Egyptian Informatics Journal最新文献

{"title":"Blockchain-enabled genetic-inspired deep neural network for secure and efficient iot offloading and routing","authors":"D. Sindhuja ,&nbsp;R. Samson Ravindran","doi":"10.1016/j.eij.2026.100927","DOIUrl":"10.1016/j.eij.2026.100927","url":null,"abstract":"<div><div>The fast pace of Internet of Things (IoT) ecosystems has come with a major problem of secure task offloading and efficient routing, especially when the network is dynamic and its resources are limited. Traditional optimization and learning methods are usually limited by premature convergence, poor trust management, slowness and excessive energy usage, which limit their use in large-scale IoT systems. The solution to these problems is important in facilitating scalable and reliable IoT infrastructures. The paper will provide a secure and energy-efficient IoT offloading and routing framework that combines blockchain-enabled trust management and a genetic-inspired deep neural network optimization approach. The given strategy focuses on the compromise in the decision-making process by collectively looking at reliability, network lifetime, and communication efficiency, but remains flexible to the heterogeneous IoT settings. Mechanisms of blockchains are used to increase trust, transparency, and integrity of data in offloading processes whereas diversity-sensitive genetic optimization assists stable and effective learning behaviour across different network states. The effectiveness of the suggested framework is tested based on large-scale experiments involving simulations and compared to different existing models of IoT offloading and routing. Numerical data show that the main indicators of evaluation are significantly improved. The proposed scheme has a precision of 94%, accuracy of 93%, recall of 92% and F1-score of 93% as well as a stable extended network life and shorter latency with larger network sizes. These results report better robustness and reliability of decisions compared to baseline approaches. In general, the findings support the conclusion that the proposed framework can provide a useful and scalable solution to secure IoT offloading and routing. Through a capable integration of trust-conscious blockchain functionalities with adaptive learning-based optimization, the model would overcome the most significant constraints of the current protocols and will enable the needs of the IoT systems of the next generation.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100927"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396700","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":"Fuzzy Inference System-Based Prognostics for Remaining Useful Life Estimation","authors":"Mahmut Sami Şaşmaztürk ,&nbsp;Ferhat Yuna","doi":"10.1016/j.eij.2026.100897","DOIUrl":"10.1016/j.eij.2026.100897","url":null,"abstract":"<div><div>Prognostics and health management (PHM) plays a critical role in ensuring the reliability and safety of complex engineering systems such as aircraft engines. In this field, estimating the Remaining Life (RUL) of systems is vital for optimizing maintenance strategies and preventing unexpected failures. This study proposes a Fuzzy Inference System (FIS)-based approach for RUL estimation. The proposed model uses expert-defined fuzzy rules and membership functions to effectively address uncertainties and nonlinear degradation patterns in sensor data. The industry-standard NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset was used for model development and validation. Multiple features extracted from the dataset were input to the developed Fuzzy Inference System, and the system’s performance was comprehensively evaluated under different operating conditions. Experimental results demonstrate that the FIS model performs competitively compared to traditional machine learning methods and produces interpretable and robust RUL estimates. This study demonstrates the potential of fuzzy logic in data-driven prognostics and makes a significant contribution to the literature by laying a solid groundwork for future hybrid approaches that integrate expert knowledge and learning algorithms.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100897"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078371","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":"Predictive analysis of drifting test cases and critical areas for enhancing embedded systems using a Gaussian distribution methodology for multi-output analysis","authors":"M.Lakshmi Prasad ,&nbsp;R.Obulakonda Reddy ,&nbsp;Sandeep Kautish ,&nbsp;G.Suresh Reddy ,&nbsp;Abdulaziz S. Almazyad ,&nbsp;Ali Wagdy Mohamed ,&nbsp;Seyed Jalaleddin Mousavirad","doi":"10.1016/j.eij.2025.100857","DOIUrl":"10.1016/j.eij.2025.100857","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Many sectors of the economy are impacted by embedded computer systems including tools, basic architecture and a range of other features that contribute to the success of these systems. It is vital to guarantee these systems’ functionality and dependability. However, instances in which drifting behaviour can occur in embedded systems as a result of things such as software upgrades, hardware deterioration, and environmental changes over time, which can lead to drifting behaviour. As a result, test cases may become antiquated or less effective in identifying important areas of concern. This study offers a new technique for the multi-output realm of Temperature Monitoring Nuclear Reactor Systems (TMCNRS) predictive analysis of drifting test cases and key regions in embedded systems using Gaussian distribution. The examination makes use of artificial intelligence practices and statistical tools to perceive and adjust to variations in the system’s behaviour. The suggested approach’s preliminary step is gathering historic test case and system behaviour data. Using this data, a baseline Gaussian distribution that replicates the anticipated behaviour of the embedded system and the test cases that go along with it is established. In the subsequent phase, the performance of the embedded system will be continuously monitored, and renewed data will gradually be collected as to its performance. Drift is the nonconformity of the system’s behaviour with the reference line distribution that has been set. Exploiting a multi-output Gaussian distribution model, the technique forecasts conceivable drift in every test case and crucial region. Advanced learning practices are incorporated in the third phase, which modifies the test cases and critical area recognition criteria based on identified drift. The algorithm may adaptively change test cases to increase their efficiency and more correctly identify new key regions by assessing the deviations from the baseline distribution. In order to authenticate the efficacy of the suggested methodology, a multitude of real-world embedded systems across diverse fields of application are subjected to intensive experimentation. According to our results, even in the face of drifting action, the predictive analysis that manipulates the multi-output Gaussian distribution greatly increases the accuracy of the test case as well as strengthens the capacity of the system to detect important locations within the system in the presence of drifting action. The creation of a reliable and flexible technique for identifying drifting test cases and crucial regions in integrated systems is where this study contributes. Through the use of Optimal Gaussian distribution (OGD) in the context of multiple outputs, the suggested methodology presents a novel way to preserve the dependability and efficiency of embedded systems, guaranteeing their capacity to function efficiently even in constantly evolving and dynamic surroundings. This study s","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100857"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718906","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":"Quality-Aware Fuzzy-Logic-Based vertical handover decision method for dependable Real-Time visual image identification","authors":"Dongliang Zhang ,&nbsp;Lei Wang","doi":"10.1016/j.eij.2025.100876","DOIUrl":"10.1016/j.eij.2025.100876","url":null,"abstract":"<div><div>Real-time visual image identification presents significant challenges due to noise, variations in illumination, and intricate backdrops, frequently resulting in misclassification and heightened processing costs. To mitigate these constraints, we offer a Fuzzy Dependency Model for Image Identification (FDM-II) that explicitly characterizes pixel interdependencies and executes adaptive feature selection. The approach incorporates fuzzification, fuzzy derivative optimization, and defuzzification to dynamically prioritize high-dependency features, minimize duplicate computation, and enhance classification robustness in uncertain settings. Utilizing the Open Images dataset, FDM-II attained 11.43% superior detection precision, 9.84% enhanced correlation rate, and 9.55% augmented classification accuracy relative to established RSS-based, TOPSIS-MADM, and fuzzy VHO methodologies, concurrently decreasing detection error and processing time by 8.77% and 10.06%, respectively. In contrast to conventional fixed-threshold or resource-intensive deep learning models, our methodology employs adaptive correlation-based refinement and dynamic feature ranking, facilitating scalable, low-latency, and reliable real-time performance appropriate for IoT and embedded applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100876"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884769","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 novel method based on variational mode decomposition for lie detection","authors":"Nevzat Olgun","doi":"10.1016/j.eij.2025.100873","DOIUrl":"10.1016/j.eij.2025.100873","url":null,"abstract":"<div><div>In this study, a novel method based on Variational Mode Decomposition (VMD) is proposed for lie detection from EEG signals (EEGs). The study was conducted using the LieWaves database, and analyses were performed on 5 −channel EEGs obtained from 27 subjects. The EEGs collected from the subjects during truthful and lying situations were divided into 2-second segments based on the moments when visual stimuli were presented, and a total of 1350 EEG signals were obtained. For lie detection, 3 channels were selected, and EEG signals were processed using the VMD technique and time domain features were extracted from each mode. Extra Trees, Random Forest, K-Nearest Neighbors and Support Vector Machine classification models were used to classify the data. As a result of the tests, the Extra Trees model achieved the highest performance<strong>,</strong> reaching 100% classification accuracy. The other classification models achieved 99.93%, 99.48% and 64.22% classification accuracy, respectively. These results show that the VMD-based method provides an effective and efficient solution for EEG-based lie detection and it is suitable for real-time applications on portable EEG devices. Moreover, the proposed method is more advantageous than the complex approaches in the literature with its low number of channels and low processing time. The results show that this method has great potential for future studies and applications in the detection of deception.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100873"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841260","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":"Research on K-Means algorithm based on adaptive association rules and its application in commodity segmentation","authors":"Sitan Liu ,&nbsp;Quanxi Feng ,&nbsp;Wu Ai ,&nbsp;Huazhou Chen ,&nbsp;Bin Lin","doi":"10.1016/j.eij.2026.100911","DOIUrl":"10.1016/j.eij.2026.100911","url":null,"abstract":"<div><div>Accurate commodity segmentation plays a crucial role in enhancing the competitiveness of sales enterprises in marketing. Currently, the retail industry widely employs cluster analysis and association rule algorithms for commodity segmentation and data mining.</div><div>The K-Means algorithm is widely used due to its simplicity, fast convergence, and suitability for large-scale datasets. However, traditional K-Means suffers from issues such as sensitivity to initial cluster centers, inability to handle mixed-type data, and ignoring relationships between attributes. While association rule mining effectively uncovers relationships between attributes, it is generally applied to categorical or discretized data and may generate an overly large set of candidate rules. To address these challenges, this paper proposes a novel clustering algorithm based on adaptive association rules, named AAP-KM.</div><div>The algorithm first uses adaptive association rules (AAP) to partition the dataset and obtain an initial division. It then calculates the initial cluster centers based on this partition, followed by the application of the K-Means algorithm for clustering. The main distinction of AAP-KM from traditional clustering methods is that it incorporates attribute relationships to determine more representative initial cluster centers. Additionally, the algorithm enhances its adaptability to different types of datasets by employing a secondary attribute transformation technique. To evaluate its effectiveness, numerical experiments are conducted on eight UCI datasets, with comparisons made against other improved K-Means algorithms. Experimental results demonstrate that AAP-KM exhibits significant performance advantages across multiple datasets. Finally, the AAP-KM algorithm is applied to the task of product segmentation.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100911"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187900","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":"Binary classification for imbalanced datasets using a novel metric method","authors":"Jian Zheng ,&nbsp;Shengye Wang ,&nbsp;Huyong Yan ,&nbsp;Haichao Sun","doi":"10.1016/j.eij.2026.100890","DOIUrl":"10.1016/j.eij.2026.100890","url":null,"abstract":"<div><div>This work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction enables adaptive exploration of minority class regions, and a Riemannian metric–guided kernel amplification mechanism effectively induces minority class migration in feature space, tightening the spatial distance inner minority class instances. Experimental validation across ten UCI benchmark datasets with class imbalance demonstrate the superior performance of our proposed method. The method achieves statistically significant superiority over all six baseline approaches on five highly imbalanced datasets (with imbalance ratios (IR) &gt; 10:1), notably achieving 0.883 F1-score on datasets with 40.22:1 imbalance ratio and 0.800 sensitivity to the minority class. Furthermore, our approach maintains competitive advantages on the remaining five moderately imbalanced datasets (IR &lt; 10:1), outperforming a subset of the baseline methods across all evaluation metrics. Furthermore, the kernel amplification mechanism boosts the sensitivity to perception minority classes by a maximum 6.35-fold enhancement on highly imbalanced datasets, and by a maximum 2.17-fold enhancement on moderately imbalanced datasets. The derived amplification factor exhibits dimension-dependent characteristics, showing independence from both sample size and imbalanced ratio——a critical advantage for high-dimensional imbalanced classification.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100890"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078375","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 and time–frequency feature framework for optimal DER planning in radial networks","authors":"Sangeeta DebBarman ,&nbsp;Kumari Namrata ,&nbsp;Manoj Gupta ,&nbsp;Pannee Suanpang ,&nbsp;Aziz Nanthaamornphong","doi":"10.1016/j.eij.2026.100928","DOIUrl":"10.1016/j.eij.2026.100928","url":null,"abstract":"<div><div>The rapid integration of photovoltaic and wind-based distributed energy resources (DERs) into radial distribution networks has introduced operational challenges such as voltage instability, increased losses, and unpredictable system behaviour under renewable variability. These issues require optimization frameworks that are both computationally efficient and capable of modelling uncertainty. This paper presents a Machine Learning-Enhanced Cheetah Optimizer (ML-EChOA) that integrates time–frequency voltage analysis with surrogate-assisted metaheuristic search to achieve fast and accurate techno-economic DER allocation. Voltage time series are transformed into spectrograms and scalograms, from which Local Binary Pattern features are extracted to capture transient behaviour. A Gradient Boosting surrogate is trained on these features to approximate power-flow outcomes, enabling the optimizer to evaluate candidate solutions with minimal computational overhead. Deterministic and probabilistic scenarios — generated through LSTM-based forecasting of solar, wind, and load profiles — ensure that the optimization remains robust under uncertainty. The proposed approach produces substantially improved voltage quality, reduced losses, and enhanced economic performance while converging faster than conventional metaheuristics. These results illustrate the potential of ML-EChOA as a scalable, intelligent, and uncertainty-aware optimization tool for renewable integration and future smart distribution networks.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100928"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396707","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":"Adaptive sampling enhanced deep learning framework for accurate interpretable stroke risk prediction","authors":"Rasha M. Abd El-Aziz,&nbsp;Alanazi Rayan","doi":"10.1016/j.eij.2026.100887","DOIUrl":"10.1016/j.eij.2026.100887","url":null,"abstract":"<div><div>Stroke is a leading cause of global mortality and long-term disability, emphasizing the urgent need for predictive models that are accurate, interpretable, and equitable to support precision medicine. Conventional risk assessment methods often rely on a limited set of clinical indicators and ignore subgroup-specific patterns, which reduces predictive performance and can bias outcomes against underrepresented populations. To address these challenges, this study proposes ASTab-Stroke (Adaptive Stratified TabNet for Stroke Prediction), a deep learning framework integrating Adaptive Stratified Sampling (ASS) with TabNet’s sequential attention mechanism. ASS dynamically reweights patient strata based on their contribution to prediction errors, ensuring fair representation of minority and high-risk subgroups without introducing synthetic data. TabNet’s sequential attention provides step-wise feature attribution, enabling clinicians to interpret the influence of predictors such as age, hypertension, heart disease, glucose level, BMI, and lifestyle factors on stroke risk. The framework was implemented in Python 3.10 and evaluated using the Stroke Prediction Dataset, which includes diverse demographic, clinical, and lifestyle variables. ASTab-Stroke achieved 98% accuracy, 0.998 AUC, 0.97 F1-score, 0.99 recall, and 0.98 precision, outperforming existing baselines by approximately 3% in accuracy while demonstrating improved sensitivity and fairness across clinically significant subgroups. The age and comorbidity features proved to be critical in ablation studies and work on cross-validation showed strong generalization. This framework is a clinically interpretable, scalable, and ethically rationalized method of stroke risk prediction, which gives dependable information to support clinical decision-making with data. The flexibility of it implies that it has a wide potential to be used in other fields of precision medicine, where interpretability and subgroup fairness are crucial in promoting equitable and informed patient care.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100887"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037846","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":"SHIELD-FL: Scalable backdoor defense in federated learning via gradient trust and data-free distillation under non-IID data","authors":"Ahmed Soliman,&nbsp;Khalid M. Amin,&nbsp;Noura A. Semary,&nbsp;Hayam Mousa","doi":"10.1016/j.eij.2026.100904","DOIUrl":"10.1016/j.eij.2026.100904","url":null,"abstract":"<div><div>Federated Learning (FL) enables collaborative model training without sharing raw data but remains vulnerable to backdoor attacks, particularly under high adversarial ratios and non-IID data distributions. Existing defenses often rely on clean public datasets or strong threat assumptions, limiting real-world applicability. We propose <strong>SHIELD-FL</strong> (Secure Hybrid Inspection for Enhanced Learning Defense in Federated Learning), a scalable, attack-agnostic framework that operates without external data. SHIELD-FL integrates: (1) <em>Gradient Trust Indexing</em>, which dynamically scores client reliability via adversarial perturbation sensitivity; (2) <em>Adaptive Clustering</em> using HDBSCAN in parameter space to isolate benign clients; and (3) <em>Robust Knowledge Distillation</em> with temperature-scaled soft labels and stochastic weight averaging. Extensive evaluations on CIFAR-10, EMNIST, and Fashion-MNIST under five adaptive backdoor attacks show SHIELD-FL achieves up to 92.5% main-task accuracy while reducing attack success rates to 3.6%, even with 60% malicious clients. It outperforms data-dependent defenses like FLTrust, maintains low communication overhead, and runs 3–4<span><math><mo>×</mo></math></span> faster than ensemble methods. SHIELD-FL is especially suitable for privacy-sensitive, resource-constrained environments including emerging MENA region applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100904"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187898","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}