alexandria engineering journal最新文献

{"title":"Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm","authors":"Mahmoud Ragab ,&nbsp;Ehab Bahaudien Ashary ,&nbsp;Faris Kateb ,&nbsp;Abeer Hakeem ,&nbsp;Rayan Mosli ,&nbsp;Nasser N. Albogami ,&nbsp;Sameer Nooh","doi":"10.1016/j.aej.2025.04.071","DOIUrl":"10.1016/j.aej.2025.04.071","url":null,"abstract":"<div><div>The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 116-130"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural networks and genetic algorithms-based self-adjustment system for a backstepping controller of an unmanned aerial vehicle","authors":"Omar Rodríguez-Abreo ,&nbsp;Marcos Aviles ,&nbsp;Juvenal Rodríguez-Reséndiz ,&nbsp;A. García-Cerezo","doi":"10.1016/j.aej.2025.04.034","DOIUrl":"10.1016/j.aej.2025.04.034","url":null,"abstract":"<div><div>Backstepping control has been widely used in drones because it considers the dynamic of the system when designing the control law and is robust to parametric uncertainties. However, the typical controller has twelve gains that must be adjusted for optimal results. This process is done manually and with a fixed value, which limits the performance of the controller. This article presents a backstepping intelligent self-tuning system for a multirotor drone. The autotuning is done based on the dynamic vehicle response, optimizing energy consumption, and minimizing its rise time, but without causing an overshoot that consumes unnecessary energy. A backpropagation neural network was trained with a database that considers the dynamic response of the system to achieve this effect. The database was obtained with a metaheuristic algorithm to ensure that only combinations that meet these conditions are used. Several independent tests were carried out to test the system. The results show that the proposed method is adequately adjusted and fulfilled, with the expected dynamic response for 95% of the tests and a dynamic response with minor overshoot and settling time, compared to a PID tuned by genetic algorithm.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 70-80"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient gesture recognition for HCI based on semantics-guided GCNs and adaptive regularization graphs","authors":"Liheng Dong ,&nbsp;Xin Xu ,&nbsp;Guiqing He ,&nbsp;Yuelei Xu ,&nbsp;Jarhinbek Rasol ,&nbsp;Chengyang Tao ,&nbsp;Zhaoxiang Zhang","doi":"10.1016/j.aej.2025.04.019","DOIUrl":"10.1016/j.aej.2025.04.019","url":null,"abstract":"<div><div>In the embedded system, real-time gesture recognition is crucial to human–computer interaction (HCI). Recently, Graph Convolutional Networks (GCNs) have been applied to inertial measurement unit-based (IMU-based) gesture recognition. However, the disadvantage of these GCN-based methods is that they use very deep networks to capture deep motion features, without considering computational efficiency. In this paper, we propose a shallow GCN as the basic framework to ensure the real-time performance of gesture recognition. To solve the problem of shallow networks’ difficulty capturing deep motion features, we provide hand-crafted semantic information about the positions of nodes (sensors) and frames to guide deep feature extraction. Furthermore, we propose a regularization module named Double-Mask (2MASK) to enhance the network’s generalization. Experiments show that the average inference time on raspberry pi 4b is less than 4 ms. Extensive testing on the self-constructed dataset indicates that the proposed method outperforms previous state-of-the-art (SOTA) methods on multiple metrics. The accuracy reaches 89.47% and 98.70% on two public datasets, outperforming other methods. Experiments in an HCI application show that our method meets the high-precision and low-latency requirements for autonomous taxiing of UAVs. The code for this paper has been uploaded to <span><span>https://github.com/oldbowls/2MAGCN-FN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 30-44"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response","authors":"H. Mancy ,&nbsp;Naglaa E. Ghannam ,&nbsp;Amr Abozeid ,&nbsp;Ahmed I. Taloba","doi":"10.1016/j.aej.2025.04.033","DOIUrl":"10.1016/j.aej.2025.04.033","url":null,"abstract":"<div><div>Water resource management and disaster response have become some of the most challenging tasks, especially when disasters pose a threat, as delays could lead to more impacts. The centralized system used for water dynamics and disaster control usually presents itself as a scalability problem since more clients present a problem, the system's latency is high, and the system is always prone to a single-point failure. The previous approach lacks flexibility and does not synchronously guarantee the integration of several subjects in real time, especially during unpredictable disaster conditions. The proposed FL-MAPPO model surpasses current methods by facilitating decentralized, privacy-protecting decision-making minimizing latency and single-point failures. In contrast to LSTM, Bi-LSTM, and DRNN, which are based on centralized data processing, FL-MAPPO provides real-time adaptability and effective resource management. Experimental results validate that it has lower MSE, higher R² scores, and quicker response times, making it better suited for flood prediction and disaster response. To this end, this study advances a solution through a Decentralized Learning-Driven Multi-Agent Autonomous System (DL-MAAS). The new feature is a Decentralized Cooperation environment in which intelligent and self-managing agents learn utilizing Reinforcement Learning (RL) and Federated Learning (FL) algorithms for enhancing smart water management and real-time disaster relief. IoT devices are adopted for sensing and data acquisition, adaptive learning for decision-making, and optimization of energy use among the agents in the system through metaheuristic algorithms. The research methodology for implementing the proposed solution involves the design of a multi-layered architecture, including data acquisition, decentralized learning, and real-time execution. With a Mean Squared Error (MSE) of 0.112, R-squared (R²) of 0.953, and Mean Absolute Error (MAE) of 0.207, the proposed method is better than existing approaches for big, real-time flood predictive systems. Data show that decentralized systems provide orders of magnitude higher efficiency in water distribution, time of response to disasters, and energy usage compared to conventional centralized systems. These results indicate the significant opportunity for decentralized multi-agent systems in the sustainability of disaster management and water resources.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 8-29"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical investigation of a fractal oscillator arising from the microbeams-based microelectromechanical system","authors":"Bin Chen ,&nbsp;Junfeng Lu ,&nbsp;Lei Chen","doi":"10.1016/j.aej.2025.04.015","DOIUrl":"10.1016/j.aej.2025.04.015","url":null,"abstract":"<div><div>In this paper, we consider a electrically excited microbeams-based microelectromechanical system (MEMS) on a fractal time space. This MEMS problem can be modelled by a fractal nonlinear oscillator. A numerical approach by combining the fractal complex transformation and the spreading residue harmonic balance method is proposed for finding the approximations to the fractal vibration system. The approximated solutions and frequencies with high accuracy are given, and compared with the approximations by the existing methods such as Runge–Kutta method, energy balance method and Li-He’s modified homotopy perturbation method. Sensitivity analysis of the approximations concerning different amplitudes and other parameters is also investigated for understanding the numerical behaviour. Numerical results confirm the efficiency of the proposed approach over some existing methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 53-59"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-light object detection via adaptive enhancement and dynamic feature fusion","authors":"Siyong Fu,&nbsp;Qinghua Zhao,&nbsp;Hesheng Liu,&nbsp;Qiuxiang Tao,&nbsp;Danjuan Liu","doi":"10.1016/j.aej.2025.04.047","DOIUrl":"10.1016/j.aej.2025.04.047","url":null,"abstract":"<div><div>Under low-light conditions, object detection tasks face challenges such as low brightness, low contrast, and noise, which can lead to missed or incorrect detections. To address this issue, this paper proposes a low-light enhancement algorithm, called DAMFCN, and an improved DarkYOLOv8 method, aimed at enhancing low-light image quality and object detection performance. DAMFCN significantly improves the quality of low-light images by integrating the Low-Light Adaptive Module and the Multi-Scale Feature Compensation Block, where LLAM effectively extracts fine details and suppresses noise, and MSFCB compensates for lost details by integrating multi-scale information. The DarkYOLOv8 framework, built on the EfficientNet backbone, combines a multi-scale attention mechanism and the Dynamic Feature Fusion Attention Module, demonstrating superior object detection performance under low-light conditions. Experimental results show that the proposed methods outperform existing state-of-the-art techniques in terms of accuracy, robustness, and efficiency, offering broad application potential.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 60-69"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lumps, solitons, modulation instability and stability analysis for the novel generalized (2+1)-dimensional nonlinear model arising in shallow water","authors":"Kalim U. Tariq ,&nbsp;Adil Jhangeer ,&nbsp;Muhammad Nasir Ali ,&nbsp;Hamza Ilyas ,&nbsp;R. Nadir Tufail","doi":"10.1016/j.aej.2025.03.110","DOIUrl":"10.1016/j.aej.2025.03.110","url":null,"abstract":"<div><div>In this study, the (2+1)-dimensional Kadomtsev–Petviashvili type equation is investigated that describes the nonlinear wave patterns of behavior and properties in oceanography, fluid dynamics, and shallow water. Firstly, the Hirota bilinear form is implemented to develop a variety of lump, strip soliton and periodic waves solutions for the governing model. Furthermore, some interesting traveling and semi-analytical solitons are generated by availing the extended modified auxiliary equation mapping technique and the Adomian decomposition algorithm. Moreover, in order to determine the absolute error, we have constructed a juxtapose of approximate and soliton results. Additionally, we deliberate the stability analysis and the modulation instability for the governing model extensively to validate the scientific computations. Moreover, the graphical portrayals which include contour plots, 2D and 3D models are illustrated that are useful for understanding the behaviors and dynamics presented by the model’s solutions. The findings of current study are quite novel and make a big contribution to soliton dynamics and mathematical physics.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 45-52"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A blockchain-based approach for secure energy-efficient IoT-based Wireless Sensor Networks for smart cities","authors":"Hamad Aldawsari","doi":"10.1016/j.aej.2025.04.052","DOIUrl":"10.1016/j.aej.2025.04.052","url":null,"abstract":"<div><div>As a result of IoT-based Wireless Sensor Networks (IoT-WSNs), resource-constrained environments are becoming more efficient and dynamic. Even though IoT-WSNs have many advantages, they also face significant challenges, including their high energy consumption, limited lifespan, and security vulnerabilities. IoT-WSNs for smart cities could be made more energy-efficient and secure by using a blockchain-based approach. Blockchain technology improves energy efficiency and reduces communication costs while ensuring decentralized, secure spectrum management. A smart contract and distributed ledger mechanism reduce redundant data transmissions and facilitate network trust. A blockchain-enabled clustering mechanism allows energy-aware sensing and resource allocation, as well as cognitive radio technology used for efficient spectrum utilization. Compared to existing techniques, the proposed method is more energy efficient, more accurate in sensing spectrums, and more secure. IoT-WSNs provide a solution to energy and security challenges in smart city infrastructure, contributing to sustainable development.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 1-7"},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of face stability for a shield tunnel in the inclined strata with soft upper and hard lower layers","authors":"Xiaopu Cui ,&nbsp;Pengfei Li ,&nbsp;Yang Chen ,&nbsp;Fei Jia ,&nbsp;Zhaoguo Ge ,&nbsp;Shaohua Li","doi":"10.1016/j.aej.2025.04.013","DOIUrl":"10.1016/j.aej.2025.04.013","url":null,"abstract":"<div><div>Tunnel excavation often encounters inclined strata with a soft upper layer and a hard lower layer at the excavation face. Currently, limited research has been conducted on the mechanisms underlying the instability of such strata. This paper proposes a new excavation face failure mechanism considering the inclination angle and soil arching effect, and classify the failure models into two types according to the inclination angle. The results of numerical simulations and existing theories are used to validate our model. Through sensitivity analysis of soil strength parameters, the analytical results indicate that as the inclination angle changes from counterclockwise to clockwise, the limit support pressure first decreases, then increases, and then gradually decreases. The internal friction angle of the inclined strata significantly affects the limit support pressure. The influence of cohesion difference on the limit support pressure gradually weakens. Under clockwise inclination angles, the limit support pressure mainly depends on the strength parameters of the upper layer. This paper provides a more accurate theoretical basis for the stability analysis of shield tunnels in inclined strata with soft upper and hard lower layers, which is of great significance for guiding the design and construction of shield tunnels in complex geological conditions.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 677-691"},"PeriodicalIF":6.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chaos and elite reverse learning – Enhanced sparrow search algorithm for IIoT sensing communication optimization","authors":"Yongmei Wang ,&nbsp;Junyong Li ,&nbsp;Xiaoyun Tan","doi":"10.1016/j.aej.2025.04.054","DOIUrl":"10.1016/j.aej.2025.04.054","url":null,"abstract":"<div><div>Sensing, communication, and collaborative optimization are currently hot topics in the Industrial Internet of Things (IIoT) research. This paper addresses minimizing energy consumption in IIoT user terminal devices by modeling energy consumption as an optimization challenge. Initially, a data - aware sharing architecture for IIoT user terminal devices is constructed to reduce device energy consumption. In scenarios involving multiple intelligent terminal devices, collaborative devices, and edge IIoT proxy devices, factors such as user device location stability, local network status, task arrival rate, and queue stability are comprehensively considered. Subsequently, this paper introduces a Chaos and Elite Reverse Learning Sparrow Search Algorithm (CERL-SSA) to solve the established model. The testing experiments use common benchmark functions to verify the superiority of the improved algorithm, and the experimental results show the good performance and effectiveness of the proposed algorithm in IIoT sensing communication and collaborative optimization.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 663-676"},"PeriodicalIF":6.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}