alexandria engineering journal最新文献

{"title":"The real-time data processing framework for blockchain and edge computing","authors":"Zhaolong Gao ,&nbsp;Wei Yan","doi":"10.1016/j.aej.2025.01.092","DOIUrl":"10.1016/j.aej.2025.01.092","url":null,"abstract":"<div><div>The rapid growth of IoT has increased the demand for large-scale data processing. However, traditional centralized methods struggle with real-time requirements and data security. This paper introduces VCD-TSNet, a novel real-time IoT data processing framework that combines blockchain and edge computing. By integrating deep learning models like VGG, ConvLSTM, and DNN, VCD-TSNet effectively performs spatial feature extraction, temporal modeling, and decision-making, while using blockchain to ensure data integrity and privacy. Experimental results demonstrate that VCD-TSNet outperforms baseline models in classification accuracy, prediction precision, and real-time performance. For instance, on the BoT-IoT dataset, the classification accuracy reaches 97.5%, throughput increases to 920 TPS, and response time stays below 85 ms. This study validates the model’s effectiveness and highlights its potential in large-scale IoT environments, offering efficient, secure solutions for real-time data processing. It also provides insights for future improvements in frameworks that combine edge computing with blockchain.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"120 ","pages":"Pages 50-61"},"PeriodicalIF":6.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BACVC: Bi-adaptive and cross-view consistency for incomplete multi-view subspace clustering","authors":"Jiaqiyu Zhan,&nbsp;Yuesheng Zhu,&nbsp;Guibo Luo","doi":"10.1016/j.aej.2025.01.089","DOIUrl":"10.1016/j.aej.2025.01.089","url":null,"abstract":"<div><div>Multi-view subspace clustering leverages complementary information from different views to uncover latent subspaces. However, incomplete multi-view data is prevalent, particularly in fields such as communication systems. Incomplete Multi-View Subspace Clustering (IMSC) addresses this challenge but faces two main challenges: (1) neglecting dissimilarities between views and samples, and (2) insufficient handling of cross-view consistency. To tackle these issues, we propose a novel IMSC framework, referred to as Bi-Adaptive and Cross-View Consistency (BACVC). BACVC improves incomplete data recovery and subspace structure discovery through view-adaptive tensor rank constraints, data-adaptive high-order correlations, and view-level contrastive learning. Specifically, we first apply tensor factorization with view-adaptive tensor rank approximation to enforce low-rank constraints on a stacked affinity tensor, capturing the view-specific subspace block-diagonal structure. We then introduce a data-adaptive non-uniform hypergraph-induced hyper-Laplacian regularization to model high-order correlations and guide the recovery of incomplete data. Finally, contrastive learning is applied to the soft clustering assignment of each view, ensuring cross-view structural consistency. Extensive experiments on four benchmark datasets show that BACVC outperforms eleven state-of-the-art methods, with improvements of up to 4.39%, 5.43%, and 3.95% in ACC, NMI, and purity, respectively. Experimental results demonstrate the robustness of BACVC in handling incomplete data and its effectiveness in practical applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 623-633"},"PeriodicalIF":6.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A genetic-based random ensemble forest learning for cloud-based automotive data transformation in internet of vehicle","authors":"Salwa Umar Qureshi ,&nbsp;Alireza Souri ,&nbsp;Nihat İnanç ,&nbsp;Jan Lansky ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.aej.2025.01.120","DOIUrl":"10.1016/j.aej.2025.01.120","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) is a constantly changing field, and the fast convergence of automotive technology and connectivity has brought about a new era marked by enormous cybersecurity risks. A crucial component of the inquiry is a thorough examination of the IoV infrastructure's vulnerabilities, which highlights potential sources of compromise and places where strong cybersecurity measures are required for data transformations in cloud-edge computing. Additionally, the Controller Area Network (CAN) and the Electronic Control Units (ECUs) are critical points in automotive networking to connect user data from smart applications to electric vehicles. Therefore, finding a safe automotive data transformation approach for incorporating Connected and Autonomous Vehicles (CAVs) and investigating particular cybersecurity issues is a critical and key challenge in the IoV ecosystem. To ensure the safe development of the IoV landscape, the research introduces two innovative genetic algorithms, Genetic Algorithm Random Forest (GA-RF) and Genetic Algorithm Ensemble Bagged Trees (GA-EBT), to improve the identification of cyber threats in the IoV context. The simulation results demonstrate that the proposed hybrid algorithm achieves exceptional performance, attaining a high accuracy rate of 99.92 %, the lowest mean absolute error of 0.0028, and the highest precision, recall, and F1 measures near to 100 %. These results are especially noteworthy on real automotive data transformation datasets. These results highlight the significance of the suggested strategy for defending IoV systems from suspicious threats.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"120 ","pages":"Pages 74-86"},"PeriodicalIF":6.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing","authors":"Zepei Li ,&nbsp;Peng Zheng ,&nbsp;Yanjia Tian","doi":"10.1016/j.aej.2025.01.020","DOIUrl":"10.1016/j.aej.2025.01.020","url":null,"abstract":"<div><div>In industrial IoT (Internet of Things) environments, accurate anomaly detection and high-quality data management are crucial yet challenging due to noisy and incomplete sensor data. This study introduces BD-IoTQNet, a novel framework designed to address these challenges by integrating data fusion, anomaly detection using the Isolation Forest algorithm, and blockchain-enabled DQM (Data Quality Management). The framework leverages blockchain technology to ensure data transparency and security, while smart contracts automate exception handling to enhance efficiency. Experiments conducted on the NASA Turbofan Engine Degradation and UCI Hydraulic Systems datasets demonstrate that BD-IoTQNet outperforms existing models in accuracy, precision, and data quality improvement, with reduced latency and enhanced robustness under noisy and missing data conditions. An ablation study validates the critical role of each component, showing significant performance drops when modules like DQM or blockchain are excluded. These findings highlight BD-IoTQNet as an effective solution for improving anomaly detection, predictive maintenance, and operational efficiency in industrial IoT systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 465-477"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming single-technology limitations in digital heritage preservation: A study of the LiPhoScan 3D reconstruction model","authors":"Yao Wang ,&nbsp;Wei Bi ,&nbsp;Xiaodong Liu ,&nbsp;Yan Wang","doi":"10.1016/j.aej.2024.12.095","DOIUrl":"10.1016/j.aej.2024.12.095","url":null,"abstract":"<div><div>With the increasing demand for the digital preservation of cultural heritage, high-precision 3D reconstruction of museum artifacts has become an important research direction. However, single technology approaches face limitations in practical applications, such as insufficient capture of geometric details, poor texture fidelity, and suboptimal geometric accuracy. To address these issues, this paper proposes the LiPhoScan hybrid 3D reconstruction model, which integrates LiDAR, photogrammetry, and structured light scanning technologies. This model leverages the high geometric accuracy of LiDAR, the high texture fidelity of photogrammetry, and the detail-capturing ability of structured light scanning to overcome the limitations of individual technologies, providing a more comprehensive and refined 3D reconstruction solution. Experimental results show that LiPhoScan improves geometric accuracy by 15% and texture fidelity by 20% compared to traditional methods. In addition, compared to existing single-technology approaches, LiPhoScan demonstrates significant advantages in detail fidelity and overall geometric consistency. This offers an innovative solution for the digital preservation of museum artifacts and lays a solid foundation for high-precision and high-detail 3D reconstruction tasks. Future research will incorporate parallel computing and efficient data processing methods to further enhance the model’s computational efficiency.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 518-530"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating pre-processing and deep learning methods in medical imaging: Combined effectiveness across multiple modalities","authors":"Thien B. Nguyen-Tat ,&nbsp;Tran Quang Hung ,&nbsp;Pham Tien Nam ,&nbsp;Vuong M. Ngo","doi":"10.1016/j.aej.2025.01.090","DOIUrl":"10.1016/j.aej.2025.01.090","url":null,"abstract":"<div><div>Medical imaging is critical in modern healthcare for accurately detecting and diagnosing various medical conditions. Advanced computational techniques, particularly preprocessing methods and deep learning models, have demonstrated significant potential for improving medical image analysis. However, determining the optimal combination of these techniques across different types of medical images remains a challenge. Using empirical experiments, this evaluation research investigates the effectiveness of five popular pairs of preprocessing techniques combined with five widely used deep learning models. Preprocessing methods evaluated include CLAHE + Butterworth, DWT + Threshold, CLAHE + median filter, Median-Mean Hybrid Filter, and Unsharp Masking + Bilateral Filter, concatenated with deep learning models: EfficiencyNet-B4, ResNet-50, DenseNet-169, VGG16 and MobileNetV2. The performance of these combinations was evaluated through experiments carried out on eight diverse and commonly used datasets encompassing various medical imaging modalities. These datasets include two X-ray collections: the COVID-19 Pneumonia Normal Chest PA Dataset and the Osteoporosis Knee X-ray Dataset; two CT scan datasets: the Chest CT-Scan Images Dataset and the Brain Stroke CT Image Dataset; two MRI datasets: the Breast Cancer Patients MRI and the Brain Tumor MRI Dataset; and two ultrasound datasets: the Ultrasound Breast Images for Breast Cancer and the MT Small Dataset. Our findings show that the Median-Mean Hybrid Filter and Unsharp Masking + Bilateral Filter are the most effective preprocessing methods, achieving an efficiency rate of 87.5%. Among the deep learning models, EfficiencyNet-B4 and MobileNetV2 are the highest performing models with an efficiency ratio of 75%, with MobileNetV2 providing up to 34% shorter runtime compared to other models. This study provides a thorough evaluation of the performance of different preprocessing methods and deep learning algorithms across commonly used medical imaging modalities. Presenting empirical results from our experiments offers practical insights into choosing the most suitable preprocessing techniques and deep learning models for various types of medical images. These findings are intended to support improvements in diagnostic accuracy and efficiency in medical imaging, offering a valuable reference for enhancing image-based diagnostic processes.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 558-586"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New super and shock like solitary structures for KdV equation with higher-order nonlinearity","authors":"H.S. Alayachi ,&nbsp;Abdulghani Alharbi ,&nbsp;E.K. El-Shewy ,&nbsp;Mahmoud A.E. Abdelrahman","doi":"10.1016/j.aej.2025.01.124","DOIUrl":"10.1016/j.aej.2025.01.124","url":null,"abstract":"<div><div>The model of dual-power law nonlinearity Korteweg–De Vries (KdV) equation describe sudden physical phenomena with higher orders of nonlinearity in fluid dynamics, plasma, fiber communications and biological systems. The model was solved by the modified F-expansion approach to produce structural solutions in the vital form of super periodic solitons, super periodic shocks, shock solutions, super-shock-soliton like solutions and cnoidal solitons. The modified F-expansion approach is an effective, powerful and straightforward method for obtaining the solitary wave solutions to the nonlinear partial differential equations (NPDEs). The effect of model parameters on the nature, properties and structures of the model solutions have been examined. It was noted that, the wave amplitude, bandwidths and phase shift are improved by changing model parameters in super periodic solitons and super periodic shocks. The new solutions obtained in this study may hold significance for the applications of electrostatic EASWs structures at critical density, which have been observed in diverse space plasma environments, including the plasma sheet boundary layers of the Earth’s magnetotail region for ions temperature ranging from 0.01 to 1 KeV.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 503-510"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph","authors":"Heling Jiang ,&nbsp;Yongping Xia ,&nbsp;Changjie Yu ,&nbsp;Zhao Qu ,&nbsp;Huaiyong Li","doi":"10.1016/j.aej.2025.01.093","DOIUrl":"10.1016/j.aej.2025.01.093","url":null,"abstract":"<div><div>This work employs support vector machine (SVM), K-Nearest Neighbors (KNN) and logistic regression models to predict the health state of the pump and to establish fault diagnosis. From the features like vibration, temperature of the motor, pressure, and flow rate, the models categorize the state of the pump into two; normal or No Fault, and Fault Detected. This makes it possible to detect specific faults and assist in creating preventive maintenance. Post analysis, it was inferred that with an accuracy of 0.92, the SVM with a linear kernel outperformed the competing models. While the KNN performed marginally worse with an accuracy of 0.85, the SVM with RBF and polynomial kernels as well as logistic regression both attained accuracy of 0.91. These findings highlight the SVM with a linear kernel’s superior generalization skills, which make it the best option for pump system defect identification. For defect detection, giving the SVM with a linear kernel priority guarantees precise predictions, allowing for proactive maintenance and minimizing downtime. To improve operational efficiency and lower long-term maintenance costs, policy ideas include standardizing data collection techniques, investing in real-time monitoring systems, and implementing machine learning-based predictive maintenance across industries.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 587-597"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the assessment and reliability of political and ideological education in colleges using deep learning methods","authors":"Yongsheng Ma ,&nbsp;Xianhui Sun ,&nbsp;Aiqun Ma","doi":"10.1016/j.aej.2025.01.114","DOIUrl":"10.1016/j.aej.2025.01.114","url":null,"abstract":"<div><div>The reliability and effectiveness of teaching outcomes are reliant upon the accurate evaluation of ideological and political (IAP) education in colleges. This study focuses on predicting assessment scores to evaluate student performance, identify areas of vulnerability, and implement targeted interventions. Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. The results demonstrated clear distinctions between the training and test errors for the models. The ANN exhibited the highest errors, with a training RMSE (root mean squares error) of 14.13 and test RMSE of 13.55, indicating weak generalization. The CNN showed substantial improvement, with a training RMSE of 9.31 and test RMSE of 9.32, reflecting moderate but consistent performance. However, the SVM emerged as the most reliable model, achieving the lowest prediction errors: training RMSE of 7.68 and test RMSE of 8.0, with minimal discrepancies between training and test results. These findings provide valuable insights for instructors and policymakers to refine curriculum delivery, monitor student outcomes, and address educational disparities effectively. By adopting robust models like the SVM, institutions can ensure reliable predictions, fostering a more inclusive and outcome-oriented education system.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 511-517"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new statistical model with optimal fitting performance: Its assessments in management sciences and reliability","authors":"Zhidong Liang","doi":"10.1016/j.aej.2025.01.094","DOIUrl":"10.1016/j.aej.2025.01.094","url":null,"abstract":"<div><div>The study of real-world phenomena fundamentally hinges on probability distributions. This understanding has inspired researchers to design new statistical models, which has resulted in a variety of methodologies. Often, these methodologies are developed with new parameters. Unfortunately, the introduction of additional parameters can sometimes create difficulties related to re-parameterization. In the context of this particular research area, we introduce a groundbreaking statistical methodology designed to enhance the distributional flexibility of probability models without the addition of new parameters. The methodology we propose, which combines the sine function with the weighted T-<span><math><mi>X</mi></math></span> strategy, is referred to as the sine weighted-<span><math><mi>G</mi></math></span> (SW-<span><math><mi>G</mi></math></span>) family. The sine weighted-Weibull (SW-Weibull) distribution is examined through the SW-<span><math><mi>G</mi></math></span> method. Essential distributional functions for the SW-Weibull distribution are presented, along with corresponding visual representations. Additionally, properties based on quartiles are explored, and the derivation of maximum likelihood estimators is presented. A simulation study is conducted to enhance the understanding of the distribution. Ultimately, the relevance of the SW-Weibull distribution is confirmed by examining two real-world data sets from the management sciences and reliability sectors. Our findings, based on particular evaluation tests, indicate that the SW-Weibull distribution provides optimal performance when analyzing the aforementioned data sets.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"119 ","pages":"Pages 545-557"},"PeriodicalIF":6.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}