Egyptian Informatics Journal最新文献

{"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}

{"title":"Innovative quantum techniques for improving system performance in cloud computing","authors":"Naher M. Alsafri ,&nbsp;Ahmed Y. Hamed ,&nbsp;A. Mindil ,&nbsp;M.R. Hassan","doi":"10.1016/j.eij.2025.100710","DOIUrl":"10.1016/j.eij.2025.100710","url":null,"abstract":"<div><div>Effective task scheduling is pivotal for optimizing the performance of cloud computing services, particularly to minimize execution time and enhance resource utilization. Traditional approaches often focus on single-objective metrics, such as task completion time, or fail to address the intricate interdependencies between multiple objectives. To overcome these limitations, we introduce QISPF, a novel multi-objective task scheduling algorithm that combines genetic algorithms with innovative quantum techniques. QISPF is designed to achieve an optimal task distribution by addressing key performance metrics makespan, scheduling length, throughput, resource utilization, energy consumption, and load balancing, through a unified measure known as system performance. QISPF leverages quantum techniques to enhance the traditional genetic algorithm framework by incorporating principles from quantum mechanics, such as probabilistic quantum encoding and superposition. The simulations were conducted for two cases. The first had 100 tasks and anything from 10 to 50 virtual machines. Furthermore, in the second case, there were a certain number of virtual machines (VMs), with the number of tasks ranging from 500 to 1000. The simulation results demonstrated the scheduling efficiency of QISPF compared to the G-MOTSA, ETVMC, TSACS, and ACO algorithms. QISPF offers a more powerful approach to exploring and exploiting the solution space. This novel method allows for a richer representation of potential solutions and improves the algorithm’s ability to find high-quality solutions in complex problem landscapes.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100710"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144229763","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":"Machine learning models for enhanced stroke detection and prediction","authors":"Shilpa Bajaj ,&nbsp;Manju Bala ,&nbsp;Mohit Angurala","doi":"10.1016/j.eij.2025.100705","DOIUrl":"10.1016/j.eij.2025.100705","url":null,"abstract":"<div><div>Stroke detection plays a vital role in medical diagnostics, where timely and accurate identification can improve patient outcomes. This research evaluates the performance of three machine learning models—OzNet-mRMR-NB, Logistics Regression, and an Ensemble CNN—using medical images for stroke prediction. The OzNet-mRMR-NB model integrates VGG19 for feature extraction, mRMR for feature selection, and Naive Bayes for classification, while Logistic Regression processes flattened feature vectors. The Ensemble CNN, leveraging ResNet and VGG19, outperforms the other models with a testing accuracy of 92.43 %, an AUC score of 0.92, precision of 0.93, and an F1-score of 0.92. Additionally, recall for both the Ensemble and OzNet models was 0.93, highlighting the Ensemble model’s capacity to sustain a robust balance between specificity and sensitivity. These results highlight the advantages of combining diverse CNN architectures for improved accuracy and generalizability. This research advances automated stroke detection, with potential clinical applications for timely and informed decision-making. Future work will refine the ensemble approach for broader clinical adoption across diverse patient populations.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100705"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241315","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":"Emotion recognition in panoramic audio and video virtual reality based on deep learning and feature fusion","authors":"Siqi Guo,&nbsp;Mian Wu,&nbsp;Chunhui Zhang,&nbsp;Ling Zhong","doi":"10.1016/j.eij.2025.100697","DOIUrl":"10.1016/j.eij.2025.100697","url":null,"abstract":"<div><div>Virtual reality technology has been widely applied in various fields of society, and its content emotion recognition has received much attention. The recognition of emotions in virtual reality content can be employed to regulate emotional states in accordance with the emotional content, to treat mental illness and to assess psychological cognition. Nevertheless, the current research on emotion induction and recognition of virtual reality scenes lacks scientific and quantitative methods for establishing the mapping relationship between virtual reality scenes and emotion labels. Furthermore, the associated methods lack clarity regarding image feature extraction, which contributes to the diminished accuracy of emotion recognition in virtual reality content. To solve the current issue of inaccurate emotion recognition in virtual reality content, this study combines convolutional neural networks and long short-term memory. The attention mechanism and multi-modal feature fusion are introduced to improve the speed of feature extraction and convergence. Finally, an improved algorithm-based emotion recognition model for panoramic audio and video virtual reality is proposed. The average accuracy of the proposed algorithm, XLNet-BIGRU-Attention algorithm, and CNN-BiLSTM algorithm was 98.87%, 90.25%, and 86.21%, respectively. The average precision was 98.97%, 97.24% and 97.69%, respectively. The proposed algorithm was significantly superior to the comparison algorithm. A performance comparison was conducted between panoramic audio and video virtual reality emotion recognition models based on the improved algorithm. The improved algorithm’s the mean square error is 0.17 and mean absolute error is 0.19, obviously better than other comparison models. In the analysis of visual classification results, the proposed model has the best classification aggregation effect and is significantly superior to other models. Therefore, the improved algorithm and the panoramic audio and video virtual reality emotion recognition model based on the improved algorithm have good effectiveness and practical value.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100697"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241316","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":"Intrusion detection system framework for cyber-physical systems","authors":"Shafiq ur Rehman ,&nbsp;Hisham Alhulayyil ,&nbsp;Taher Alzahrani ,&nbsp;Hatoon AlSagri ,&nbsp;Muhammad U. Khalid ,&nbsp;Volker Gruhn","doi":"10.1016/j.eij.2024.100600","DOIUrl":"10.1016/j.eij.2024.100600","url":null,"abstract":"<div><div>Cyber-Physical Systems (CPS) have become integral components across diverse sectors, including autonomous vehicle systems, healthcare, power distribution, and manufacturing. These systems leverage physical components enhanced with intelligent capabilities, enabling autonomous functionality and increased efficiency. Security is a critical concern for CPS due to their close integration with essential infrastructure, where failures can have severe consequences for both the physical environment and human lives. Intrusion Detection Systems (IDS) can be a vital tool for secure CPS, detecting and alerting against threats such as malicious activities. However, conventional IDS designs are often inadequate for CPS environments, typically focusing solely on the network (Network-based Intrusion Detection System or NIDS) or application layer (Host-based Intrusion Detection System or HIDS), while neglecting the physical layer. Therefore, this research proposes a novel IDS framework that employs a hybrid detection approach, along with comprehensive guidelines for intrusion detection specifically tailored to CPS. This initiative contributes towards establishing a cohesive IDS framework for CPS, empowering practitioners in navigating this domain and crafting bespoke intrusion detection solutions. The proposed approach has been rigorously evaluated through a comparative analysis of different methodologies, demonstrating the effectiveness of the guidelines and requirements in addressing all relevant security aspects for IDS design. This research provides CPS practitioners and researchers with actionable guidelines designed to effectively enhance the security posture of their systems. By implementing these guidelines, they can better protect against threats and mitigate their potential consequences, thereby contributing to the security of Industry 4.0. This proactive approach not only secure critical infrastructure but also fosters a more resilient and secure operational environment in the face of evolving cyber threats.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100600"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212211","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":"An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm","authors":"Ibrahim Musa Conteh ,&nbsp;Gibril Njai ,&nbsp;Abass Conteh ,&nbsp;Qingguo Du","doi":"10.1016/j.eij.2025.100709","DOIUrl":"10.1016/j.eij.2025.100709","url":null,"abstract":"<div><div>This paper proposes an improved feature selection method based on an improved Slime Mould Algorithm (SMA), called the Triangular Mutation Rule Restart Strategy Slime Mould Algorithm (TRSMA), to overcome some of the shortcomings of the SMA, including premature convergence, poor population diversity, and local optima entrapment. The TRSMA uses three main strategies: (1) Good Point Set (GPS) initialization for better initial population diversity, (2) Triangular Mutation Rule (TMR) for better global exploration and finding higher-quality areas in the solution space, and (3) a Restart Strategy (RS) to reinitialize weak individuals to escape from local optimum. Then we combine the TRSMA with Support Vector Machines (SVM) and propose the TRSMA-SVM model to select the joint feature and classifier parameters. Experimental results on nine University of California, Irvine (UCI) datasets and a real-world malaria dataset show that TRSMA-SVM consistently outperforms recent state-of-the-art methods regarding classification accuracy with fewer selected features. Comparison with benchmark testing on CEC2017 functions confirms TRSMA’s ability to perform global optimization. Statistical tests using the Wilcoxon rank-sum and Friedman tests also verify these performance gains. The results illustrate that TRSMA is powerful and can handle complex high-dimensional optimization problems.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100709"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220959","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":"Advanced skin lesion segmentation and classification using adaptive contextual GLCM and deep learning hybrid models","authors":"Vinit Kumar ,&nbsp;D.L. Shanthi ,&nbsp;Tummala Ranga Babu ,&nbsp;Narendra Kumar ,&nbsp;Rakesh Kumar Godi ,&nbsp;Dr Arun G","doi":"10.1016/j.eij.2025.100706","DOIUrl":"10.1016/j.eij.2025.100706","url":null,"abstract":"<div><div>One of the most common and dangerous types of cancer, skin cancer, especially melanoma, is distinguished by its quick spread and high death rate if left untreated. It is impossible to overestimate the significance of early detection and precise diagnosis as they are essential for successful treatment and greatly raise patient survival rates. To improve skin lesion segmentation and classification, this work presents a state-of-the-art image analysis technique that uses hybrid deep learning models in conjunction with the Adaptive Contextual Gray Level Co-occurrence Matrix (GLCM). DeepLabV3 + is used to segment the skin lesion from pre-processed input images with high accuracy. A Long Short-Term Memory (LSTM) network trained on characteristics collected by the Adaptive Contextual GLCM is included to further evaluate the segmented images. Because of this special combination, the model can accurately capture contextual information and tiny texture differences, both of which are essential for differentiating between benign and malignant skin lesions. Our suggested approach performs very well in tasks involving both segmentation and classification. In particular, it attains a remarkable 98.34 % accuracy, 99.13 % specificity, 97.25 % sensitivity, 97.15 % Dice coefficient, and 98.6 % F1 score. These findings demonstrate the method’s stability and dependability, which makes it a potentially useful instrument for raising the detection accuracy of skin cancer. This approach has the potential to transform early detection procedures by providing improved diagnostic accuracy and resilience, eventually contributing to better patient outcomes and more effective management of skin cancer cases.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100706"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212212","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":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(25)00114-8","DOIUrl":"10.1016/S1110-8665(25)00114-8","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100721"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321113","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":"Intelligent VANET-based traffic signal control system for emergency vehicle prioritization and improved traffic management","authors":"Pijush Bairi ,&nbsp;Sujata Swain ,&nbsp;Anjan Bandyopadhyay ,&nbsp;Khursheed Aurangzeb ,&nbsp;Musaed Alhussein ,&nbsp;Saurav Mallik","doi":"10.1016/j.eij.2025.100700","DOIUrl":"10.1016/j.eij.2025.100700","url":null,"abstract":"<div><div>Emergency vehicles (EmVs) are essential for saving lives and reducing damage in critical situations, yet their movement is often hindered by urban traffic congestion and inefficient signal control. Traditional fixed-time and pre-timed traffic signals lack the adaptability needed to prioritize EmVs, causing significant delays. This paper proposes an advanced, intelligent traffic signal control system based on Vehicular Ad-hoc Networks (VANETs) and Vehicle-to-Infrastructure (V2I) communication to optimize EmV passage and improve traffic flow. The system dynamically adjusts signal timings in real time, utilizing an adaptive control algorithm that calculates EmV arrival times, adjusts signal phases, and maintains balance to minimize disruptions to regular traffic. By leveraging V2I communication, traffic controllers receive instant updates on EmV locations and traffic conditions, enabling prioritized EmV passage. Simulation results using SUMO and OMNeT++ demonstrate that this approach can reduce EmV travel time compared to conventional systems, with minimal impact on regular traffic. The system also achieves a high success rate of preemption requests, ensuring that EmVs can pass through intersections without stopping. Furthermore, optimization results reveal that the proposed system outperforms Fixed-Time Control Methods (FTCM) with an average of 66.45% reduction in EmV travel times, Flexible Signal Preemption Methods (FSPM) by an average of 57.08%, and Intrusive Signal Preemption Methods (ISPM) by an average of 12.90%. Above findings highlight the potential of the proposed method in optimizing traffic flow, reducing emergency response times, and improving overall road safety. This research provides a scalable, real-world applicable model for enhancing emergency response efficiency in urban environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100700"},"PeriodicalIF":5.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281027","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":"Novel framework on double event-triggered consensus of positive multi-agent systems","authors":"Junfeng Zhang ,&nbsp;Lishuo Dong ,&nbsp;Di Wu ,&nbsp;Ahmed Bakr","doi":"10.1016/j.eij.2025.100695","DOIUrl":"10.1016/j.eij.2025.100695","url":null,"abstract":"<div><div>The paper investigates double adaptive event-triggered consensus (DAETC) for positive multi-agent systems (PMASs). The DAETC refers to the observer and control protocols. First, an adaptive event-triggered observer is constructed for the systems by designing an observer gain and the corresponding auxiliary gain. Then, an adaptive event-triggered consensus protocol is proposed based on the designed observer. Two kinds of adaptive event-triggered conditions are established for the observer and control protocols. Thus, a novel consensus framework is constructed in this paper for PMASs by introducing an additional term. Meanwhile, a double event-triggering mechanism is presented for PMASs. The gain matrices of the observer and control protocol are designed by means of linear programming (LP) and matrix decomposition techniques and some sufficient conditions are addressed to ensure positivity and consensus of the closed-loop systems. The contributions of the paper lie in that: (i) A novel DAETC framework is constructed, (ii) A tractable design approach is established to obtain the gains of observer and control protocols, and (iii) A simple analysis and computation approach containing co-positive Lyapunov function (CLF) and linear programming (LP) is presented for the systems. Finally, an example is given to verify the effectiveness of the theoretical results and the corresponding comparison simulations are provided by choosing different initial conditions. It is shown from these simulations that all states are driven to a non-negative region rather than zero and the sampling frequency of observer and the control protocol is reduced. A graphical abstract is shown.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100695"},"PeriodicalIF":5.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169446","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}