Egyptian Informatics Journal最新文献

{"title":"A modified genetic algorithm for large-scale and joint satellite mission planning","authors":"Qingbiao Zheng ,&nbsp;Yuanwen Cai ,&nbsp;Peng Wang","doi":"10.1016/j.eij.2025.100713","DOIUrl":"10.1016/j.eij.2025.100713","url":null,"abstract":"<div><div>In the context of global space technology’s rapid advancements, an increasing number of Earth observation satellites are being deployed to perform remote sensing missions, including target identification and regional surveillance. However, the inherent limitations of individual satellite systems — such as restricted observational coverage, temporal constraints, and resource capacities — necessitate collaborative multi-constellation operations to fulfill complex mission demands. This integration introduces a large-scale, multi-dimensional optimization challenge characterized by conflicting objectives (e.g., maximizing mission success rates and observational utility) and intricate constraints (e.g., satellite payload limitations and task-specific requirements). To address these complexities, we propose an enhanced hybrid genetic algorithm (GA) framework that integrates three complementary strategies: (1) an adaptive parameter tuning mechanism to balance exploration–exploitation trade-offs during evolution dynamically, (2) a tabu search-based local optimization module to refine solution quality while avoiding premature convergence, and (3) an elitist preservation protocol to retain high-performance candidates across generations. Simulation experiments conducted on representative mission scenarios demonstrate that the proposed methodology achieves superior performance compared to conventional algorithms, particularly in scenarios requiring stringent resource allocation and real-time responsiveness. The results validate the ability of the framework to solve large-scale satellite mission planning problems within relevant constraints effectively.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100713"},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517746","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}

{"title":"Analysis of classification metric behaviour under class imbalance","authors":"Jean-Pierre van Zyl ,&nbsp;Andries Petrus Engelbrecht","doi":"10.1016/j.eij.2025.100711","DOIUrl":"10.1016/j.eij.2025.100711","url":null,"abstract":"<div><div>Class imbalance is the phenomenon defined as skewed target variable distributions in a dataset. In other words class imbalance occurs when a dataset has an unequal proportion of target variables assigned to the instances in the dataset. Although the level of class imbalance is simply an inherent property of a dataset, highly skewed class imbalances cause misleading performance evaluations of a classification model to be reported by certain evaluation metrics. This paper reviews the history of existing performance evaluation metrics for classification, and uses a normalisation process to create new variations of these existing metrics which are more robust to class imbalance. Conclusions about the performance of the analysed metrics are drawn by performing the first extensive global sensitivity analysis of classification metrics. A statistical analysis technique, <em>i.e.</em> analysis of variance, is used to analyse the robustness to class imbalance of the existing metrics and the proposed metrics. This paper finds that most performance evaluation metrics for classification problems are highly sensitive to class imbalance, while the newly proposed alternative metrics tend to be more robust to class imbalance.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100711"},"PeriodicalIF":5.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517743","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}

{"title":"Efficient stochastic framework for availability improvement of stone door frame manufacturing plants using artificial neural networks and regression analysis","authors":"Naveen Kumar ,&nbsp;Ashish Kumar ,&nbsp;Monika Saini ,&nbsp;Khalid A. Alnowibet ,&nbsp;Seyed Jalaleddin Mousavirad ,&nbsp;Ali Wagdy Mohamed","doi":"10.1016/j.eij.2025.100718","DOIUrl":"10.1016/j.eij.2025.100718","url":null,"abstract":"<div><div>The main objective of this study is to introduce an efficient stochastic framework to improve the availability of the stone door frame manufacturing plants along with the reliability, maintainability, and dependability (RAMD) investigation and prediction of steady state availability of the plant using regression analysis (RA) and artificial neural networks (ANNs). The plant has five subsystems connected in series configuration. The RAMD methodology is employed to identify critical components that significantly impact the system’s overall performance. For this purpose, a mathematical model is developed using Markov birth–death process and Chapman-Kolmogorov differential difference equations derived for steady state availability evaluation. The incorporation of exponential distribution for failure and repair rates, coupled with the Markovian technique, yields insights into the intricate variations within the system. Several goodness-of-fit metrics, such as R², MAE, RMSE, and collinearity diagnostics, are used to evaluate the performance of the proposed model. Results show that in this application, ANN performs better than regression analysis. The findings showcase the efficacy of the proposed stochastic framework in achieving remarkable improvements in availability. Numerical outcomes, meticulously presented in structured tables and figures, provide tangible evidence of the framework’s success. The novelty of the study lies in the strategic combination of these methodologies to achieve enhanced insights into availability improvement. By enhancing availability, the proposed framework directly influences production efficiency and overall plant performance. The findings of present work are valuable insights for industrial practitioners seeking resilient operational strategies.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100718"},"PeriodicalIF":5.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490499","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}

{"title":"Dynamic oversampling-driven Kolmogorov–Arnold networks for credit card fraud detection: An ensemble approach to robust financial security","authors":"Mohamed Akouhar ,&nbsp;Mohamed Ouhssini ,&nbsp;Mohamed El Fatini ,&nbsp;Abdallah Abarda ,&nbsp;Elhafed Agherrabi","doi":"10.1016/j.eij.2025.100712","DOIUrl":"10.1016/j.eij.2025.100712","url":null,"abstract":"<div><div>Credit card fraud detection remains a persistent challenge in digital finance due to severe class imbalance, evolving fraud tactics, and the need for real-time analysis. Traditional detection systems often rely on static oversampling techniques and fixed feature sets, which limit their adaptability and robustness. This paper addresses these gaps by proposing a novel deep learning framework that combines Kolmogorov–Arnold Networks (KAN) with dynamic oversampling and ensemble feature selection. The dynamic oversampling strategy leverages both SMOTE and Generative Adversarial Networks (GANs) with variable sampling rates, reducing overfitting and enhancing generalization. Meanwhile, an ensemble feature selection mechanism integrates multiple metaheuristic algorithms to identify the most relevant features for fraud detection. The proposed approach, evaluated on three benchmark datasets, demonstrates strong improvements in adaptability over conventional deep learning models. This work offers a scalable, data-efficient solution for real-world fraud detection, improving resilience to data imbalance and evolving fraud patterns.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100712"},"PeriodicalIF":5.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480764","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}

{"title":"FeistelX network-based image encryption leveraging hyperchaotic fusion and extended DNA coding","authors":"Kristiawan Nugroho ,&nbsp;De Rosal Ignatius Moses Setiadi ,&nbsp;Eri Zuliarso ,&nbsp;Aceng Sambas ,&nbsp;Omar Farooq","doi":"10.1016/j.eij.2025.100716","DOIUrl":"10.1016/j.eij.2025.100716","url":null,"abstract":"<div><div>The rising frequency of cyberattacks has heightened the need for more secure and efficient image encryption techniques. Traditional chaotic and DNA-based methods often struggle with limited key space, low diffusion efficiency, or vulnerability to statistical attacks, especially when handling large or high-dimensional image data. This study introduces an image encryption technique that integrates the FeistelX Network with extended DNA cryptography and two distinct two-dimensional hyperchaotic maps, namely the two-dimensional symbolic chaotic map (2D-SCM) and the two-dimensional hyperchaotic exponential adjusted Logistic and Sine map (2D-HELS), to bolster data security. The proposed method synergizes three key components: the FeistelX Network offers a robust encryption framework with bijectivity ensured by property H; the extended DNA cryptography expands the key space and minimizes pixel correlation through advanced DNA operations; and the two hyperchaotic maps generate highly intricate chaotic sequences, ensuring greater randomness and resilience. Compared to existing schemes, the proposed method demonstrates improved diffusion, randomness, and resistance to statistical attacks. Experimental results show that this method achieves high-security indicators, with Chi-square values consistently below the critical threshold, average entropy values of 7.9994, and UACI and NPCR metrics remaining within the optimal theoretical ranges. Moreover, the method passed all sixteen NIST randomness tests with an average p-value of 0.6278. It demonstrated resilience to noise and data loss with PSNR values above 18 dB under attack scenarios. This combination of FeistelX structure, extended DNA operations, and dual hyperchaotic maps offers a novel and effective solution for enhancing image encryption security beyond traditional approaches.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100716"},"PeriodicalIF":5.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322339","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}

{"title":"Named entity recognition using Bi-LSTM model with pointer cascade conditional random field for selecting high-profit products","authors":"C. Gayathri ,&nbsp;Dr. R. Samson Ravindran","doi":"10.1016/j.eij.2025.100703","DOIUrl":"10.1016/j.eij.2025.100703","url":null,"abstract":"<div><div>Named entity recognition (NER) refers to recognizing objects mentioned in texts and is considered one of the most fundamental tasks in natural language processing. The authentication of named entities is not merely a matter of extracting information independently. The rise of this sector has benefited from rapid growth, especially in the e-commerce sector; numerous reviews are published that reflect consumer sentiments on different aspects of products and services such as quality, price, and more.A critical challenge lies in improving the accuracy and robustness of NER systems to address issues such as ambiguous contexts, intricate sentence structures, and domain-specific variations. Previous works on NER usually use conventional machine learning methods. However, there is still a need to improve the accuracy of identifying entities. To accomplish this goal, this work proposes a pointer cascade conditional random field-based named entity recognition procedure. A word embedding approach is initially applied to segment the word for further processing. Word vectors are provided as input to a bidirectional LSTM (Bi-LSTM) model, which extracts features from sentence or word vectors. To improve the performance of BiLSTM, a pointer network is used to generate pointer sequences for the elements of the input array. After features are extracted, the Cascade Conditional Random Field (CCRF) layer checks tag validity by learning the correlation between tags. A Python 3.7 framework is used to implement the proposed model. According to the results of the experiments, this work achieves a high accuracy of 98.54 %.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100703"},"PeriodicalIF":5.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312957","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}

{"title":"MalwareNet: an intelligent malware detection and classification using advanced extreme leaning machine in edge computing environment","authors":"P. Shailaja ,&nbsp;Thanveer Jahan ,&nbsp;Karramreddy Sharmila ,&nbsp;P. Bharath Siva Varma ,&nbsp;Swetha Arra ,&nbsp;Pala Mahesh Kumar","doi":"10.1016/j.eij.2025.100714","DOIUrl":"10.1016/j.eij.2025.100714","url":null,"abstract":"<div><div>Malware continues to wreak havoc on global digital ecosystems, with companies facing an average financial loss of $4.35 million per data breach in recent years. At the same time, individual users suffer from identity theft, affecting over 1.1 billion personal records annually. Existing malware detection systems often struggle with high latency in centralized cloud environments and fail to generalize across diverse malware variants generated by edge devices. To address these challenges, this work introduces MalwareNet, a novel multiclass malware detection network designed specifically for edge computing environments. MalwareNet innovatively processes data directly on edge devices, enabling real-time detection and classification with minimal latency and enhanced data privacy. The system employs a robust preprocessing pipeline to clean raw data, followed by Independent Component Analysis (ICA) to extract discriminative features while reducing dataset dimensionality. A Hybrid Wrapper-Filter (HWF) feature selection method optimizes feature subsets by integrating wrapper and filter techniques, ensuring compatibility with the chosen machine-learning classifier to maximize classification accuracy. The Extreme Learning Machine (ELM), selected for its rapid training and strong generalization, classifies malware into distinct categories, effectively identifying threats in edge settings. By combining edge-based processing, advanced feature engineering, and efficient classification, MalwareNet offers a scalable and reliable solution, significantly advancing malware detection capabilities for resource-constrained environments and providing a foundation for future adaptive security systems. Experimental evaluations on a large-scale malware dataset demonstrate the effectiveness of the proposed approach with an accuracy of 99.7 %, and F-measure of 99.55 %. The system also achieves high Jaccard index with an increment of 2.63 % in detecting and classifying malware, providing reliable security measures in edge computing environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100714"},"PeriodicalIF":5.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322338","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}

{"title":"A federated supply chain finance risk control method based on personalized differential privacy","authors":"Chao Ma,&nbsp;Haiyu Zhao,&nbsp;Kaiqi Zhang,&nbsp;Luogang Zhang,&nbsp;Hai Huang","doi":"10.1016/j.eij.2025.100704","DOIUrl":"10.1016/j.eij.2025.100704","url":null,"abstract":"<div><div>With the rapid development of supply chain finance, effectively managing risks while safeguarding participant data privacy has become a critical area of research. However, existing traditional risk control models predominantly rely on centralized data processing, which leads to the phenomenon of ”data silos,” hindering the flow and sharing of information. Furthermore, the significant privacy risks associated with centralized processing restrict collaboration among financial institutions, exacerbating the challenges of risk management. In this context, this study proposes a federated risk control method for supply chain finance based on personalized differential privacy optimization. This approach introduces a personalized differential privacy mechanism, enabling different institutions to collaboratively optimize model parameters without directly exchanging sensitive data. This methodology not only effectively safeguards data privacy but also enhances the overall performance of risk control, facilitating multi-party collaboration. Experimental results indicate that, compared to traditional centralized risk control models and other privacy protection methods, the proposed solution demonstrates favorable outcomes in terms of predictive accuracy and model performance while adhering to data privacy protection requirements. This research lays a theoretical foundation for the future development of safer and more efficient cross-institutional risk control systems and provides new insights and technical support for innovative risk management in the field of supply chain finance.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"31 ","pages":"Article 100704"},"PeriodicalIF":5.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312956","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}

{"title":"Analysis of social networks content to identify fake news using stacked combination of deep neural networks","authors":"Yujie Li ,&nbsp;Yushui Xiao ,&nbsp;Yong Huang ,&nbsp;Rui Ma","doi":"10.1016/j.eij.2025.100707","DOIUrl":"10.1016/j.eij.2025.100707","url":null,"abstract":"<div><div>In today’s fast-paced world, the unprecedented expansion of social networks and the huge volume of information has made automatic detection of fake news an undeniable necessity. The dissemination of fake news and misinformation can have a devastating impact on public opinion and social decision-making. This challenge requires new and powerful approaches in the fields of deep learning and natural language processing to accurately and quickly identify fake news and prevent its dissemination. For that purpose, this current work presents a new and efficient solution to detecting and spotting spurious news on social media. This method, through deep text content analysis and the employment of advanced deep learning techniques, aims to provide an expansive and accurate response to solve this problem. The proposed method consists of three determining steps: 1) The input data is initially prepared for the next steps using preprocessing techniques. This is done through noise removal, text normalization, and data conversion into a format that can be processed by deep learning models. 2) A hybrid method is then used to extract text features, which is a combination of a list of statistical features (e.g., text length, word count, and links), GloVe-based semantic features (to represent the word relationships), and Character N-Grams (CNG) (to improve misspelling and linguistic anomaly robustness). 3) Finally, for each set of features, a particular deep model is trained to predict based on each component. Specifically, a Multilayer Perceptron (MLP) model is used for statistical feature analysis, and Convolutional Neural Network (CNN) models are used for GloVe and CNG features. Both models generate individual predictions from the input features presented to them, and the predicted labels and the posterior probability vector for each of the models are combined to output a vector to be forwarded to the <em>meta</em>-learner (a MLP model). By learning patterns in the combinations of outputs and the probability vectors of the individual base models, the MLP model can correctly identify fake news or real news. Experimental results conducted on two authentic datasets, GossipCop and Politifact, show that our proposed method achieves 99.45 % and 97.40 % accuracies, respectively. This achievement indicates the very good and effective performance of our method in detecting fake news on both datasets.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100707"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196380","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}

{"title":"Cancelable finger vein authentication using multidimensional scaling based on deep learning","authors":"Mohamed Hammad ,&nbsp;Mohammed ElAffendi ,&nbsp;Ahmed A. Abd El-Latif","doi":"10.1016/j.eij.2025.100708","DOIUrl":"10.1016/j.eij.2025.100708","url":null,"abstract":"<div><div>In the field of identity verification and identification, biometrics has evolved as a reliable approach for identifying individuals based on their unique physical or behavioral characteristics. The utilization of finger vein authentication has generated significant attention as a biometric modality owing to its strong resilience, resistance against spoofing attacks, and consistent patterns. In this work, we proposed a novel cancelable finger vein authentication system using multidimensional scaling (MDS) based on deep learning. Our method addressed the limitations of previous biometric authentication systems by integrating MDS with a lightweight convolutional neural network (CNN) model for feature extraction. The cancelable approach ensured privacy and security by generating distinct templates for each user. We evaluated our system on <em>three</em> publicly available datasets for finger veins using various performance metrics, including accuracy, precision, recall, and equal error rate (EER). The results demonstrated the effectiveness of our method, which achieved high accuracy, low error rates, and strong performance in diversity and irreversibility tests. Additionally, our system maintained high authentication accuracy while preserving user privacy, making it suitable for practical applications in biometric authentication.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100708"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220953","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}