Egyptian Informatics Journal最新文献_第5页

Multi-task detection of harmful content in code-mixed meme captions using large language models with zero-shot, few-shot, and fine-tuning approaches 使用带有零镜头、少镜头和微调方法的大型语言模型对代码混合模因标题中的有害内容进行多任务检测

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-19 DOI: 10.1016/j.eij.2025.100683

A.K. Indira Kumar , Gayathri Sthanusubramoniani , Deepa Gupta , Aarathi Rajagopalan Nair , Yousef Ajami Alotaibi , Mohammed Zakariah

{"title":"Multi-task detection of harmful content in code-mixed meme captions using large language models with zero-shot, few-shot, and fine-tuning approaches","authors":"A.K. Indira Kumar , Gayathri Sthanusubramoniani , Deepa Gupta , Aarathi Rajagopalan Nair , Yousef Ajami Alotaibi , Mohammed Zakariah","doi":"10.1016/j.eij.2025.100683","DOIUrl":"10.1016/j.eij.2025.100683","url":null,"abstract":"<div><div>In today’s digital world, memes have become a common form of communication, shaping online conversations and reflecting social events. However, some memes can negatively impact people’s emotions, especially when they involve sensitive topics or mock certain groups or individuals. To address this issue, it is important to create a system that can identify and remove harmful memes before they cause further harm. Using Large Language Models for text classification in this system offers a promising approach, as these models are skilled at understanding complex language structures and recognizing patterns, including those in code-mixed language. This research focuses on evaluating how well different Large Language Models perform in identifying memes that promote cyberbullying. It covers tasks like cyberbullying detection, sentiment analysis, emotion recognition, sarcasm detection, and harmfulness evaluation. The results show significant improvements, with a 7.94% increase in accuracy for cyberbullying detection, a 2.68% improvement in harmfulness evaluation, and a 1.7% boost in sarcasm detection compared to previous top models. There is also a 1.07% improvement in emotion detection. These findings highlight the ability of Large Language Models to help tackle cyberbullying and create safer online spaces.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100683"},"PeriodicalIF":5.0,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Simulink-Driven Digital Twin Implementation for Smart Greenhouse Environmental Control 智能温室环境控制的simulink驱动数字孪生实现

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100679

Jehangir Arshad , Ch. Ahsan Abbas Sheheryar , Mohammad Khalid Imam Rahmani , Abdul Qayyum , Roumaisa Nasir , Sohaib Tahir Chauhdary , Khalid Jaber Almalki

{"title":"Simulink-Driven Digital Twin Implementation for Smart Greenhouse Environmental Control","authors":"Jehangir Arshad , Ch. Ahsan Abbas Sheheryar , Mohammad Khalid Imam Rahmani , Abdul Qayyum , Roumaisa Nasir , Sohaib Tahir Chauhdary , Khalid Jaber Almalki","doi":"10.1016/j.eij.2025.100679","DOIUrl":"10.1016/j.eij.2025.100679","url":null,"abstract":"<div><div>Sustainable food production must grow unprecedentedly in the face of the growing global hunger crisis. This proposal significantly reduces global hunger by creating an environmentally friendly approach to a smart greenhouse that aligns with zero hunger and sustainable development. This novel study is dissimilar to the conventional implementation of small-scale greenhouse farming as it implements modern sophisticated techniques applied specifically in greenhouses. The novelty of work lies in the integration of Simulink, the digital twin model into the smart greenhouse environment, capable of providing intelligent insights about plant growth patterns, enabling the farmers to make the right decision at the right time with remote monitoring capabilities, while maximizing the yield potential, trained via boosted trees algorithm with 8.4684 RMSE and 85% validation accuracy. Additionally, we have used state-of-the-art CNN model, Internet of Things (IoT) sensors and image-processing techniques to identify and classify diseases of crops in a greenhouse with 98.39% validation accuracy. The reason for this is quite long-term too as it involves not only dealing with the woes befalling greenhouse agriculture but reforming a more sustainable approach to food production.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100679"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Network information security protection method based on additive Gaussian noise and mutual information neural network in cloud computing background 云计算背景下基于加性高斯噪声和互信息神经网络的网络信息安全保护方法

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100673

Yu Zhong , Xingguo Li

{"title":"Network information security protection method based on additive Gaussian noise and mutual information neural network in cloud computing background","authors":"Yu Zhong , Xingguo Li","doi":"10.1016/j.eij.2025.100673","DOIUrl":"10.1016/j.eij.2025.100673","url":null,"abstract":"<div><div>In the cloud computing environment, data security and privacy have received unprecedented attention, but current information security protection methods cannot simultaneously balance data utility and privacy protection effects. Therefore, a network information security protection method based on Gaussian denoising and mutual information neural network is proposed. The research aims to protect network information while maintaining high data utility. This study utilizes Gaussian noise and K-dimensional perturbation trees to establish a privacy protection scheme, and introduces a Bayesian network-based network intrusion detection method to combine the two for information privacy protection. Afterwards, mutual information is used to evaluate the effectiveness of privacy protection and further optimize the parameters of the protection scheme. The experimental results showed that the proposed method achieved a data utility retention rate of 85%, and the number of privacy breaches did not exceed 3 times. In long-term experiments, through continuous optimization, the number of breaches gradually remained at 0. From this, the proposed privacy protection method can effectively improve the data security and privacy in cloud computing environments, and ensure data utility during transmission and storage processes.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100673"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Automatic identification method of foreign body intrusion in railway transportation track based on improved LeaderRank identification of key points 基于改进LeaderRank关键点识别的铁路运输轨道异物自动识别方法

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100682

Linjie Niu

{"title":"Automatic identification method of foreign body intrusion in railway transportation track based on improved LeaderRank identification of key points","authors":"Linjie Niu","doi":"10.1016/j.eij.2025.100682","DOIUrl":"10.1016/j.eij.2025.100682","url":null,"abstract":"<div><div>To accurately identify foreign object intrusion behaviors in key areas of railway transport tracks, an automatic recognition method is proposed. This method is based on an improved LeaderRank algorithm and is designed to detect foreign object intrusions on railway tracks. First, the improved LeaderRank algorithm identifies key points of trajectories, which are then used as layout points for installing monitoring equipment. Real-time monitoring devices collect video images of key track areas. Next, an improved Gaussian mixture model is used for image segmentation in track monitoring, extracting potential foreground images containing foreign object intrusions. These images are then input into a hybrid deep learning-based automatic recognition model for foreign object intrusion. The firefly algorithm trains this model, constructing a structurally stable hybrid deep learning model that learns the relationship between image combination features and foreign object intrusion behaviors, enabling accurate recognition of foreign object intrusions. Experimental results demonstrate that this method accurately identifies foreign object intrusion, enhancing detection accuracy and reliability. The proposed method, combining the improved LeaderRank algorithm with hybrid deep learning, offers an efficient and accurate solution, providing a new technical approach for railway transport safety management.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100682"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications 可教机器、移动网络和YOLO在目标检测中的有效性：实际应用的比较研究

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-14 DOI: 10.1016/j.eij.2025.100680

Sheikh Muhammad Saqib , Muhammad Iqbal , Tehseen Mazhar , Tariq Shahzad , Khmaies Ouahada , Habib Hamam

{"title":"Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications","authors":"Sheikh Muhammad Saqib , Muhammad Iqbal , Tehseen Mazhar , Tariq Shahzad , Khmaies Ouahada , Habib Hamam","doi":"10.1016/j.eij.2025.100680","DOIUrl":"10.1016/j.eij.2025.100680","url":null,"abstract":"<div><div>In this comparative study, the effectiveness of three prominent object detection models—Teachable Machine, MobileNet, and YOLO—was evaluated using a diverse dataset consisting of images from four distinct categories: bird, horse, laptop, and sandwich. The objective was to identify the most efficient model in terms of accuracy, speed, and usability for practical applications in fields such as self-driving vehicles, robotics, security systems, and augmented reality. The dataset was meticulously curated and subjected to training across the three models. Results from the comprehensive analysis indicated that the Teachable Machine model surpassed both MobileNet and YOLO in performance, demonstrating superior accuracy and effectiveness in detecting objects across the specified categories. This research contributes significantly to the domain of artificial intelligence by providing detailed insights and comparisons of model performances, offering a valuable resource for further advancements in object detection technologies. The study not only showcases the Teachable Machine’s superiority in handling multi-class object detection problems but also sets a benchmark for future explorations in enhancing object detection methodologies.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100680"},"PeriodicalIF":5.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Temporal record linkage for heterogeneous big data records 异构大数据记录的时态记录联动

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-14 DOI: 10.1016/j.eij.2025.100642

Reham I. Abdel Monem, Ehab E. Hassanein, Ali Z. El Qutaany

{"title":"Temporal record linkage for heterogeneous big data records","authors":"Reham I. Abdel Monem, Ehab E. Hassanein, Ali Z. El Qutaany","doi":"10.1016/j.eij.2025.100642","DOIUrl":"10.1016/j.eij.2025.100642","url":null,"abstract":"<div><div>Temporal Record Linkage (TRL) or Temporal Entity Matching (TEM) is the process of identifying records/entities that refer to the same real-world object in different lifetime states. TRL is a well-known problem in different data engineering contexts e.g. data analysis, data warehousing, data mining, and/or machine learning to identify entities denoting the same real-world object over time. Unlike traditional record linkage which considers differences between records of the same entity as contradictions; temporal record linkage considers such differences as normal entity growth over time. Existing frameworks which are limited to, No model, Decay, Disprob, Mixed, and Agreement First Dynamic Second (AFDS) which deal with temporal record linkage achieve high accuracy but with high computation cost. They condition the presence of the time dimension to detect similar entities that refer to the same real-world object. In this research, we present a framework called Tracking Similar Entities in Heterogeneous Temporal Records (TSE-HTR) to track similar entities in heterogeneous, big, low-quality, and temporal data regardless of the presence of the time dimension. It introduces data cleansing and state ranking modules to detect anomalies within similar entities, find the final and accurate set of them, and explain anomalies to the users or domain experts in a comprehensible manner that not only offers increased business intelligence but also opens opportunities for improved solutions. It presents to the user the records of different states of the same real-world object ranked according to different quality measures like completeness, validity, and accuracy. Performance evaluation of the proposed framework against existing frameworks over real and big data shows a great improvement in both effectiveness and efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100642"},"PeriodicalIF":5.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The application and performance optimization of multi-controller-based load balancing algorithm in computer networks 基于多控制器的负载均衡算法在计算机网络中的应用及性能优化

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-14 DOI: 10.1016/j.eij.2025.100678

Fengfeng Guo , Ailing Ye

{"title":"The application and performance optimization of multi-controller-based load balancing algorithm in computer networks","authors":"Fengfeng Guo , Ailing Ye","doi":"10.1016/j.eij.2025.100678","DOIUrl":"10.1016/j.eij.2025.100678","url":null,"abstract":"<div><div>This paper addresses the critical issue of network congestion caused by the increase in network traffic in contemporary society. The computer networks serve as the foundation for information exchange and online services, and their efficiency is essential. Traditional load-balancing algorithms face challenges in handling dynamic workloads, leading to inefficient resource utilization and extended response time. To address this problem, a novel method called Genetic-Bird Swarm Optimization (GBSO) is introduced, focusing on multi-controller-based load balancing. This method involves problem modeling, analysis, and selection processes, including the selection of switches and target controllers within the network segment. The results showed that the throughput of the proposed GBSO method was about 3800, and the load index after load balancing was 0.6, indicating that the workload distribution was balanced. The accuracy of the proposed GBSO algorithm was 92.15 %, the precision was 89 %, the recall rate was 88 %, and the F1 score was 85 %, all of which were higher than the existing Naive Bayes algorithm. This study emphasizes the importance of load balancing in optimizing computer network performance. The new algorithm proposed in this article provides a reliable solution for uniform network traffic distribution, reducing the limitations of existing methods.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100678"},"PeriodicalIF":5.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

TFKAN: Transformer based on Kolmogorov–Arnold Networks for Intrusion Detection in IoT environment TFKAN：基于柯尔莫哥洛夫-阿诺德网络的变压器，用于物联网环境中的入侵检测

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-11 DOI: 10.1016/j.eij.2025.100666

Ibrahim A. Fares , Mohamed Abd Elaziz , Ahmad O. Aseeri , Hamed Shawky Zied , Ahmed G. Abdellatif

{"title":"TFKAN: Transformer based on Kolmogorov–Arnold Networks for Intrusion Detection in IoT environment","authors":"Ibrahim A. Fares , Mohamed Abd Elaziz , Ahmad O. Aseeri , Hamed Shawky Zied , Ahmed G. Abdellatif","doi":"10.1016/j.eij.2025.100666","DOIUrl":"10.1016/j.eij.2025.100666","url":null,"abstract":"<div><div>This work proposes a novel Transformer based on the Kolmogorov–Arnold Network (TFKAN) model for Intrusion Detection Systems (IDS) in the IoT environment. The TFKAN Transformer is developed by implementing the Kolmogorov–Arnold Networks (KANs) layers instead of the Multi-Layer Perceptrons (MLP) layers. Unlike the MLPs feed-forward layer, KAN layers have no fixed weights but use learnable univariate function components, enabling a more compact representation. This means a KAN can achieve comparable performance with fewer trainable parameters than a larger MLP. The RT-IoT2022, IoT23, and CICIoT2023 datasets were used in the evaluation process. The proposed TFKAN Transformer outperforms and obtains higher accuracy scores of 99.96%, 98.43%, and 99.27% on the RT-IoT2022, IoT23, and CICIoT2023 datasets, respectively. The results indicate that the developed Transformer using KAN shows promising performance in IDS within IoT environments compared to MLP layers.Transformers based on KANs are on average 78% lighter, in parameter count, than Transformers using MLPs. This makes KANs promising to be a replacement for MLPs.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100666"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 control-driven transition strategy for enhanced multi-level threshold image segmentation optimization 一种增强多级阈值图像分割优化的控制驱动过渡策略

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-11 DOI: 10.1016/j.eij.2025.100646

Laith Abualigah , Mohammad H. Almomani , Saleh Ali Alomari , Raed Abu Zitar , Vaclav Snasel , Kashif Saleem , Aseel Smerat , Absalom E. Ezugwu

{"title":"A control-driven transition strategy for enhanced multi-level threshold image segmentation optimization","authors":"Laith Abualigah , Mohammad H. Almomani , Saleh Ali Alomari , Raed Abu Zitar , Vaclav Snasel , Kashif Saleem , Aseel Smerat , Absalom E. Ezugwu","doi":"10.1016/j.eij.2025.100646","DOIUrl":"10.1016/j.eij.2025.100646","url":null,"abstract":"<div><div>This work proposes an image segmentation approach based on a multi-threshold segmentation method and the enhanced Flood Algorithm combined with the Non-Monopolize search (named Improved IFLANO). The introduced approach, depending on IFLANO, offers much better segmentation quality for various images. Based on the existing structure, two different types of optimization techniques are added within IFLANO to enhance the update dynamics during optimization. The random strategy used in the Aquila optimization procedure enhances the performance of FLA, and a self-transition adaptation enhances the exploration ability of the image analysis. IFLANO framework is implemented for multi-level threshold image segmentation wherein the evaluation metric is Kapur’s entropy-based between-class variance. Benchmarking studies against standard test images show that IFLANO works not only faster but also yields better, more stable outcomes in image segmentations within similar time frames. IFLANO is shown to put any solution a step forward in its search for more accurate alternatives than any of the considered techniques by getting 96% improvement. We also find that the proposed method got better results in solving large medical clustering applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100646"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 robust and efficient algorithm for graph coloring problem based on Malatya centrality and sequent independent sets 基于Malatya中心性和序列独立集的图着色问题鲁棒高效算法

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-11 DOI: 10.1016/j.eij.2025.100676

Selman Yakut

{"title":"A robust and efficient algorithm for graph coloring problem based on Malatya centrality and sequent independent sets","authors":"Selman Yakut","doi":"10.1016/j.eij.2025.100676","DOIUrl":"10.1016/j.eij.2025.100676","url":null,"abstract":"<div><div>The Graph Coloring Problem (GCP) is an NP-hard problem that aims to color the vertices of a graph using the minimum number of distinct colors, ensuring that adjacent vertices do not share the same color. GCP is widely applied in real-world scenarios and graph theory problems. Despite numerous studies on solving GCP, existing methods face limitations, often performing well on specific graph types but failing to deliver efficient solutions across diverse structures. This study introduces the Malatya Sequent Independent Set Coloring Algorithm as an effective solution for GCP. The algorithm utilizes the Malatya Centrality Algorithm to compute Malatya Centrality (MC) values for graph vertices, where an MC value is defined as the sum of the ratios of a vertex’s degree to its neighbors’ degrees. The algorithm selects the vertex with the lowest MC value, adds it to an independent set, and removes it along with its neighbors and edges. This process repeats until the first sequent independent set is identified. The removed set is then excluded from the original graph, and the process continues on the remaining structure to determine additional sequent independent sets, ensuring that each set corresponds to a single color group in GCP. The algorithm was tested on social network graphs, random graphs, and benchmark datasets, supported by mathematical analyses and proofs. The results confirm that the algorithm provides efficient, polynomial-time solutions for GCP and maintains high performance across various graph types, independent of constraints.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100676"},"PeriodicalIF":5.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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