Egyptian Informatics Journal最新文献

{"title":"Prediction of one- and three-months yoga practices effect on chronic venous insufficiency based on machine learning classifiers","authors":"Xue Han ,&nbsp;Nan Hu","doi":"10.1016/j.eij.2024.100507","DOIUrl":"10.1016/j.eij.2024.100507","url":null,"abstract":"<div><p>The rise of technology has heightened work demands, adversely impacting mental health and fitness. The COVID-19 pandemic exacerbates psychological stress, emphasizing the need for non-pharmacological interventions like yoga. Yoga positively influences the autonomic nervous system, benefiting cardio-respiratory health, metabolic efficiency, and conditions like Type-2 diabetes, Chronic Venous disease, and obesity. This study employs a dataset with 100 samples and 43 features related to Chronic Venous Insufficiency (CVI). Logistic and Random Forest classifiers are validated using K-fold cross-validation, with feature selection optimizing prediction accuracy. Hybrid models, enhanced with optimization algorithms, predict Venous Clinical Severity Score (VCSS) before, one, and three months after yoga practices. The Random Forest classifier, particularly RFGT, proves highly accurate in categorizing baseline severity and identifying Mild and Moderate CVI cases. RFGT demonstrated AUC score of 0.9072, 0.8714, 0.7709, and 0.7200 in Absent, Mild, Moderate, and Severe patient groups classification before yoga practices (VCSS-Pre). These values were 0.9158, 0.8644, 0.8142, and 0.6333 for VCSS-1 and reported as 0.9269, 0.8399, 0.7838, and 0.7500 for patients’ classification in VCSS-3. Predicting VCSS scores before yoga intervention assists in categorizing participants for personalized care and efficient resource allocation. The RFC-based models, notably RFGT, show high accuracy in identifying baseline severity, enabling early intervention for high-risk individuals. These models, especially RFGT, perform well in classifying Mild and Moderate CVI cases, informing lifestyle modifications. Predicting VCSS-1 scores evaluates the short-term impact of yoga practices, identifying individuals requiring additional support. RFGT aids in personalized recommendations based on specific factors, crucial for severe conditions. Predicting VCSS-3 scores assesses the sustained impact over three months, identifying intervention responders, particularly in Severe and Moderate groups. RFGT demonstrates optimal predictions, contributing to future interventions tailored to individual responses and improved outcomes.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000707/pdfft?md5=6d018a619ca30f87b685d3fe87c6ee4f&pid=1-s2.0-S1110866524000707-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950143","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":"Multilevel thresholding Aerial image segmentation using comprehensive learning-based Snow ablation optimizer with double attractors","authors":"Mohamed Abd Elaziz ,&nbsp;Mohammed A.A. Al-qaness ,&nbsp;Rehab Ali Ibrahim ,&nbsp;Ahmed A. Ewees ,&nbsp;Mansour Shrahili","doi":"10.1016/j.eij.2024.100500","DOIUrl":"10.1016/j.eij.2024.100500","url":null,"abstract":"<div><p>Aerial photography is a remote sensing technique used for target detection, enabling both qualitative and quantitative analysis. The segmentation process is considered one of the most important processes to improve the analysis of Aerial images. In this study, we introduce an alternative multilevel threshold image segmentation based on a modified Snow ablation optimizer (SAO) algorithm. This modification is conducted using the strengths of Comprehensive learning and Double attractors which aims to enhance the exploration and exploitation abilities of the SAO during the process of discovering the optimal threshold levels that are used to segment the Aerial photography image. To validate the quality of the modified version of SAO, named DCSAO, a set of experimental series is conducted using the CEC2022 benchmark function and sixteen Aerial images at different threshold levels. In addition, we compared the results of DCSAO with different well-known Metaheuristic techniques. The results show the superior performance of DCSAO in comparison to other algorithms according to the performance metrics.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111086652400063X/pdfft?md5=ac54158e3b264721813ec98c7c4add6f&pid=1-s2.0-S111086652400063X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736343","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":"Determining the optimal number of clusters by Enhanced Gap Statistic in K-mean algorithm","authors":"Iliyas Karim Khan ,&nbsp;Hanita Binti Daud ,&nbsp;Nooraini Binti Zainuddin ,&nbsp;Rajalingam Sokkalingam ,&nbsp;Muhammad Farooq ,&nbsp;Muzammil Elahi Baig ,&nbsp;Gohar Ayub ,&nbsp;Mudasar Zafar","doi":"10.1016/j.eij.2024.100504","DOIUrl":"10.1016/j.eij.2024.100504","url":null,"abstract":"<div><p>Unsupervised learning, particularly K-means clustering, seeks to partition data into clusters with distinct intra-class cohesion and inter-class disparity. However, the arbitrary selection of clusters in K-means introduces challenges, leading to trial and error in determining the Optimal Number of Clusters (ONC). To address this, various methodologies have been devised, among which the Gap Statistic is prominent. Gap Statistic reliance on expected values for reference data selection poses limitations, especially in scenarios involving diverse scale, noise, and overlapping data.</p><p>To tackle these challenges, this study introduces Enhanced Gap Statistic (EGS), which standardizes reference data using an exponential distribution within the Gap Statistic framework, integrating an adjustment factor for a more dependable estimation of the ONC. Application of EGS to K-means clustering facilitates accurate ONC determination. For comparison purposes, EGS is benchmarked against traditional Gap Statistic and other established methods used for ONC selection in K-means, evaluating accuracy and efficiency across datasets with varying characteristics. The results demonstrate EGS superior accuracy and efficiency, affirming its effectiveness in diverse data environments.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000677/pdfft?md5=b38f7fc240484c948d461e5afbf4d41b&pid=1-s2.0-S1110866524000677-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636737","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 proposed secure framework for protecting cloud-based educational systems from hacking","authors":"Hosam El-Sofany ,&nbsp;Samir A. El-Seoud ,&nbsp;Omar H. Karam ,&nbsp;Belgacem Bouallegue ,&nbsp;Abdelmoty M. Ahmed","doi":"10.1016/j.eij.2024.100505","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100505","url":null,"abstract":"<div><p>Educational institutions and users involved in the whole learning process frequently have concerns about the storage and processing of sensitive data and essential apps in the cloud. Security and privacy issues have emerged as a major challenge, limiting cloud computing’s implementation in educational environments. Several users have yet to meet this security challenge, which is linked to the system’s multi-tenancy nature and the outsourcing of resources and data. This study proposes a secure framework for protecting cloud-based educational systems from hacking using a unique encryption technique, as well as a deep learning-based classification for cloud attack detection. Initially, we preprocess the data and extract features using a gray-level covariance matrix (GLCM). Next, we propose a classification based on multiple convolutional neural networks (M−CNN) to detect attacks in the cloud environment. Finally, we propose a modified digital signature algorithm (MDSA) for data encryption and decryption. The proposed technique achieved high security rates, with an accuracy of 97.7%, sensitivity of 96%, specificity of 94.3%, precision of 99.6%, and recall of 97%. Comparative evaluations showed that the proposed mechanism outperformed other encryption techniques. This novel model enhances the security of cloud-based educational systems and promotes users’ confidence in such platforms.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000689/pdfft?md5=07241a1a253dd8d5eda6323f38ea4e0e&pid=1-s2.0-S1110866524000689-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606941","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":"Improving intrusion detection using LSTM-RNN to protect drones’ networks","authors":"Menna Gamal ,&nbsp;Mohamed Elhamahmy ,&nbsp;Sanaa Taha ,&nbsp;Hesham Elmahdy","doi":"10.1016/j.eij.2024.100501","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100501","url":null,"abstract":"<div><p>The expanding use of Unmanned Aerial Vehicle (UAVs)/drones has been noticeable in recent years. Drones have several uses in a wide range of industries, including the military, delivery, agricultural, and surveillance. This led to a visible increase in malicious activities targeting drones’ network. Consequently, it has become imperative to develop intrusion detection systems. The network intrusion detection system (NIDS) uses deep learning to identify network anomalies. In this paper, a new approach is proposed to enhance IDS in drone communications. The proposed model utilizes the Recurrent Neural Network (RNN) with a Long Short-Term Memory Network (LSTM) combined with pre-processing algorithms. Simulating real network traffic was necessary to do benchmark datasets to evaluate the IDS performance. Due to the artificial part in datasets, there is unbalancing between the normal and attack traffic. Training models on high-dimensional datasets with redundant features can be computationally expensive, need more storage, and lead to low performance. The cleaning of the dataset is accompanied by the most effective pre-processing techniques. SMOTE for unbalancing, one-hot encoding, and min–max scaling techniques are used to mitigate the dataset issues. The model is evaluated using the most up-to-date version of the dataset CICIDS2017 (13 May 2023). The model successfully achieves 99.84 % classification accuracy, 99.84 % F1-score, 99.99 % Precision, and 99.70 % recall. The proposed model outperformed the Naïve Bayes and five other legacy protocols in accuracy and False Positive rate.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000641/pdfft?md5=cf102b39e8cb9e5585b6bed51ef13f17&pid=1-s2.0-S1110866524000641-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583200","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":"MC-ShuffleNetV2: A lightweight model for maize disease recognition","authors":"Shaoqiu Zhu ,&nbsp;Haitao Gao","doi":"10.1016/j.eij.2024.100503","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100503","url":null,"abstract":"<div><p>Maize has a long history of cultivation and is renowned for its high yield, superior quality, and adaptability. Currently, maize holds a significant position in grain cultivation and occupies a significant place in the agricultural structure. However, maize is susceptible to various diseases during its growth process, which can have a significant impact on the quality and yield. Traditional machine learning is heavily reliant on feature extraction, whereas deep learning has demonstrated notable success in image recognition for computer vision.The use of bloated models and the resulting wastage of computational resources represent significant challenges. The paper proposes a lightweight model, MC-ShuffleNetV2 (Mish + Convolutional Block Attention Module + ShuffleNetV2), to meet the practical needs of convolutional neural networks in maize disease image recognition. The model has designed with a focus on network lightweighting and accurate feature extraction. The model was constructed upon the foundation of the high-performance ShuffleNetV2 1 × network. The Convolutional Block Attention Module was integrated into the network architecture to enhance the model’s adaptive expressiveness. The depthwise separable convolution kernel of the depth-separable module was modified from a 3 × 3 kernel to a 5 × 5 kernel. This modification was implemented with the objective of expanding the image receptive field and extracting more detailed features of the image. It was necessary to modify the activation function in each stage for Mish. The model was compressed through the application of pruning operations. In the maize disease dataset test, the accuracy of the test set recognition accuracy of the network model constructed in this paper reaches 99.86 %, the model parameters are only 873,936, and the FLOPs (Floating-point Operations) are only 1,751,286. Compared with LeNet, AlexNet, MobileNetV2, and EfficientNetV2 models, the MC-ShufflenetV2 model’s recognition ability and size have obvious advantages, and it is more conducive to the actual deployment of the agricultural mobile terminal.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000665/pdfft?md5=b6fc6748f084b0530dace13f462b152c&pid=1-s2.0-S1110866524000665-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141583201","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 deep hash coding network for content-based medical image retrieval for healthcare applications","authors":"Lichao Cui ,&nbsp;Mingxin Liu","doi":"10.1016/j.eij.2024.100499","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100499","url":null,"abstract":"<div><p>The proliferation of medical imaging in clinical diagnostics has led to an overwhelming volume of image data, presenting a challenge for efficient storage, management, and retrieval. Specifically, the rapid growth in the use of imaging modalities such as Computed Tomography (CT) and X-rays has outpaced the capabilities of conventional retrieval systems, necessitating more sophisticated approaches to assist in clinical decision-making and research. Our study introduces a novel deep hash coding-based Content-Based Medical Image Retrieval (CBMIR) framework that uses a convolutional neural network (CNN) combined with hash coding for efficient and accurate retrieval. The model integrates a Dense block-based feature learning network, a hash learning block, and a spatial attention block to enhance feature extraction specific to medical imaging. We reduce dimensionality by applying the Reconstruction Independent Component Analysis (RICA) algorithm while preserving diagnostic information. The framework achieves a mean average precision (mAP) of 0.85 on ChestX-ray8, 0.82 on TCIA-CT, 0.84 on MIMIC-CXR, and 0.82 on LIDC-IDRI datasets, with retrieval times of 675 ms, 663 ms, 735 ms, and 748 ms, respectively. Comparisons with ResNet and DenseNet confirm the effectiveness of our model, enhancing medical image retrieval significantly for clinical decision-making and research.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000628/pdfft?md5=05a3f989f6a810a48166e144284468ba&pid=1-s2.0-S1110866524000628-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541991","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":"Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms","authors":"Abdullah Ayub Khan ,&nbsp;Yen-Lin Chen ,&nbsp;Fahima Hajjej ,&nbsp;Aftab Ahmed Shaikh ,&nbsp;Jing Yang ,&nbsp;Chin Soon Ku ,&nbsp;Lip Yee Por","doi":"10.1016/j.eij.2024.100502","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100502","url":null,"abstract":"<div><p>Owing to the major development of social media platforms, the usage of technological adaptation increases by means of editing software tools. Posting media in social communication environments has become one of our common daily routines. Before posting, various editing generators are used to manipulate pixel values, such as for enhancing brightness and contrast. Undoubtedly, this software helps bring posting media from ordinary to outstanding. But such a type of editing crosses the line in terms of creating fakes—anything that comes from anywhere and does not retain its originality anyway. It poses a series of issues in the process of multimedia forensics investigation and chain of custody. In order to restrict the attempts at deep faking and make the investigation hierarchy more effective, efficient, and reliable in the socio-cyber space (SCS), this paper presents a novel framework called DF-SCW. A digital forensics-enabled socio-cyber world with artificial intelligence (AI), especially deep neural networks (DNNs), for detecting and analyzing deep fake media investigations on social media platforms. It compares pixels with their neighboring values in the same media (such as images and videos) to identify information about the original one. There is a media flag designed to filter out malicious and dangerous attempts, like a powerful leader declaring war. Putting flags on such fakes helps digital investigators resist sharing the posts. In addition, the other prospect of this research is to make the digital forensics ecosystem more appropriate to take qualitative judgments in real-time while media is uploaded on social media platforms. The simulation of the proposed DF-SCW is tested on three different platforms, such as Instagram, Facebook, and Twitter. Through the experiment, the DF-SCW outperformed in terms of detection, identification, and analysis of deepfake media by an increased rate of 3.77%.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000653/pdfft?md5=ba1bae1de5468575813f6406e7b268fd&pid=1-s2.0-S1110866524000653-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484854","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":"Grey Wolf Optimization algorithm with random local optimal regulation and first-element dominance","authors":"Xuan Yanzhuang,&nbsp;Xuan Shibin","doi":"10.1016/j.eij.2024.100486","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100486","url":null,"abstract":"<div><p>Due to the classical Grey Wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first-element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000495/pdfft?md5=87190373e1a25341b5285339e1360faa&pid=1-s2.0-S1110866524000495-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484855","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":"Efficient and Secure Color Image Encryption System with Enhanced Speed and Robustness Based on Binary Tree","authors":"Marwa A. Elmenyawi ,&nbsp;Nada M. Abdel Aziem ,&nbsp;Ayman M. Bahaa-Eldin","doi":"10.1016/j.eij.2024.100487","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100487","url":null,"abstract":"<div><p>Recently, there has been a growing demand for image encryption techniques that offer robust protection and minimize processing time. The proposed paper proposes an efficient color image encryption system that excels in speed and security. The encryption system comprises three fundamental phases. The initial phase generates a unique encryption key by combining user-defined input with the original image and applying various operations and hash functions. In the confusion phase, the image is divided into blocks, forming a Binary Tree (BT) using primary color blocks, ensuring that the root and leaves belong to different colors. The confused matrix is derived through an inorder traversal that ensures non-adjacency of pixels of the same color, introducing an added layer of security. Finally, each pixel is scrambled by applying BT to its binary form to add more security and complexity. A DNA sequence is generated, and operations are executed based on two different chaotic maps, enhancing unpredictability and attack resistance. Extensive testing has validated the effectiveness of the proposed system, revealing a remarkable 28–45% reduction in processing time compared to recent techniques. Moreover, the system successfully withstands various attacks, as demonstrated through rigorous evaluations, including high-performance, visual perception, and cryptosystem strength evaluations. These results underscore the practical applicability and robust security offered by our efficient color image encryption solution, which provides a practical solution for applications prioritizing efficiency.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000501/pdfft?md5=2cc22c7586bb2170eaeadce60d7767ab&pid=1-s2.0-S1110866524000501-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433946","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}