alexandria engineering journal最新文献_第8页

Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis 增强航空安全：通过先进的数据分析进行实时ADS-B注射检测的机器学习

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-30 DOI: 10.1016/j.aej.2025.04.045

Md. Atiqur Rahman , Touhid Bhuiyan , M. Ameer Ali

{"title":"Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis","authors":"Md. Atiqur Rahman , Touhid Bhuiyan , M. Ameer Ali","doi":"10.1016/j.aej.2025.04.045","DOIUrl":"10.1016/j.aej.2025.04.045","url":null,"abstract":"<div><div>Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system’s performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen’s kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 262-276"},"PeriodicalIF":6.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887508","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}

引用次数: 0

Statistical analysis of disability: Utilizing the modified kies power unit inverse Lindley model 残障统计分析：利用改进的kies动力单元逆林德利模型

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-30 DOI: 10.1016/j.aej.2025.04.042

Mohamed A. Abd Elgawad , Safar M. Alghamdi , Rana H. Khashab , Etaf Alshawarbeh , Ehab M. Almetwally , Mohammed Elgarhy

{"title":"Statistical analysis of disability: Utilizing the modified kies power unit inverse Lindley model","authors":"Mohamed A. Abd Elgawad , Safar M. Alghamdi , Rana H. Khashab , Etaf Alshawarbeh , Ehab M. Almetwally , Mohammed Elgarhy","doi":"10.1016/j.aej.2025.04.042","DOIUrl":"10.1016/j.aej.2025.04.042","url":null,"abstract":"<div><div>In this article, we propose a new extension of a new unit probability statistical model, the so-called modified kies power unit inverse Lindley (MKPUILD) distribution. The MKPUILD is very flexible model because it’s probability density function and hazard rate function have different shapes as unimodal, right-skewed, left-skewed, bathtub, increasing, N-shaped. The new suggested model is very flexible and suitable for the disability data in the Kingdom of Saudi Arabia. This study demonstrates the flexibility and applicability of the MKPUILD by analyzing two real-world datasets: the relative distribution of individuals with mild difficulties and the relative distribution of individuals with disabilities in Saudi Arabia, categorized by age groups. These data sets exhibit diverse statistical characteristics, enabling a comprehensive evaluation of the performance of the model. To validate the efficacy of the proposed model, goodness-of-fit statistics were utilized, comparing the MKPUILD with existing competing models. The findings highlight the robustness of the MKPUILD in capturing complex statistical patterns across varying datasets. The model parameters are determined by several estimation techniques. Simulation tests were performed using MKPUILD to analyze the efficacy of various estimation techniques. The essential characteristics of the model have been established, including the quantile function, moments and order statistics.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 181-195"},"PeriodicalIF":6.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891035","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}

引用次数: 0

Harnessing machine learning for advanced hazardous waste prediction 利用机器学习进行先进的危险废物预测

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-30 DOI: 10.1016/j.aej.2025.04.082

Abderrahim Lakhouit , Sumaya Y.H. Abbas

{"title":"Harnessing machine learning for advanced hazardous waste prediction","authors":"Abderrahim Lakhouit , Sumaya Y.H. Abbas","doi":"10.1016/j.aej.2025.04.082","DOIUrl":"10.1016/j.aej.2025.04.082","url":null,"abstract":"<div><div>Hazardous waste (HW) poses significant risks to human health and the environment, necessitating thorough analysis and data collection. This study investigates HW data from the Gulf Cooperation Council (GCC) countries between 2010 and 2020, emphasizing key findings and the utilization of Response Surface Methodology (RSM) for precise estimation. By integrating historical data with advanced predictive modeling techniques, RSM proves to be a valuable tool for forecasting waste generation patterns and facilitating targeted resource allocation. The study underscores the importance of these algorithms in predicting waste trends, aiding authorities in identifying critical areas for intervention. The incorporation of RSM yields promising results, with R2-scores of 0.92, 0.73, and 0.93 for total hazardous wastes, medical wastes, and industrial wastes, respectively, demonstrating the effectiveness of RSM in waste management practices. By presenting these significant findings, this study contributes to better understanding and management of HW streams. To the best of our knowledge, this work represents the first attempt to employ machine-learning techniques in evaluating HW.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 277-284"},"PeriodicalIF":6.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887509","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}

引用次数: 0

A novel U-net model for brain tumor segmentation from MRI images 一种新的U-net模型用于从MRI图像中分割脑肿瘤

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-30 DOI: 10.1016/j.aej.2025.04.051

Marwa Obayya , Asma Alshuhail , Khalid Mahmood , Meshari H. Alanazi , Mohammed Alqahtani , Nojood O. Aljehane , Hamad Almansour , Mohammed Abdullah Al-Hagery

{"title":"A novel U-net model for brain tumor segmentation from MRI images","authors":"Marwa Obayya , Asma Alshuhail , Khalid Mahmood , Meshari H. Alanazi , Mohammed Alqahtani , Nojood O. Aljehane , Hamad Almansour , Mohammed Abdullah Al-Hagery","doi":"10.1016/j.aej.2025.04.051","DOIUrl":"10.1016/j.aej.2025.04.051","url":null,"abstract":"<div><div>Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 220-230"},"PeriodicalIF":6.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887505","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}

引用次数: 0

CNN-based cancer prediction scheme using 5G-assisted federated learning for healthcare Industry 5.0 基于cnn的基于5g辅助联邦学习的医疗行业5.0癌症预测方案

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.04.043

Aparna Kumari , Divya Patadia , Sudeep Tanwar , Giovanni Pau , Fayez Alqahtani , Amr Tolba

{"title":"CNN-based cancer prediction scheme using 5G-assisted federated learning for healthcare Industry 5.0","authors":"Aparna Kumari , Divya Patadia , Sudeep Tanwar , Giovanni Pau , Fayez Alqahtani , Amr Tolba","doi":"10.1016/j.aej.2025.04.043","DOIUrl":"10.1016/j.aej.2025.04.043","url":null,"abstract":"<div><div>Healthcare Industry 5.0 has witnessed a transformative change by incorporating Artificial Intelligence (AI), Internet-of-things (IoT), and genomics to provide individualized and patient-centered healthcare. It prioritizes comprehensive health, proactive strategies for chronic diseases like tumors, cancer, etc., and effective cooperation among participants. Much work has been done for cancer prediction using machine learning and deep learning approaches. However, it has yet to be fully explored to protect data privacy and security by limiting data sharing. So, this paper proposes a Convolutional Neural Networks (CNN)-based Cancer Prediction scheme, i.e., <em>CNN-CPS</em> within the context of Healthcare Industry 5.0. The proposed scheme harnesses the capabilities of 5G-assisted Federated Learning (FL), a revolutionary method for predicting cancer occurrences with data privacy, security, and limiting data sharing. Then, it also leverages model data from dispersed systems to boost efficiency. By capitalizing on the speed and efficiency of 5G networks and the collaborative nature of FL, precise cancer predictions can be attained, all while upholding the confidentiality of sensitive data. This integration of technologies has the potential to significantly reshape cancer diagnosis and therapeutic approaches in the Industry 5.0 landscape. Here, 5G can enable real-time data interchange across remote data centers with rapid transmission of data capabilities and low-latency connectivity, enabling more effective and precise model training. Hence, this study examines the FL to predict breast cancer and combines the optimizer weights from several clients to continually enhance the performance of the overall model. The experimental results show the efficacy of the <em>CNN-CPS</em> compared to the existing approaches based on several parameters like low latency (40.77% improvement), accuracy (75%), and loss (0.54).</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 131-142"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873218","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}

引用次数: 0

Identification and analysis of driving factors for product evolution: A text data mining approach 产品演化驱动因素的识别与分析：一种文本数据挖掘方法

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.04.073

Shifeng Liu , Jianning Su , Shutao Zhang , Kai Qiu , Shijie Wang

{"title":"Identification and analysis of driving factors for product evolution: A text data mining approach","authors":"Shifeng Liu , Jianning Su , Shutao Zhang , Kai Qiu , Shijie Wang","doi":"10.1016/j.aej.2025.04.073","DOIUrl":"10.1016/j.aej.2025.04.073","url":null,"abstract":"<div><div>Products continuously evolve over time as a result of changes in various factors, including technological advancements, customer requirements, and material processes. The complexity and interconnections among these factors present significant challenges for their identification and description. Traditional studies primarily rely on inductive summarization, which often faces issues of subjectivity, uncertainty, and low reliability. This research presents a method combining the Bidirectional Encoder Representations from Transformers (BERT) model and Dynamic Topic Model (DTM) to analyze the driving factors of product evolution. First, the BERT model was employed to enhance the DTM model, and a text corpus related to product evolution was constructed to identify its driving factors. Then, similar algorithms and co-occurrence network analysis methods are applied to study the spatiotemporal evolution of these driving factors at different granular levels and their impact on designers' cognition. Finally, a case study on the evolution of automobiles is conducted to verify the effectiveness and applicability of the proposed model. The results indicate that incorporating the BERT model to enhance the DTM model improves semantic extraction from textual data. Moreover, significant interdependencies were identified among the driving factors, with their specific meanings progressively evolving towards domains such as human emotions, culture, and experiences. From a data mining perspective, this approach addresses the challenges of identifying product evolution driving factors, assisting designers and decision-makers in executing iterative product development more efficiently and scientifically.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 143-159"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873219","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}

引用次数: 0

Syntax-enhanced Chinese–Vietnamese neural machine translation with linguistic feature template integration 基于语言特征模板集成的语法增强汉越神经机器翻译

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.04.032

Hongfei Zhang , Zhiqiang Yu , Ting Wang , Zuo Jiang , Yi Tang

{"title":"Syntax-enhanced Chinese–Vietnamese neural machine translation with linguistic feature template integration","authors":"Hongfei Zhang , Zhiqiang Yu , Ting Wang , Zuo Jiang , Yi Tang","doi":"10.1016/j.aej.2025.04.032","DOIUrl":"10.1016/j.aej.2025.04.032","url":null,"abstract":"<div><div>Template based translation have become a mainstream technology in the field of neural machine translation. Unlike conventional machine translation methods that employ strategies such as data augmentation or network structure optimization, template-based machine translation excels at incorporating target-side semantics. However, this technological paradigm overly focuses on using the target sentence as a template and fails to effectively utilize the linguistic features in the source sentence and template. To this end, we introduce an innovative method for extracting linguistic features from Chinese–Vietnamese language pair, which serves as a template to steer the translation. This work templates typical language features (modifiers reversed) in Chinese and Vietnamese, and an integration approach is presented for integrating the linguistic feature template into sequence-to-sequence translation framework. The experimental results demonstrate that the proposed method outperforms the strong baseline models with an average 1.15 BLEU score in Chinese–Vietnamese translation tasks, and also achieves significant improvements on other machine translation evaluation metrics. Additionally, the importance of the linguistic feature template has been substantiated through its application in the analysis of Chinese–Vietnamese language characteristics.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 105-115"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873308","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}

引用次数: 0

Incremental-learning-based graph neural networks on edge-forwarding devices for network intrusion detection 边缘转发设备上基于增量学习的图神经网络用于网络入侵检测

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.03.102

Qiang Gao , Samina Kausar , HuaXiong Zhang

{"title":"Incremental-learning-based graph neural networks on edge-forwarding devices for network intrusion detection","authors":"Qiang Gao , Samina Kausar , HuaXiong Zhang","doi":"10.1016/j.aej.2025.03.102","DOIUrl":"10.1016/j.aej.2025.03.102","url":null,"abstract":"<div><div>Graph neural networks have become one of the research hotspots for network intrusion detection due to their natural suitability for representing computer networks. However, most of the related research on training GNNs is centralized, and this approach involves long-distance transmission and dumping of network data, so it is inefficient to perform, has the potential for privacy leakage, and introduces an additional transmission burden to the network. To address these challenges, this paper investigates the feasibility of offloading both graph neural networks' training and inference phases to edge-forwarding devices such as switches. We propose a distributed framework that aggregates residual computational resources from edge-forwarding devices into a micro-computing network. This framework then migrates GNN execution to edge-forwarding devices through a hybrid parallelism paradigm, thus locally detecting network anomalies to reduce network data transmission significantly. Meanwhile, to address the problem of computational and memory constraints of edge-forwarding devices, we propose a novel attention heatmap-driven memoryless incremental learning algorithm that learns network features and detects anomalies with minimal resources while avoiding catastrophic forgetting. Finally, we implement and verify the feasibility of the above framework and algorithm using a general-purpose embedded system and open-source software. The experiments show that although each edge-forwarding device's computational and memory load is light, the framework performs similarly to traditional approaches. To the best of our knowledge, this is the first approach that offloads a graph neural network model to edge-forwarding devices.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 81-89"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873306","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}

引用次数: 0

SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection SAMFNet：多模态三维物体检测的场景感知采样和多阶段融合

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.03.129

Baotong Wang , Chenxing Xia , Xiuju Gao , Bin Ge , Kuan-Ching Li , Xianjin Fang , Yan Zhang , Yuan Yang

{"title":"SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection","authors":"Baotong Wang , Chenxing Xia , Xiuju Gao , Bin Ge , Kuan-Ching Li , Xianjin Fang , Yan Zhang , Yuan Yang","doi":"10.1016/j.aej.2025.03.129","DOIUrl":"10.1016/j.aej.2025.03.129","url":null,"abstract":"<div><div>Recently, multimodal 3D object detection (M3OD) that fuses the complementary information from LiDAR data and RGB images has gained significant attention. However, the inherent structural differences between point clouds and images pose fusion challenges, significantly hindering the exploration of correlations within multimodal data. To address this issue, this paper introduces an enhanced multimodal 3D object detection framework (SAMFNet), which leverages virtual point clouds generated from depth completion. Specifically, we design a scene-aware sampling module (SASM) that employs tailored sampling strategies for different bins based on the density distribution of point clouds. This effectively alleviates the detection bias problem while ensuring the key information of virtual points, significantly reducing the computational cost. In addition, we introduce a multi-stage feature fusion module (MSFFM) that embeds point-level and regional-adaptive feature fusion strategies to generate more informative multimodal features by fusing features with different granularities. To further improve the accuracy of model detection, we also introduce a confidence prediction branch unit (CPBU), which improves the detection accuracy by predicting the confidence of feature classification in the intermediate stage. Extensive experiments on the challenging KITTI dataset demonstrate the validity of our model.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"126 ","pages":"Pages 90-104"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873307","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}

引用次数: 0

Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation 农业消费物联网设备：优化数据聚合的方法

IF 6.2 2区工程技术

alexandria engineering journal Pub Date : 2025-04-26 DOI: 10.1016/j.aej.2025.03.134

Hanan Abdullah Mengash , Hany Mahgoub , Asma Alshuhail , Abdulbasit A. Darem , Jihen Majdoubi , Ayman Yafoz , Raed Alsini , Omar Alghushairy

{"title":"Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation","authors":"Hanan Abdullah Mengash , Hany Mahgoub , Asma Alshuhail , Abdulbasit A. Darem , Jihen Majdoubi , Ayman Yafoz , Raed Alsini , Omar Alghushairy","doi":"10.1016/j.aej.2025.03.134","DOIUrl":"10.1016/j.aej.2025.03.134","url":null,"abstract":"<div><div>With the advent of state-of-the-art computer and digital technology, modern civilisation has been immensely facilitated and optimised. The Internet of Things (IoT) has grown in importance in recent years, allowing us to monitor our physical environments and broadening our horizons. The \"practice, science, or art\" of farming is defined as tending to land, growing crops with the use of different tools and techniques, and then selling the harvested food. If farmers optimise their operations with the help of a Wireless Sensor Network (WSN), they will be able to work much more efficiently and effectively. Data aggregation involves collecting information from multiple sensors. The data aggregation process is optimised by applying metaheuristic techniques. A Genetic Algorithm (GA) is a method for modelling evolution that uses mutation, crossover, and natural selection as its building blocks. The key benefit of the Artificial Bee Colony (ABC) approach is that it simultaneously considers local and global search, and it doesn't get trapped calculating its local minima. Naturalistic algorithms like ALO model their hunting behaviour after that of ant-lions and doodlebugs. It manages to find a happy medium between exploration and exploitation with just one operator. Experimental evidences show that the proposed metaheuristic technique, ABC-ALO, which combines the best elements of Artificial Bee Colony and Ant Lion Optimisation, is superior to existing metaheuristic approaches in terms of lifetime computation, or the number of alive nodes at different round counts.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"125 ","pages":"Pages 692-699"},"PeriodicalIF":6.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873522","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}

引用次数: 0