{"title":"Different copula types and reliability applications for a new fisk probability model","authors":"","doi":"10.1016/j.aej.2024.09.024","DOIUrl":"10.1016/j.aej.2024.09.024","url":null,"abstract":"<div><div>In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438147","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":"Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems","authors":"","doi":"10.1016/j.aej.2024.09.115","DOIUrl":"10.1016/j.aej.2024.09.115","url":null,"abstract":"<div><div>IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438145","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":"Constructing a new estimator for estimating population mean utilizing auxiliary information in probability proportional to size sampling","authors":"","doi":"10.1016/j.aej.2024.10.029","DOIUrl":"10.1016/j.aej.2024.10.029","url":null,"abstract":"<div><div>In some instances, the size of the target population might exhibit significant variation. In the medical investigation, the number of individuals troubled with a certain infection and the scale of the medical facilities may differ. Probability proportional to size (PPS) sampling helps to collect data in household income surveys when the number of siblings in houses fluctuates. This work aims to develop an improved estimator for estimating finite population mean using auxiliary information under PPS sampling. Utilizing the Taylor series approach, a novel and enhanced estimator is introduced to determine the expression of the mean square error up to the first degree of approximation. This estimator performs better as compared to some current existing estimators using theoretical efficiency constraints. The performance of the existing and newly designed estimators was evaluated by analyzing two actual data sets. The performance was assessed based on maximizing the percentage relative efficiency and minimizing the marginal mean square error. Compared to other estimator which is examined in this work, we found that the proposed technique exhibited superior performance and increased efficiency.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438146","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":"Damage evolution of slab tracks with complex temperature distribution","authors":"","doi":"10.1016/j.aej.2024.10.048","DOIUrl":"10.1016/j.aej.2024.10.048","url":null,"abstract":"<div><div>High-speed railways extensively utilize longitudinally continuous slab tracks, which are susceptible to significant structural damage caused by temperature change. This study emphasizes the impact of complex temperature distribution characteristics of the tracks on track damage including interfacial debonding, slab end arching and concrete joint failure. Firstly, the nonlinear temperature distribution in track structures was characterized using field measurement data. Secondly, a numerical model of the longitudinally continuous slab track model was tailored and validated. Next, the numerical model was used to investigate the damage evolution of the track caused by its complex nonlinear temperature distribution. Finally, the study thoroughly examined the effects of variations of temperature distribution characteristics on the track's mechanical performance. Results show that: (1) Considering a vertical nonlinearity of 0.3 and a lateral gradient of 10 ℃/m can improve the maximum vertical displacement by 31.6 %. (2) The critical air temperature for interface damage initiation can be approximately 5 ℃ lower when the vertical nonlinearity and the lateral gradient are not considered than when they are considered. The distribution of damage on the interface between track slabs and mortar layers also varies depending on the temperature assumptions. (3) The maximum compression damage of the pre-damaged T-shaped concrete joint decreases with increasing vertical nonlinearity and lateral gradient. These new findings are of significant theoretical importance as they emphasize the need to consider temperature distribution characteristics that have been overlooked in previous studies. Additionally, the insights from this study have practical value as they provide track engineers with a state-of-the-art recommendation for incorporating realistic temperature distribution in the evaluation of longitudinally continuous slab tracks' performance.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432852","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":"An efficient convolution neural network method for copy-move video forgery detection","authors":"","doi":"10.1016/j.aej.2024.10.030","DOIUrl":"10.1016/j.aej.2024.10.030","url":null,"abstract":"<div><div>Unmanned systems play a pivotal role in military surveillance, critical infrastructure protection, law enforcement, search and rescue operations, and border security, showcasing their multifaceted importance across diverse applications. Video fraud detection is integral to multimedia security, where our task involves the precise identification of modified segments within video sequences. Current approaches to video fraud detection often rely on manual feature selection and models tailored to detect specific tampering types, such as copy-move or splicing. The general representation powers of deep learning models and the connection of multiple forensic characteristics are still not fully explored. This research uses a convolutional neural network (CNN) model to identify copy-move video forgeries. Copy-move forgery is a type of video tampering whereby a portion of the video is copied and pasted somewhere different in the same video to cover an essential video characteristic. The method that is being proposed involves dividing the video into individual frames, extracting features from each frame by using a CNN model that has already been trained, and then utilizing these features to train a new CNN model that would classify each frame as either legitimate or fabricated. The proposed method effectively detects copy-move video forgery with an exceptionally high accuracy rate, exceeding current methods of accuracy and computational effort. The proposed method outperformed all other approaches on the SULFA, GRIP, and VTD datasets. The model's accuracy was 85.42 %, 86.16 %, and 81.87 %, respectively, with the shortest times recorded being 9.6 sec, 11.4 sec, and 13.7 sec, respectively. Consequently, specialists can employ the suggested method as a machine-learning instrument for detecting fake videos in real-time.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432948","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":"Utilization of gold nanoparticles-bismuth vanadate photocatalyst-based photoelectrochemical sensor for caffeine detection in biological samples of athletes: A sporting perspective","authors":"","doi":"10.1016/j.aej.2024.09.119","DOIUrl":"10.1016/j.aej.2024.09.119","url":null,"abstract":"<div><div>This study presents a photo sensor for caffeine (CA) detection, employing gold nanoparticles (Au NPs) immobilized on bismuth vanadate on indium tin oxide substrate (Au-BiVO<sub>4</sub>/ITO). The characterization of the Au-BiVO<sub>4</sub>/ITO electrode was investigated by surface analysis, crystallography and electrochemical methods. The incorporation of Au NPs into the BiVO<sub>4</sub> structure has significantly increased the photocatalytic performance of the material, especially its photocurrent response. This modification has increased the photocurrent from the initial value of 200 µA to more than 300 µA. The Au-BiVO<sub>4</sub>/ITO PEC sensor demonstrated enhanced photocurrent responses to CA under visible light, compared to BiVO<sub>4</sub>/ITO. Under optimized conditions, the PEC sensor showed a linear correlation between analytical signal and CA concentration from 0.3 to 270 μM, with obtained detection limit of 0.08 μM. Using the standard addition way, it measured CA in serum and urine samples from athletes, achieving average recovery rates of 99.4 % in serum and 97.8 % in urine, which indicates high accuracy. Moreover, the results showing strong agreement with standard HPLC analysis and demonstrating high recovery efficiency. Furthermore, the sensor demonstrated excellent performance with acceptable figure of merit for CA measurement, making it suitable for reliable analytical applications in biological samples.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432945","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":"Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT","authors":"","doi":"10.1016/j.aej.2024.10.009","DOIUrl":"10.1016/j.aej.2024.10.009","url":null,"abstract":"<div><div>In Industry 4.0, information and communication technology (ICT) was employed in numerous significant infrastructures, like financial networks, smart factories, and power plants, to automate and certify industrial systems. In power control systems, ICT technologies such as IIoT have improved automated monitoring, but legacy methods, originally autonomous, now connect with external networks. This progress has presented safety vulnerabilities from legacy ICT systems. Hence, various cybersecurity approaches are developed and examined to deal with cyberattacks and vulnerabilities. Utilizing new cybersecurity models in power control systems poses risks due to their uncertified safety. Ensuring their stability and efficiency is significant for maintaining reliable power delivery and incorporating these technologies into power control systems. Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. The main cause of the NGCAD-EDLM technique is the automatic recognition of cyber-attacks. In the NGCAD-EDLM approach, the primary data normalization phase utilizing min-max normalization is performed. Next, the honey-badger algorithm (HBA) approach selects the feature subsets. Furthermore, an ensemble deep learning (DL) of two methods, namely convolutional neural networks (CNNs) and deep belief networks (DBNs) methods, are employed for classification. In addition, the DL techniques' hyperparameter selection is accomplished using the lotus effect optimization algorithm (LEOA) method. A complete set of simulation validation is performed to establish the experimental analysis of the NGCAD-EDLM method. The performance validation of the NGCAD-EDLM method exhibited a superior accuracy value of 99.21 % over other existing techniques.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432949","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":"Lateral behavior of high-speed railway bridge pile foundation in soft soils under adjacent surcharge loads considering time-dependent characteristics","authors":"","doi":"10.1016/j.aej.2024.10.044","DOIUrl":"10.1016/j.aej.2024.10.044","url":null,"abstract":"<div><div>The adjacent surcharge caused by improper soil dumping and irregular backfilling poses a huge threat to the safe service of high-speed railway bridge pile foundations in soft soils. In this study, multiple-case field prototype tests including different surcharge distances and loading values and a numerical model embedded with a soft soil material subroutine were carried out to investigate the time-dependent lateral behavior of bridge piles. The time-dependent mechanism of pile-soil interaction was revealed by characterizing the variations of the additional lateral load acting on the pile shaft, the soil-arching stress between piles, and the plastic deformation in the soil around piles. The results show that with increasing load duration, the bending moment and deflection of the pile increase gradually, and their distribution is closely related to the thickness and location of the soft soil layer. Furthermore, the horizontal soil-arching between piles underwent the stages of stabilization, local damage, and plastic flow, in which the passive load acting on the pile side continued to increase until it stabilized, resulting in time-dependent lateral deflection of the pile foundation. Consolidation parameters and pile-soil stiffness ratios also have a significant effect on the time-dependent behavior of pile responses. The conclusions obtained can provide a valuable reference for engineering applications to predict the long-term behavior of bridge piles.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432950","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":"Nonlinear forced vibration of the FGM piezoelectric microbeam with flexoelectric effect","authors":"","doi":"10.1016/j.aej.2024.10.028","DOIUrl":"10.1016/j.aej.2024.10.028","url":null,"abstract":"<div><div>In this paper, based on the extended dielectric theory, Euler beams theory and von Karman’s geometric nonlinearity, a nonlinear FGM piezoelectric microbeam model is established with flexoelectric effect. The governing equations, initial conditions and boundary conditions are obtained by applying Hamilton’s principle and then solved by combining the differential quadrature method (DQM) and iteration method. The innovation of this paper is to construct a nonlinear forced vibration model of piezoelectric microbeams. The coupling response between the inverse flexoelectric effect and the inverse piezoelectric effect is investigated. Various effects are examined, including the functional gradient index <em>m</em> and transverse distributed load <em>q</em> affecting the distribution of electric potential. Results indicated that the functional gradient index <em>m</em>, beam thickness <em>h</em>, and span-length ratio <em>L/h</em> have a significant impact on the dimensionless deflection of the FGM microbeam. The influence of the flexoelectric effect on dimensionless deflection increases with the decrease of scale. In addition, transverse load <em>q</em> and the functional gradient index <em>m</em> also have a significant impact on the distribution of electric potential. This paper will provide useful theoretical guidance for the design of micro-sensors and micro-actuators.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432946","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 phase factor generation using RNNs deep learning algorithm-based PTS method for PAPR reduction of beyond 5G FBMC waveform","authors":"","doi":"10.1016/j.aej.2024.10.040","DOIUrl":"10.1016/j.aej.2024.10.040","url":null,"abstract":"<div><div>Filter Bank Multicarrier (FBMC) is considered one of the strong applicants for a radio system beyond the fifth generation (B5G) that improves spectral access and lowers interference. It utilizes a prototype filter for each sub-carrier, making it best for the beyond fifth generation (B5G) framework. The performance of the FBMC is hugely impacted by the high peak-to-average power ratio (PAPR), which lowers the effectiveness of the power amplifier (PA) used in the 5G-based FBMC waveform. The conventional partial transmission sequence (PTS) technique requires high computational complexity due to the need for multiple Inverse Fast Fourier Transforms (IFFTs) and phase optimization, which can increase processing time and system latency. This article proposes a hybrid method combining a partial transmission sequence and recurrent neural network (RNN) known as PTS-RNNs. RNNs improve the performance of the PTS by efficiently predicting optimal phase factors, reducing computational complexity, and lowering the PAPR of the FBMC waveform. The parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) are estimated for 256 sub-carriers under the Rayleigh and Rician channels for FBMC and orthogonal frequency division multiplexing (OFDM). The experiment results reveal that the proposed PTS-RNNs method achieves an efficient 55.45 % and 67.56 % power saving performance for Rayleigh and Rician channels, with enhanced PSD performance while preserving the BER compared to the traditional selective mapping (SLM) and PTS methods. It is also noticeable that by adding more sub-blocks and phase parameters, PAPR can be further optimised.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432951","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}