers and achieve a 96 % accuracy level. It helps in enhancing road safety by devising effective drowsiness detection mechanisms, potentially preventing accidents and saving lives. Recall, accuracy, f1-score, and precision are the performance metrics to measure the drowsiness condition.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000926/pdfft?md5=4b44a300e027216aa003ae0c9eaabd67&pid=1-s2.0-S1110866524000926-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches","authors":"Mohamed Ouhssini ,&nbsp;Karim Afdel ,&nbsp;Mohamed Akouhar ,&nbsp;Elhafed Agherrabi ,&nbsp;Abdallah Abarda","doi":"10.1016/j.eij.2024.100517","DOIUrl":"10.1016/j.eij.2024.100517","url":null,"abstract":"<div><p>This comprehensive study examines cutting-edge strategies for combating Distributed Denial of Service (DDoS) attacks in cloud environments, addressing a critical gap in recent literature. Through a systematic review of the latest advancements, we propose a framework for identifying, preventing, and mitigating DDoS threats specifically tailored to cloud infrastructures. Our research highlights the urgent need for robust defense mechanisms to enhance cloud security, minimize service disruptions, and safeguard against data breaches. By analyzing the strengths and limitations of current models, we underscore the importance of continued innovation in this rapidly evolving field. This study provides essential insights for academics and industry professionals aiming to enhance the resilience of cloud infrastructure against the ongoing and adaptive menace of DDoS attacks.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400080X/pdfft?md5=854347fdaf4c610fcd5ee848317be212&pid=1-s2.0-S111086652400080X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing cloud security: Unveiling the protective potential of homomorphic secret sharing in secure cloud computing","authors":"Sijjad Ali ,&nbsp;Shuaib Ahmed Wadho ,&nbsp;Aun Yichiet ,&nbsp;Ming Lee Gan ,&nbsp;Chen Kang Lee","doi":"10.1016/j.eij.2024.100519","DOIUrl":"10.1016/j.eij.2024.100519","url":null,"abstract":"<div><p>Cloud computing security and data protection are becoming increasingly critical as its use increases. The research we present demonstrates how undercover sharing techniques and homomorphic encryption can be combined to protect private information in cloud computing scenarios. We create a reliable, private, and confidential computation platform by utilizing this dual approach. Our strategy involves protecting data while dividing it among multiple servers. By using this distribution, the system is less likely to suffer from single points of failure and has a higher security level. To ensure information privacy and security, data encryption restricts access to authorized individuals only. As an additional feature, we employ homomorphic encryption to enable operations on encrypted data without direct access to the originals. By using this feature, sensitive data is protected from disclosure or misuse while being processed. Therefore, original data confidentiality can be preserved when computing on encrypted shares. Several performance tests were conducted to prove our strategy’s practicality and effectiveness. Our considerations extended beyond encryption and decryption time and processing overhead. In our research, we demonstrate that our method strikes the right balance between security and computational efficiency.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000823/pdfft?md5=eb8b1bdbebde7024362d578d4e4df7a8&pid=1-s2.0-S1110866524000823-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tachyon: Enhancing stacked models using Bayesian optimization for intrusion detection using different sampling approaches","authors":"T. Anitha Kumari,&nbsp;Sanket Mishra","doi":"10.1016/j.eij.2024.100520","DOIUrl":"10.1016/j.eij.2024.100520","url":null,"abstract":"<div><p>The integration of sensors in the monitoring of essential bodily measurements, air quality, and energy consumption in buildings demonstrates the importance of the Internet of Things (IoT) in everyday life. These security breaches are caused by rudimentary and immature security protocols that are implemented on IoT devices. An intrusion detection system is used to detect security threats and system-level applications to detect malicious activities. This paper introduces Tachyon, a combination of various statistical and tree-based Artificial Intelligence (AI) techniques, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Bidirectional Auto-Regressive Transformers (BART), Logistic Regression (LR), Multivariate Adaptive Regression Splines (MARS), Decision Tree (DT), and a top k stack ensemble to distinguish between normal and malicious attacks in a binary classification setting. The IoTID2020 dataset used in this study consists of 6,25,783 samples with 83 features. An initial examination of the data reveals its unbalanced nature. To create a balanced dataset, a range of sampling techniques were used, including Oversampling, Undersampling, Synthetic Minority Oversampling Technique (SMOTE), Random Oversampling Examples (ROSE), Borderline Synthetic Minority Oversampling Technique (b-SMOTE), and Adaptive Synthetic (ADASYN). In addition, principal component analysis (PCA) and partial least squares (PLS) were used to determine the most significant features. The experimental results demonstrate that the stacked ensemble achieved a performance of 99.8%, which is better than the baseline approaches. An ablation study of ensemble models was also conducted to assess the performance of the proposed model in various scenarios.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000835/pdfft?md5=7cf69161e9063af8d9dfa578dc2f9947&pid=1-s2.0-S1110866524000835-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A knowledge graph based intelligent auxiliary diagnosis and treatment system for primary tinnitus using traditional Chinese medicine","authors":"Ziming Yin ,&nbsp;Lihua Wang ,&nbsp;Haopeng Zhang ,&nbsp;Zhongling Kuang ,&nbsp;Haiyang Yu ,&nbsp;Ting Li ,&nbsp;Ziwei Zhu ,&nbsp;Yu Guo","doi":"10.1016/j.eij.2024.100525","DOIUrl":"10.1016/j.eij.2024.100525","url":null,"abstract":"<div><p>Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000884/pdfft?md5=05320fbde686302d851578b9db60a119&pid=1-s2.0-S1110866524000884-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of vulnerabilities and attack strategies in cancelable biometric systems","authors":"Zineb Maaref ,&nbsp;Foudil Belhadj ,&nbsp;Abdelouahab Attia ,&nbsp;Zahid Akhtar ,&nbsp;Muhammed Basheer Jasser ,&nbsp;Athirah Mohd Ramly ,&nbsp;Ali Wagdy Mohamed","doi":"10.1016/j.eij.2024.100511","DOIUrl":"10.1016/j.eij.2024.100511","url":null,"abstract":"<div><p>Cancelable biometrics (CB) has been principally proposed to solve some issues related to the security, privacy, and revocability of users’ stored templates in traditional biometric systems. Its basic idea is to design a transformation function that creates a pseudo identity starting from the original biometric template while respecting mainly two properties irreversibility and revocability. The first property seeks the protection of the user data by ensuring the impossibility of recovering the original template from the transformed one. The second property permits to issue multiple pseudo identities related to one biometric trait originated from the same user. Although great efforts have been made in the literature to ensure these two properties, most of the proposed transform functions are vulnerable to several attacks and their effectiveness is still under study. Thus, the purpose of this paper is to boost the security analysis of CB by reviewing existing attacks against cancelable biometric systems. We discuss the vulnerabilities of some protection schemes that attract multiple security issues and enable the attacker to penetrate the protection system. The robustness evaluation of such schemes against some known attacks has been outlined. Also, some taxonomies related to attack approaches are presented. Furthermore, we provide comparisons between multiple attacks on cancelable biometric systems in terms of many valuable factors, after which we build a rigorous framework to evaluate a protection scheme and mitigate these attacks. As a result, our study serves as a wake-up call for the research community focused on cancelable biometric template protection, drawing attention to the vulnerabilities in these protection systems and raising awareness in this area to mitigate serious attacks. By identifying weaknesses and assessing their impacts, we hope to stimulate further research and development to enhance the security of CB systems.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000744/pdfft?md5=79c720e6a0f0ddd9d6796aeaadd6ad36&pid=1-s2.0-S1110866524000744-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent fuzzy reinforcement learning-based routing algorithm with guaranteed latency and bandwidth in SDN: Application of video conferencing services","authors":"Zhiqun Wang ,&nbsp;Zikai Jin ,&nbsp;Zhen Yang ,&nbsp;Wenchao Zhao ,&nbsp;Mahdi Mir","doi":"10.1016/j.eij.2024.100524","DOIUrl":"10.1016/j.eij.2024.100524","url":null,"abstract":"<div><p>Online video conferencing services connection by multipoint control units leads to communication bottleneck and high latency. Due to the dynamic traffic in routing connection requests to video conferences, the existing routing algorithms face problems such as communication bottlenecks, high latency, congestion, path redundancy, and local optimum. In recent years, the implementation of video conferencing systems using Software-Defined Networking (SDN) provides new opportunities to improve Quality of Service (QoS) with low latency. In this paper, an <u>I</u>ntelligent <u>F</u>uzzy reinforcement learning-based <u>R</u>outing <u>A</u>lgorithm is proposed, which simultaneously <u>G</u>uarantees <u>L</u>atency and <u>B</u>andwidth for online video conferencing services in SDN (named IFRA-GLB). Fuzzy logic mainly performs online routing between an ingress–egress pair. Meanwhile, through ongoing training, reinforcement learning lowers the average number of hops on the path determined by fuzzy model. Enhancements to the convergence capability of IFRA and reduced reliance on network topology are achieved by adjusting link weights using a weighted shortest path algorithm focused on critical nodes. When the network encounters congestion, a deferral module is applied to assign higher priority to requests with lower resource demands. Experimental results demonstrate that IFRA-GLB significantly improves performance and convergence compared to existing solutions, enhancing the QoS for video conferencing services. Specifically, IFRA-GLB increases the average admission rate by 1.96% for MIRA topology and 2.71% for ANSNET topology with 3.5% lower latency.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000872/pdfft?md5=4f2b33eca87a92aa4eaf584a59c7530f&pid=1-s2.0-S1110866524000872-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}