Egyptian Informatics Journal最新文献

{"title":"Improved lightweight node storage solutions in blockchain","authors":"Hongping Cao ,&nbsp;Raja Kumar Murugesan ,&nbsp;Hongxing Cao ,&nbsp;Hengfeng Shen","doi":"10.1016/j.eij.2025.100648","DOIUrl":"10.1016/j.eij.2025.100648","url":null,"abstract":"<div><div>Lightweight nodes in blockchain systems face challenges in terms of dependence, verification efficiency, and security due to their limited storage and growing data volume. This article focuses on two types of lightweight nodes: lightweight clients (e.g., Bitcoin wallets) and DHT (Distributed Hash Table) cluster nodes. Lightweight clients rely entirely on full nodes for transaction verification, resulting in dependence and vulnerability. DHT cluster nodes share storage; thereby, each node maintains a fraction of the data and retrieves the remaining data from other nodes. This will introduce processing latency when verifying new transactions. Testing conducted on Bitcoin indicates that nodes maintaining recent blocks can locally verify most new transactions. Based on this, this article proposes a new design, RBS (Recent Block Storage), where each cluster node stores recent blocks and shares older ones. Lightweight clients expand storage for recent blocks. Test results on Bitcoin show this design can reduce remote data retrieval and associated processing delays for lightweight nodes by 90 % with only 8 GB of extra storage per node. This design improves the independence and security of lightweight clients and reduces inter-node data retrieval within DHT clusters. It will facilitate the broader application of blockchain technology across various fields.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100648"},"PeriodicalIF":5.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680330","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":"Clipper: An efficient cluster-based data pruning technique for biomedical data to increase the accuracy of machine learning model prediction","authors":"M.B. Karadeniz ,&nbsp;Ebru Efeoğlu ,&nbsp;Burak Çelik ,&nbsp;Adem Kocyigit ,&nbsp;Bahattin Türetken","doi":"10.1016/j.eij.2025.100641","DOIUrl":"10.1016/j.eij.2025.100641","url":null,"abstract":"<div><div>The exponential rise in clinical research costs can potentially be mitigated by half through the implementation of machine learning-driven efficient data processing techniques. Traditional methods like data preprocessing and hyperparameter tuning, which are effective for model optimization, often introduce complexities that can diminish the benefits of machine learning integration. To overcome this issue, we present Clipper: a novel, cluster-based data pruning approach designed specifically for biomedical data, aiming to enhance the predictive accuracy of machine learning models. Clipper’s key advantage lies in its ability to automate the data pruning process, optimizing accuracy without the need for manual hyperparameter adjustments—a typically cumbersome aspect of machine learning tasks. Upon comprehensive comparative analysis, the proposed Clipper methodology demonstrates superior performance across various medical and biological datasets. Our experiments reveal Clipper’s consistent superiority over baseline models, with significant accuracy improvements: 44% for Heart Disease, 7% for Breast Cancer, 40% for Parkinson’s, and 20% for Raisin classification. Specifically, the model achieves remarkable predictive accuracy, with classification rates of 99.5% for Heart Disease, 99.64% for Breast Cancer, 99.47% for Parkinson’s Disease, and 93% for Raisin Classification, thereby substantially outperforming contemporary state-of-the-art computational techniques. The empirical evidence suggests that Clipper serves as an effective accuracy enhancer for baseline models, eliminating the need for parameter tuning or complex preprocessing steps. Furthermore, Clipper produces robust outputs even at very low split rates, where baseline models typically perform poorly.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100641"},"PeriodicalIF":5.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680331","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 real-time system for monitoring and classification of human falls on stairs using 2.4 GHz XBee3 micro modules with a tri-axial accelerometer and KNN algorithms","authors":"Apidet Booranawong ,&nbsp;Sittiporn Sukveeraphan ,&nbsp;Liangrui Pan ,&nbsp;Nattha Jindapetch ,&nbsp;Pornchai Phukpattaranont ,&nbsp;Hiroshi Saito","doi":"10.1016/j.eij.2025.100643","DOIUrl":"10.1016/j.eij.2025.100643","url":null,"abstract":"<div><div>In this paper, a monitoring and classification system for human activities on stairs is presented. The contribution of this work is that, first, we develop the real-time wireless sensor monitoring system for measuring human motion data using 2.4 GHz IEEE 802.15.4 XBee3 micro modules as the low-power wireless modules, where the GY-521 accelerometer sensor is attached. Here, human activities on stairs, including stair ascent, stair descent, turning around, and falling, are mainly focused on preventing any dangerous accidents. Second, using the measured data, the signal vector magnitude (SVM) calculation, signal filtering using an exponentially weighted moving average (EWMA), feature extraction using the mean, maximum, interquartile range (IQR), standard deviation (STDEV), variance, and peak-to-peak (PTP) amplitude, and classification using the K-nearest neighbors (KNN) algorithm are applied. Experiments have been conducted in a home scenario. Results indicate that the proposed system can efficiently monitor human activities on stairs in real-time with reliable communications, as indicated by a strong level of the received signal strength indicator (RSSI), and a packet delivery ratio (PDR) of 100 % for both line-of-sight (LoS) and non-line-of-sight (NLoS) communications. Additionally, the proposed system using only one variance feature and the KNN classifier provides classification accuracy of 89 % for stair ascent, 70 % for stair descent, 95 % for turning around, and 100 % for falling (a critical or focused event); 88 % on average results. Thus, our system, which includes devices and classification algorithms, has the potential to monitor and categorize human falls on stairs via wireless communication, and it can be applied in practical situations.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100643"},"PeriodicalIF":5.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643099","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 innovative approach to advanced voice classification of sacred Quranic recitations through multimodal fusion","authors":"Esraa Hassan ,&nbsp;Abeer Saber ,&nbsp;Omar Alqahtani ,&nbsp;Nora El-Rashidy ,&nbsp;Samar Elbedwehy","doi":"10.1016/j.eij.2025.100640","DOIUrl":"10.1016/j.eij.2025.100640","url":null,"abstract":"<div><div>The Quran is the most important book we have ever read or recited. Perfecting recitation of the Holy Quran is challenging. In this paper, we integrate the use of multimodal fusion to result in advanced voice classification of sacred Quranic recitations. The proposed work called Voice Shortcut Connection Fusion (VSCF) architecture also targets restrictions corresponding to the dataset size and reciters’ variations into which Residual Neural Network (ResNet50) with the Fusion Layer incorporated in voice classification is integrated. The VSCF architecture is designed in a highly complex manner and is designed to be highly sophisticated about the extent to which it can approximate high-level features as well as higher-level features within a wide range of acoustic signals. The Fusion Layer proves to be an important layer that combines the ResNet50 model’s final layer with the Global Average Pooling of the raw MFCC features of the audios. This synergistic fusion enhances the ability of the model by a vast extent to identify the underlying stylistic features inherent in each reciter’s performance. The dataset consists of a Quranic Recitation Dataset having 7144 WAV format audio files from 12 Quran reciters. Compared with the traditional voice classification strategies, VSCF aims at solving issues regarding limitations of the adopted datasets and variations among different reciters. The results from our experiment showcase the effectiveness of the VSCF architecture, achieving an accuracy of 0.97683%. Further metrics include sensitivity at 0.9752, specificity at 0.9785, precision at 0.9875, and an F1 score of 0.9813.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100640"},"PeriodicalIF":5.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641991","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":"PRFE-driven gene selection with multi-classifier ensemble for cancer classification","authors":"Smitirekha Behuria ,&nbsp;Sujata Swain ,&nbsp;Anjan Bandyopadhyay ,&nbsp;Mohammad Khalid Al-Sadoon ,&nbsp;Saurav Mallik","doi":"10.1016/j.eij.2025.100637","DOIUrl":"10.1016/j.eij.2025.100637","url":null,"abstract":"<div><div>In this era, cancer remains a paramount concern due to its pervasive impact on individuals and societies, persistent challenges in treatment and prevention, and the ongoing need for global collaboration and innovation to improve outcomes and reduce its burden. Cancer marked by uncontrolled cell growth is a leading global cause of mortality, necessitating advanced methods for accurate diagnosis. This study introduces an innovative unsupervised feature selection technique Principal Recursive Feature Eliminator (PRFE) for selection of genes and cancer classification. Subsequently, seven different classifiers—Support Vector Machine, Random Forest, CatBoost, Light Gradient Boosting Method, Artificial Neural Network, Convolutional Neural Network, Long Short-Term Memory are used to increase the model’s robustness. The proposed approach is evaluated on nine benchmark gene expression datasets with a combination of two different algorithms. A series of experiments are conducted to assess the proposed method, focusing on the selected features and identifying optimal classifiers. We have calculated F1-Score, accuracy, recall, and precision. The suggested strategy performs better than expected, as the results highlight its potential to improve cancer classification techniques with an accuracy of 99.98%. We conclude from this analysis that, across many datasets, the CatBoost and CNN model outperforms the other models. This research contributes to the ongoing efforts to improve diagnostic precision and treatment outcomes in cancer research.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100637"},"PeriodicalIF":5.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631904","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 IoT-based architecture for human resource management in construction sites using software defined networking (SDN) and game theory","authors":"Xuejie Niu","doi":"10.1016/j.eij.2025.100629","DOIUrl":"10.1016/j.eij.2025.100629","url":null,"abstract":"<div><div>Construction sites are notoriously hazardous. This paper explores how the Internet of Things (IoT) can improve worker safety and resource management in these complex environments. Traditional monitoring systems struggle with managing diverse sensor data and ensuring scalability on large construction projects. This research proposes a novel architecture that leverages Software-Defined Networking (SDN) to manage an IoT network of environmental, biometric, and surveillance sensors. This centralized control enables efficient topology control and improved network performance. Furthermore, a game-theoretic approach is employed to optimize resource allocation and routing decisions, minimizing interference and maximizing network throughput. The proposed approach achieves significant enhancements in key network metrics: delay is reduced by 59.49%, energy consumption drops by 18.31%, and packet delivery rate increases by 7.70%. This research demonstrates the potential of SDN-based IoT architectures to revolutionize safety and resource management in construction, paving the way for future advancements in on-site worker well-being and project efficiency.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100629"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563156","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":"IFC: Editorial","authors":"","doi":"10.1016/S1110-8665(25)00045-3","DOIUrl":"10.1016/S1110-8665(25)00045-3","url":null,"abstract":"","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100652"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678989","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":"Mechanical fault diagnosis based on combination of sparsely connected neural networks and a modified version of social network search","authors":"Shang Xu","doi":"10.1016/j.eij.2025.100633","DOIUrl":"10.1016/j.eij.2025.100633","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Swift and precise fault diagnosis is a significant category to guarantee machinery operates reliably and avoid major failures. Conventional methods for monitoring bearing health rely on large datasets of labeled faulty samples, which can be time-consuming and costly to gain. Intelligent fault identification techniques are limited by a lack of defective samples. Although Convolutional Neural Networks (CNNs) are effective tools for diagnosing mechanical faults, they may perform poorly on unseen data due to overfitting when trained with few faulty samples. The small sample problem poses a significant difficulty in mechanical fault diagnosis, as insufficient faulty samples can induce overfitting in models such as CNNs that&lt;!--&gt; &lt;!--&gt;leads to inadequate generalization. For instance, conventional CNNs may extremely adapt to the unique traits of limited training data, whereas SCNNs, characterized by their sparse connectivity, reduce this concern. Additionally, optimizing the hyperparameters of SCNNs can be complex.&lt;!--&gt; &lt;!--&gt;However, the M-SNS algorithm, using&lt;!--&gt; &lt;!--&gt;Lévy flight and self-adjusting population mechanisms&lt;!--&gt; &lt;!--&gt;proficiently solves this issue by enhancing exploitation and exploration. This study suggests a novel approach to solve the small sample problem Sparsely Connected Neural Networks (SCNNs) enhanced by optimizing its hyperparameters based on an improved version of Social Network Search (M-SNS). While standard SNS-based optimizers struggle with local optima, the improved version incorporates Lévy flight to meaningly improve global search performance, guaranteeing better generalization even in small sample scenarios. The proposed SCNN/M-SNS is employed to use a tool for the fault diagnosis. To guarantee the efficiency of the model, its results are applied to a benchmark, called Case Western Reserve University (CWRU) Bearing Dataset which includes a flexible test rig with a 2 hp motor to simulate various load conditions (0, 1, 2, and 3 hp) and controlled fault introduction using Electrical Discharge Machining (EDM) to precisely introduce faults in bearings with different diameters (7, 14, 28, and 40 mils) and at different locations (roller, inner, and outer race faults). Accelerometers are used to collect data at two sampling rates (12 kHz and 48 kHz). The results are compared with some similar state of the art models based on SCNN, including fuzzy neural network model (FNN), Convolution Neural Network (CNN), and LSTM neural network. The results show that the proposed model achieves a mean squared error (MSE) of 0.021 on the test dataset which is higher than the fully connected network with an MSE of 0.035. When compared to other metaheuristic algorithms, the M-SNS-based SCNN model achieves an MSE of 0.021, while Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) achieve MSE values of 0.023 and 0.025, respectively. The proposed model also outperforms other techniques on the CWRU dataset, with a classification ","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100633"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609488","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 improved routing technique for energy optimization and delay reduction for Wireless body area networks","authors":"Swati Goel ,&nbsp;Kalpna Guleria ,&nbsp;Surya Narayan Panda ,&nbsp;Fahd S. Alharithi ,&nbsp;Aman Singh ,&nbsp;Aitizaz Ali","doi":"10.1016/j.eij.2025.100630","DOIUrl":"10.1016/j.eij.2025.100630","url":null,"abstract":"<div><div>Wireless Body Area Networks (WBANs) have a lot of potential applications in the medical domain for remote health monitoring. The critical concerns which have a significant impact on WBAN’s performance are energy efficiency and end-to-end latency. This paper proposes an energy-efficient routing technique for WBAN that decreases network delay, lowers the bit error rate, and increases the network’s total lifespan. To improve the performance of WBAN the proposed Energy-Efficient Adaptive Routing Technique (EEART) performs clustering, scheduling, and optimisation namely, Glowworm Swarm Optimisation (GSO). The network is initially split into clusters, and each cluster is assigned a Cluster Head (CH). During scheduling, the load is distributed among the cluster nodes based on time, priorities, and energy of nodes for effective operation of the network. The GSO adjusts the configuration dynamically in accordance with the conditions of the network for finding an optimal path. The simulation results demonstrate that the energy and latency of the WBAN network improves considerably with an increased efficiency of 30–35%. The proposed technique performs better in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and end-to-end delay in comparison to other existing routing protocols.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100630"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508594","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 machine learning neural network architecture for the accelerating universe based modified gravity","authors":"Zulqurnain Sabir ,&nbsp;Basma Souayeh ,&nbsp;Zahraa Zaiour ,&nbsp;Alyn Nazal ,&nbsp;Mir Waqas Alam ,&nbsp;Huda Alfannakh","doi":"10.1016/j.eij.2025.100635","DOIUrl":"10.1016/j.eij.2025.100635","url":null,"abstract":"<div><div>The current investigations present the numerical outputs of the mathematical accelerating universe based modified gravity model (MAUMGM) by designing a computational stochastic structure using the Bayesian regularization neural network. The classification of the mathematical MAUMGM is presented into five different nonlinear classes. A dataset is designed using the explicit Runge-Kutta scheme, which is divided into training as 82% and 9%, 9% for testing and verification. The designed stochastic process for solving the MAUMGM contains log-sigmoid activation function, thirty neurons in the hidden layer, dataset based explicit Runge-Kutta, and Bayesian regularization for the optimization. The correctness of the stochastic solver is perceived by comparing the outcomes along with absolute error 10^-06 to 10^-09. The best training values are reported around 10^-13 to 10^-14, which also signify the solver’s perfection. To authenticate the accuracy, and competence of the solver, some tests have been taken using the parameters of regression, state transition, and error histogram.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"29 ","pages":"Article 100635"},"PeriodicalIF":5.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535112","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}