Egyptian Informatics Journal最新文献

{"title":"Designing lightweight secure and energy-efficient wireless acoustic sensor networks for optimized data transmission and processing","authors":"Utpal Ghosh ,&nbsp;Uttam kr. Mondal ,&nbsp;Abdelmoty M. Ahmed ,&nbsp;Ahmed A. Elngar","doi":"10.1016/j.eij.2025.100883","DOIUrl":"10.1016/j.eij.2025.100883","url":null,"abstract":"<div><div>The deployment of effective data transmission with minimal resources, minimum architecture, low power consumption, and improved security makes this proposed lightweight wireless acoustic sensor network (WASNs) an appealing solution. This paper addresses the challenges of secure and energy-efficient audio broadcasting in WASNs. To transfer the entire gathered signal from source to recipient, a common setup for this application would be to send it over multi-hop communication to a distant server. On the other hand, persistent data streaming may induce an abrupt reduction in sensor energy, which may shorten the network lifetime and raise concerns about the application’s feasibility. This suggested method is supplemented during the design phase with several methods or processes for reducing the overhead of architectural design, specifically regarding network resource consumption and development effort. This method aims to reduce the amount of energy used by the acoustic origin sensor and free up network bandwidth by carrying less unnecessary data. The proposed method guarantees secure transfer through an enhanced Elliptic Curve Cryptography (ECC). The method introduces a session key mechanism and a chaos-based private key generation approach to enhance resilience against cryptographic attacks. A novel feature extraction strategy utilizing a variety of extraction characteristics and classifications is suggested in this study. Based on experimental results, the suggested method saves 74.35% of energy and obtains 89% of feature extraction accuracy when compared to streaming the complete acoustic data to a distant server. The proposed method achieves superior security against known attacks while reducing computational overhead by over 97%.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100883"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927212","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":"Localized angle-based unsupervised outlier detection","authors":"Wei Zheng ,&nbsp;Lili Huang ,&nbsp;Haiqiang Liu ,&nbsp;Fa Zhu ,&nbsp;Achyut Shankar ,&nbsp;Imad Rida ,&nbsp;Davide Moroni","doi":"10.1016/j.eij.2025.100850","DOIUrl":"10.1016/j.eij.2025.100850","url":null,"abstract":"<div><div>The angle-based outlier detection (ABOD) is proposed to tackle the “curse of dimensionality” that exists in distance-related or density-related outlier detectors. However, ABOD may fail on multimodal datasets since it only considers global information. Furthermore, ABOD needs to calculate the angles between difference vectors from an instance to each pair of instances in the dataset except itself. Its time complexity reaches <em>O</em> (<em>n³</em>). In order to address these two issues, this paper proposes localized angle-based outlier detection (LABOD) which first finds the influence set, and then calculates the variance of angles between the difference vector from an instance to the mean of its neighbors in the influence set and the difference vectors from the instance to its neighbors in the influence set. The influence set consists of the nearest neighbor set and the reverse nearest neighbor set. Because the variance is defined by the angles in a local region, the proposed method can overcome the drawbacks of ABOD. The experiments performed on both synthetic and benchmark datasets demonstrate that LABOD is superior to ABOD.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100850"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145719068","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":"Bibliometric analysis of deep learning in plant disease management","authors":"Freedom M. Khubisa,&nbsp;Oludayo O. Olugbara","doi":"10.1016/j.eij.2025.100880","DOIUrl":"10.1016/j.eij.2025.100880","url":null,"abstract":"<div><div>Deep learning has gained significant importance in manifold disciplines such as natural language processing, supply chain optimization, computer vision, financial analysis, mechatronics and robotics, cybersecurity, and healthcare. It offers alternative methods to proactively manage plant diseases to ensure healthy crop yields, minimize economic losses, contribute to global food security, and promote sustainable agricultural practices. Nevertheless, despite a huge volume of publications on plant disease management using deep learning, a gap exists in the methodical evaluation of the contributions, impacts, trends, and exploration of intellectual structures of the publication elements using bibliometric analysis. Therefore, a bibliometric analysis was performed on 4,317 publications indexed in the Scopus database from 2016 to 2025 regarding plant disease management utilizing deep learning methods. Bibliometric performance analysis was based on publication, citation, and citation-and-publication metrics. Science mapping was conducted based on citation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis using Biblioshiny and VOSviewer tools. The bibliometric analysis confirmed that Computers and Electronics in Agriculture and IEEE Access are the most impactful publication sources according to the metrics of h-index and citations. A publication written by Mohanty SP in 2016 was found to be the most globally cited. Five distinctive clusters were identified using bibliographic coupling of publications and co-word analysis of author keywords to provide useful insights into the knowledge structure of plant disease management using deep learning. The analysis findings can provide valuable insights into the broader impact of the extant literature on deep learning applications, offering a footing for progressing artificial intelligence applications in plant disease management and guiding future research directions.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100880"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884766","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":"Advancing personalized neurology with explainable AI in Alzheimer’s classification using NeuroX-DualFusion framework","authors":"A.P. Ponselvakumar,&nbsp;S. Anandamurugan","doi":"10.1016/j.eij.2026.100926","DOIUrl":"10.1016/j.eij.2026.100926","url":null,"abstract":"<div><div>Alzheimer’s disease classification from MRI slices is a cornerstone of personalized neurology, enabling patient-specific diagnosis and treatment planning. Traditional machine learning approaches often fail in this domain due to class imbalance, limited feature representation, and poor interpretability, which restrict their clinical adoption and typically leading to biased predictions and unstable subject-level outcomes. This research introduces NeuroX-DualFusion, a hybrid framework that integrates a local attention stream and a global convolutional stream to capture both fine-grained and contextual features. The pipeline begins with standardized preprocessing and data augmentation to enhance anatomical clarity and mitigate class imbalance. Segmentation via attention-based U-Net isolates critical brain regions, while proposed NeuroX-DualFusion, dual-stream feature extraction enables robust representation learning. Additionally, Grad-CAM visualizations provide transparent, class-specific interpretability, highlighting discriminative regions aligned with clinical markers. Quantitative evaluation across Accuracy (97.5%), Precision (96.5%), Recall (97.5%), F1-score (96.5%), and Specificity (98.5%), demonstrates that NeuroX-DualFusion outperforms individual models, achieving subject-level accuracy. These findings underscore the potential of NeuroX-DualFusion to advance personalized neurology by delivering reliable, interpretable, and patient-centered dementia stage classification using MRI data, bridging the gap between computational innovation and clinical practice.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100926"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396703","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 study on front vehicle collision warning method based on lightweight YOLOv8 and DeepSort","authors":"Wenyu Zhang ,&nbsp;Yajing Li ,&nbsp;Jiaxuan Hu ,&nbsp;Ning Wang","doi":"10.1016/j.eij.2025.100861","DOIUrl":"10.1016/j.eij.2025.100861","url":null,"abstract":"<div><div>With the continuous increase in vehicle ownership, the frequency of traffic accidents has risen significantly, and higher demands have consequently been placed on active vehicle safety technologies. To address the challenges of insufficient real-time performance and high model complexity in traditional object detection methods under complex traffic conditions, an improved front-vehicle collision warning system has been proposed by integrating YOLOv8 and DeepSort. In this approach, the original YOLOv8 backbone network is replaced by the lightweight MobileNet V4, and the Convolutional Block Attention Module (CBAM) is incorporated to enhance feature extraction capabilities. A comprehensive algorithmic framework has been constructed, integrating multi-object recognition, front-vehicle distance estimation, ego-vehicle speed calculation, and hierarchical warning level output. Experimental results on the KITTI dataset have demonstrated a detection accuracy of 95.5 % and a total detection time of 2.6 ms per frame. Additionally, a 2.6 % improvement in mAP50–95 has been observed, accompanied by only a 0.1 % decrease in the recall rate. These findings suggest that the proposed method provides effective technical support for front-vehicle collision warning in intelligent transportation environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100861"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791745","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":"Epileptic seizure detection using information Gain-Based hybrid Features: Deep Neural network and comparative Machine learning approaches","authors":"Nuri Ikizler,&nbsp;Gunes Ekim","doi":"10.1016/j.eij.2026.100889","DOIUrl":"10.1016/j.eij.2026.100889","url":null,"abstract":"<div><div>Automatic detection of epileptic seizures is crucial in clinical diagnosis to enable early intervention and ensure patient safety. However, systematic comparisons across multi-class combinations and quantitative evaluation of discriminative features remain limited in the literature. This study aims to identify the most effective features for seizure detection and to develop a high-accuracy classification model. Statistical, spectral, and wavelet-based features from time, frequency, and time–frequency domains were selected using the Information Gain method, and four models were integrated into a hybrid framework. The approach was evaluated on 26 class combinations using Random Forest, Support Vector Machines, k-Nearest Neighbors, Gradient Boosting, and a Deep Neural Network. The proposed method achieved an average accuracy of 99%, with the Deep Neural Network reaching 99.69% in combinations including class E, demonstrating strong generalizability in multi-class scenarios. The main novelty of this work lies in combining Information Gain-based hybrid feature selection with a systematic multi-class analysis, a gap not fully addressed in previous studies. This approach enhances accuracy, interpretability, and generalizability, thereby contributing to improved clinical decision-making in epilepsy diagnosis.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100889"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037844","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":"Predicting preterm birth with privacy-preserving AI models: Federated learning and explainable AI","authors":"Md Tanjum An Tashrif ,&nbsp;Shahariar Hossain Mahir ,&nbsp;Dipanjali Kundu ,&nbsp;Anichur Rahman ,&nbsp;Fahmid Al Farid ,&nbsp;Sarina Mansor ,&nbsp;Abu Saleh Musa Miah","doi":"10.1016/j.eij.2026.100901","DOIUrl":"10.1016/j.eij.2026.100901","url":null,"abstract":"<div><div>Preterm birth remains a significant public health challenge, closely associated with infant mortality and long-term morbidity. The complexity of its causes complicates accurate prediction. In this study, we present an AI-driven model designed to predict preterm birth, integrating federated learning (FL), deep learning (DL), and explainable artificial intelligence (XAI) to prioritize both data privacy and interpretability. We utilized a primary dataset of 58 electrohysterogram (EHG) recordings from pregnant women, each collected over 1000-second intervals, and applied the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. To rigorously assess generalizability, we performed external validation on the independent TPEHGDB dataset comprising 300 EHG recordings from a different institution and time period Our approach evaluated a range of models, from established machine learning algorithms like XGBoost, LightGBM, and CatBoost, to advanced frameworks such as a Transformer-based architecture and quantum convolutional neural networks (QCNN). By leveraging FL, we enabled secure, collaborative training across institutions while maintaining patient data confidentiality. Additionally, XAI techniques, particularly SHAP, were employed to elucidate the key risk factors influencing predictions, thereby enhancing clinical transparency. XGBoost and Transformer models achieved 96.17% and 94.94% accuracy on internal validation, respectively, and demonstrated robust generalization with 88.67% and 89.33% accuracy on external validation, maintaining clinically critical recall rates of 78.95% and 81.58% for preterm detection. Critically, federated learning introduced minimal performance degradation (more than 2%) compared to centralized training, validating privacy-preserving collaborative learning. Although QCNN showed promise as an innovative approach, its performance lagged slightly behind classical models on external data. This underscores the potential of our approach as a scalable, privacy-preserving, and interpretable tool for early detection of preterm birth, with demonstrated generalizability across independent clinical populations.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100901"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187895","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":"Movie Recommendation system with sentiment analysis using deep learning algorithms","authors":"Agboola A.O.,&nbsp;Ladoja K.T.,&nbsp;Onifade O.F.W.","doi":"10.1016/j.eij.2026.100905","DOIUrl":"10.1016/j.eij.2026.100905","url":null,"abstract":"<div><div>In the era of digital media saturation, recommendation systems have become essential tools for delivering personalized content to users. While traditional approaches rely on user–item interactions and content similarity, they often overlook the emotional nuances expressed in user reviews. This study presents a sentiment-aware hybrid recommendation system that integrates deep learning-based sentiment classification with user demographics and item features to enhance movie recommendation accuracy. The proposed model employs Bidirectional Encoder Representations from Transformers (BERT) to classify user reviews into five nuanced sentiment polarities viz positive, slightly positive, neutral, slightly negative, and negative. These sentiment scores are embedded into a Deep Factorization Machine (DeepFM) architecture, which captures complex relationships among users, items, and emotional cues. A multi-filtering strategy incorporating user age, gender, occupation, location, and movie genre is utilized to mitigate cold-start problems and refine recommendations. Experimental evaluation using the MovieLens dataset, complemented with IMDb user reviews, demonstrates improvements in ROC-AUC (84.47%), Balanced Accuracy (76.36%), and PR-AUC (82.13%) compared to traditional systems. The findings highlight the effectiveness of integrating fine-grained sentiment analysis into the recommendation process, offering deeper insights into user intent and improving the personalization of suggestions. The proposed framework presents a scalable and efficient solution for building emotionally intelligent recommendation systems, fostering deeper user engagement, informed decision-making, and more meaningful media experiences.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100905"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188597","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":"Graph-based temporal anomaly detection with self-supervised contrastive learning and dynamic adaptive thresholding for acoustic howling suppression","authors":"Xiaoqian Fan ,&nbsp;Francisco Hernando-Gallego ,&nbsp;Diego Martín ,&nbsp;Mohammad Khishe","doi":"10.1016/j.eij.2026.100892","DOIUrl":"10.1016/j.eij.2026.100892","url":null,"abstract":"<div><div>Acoustic howling due to feedback loops in audio systems is a major challenge in such fields as hearing aids or public address systems. Traditional approaches such as notch filters and adaptive feedback cancellation often have limitations such as lack of adaptability in dynamic environments, and a need for a large amount of labelled data. To overcome these shortcomings, a new deep learning approach, Dynamic Adaptive Thresholding and Self-Supervised Contrastive Learning for Graph-based Temporal Anomaly Recognition (GTAD-CL), is proposed in this paper. By representing audio signals as graphs, GTAD-CL uses graph neural networks to represent complex spatial–temporal patterns to detect howling with high precision as an anomaly. Self-supervised contrastive learning removes the requirement of having labeled datasets which improves the scalability and generalization of the AI models. A dynamic adaptive thresholding mechanism guarantees robust performance under different acoustic conditions, e.g. low signal to noise ratio environments. Integrated with neural filtering in real time, GTAD-CL makes howling suppression easy. Experimental results on a 100-hour custom dataset and six public benchmarks indicate that GTAD-CL has a precision of 0.92 (compared to 0.88, the best baseline, HybridAHS, showing a gain of 4.5%), recall of 0.90 (compared to 0.85, a gain of 5%) and F1-score of 0.91 (compared to 0.865, a gain of 4.5%). In suppression quality GTAD-CL achieves a PESQ score of 3.02 (compared to 2.68 for HybridAHS, i.e. ∼12.7% better), and a STOI of 0.90 (compared to 0.86, i.e. ∼4.7% better). Moreover, GTAD-Cl runs with a real-time factor of 0.36× which is better than HybridAHS’s 0.42× (approx. 14% faster). These results give validation to GTAD-CL as a powerful, scalable, and low-latency solution and high-fidelity solution that is superior to state-of-the-art results for varying acoustic scenarios.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100892"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078372","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":"Reliability-oriented offloading of dependent tasks based on topology reconstruction in Industrial Internet Edge Computing","authors":"Qiong Gu ,&nbsp;Huilong Wu ,&nbsp;Jialei Liu ,&nbsp;Bin Ning ,&nbsp;Chunyang Hu ,&nbsp;Qiaozhi Hua ,&nbsp;Meng Zeng ,&nbsp;Kexin Zhang ,&nbsp;Yanyan Zhu ,&nbsp;Zhiyuan Yuan ,&nbsp;JiCheng Wu","doi":"10.1016/j.eij.2026.100909","DOIUrl":"10.1016/j.eij.2026.100909","url":null,"abstract":"<div><div>To address the reliability challenges arising from dynamic network topology and complex task dependencies in Industrial Internet Edge Computing (IIEC) environment, we propose a topology reconstruction-based reliability-optimized computing offloading method. First, we construct a system model encompassing edge-cloud network platform, industrial cloud platform, Internet of Things (IoT) devices and IoT applications, and establish a mathematical framework integrating transmission delay and reliability models, with the IoT application completion time as the core reliability metric. Second, we creatively combine the Ford–Fulkerson approximation algorithm with a Deep Q-network to optimize microservice topology reconstruction and dynamic computing offloading, thereby reducing communication costs and enhancing service reliability. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in IoT application completion time and reliability levels, providing a novel technical pathway for achieving efficient and reliable operations in IIEC.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"33 ","pages":"Article 100909"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187892","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}