Egyptian Informatics Journal最新文献_第4页

Digital image super-resolution reconstruction method based on stochastic gradient descent algorithm 基于随机梯度下降算法的数字图像超分辨率重建方法

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-09-01 DOI: 10.1016/j.eij.2025.100778

Yinghuai Yu, Xiaohong Peng, Xiaoxia Ye

{"title":"Digital image super-resolution reconstruction method based on stochastic gradient descent algorithm","authors":"Yinghuai Yu, Xiaohong Peng, Xiaoxia Ye","doi":"10.1016/j.eij.2025.100778","DOIUrl":"10.1016/j.eij.2025.100778","url":null,"abstract":"<div><div>Digital image super-resolution (SR) techniques have gained significant attention in computational imaging for reconstructing high-quality images from low-resolution inputs. Traditional SR methods often struggle with preserving fine details, texture consistency, and edge sharpness while maintaining computational efficiency, limiting their practical applications in real-time systems. The research proposes an Adaptive Dynamic Efficient Parameter Tuning for Super-Resolution (ADEPT-SR) framework based on an optimized stochastic gradient descent (SGD) algorithm. The technique transforms low-resolution (LR) images into high-resolution (HR) counterparts, addressing fundamental limitations in imaging hardware. ADEPT-SR implements an adaptive SGD framework with momentum-based parameter optimization to minimize reconstruction error between predicted and ground-truth HR images. The key innovation in ADEPT-SR lies in a hybrid loss function combining structural similarity index measure (SSIM) and perceptual loss with dynamic weighting that adjusts during training iterations. The approach enables superior edge preservation and texture reconstruction compared to conventional methods. An adaptive learning rate schedule dynamically responds to local optimization landscapes, reducing convergence time by 37 % while avoiding local minima. ADEPT-SR offers significant applications in medical imaging, satellite imagery analysis, surveillance systems, and consumer electronics, where hardware limitations constrain native resolution. Experimental validation across standard benchmark datasets demonstrates that ADEPT-SR achieves a peak signal-to-noise ratio (PSNR) improvement of 1.8 dB over standard bicubic interpolation and 0.7 dB over recent deep learning approaches for a 4 × upscaling factor. The method reduces computational complexity by 43 % compared to deep learning methods while maintaining visual quality improvement.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100778"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988582","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

Badminton actions detection from sensor data based on Deep Belief network optimized by Advanced Snake optimizer 基于深度信念网络的羽毛球动作检测，基于高级Snake优化器

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-09-01 DOI: 10.1016/j.eij.2025.100776

Bai-ren Zhou , Yusong Qi , Jie Lian

{"title":"Badminton actions detection from sensor data based on Deep Belief network optimized by Advanced Snake optimizer","authors":"Bai-ren Zhou , Yusong Qi , Jie Lian","doi":"10.1016/j.eij.2025.100776","DOIUrl":"10.1016/j.eij.2025.100776","url":null,"abstract":"<div><div>Badminton is a kind of racquet sport which has fast and accurate movements that highlights the need for badminton action’s automatic identification within training, sport analysis, and coaching. However, the identifying actions of badminton from sensor data is difficult according to the complex nature of human actions. Such movement identification systems focus on general activities like sitting, walking, and running rather than actions that are badminton-specific. Also, the badminton players’ sensor data show different measures that makes the traditional feature normalization methods useless. This study presents a new approach for badminton action identification through use of DBNs or Deep Belief Networks, which are optimized by the ASO or snake optimizer’s improved version. The proposed DBN/ASO model is tested on the Badminton Sensor Dataset (BSS), which includes 25 players performing 10 types of strokes (1,140 samples). The model performed satisfactorily in experiments, achieving 93.2 % accuracy, 94.1 % sensitivity, 92.3 % precision, 93.8 % specificity, 93.2 % F1-score, and a Matthews Correlation Coefficient (MCC) of 0.915, surpassing CNN/LSTM, MM-AGNES, NDT-GCN, 3D:VIBE, and Multi-Sensor (M−S) state-of-the-art model performances. AUC = 0.92 from the Receiver Operating Characteristic (ROC) analysis, confirms its strong discriminative ability. Comparatively benchmarking on CEC test functions ASO has yielded the mean best fitness 9.12e<sup>−7</sup> on F1 (Sphere) and 1.43e<sup>−3</sup> on F2 (Rosenbrock), associated with the lowest standard deviation across all functions, thus demonstrating better convergence and robustness. This substantiates the efficacy of the proposed framework for the accurate recognition of complex badminton actions from wearable sensor data, in turn laying down a pathway for intelligent coaching and real-time performance analytics.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100776"},"PeriodicalIF":4.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009920","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

Exploring q-rung Orthopair fuzzy sets based on multi criteria decision making: A systematic literature review 基于多准则决策的q阶矫形模糊集研究：系统文献综述

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-28 DOI: 10.1016/j.eij.2025.100755

Sinan Öztaş

引用次数: 0

ALMANET: A hybrid online learning IDS for real-time IoT security ALMANET：用于实时物联网安全的混合在线学习IDS

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-28 DOI: 10.1016/j.eij.2025.100764

Promise Ricardo Agbedanu , Shanchieh (Jay) Yang , Richard Musabe , Ignace Gatare , James Rwigema

{"title":"ALMANET: A hybrid online learning IDS for real-time IoT security","authors":"Promise Ricardo Agbedanu , Shanchieh (Jay) Yang , Richard Musabe , Ignace Gatare , James Rwigema","doi":"10.1016/j.eij.2025.100764","DOIUrl":"10.1016/j.eij.2025.100764","url":null,"abstract":"<div><div>Although some modern Intrusion Detection Systems (IDSs) for Internet of Things (IoT) have explored online machine learning (ML) approaches to build these IDSs, most IoT-based IDSs are designed using offline ML techniques. IDSs built with offline ML approaches cannot adapt to rapidly changing IoT network conditions. They need continuous retraining and require a lot of computational power. To address these limitations, we propose ALMANET (ALMA+NET), a hybrid intrusion detection approach combining Approximate Large Margin Algorithm (ALMA) with Stochastic Weight Averaging (SWA) and an online neural network (NET). ALMANET leverages the power of online learning, which updates models incrementally and allows real-time adaptation to evolving network traffic, making it suitable for IoT environments. We validated ALMANET on four benchmark datasets, namely, NF BoT IoT, NF ToN IoT, NF UNSW, and NF CSE 2018 datasets. We demonstrated the proposed technique’s performance in terms of accuracy, recall, ROCAUC, and robustness against adversarial attacks. We compared the performance of ALMANET against RF, SVM, LR, and ALMA. ALMANET records up to 98.58% ROCAUC and demonstrates high throughput, low false positive rates, and efficient memory usage of 14.64 KB across all datasets, making it feasible for deployment on edge devices.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100764"},"PeriodicalIF":4.3,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908611","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

Novel CP model and CP-assisted meta-heuristic algorithm for flexible job shop scheduling with preventive maintenance 具有预防性维护的柔性作业车间调度新CP模型及其辅助元启发式算法

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-25 DOI: 10.1016/j.eij.2025.100759

Lixin Zhao , Leilei Meng , Weiyao Cheng , Yaping Ren , Biao Zhang , Hongyan Sang

{"title":"Novel CP model and CP-assisted meta-heuristic algorithm for flexible job shop scheduling with preventive maintenance","authors":"Lixin Zhao , Leilei Meng , Weiyao Cheng , Yaping Ren , Biao Zhang , Hongyan Sang","doi":"10.1016/j.eij.2025.100759","DOIUrl":"10.1016/j.eij.2025.100759","url":null,"abstract":"<div><div>The research investigates the flexible job shop scheduling problem with preventive maintenance (FJSP-PM) by considering two maintenance strategies namely fixed preventive maintenance (FJSP-FPM) and periodic preventive maintenance (FJSP-PPM). The objective is minimizing the makespan. We first propose two novel constraint programming (CP) models for FJSP-FPM and FJSP-PPM to obtain optimal solutions. Then, we design a CP-assisted meta-heuristic framework, and develop a CP-assisted Q-learning-based collaborative variable neighborhood search algorithm (CVNSQ-CP) as a representative example to effectively address large-scale instances. Finally, the experimental evaluation on benchmark instances validates the capability of the CP model and CVNSQ-CP. Specifically, compared with existing mathematical models, the proposed CP model proves 3 new optimal solutions and improves 11 current best-known solutions for FJSP-FPM, and it improves 13 current best-known solutions for FJSP-PPM. Meanwhile, CVNSQ-CP outperforms current state-of-the-art methods by improving 9 current best-known solutions for FJSP-FPM and 3 current best-known solutions for FJSP-PPM.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100759"},"PeriodicalIF":4.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893408","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

Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks 自主车辆网络中用于隐私保护和可扩展能量优化的加权可解释联邦学习

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-20 DOI: 10.1016/j.eij.2025.100758

Muhammad Saleem , Ali Arishi , Muhammad Sajid Farooq , M.A. Khan , Khan M. Adnan

{"title":"Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks","authors":"Muhammad Saleem , Ali Arishi , Muhammad Sajid Farooq , M.A. Khan , Khan M. Adnan","doi":"10.1016/j.eij.2025.100758","DOIUrl":"10.1016/j.eij.2025.100758","url":null,"abstract":"<div><div>The rise of electric and autonomous vehicles in smart cities poses significant challenges in vehicular energy management, including unoptimized consumption, inefficient grid utilization, and unpredictable traffic dynamics. Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) often struggle with real-time adaptability, high-dimensional data processing, and privacy concerns. While Federated Learning (FL) offers a decentralized solution by enabling edge devices to collaboratively train models without sharing raw data, conventional FL typically treats all client contributions equally—regardless of their data volume, quality, or contextual relevance. This limits model generalization in heterogeneous vehicular environments. To address this, we propose a Weighted Explainable Federated Learning (WEFL) framework that enhances conventional FL by assigning dynamic importance to client updates based on factors such as data relevance and local model performance. The framework also integrates Explainable AI (XAI) methods to improve interpretability, transparency, and regulatory compliance. The proposed WEFL-XAI model ensures privacy-preserving, real-time, and adaptive vehicular energy optimization, leveraging traffic patterns, vehicle energy states, and grid load conditions. Experimental evaluations demonstrate that our Multi-Layer Perceptron (MLP)-based Weighted Federated Learning (WFL) model achieves an R<sup>2</sup> of 86.84% for energy consumption and 74.16 % for traffic density, reflecting a strong trade-off between performance and privacy. While these values are lower than centralized MLP benchmarks, the WFL model outperforms standard FL baselines by offering enhanced privacy preservation, interpretability, and decentralized scalability, making it a more viable choice for real-world smart mobility deployments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100758"},"PeriodicalIF":4.3,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878382","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

Advancing pedestrian trajectory prediction with interaction-aware 3D-dual contextualized modeling 利用交互感知3d双情境化建模推进行人轨迹预测

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-19 DOI: 10.1016/j.eij.2025.100742

Zain Ul Abideen , Nisar Ahmed , Hafiz Shafiq Ur Rehman Khalil , Muhammad Shahbaz

{"title":"Advancing pedestrian trajectory prediction with interaction-aware 3D-dual contextualized modeling","authors":"Zain Ul Abideen , Nisar Ahmed , Hafiz Shafiq Ur Rehman Khalil , Muhammad Shahbaz","doi":"10.1016/j.eij.2025.100742","DOIUrl":"10.1016/j.eij.2025.100742","url":null,"abstract":"<div><div>In the contemporary landscape, numerous models have emerged to predict pedestrian trajectories. However, a significant limitation of many existing models is their exclusive reliance on historical motion data, which may lead to undesirable outcomes such as pedestrians intersecting with roadside features. Furthermore, current research predominantly relies on spatial assumptions, making it challenging to adjust the graph arrangement for un-specified environments in online systems, and there is a notable absence of an evaluation methodology to assess the impact of relational modeling on prediction execution. This study addresses these limitations by developing a trajectory prediction model incorporating environmental factors affecting pedestrians. The proposed 3D-dual contextualized model (DCM) utilizes adaptive relational aggregation to capture the intricate relationships between pedestrians and their contextual data. Moreover, integrating a Graph Convolutional Network (GCN) with the Pedestrian Visual Acuity Module (PVAM) aims to replicate pedestrians’ perception of their surroundings, eliminating extraneous data and reducing computational complexity. Supplementary environmental data was introduced to enrich the information set. Evaluation of the dataset demonstrates that the proposed model, incorporating dual-contextualized information such as background and vision information, outperforms the prediction accuracy of cutting-edge baseline models. Experimental results demonstrate that the 3D-DCM outperforms state-of-the-art models, achieving significant improvements in prediction accuracy, particularly in scenarios with dynamic crowd behavior and environmental influences. This work contributes to the advancement of trajectory prediction by providing a robust framework that incorporates both environmental and visual data, setting the stage for more accurate and scalable applications in intelligent transportation systems and autonomous driving.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100742"},"PeriodicalIF":4.3,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863509","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

Improving medical image classification based on Boosted Beluga Whale Optimizer with Triangular Mutation and Cross Vision transformer models 基于三角突变和交叉视觉变换模型的增强白鲸优化器改进医学图像分类

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-14 DOI: 10.1016/j.eij.2025.100740

Abdelghani Dahou , Mohamed Abd Elaziz , Ahmad O. Aseeri , Ahmed A. Ewees , Mohammed A.A. Al-qaness , Rehab Ali Ibrahim

{"title":"Improving medical image classification based on Boosted Beluga Whale Optimizer with Triangular Mutation and Cross Vision transformer models","authors":"Abdelghani Dahou , Mohamed Abd Elaziz , Ahmad O. Aseeri , Ahmed A. Ewees , Mohammed A.A. Al-qaness , Rehab Ali Ibrahim","doi":"10.1016/j.eij.2025.100740","DOIUrl":"10.1016/j.eij.2025.100740","url":null,"abstract":"<div><div>In this study, we introduce a framework for medical image classification that combines deep learning models with modified Beluga Whale Optimization as a feature selection technique. Our approach utilizes a feature extraction technique called CrossViT, a vision transformer model architecture design of ViT. The CrossViT is used to extract relevant features from medical images. A modified version of the Beluga Whale Optimization (BWO) method is also employed to select the relevant feature. The modified BWO incorporates the Triangular Mutation Operator (TMO) approach to enhance the BWO’s ability to exploit the problem space. A set of twelve functions from the CEC2022 benchmark is used to evaluate the Modified BWO (MBWO) performance and compare it with traditional BWO, followed by assessing the proposed medical image classification framework on several benchmark datasets, demonstrating excellent performance results. This study presents an innovative and effective approach to medical image classification by combining the strengths of deep learning and metaheuristic optimization methods.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100740"},"PeriodicalIF":4.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828522","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

Recognition algorithm of piano playing music notes based on improved hidden Markov model 基于改进隐马尔可夫模型的钢琴演奏音符识别算法

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-12 DOI: 10.1016/j.eij.2025.100746

Ziwei Wang

{"title":"Recognition algorithm of piano playing music notes based on improved hidden Markov model","authors":"Ziwei Wang","doi":"10.1016/j.eij.2025.100746","DOIUrl":"10.1016/j.eij.2025.100746","url":null,"abstract":"<div><div>The recognition process of piano playing music notes is affected by low-frequency components, resulting in suboptimal features that affect recognition accuracy. Therefore, an improved hidden Markov model based recognition algorithm for piano playing music notes is proposed. The audio signal of piano playing music in real life is collected, to pre emphasize and pre process the audio signal after obtaining it, and improve the quality of audio signal of piano playing music; Extract signal features and emotional features from piano playing music audio signals in sequence, fuse the two features to form a piano playing music signal feature set, input the fused features into a hidden Markov model, and calculate the output probability score of the features on HMM through time series modeling. Based on the score, select the optimal features. And input the obtained optimal features into the GA-RBF neural network for learning. Reduce the impact of low-frequency components on recognition results to obtain the best piano playing music note recognition results. Experimental verification shows that this method can effectively improve the quality of collected audio signals, recognize corresponding notes during different piano playing periods, and recognize corresponding notes at different frequencies. And in the process of note recognition, only 0.3 ms is needed. The proposed method optimizes HMM and combines GA-RBF neural network to significantly reduce computational complexity while ensuring accuracy, thereby achieving fast and accurate piano playing music note recognition.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100746"},"PeriodicalIF":4.3,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826802","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

Enhancing medical image segmentation through stacked u-net architectures with interconnected convolution layers 通过具有互连卷积层的堆叠u-net架构增强医学图像分割

IF 4.3 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-08-11 DOI: 10.1016/j.eij.2025.100753

Abeer Aljohani

{"title":"Enhancing medical image segmentation through stacked u-net architectures with interconnected convolution layers","authors":"Abeer Aljohani","doi":"10.1016/j.eij.2025.100753","DOIUrl":"10.1016/j.eij.2025.100753","url":null,"abstract":"<div><div>The effective integration of convolutional neural networks (CNNs), particularly as exemplified by the U-Net architecture, has led to notable advancements in medical image segmentation. Among various methodologies, the U-Net has demonstrated unparalleled proficiency across intricate medical image segmentation tasks, particularly when training and testing data emerge homogeneously from the same source domain. In this vein, our research introduces a pioneering paradigm by devising a novel stacked U-Net architecture. Our innovative model extends the conventional U-Net design through the strategic concatenation of two U-Net instances. Even though putting together multiple models is powerful, it comes with the problem of model parameters getting bigger, which makes them more likely to overfit or underfit because information can not get through the layers as easily during backpropagation. To counteract this quandary, we harness a mechanism of direct interconnections between the convolutional layer. To ascertain the robustness and supremacy of our proposed model, a trifecta of distinct medical image datasets is enlisted for comprehensive evaluation. Notably, a comparison of the conventional U-Net, Res-U-Net, and Inception-U-Net with cutting-edge U-Net architectures is made. The comparison clearly shows that our proposed architecture achieves superior segmentation performance across all datasets. In summation, our work augments the scientific discourse by presenting a potent avenue for advancing medical image segmentation paradigms.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100753"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810597","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