Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning(Multidisciplinary Digital Publishing Institute, 2025) Mouzai, Mustapha; Riahla, Mohamed Amine; Keziou, Amor; Fouchal, HacèneAll current transportation systems (vehicles, trucks, planes, etc.) rely on the Global Positioning System (GPS) as their main navigation technology. GPS receivers collect signals from multiple satellites and are able to provide more or less accurate positioning. For civilian applications, GPS signals are sent without any encryption system. For this reason, they are vulnerable to various attacks, and the most prevalent one is known as GPS spoofing. The main consequence is the loss of position monitoring, which may increase damage risks in terms of crashes or hijacking. In this study, we focus on UAV (unmanned aerial vehicle) positioning attacks. We first review numerous techniques for detecting and mitigating GPS spoofing attacks, finding that various types of attacks may occur. In the literature, many studies have focused on only one type of attack. We believe that targeting the study of many attacks is crucial for developing efficient mitigation mechanisms. Thus, we have explored a well-known datasetcontaining authentic UAV signals along with spoofed signals (with three types of attacked signals). As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. Our main findings are as follows: (a) random forest has significant capability in detecting and classifying GPS spoofing attacks, outperforming the other models. (b) We have been able to detect most types of attacks and distinguish themItem Enhancing Porosity Prediction in Reservoir Characterization through Ensemble Learning: A Comparative Study between Stacking, Bayesian Model Optimization, Boosting, and Random Forest(Slovnaft VURUP a.s, 2024) Youcefi, Mohamed Riad; Alshokri, Ayman Inamat; Boussebci, Walid; Ghalem, Khaled; Hadjadj, AsmaAccurate estimation of porosity is a critical factor in reservoir characterization. This study aims to enhance porosity prediction through the implementation and comparison of various stacking ensemble learning strategies. A dataset comprising 273 points, which consists of well logs and core measurements, was collected from two wells for model development. Four base learners, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and XGBoost, were trained on this dataset. These models were then integrated using multiple stacking ensemble techniques, such as weighted averaging, Bayesian model averaging, and RFR as a meta-learner. Meta-learners were trained on predictions from the base learners, generated through cross-validation on leave-out data. Performance evaluations of both base and meta learners were conducted on a separate testing dataset using statistical and graphical error analysis. Results indicate that all learners demonstrated robust performance, with weighted averaging outperforming other strategies on testing data. The stacking ensemble approach, particularly through weighted averaging, effectively improved base learner performance on testing data by leveraging individual model strengths and mitigating weaknesses. The findings of this study are valuable for geoscientists and reservoir engineers in achieving accurate reservoir characterization and facilitating exploration activities.Item Modeling wax disappearance temperature using robust white-box machine learning(Elsevier Ltd, 2024) Nait Amar, Menad; Zeraibi, Noureddine; Benamara, Chahrazed; Djema, Hakim; Saifi, Redha; Gareche, MouradWax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.Item Intelligent multi-fault identification and classification of defective bearings in gearbox(SAGE Publications Inc., 2024) Damou, Ali; Ratni, Azeddine; Benazzouz, DjamelBearing faults in gearbox systems pose critical challenges to industrial operations, needing advanced diagnostic techniques for timely and accurate identification. In this paper, we propose a new hybrid method for automated classification and identification of defective bearings in gearbox systems with identical rotating frequencies. The method successfully segmented the signals and captured specific frequency components for deeper analysis employing three distinct signal processing approaches, ensemble empirical mode decomposition EEMD, wavelet packet transform WPT, empirical wavelet transform EWT. By decomposing vibration signals into discrete frequency bands using WPT, relevant features were extracted from each sub-band in the time domain, enabling the capturing of distinct fault characteristics across various frequency ranges. This extensive set of features is then served as inputs for machine learning algorithm in order to identify and classify the defective bearing in the gearbox system. Random forest RF, decision tree DT, ensemble tree ET classifiers showcased a notable accuracy in classifying different fault types and their localizations. The new approach shows the high performance of the diagnostic gearbox with a minimum of accuracy (Min = 99.95 %) and higher stability (standard deviation = 0.1).Item Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification(Elsevier Ltd, 2024) Ben Seghier, Mohamed El Amine; Mohamed, Osama Ahmed; Ouaer, HocineRapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.Item Artificial Intelligence Technique in earth sciences for porosity prediction in shaly petroleum reservoir from geophysical well-logs data. Application to Hassi R'mel field, Algeria(MEACSE Publication, 2024) Aliouane, Leila; Ouadfeul, Sid-aliMachine learning techniques are becoming very popular in earth sciences, mainly in petroleum exploration and exploitation. Reservoir characterization using geophysical well-logs data analysis is commonly conducted and plays a central role in formation evaluation in petroleum domain. The most petrophysical parameters that describe the reservoir are the porosity, the permeability and the water saturation where the porosity is the main key. Using conventional methods, the estimation of the porosity is very difficult, mainly in shaly reservoirs where the presence of clay affects considerably, the porosity and the permeability. For that, we propose to accurately predict the porosity from geophysical recordings crossed the formation of wells using machine learning methods such as multi-layer neural network. The input layer are constituted by the petrophysical well-logs data and the output layer presented by one neuron corresponding to the predicted porosity. The training step of neural network machine (NNM) is processed using core data (CORPOR) by minimizing the root mean square error using Radial Basis Function algorithm (RBF). Once trained, the model is then applied to the target wells to predict porosity (PORRBF). The predicted porosity match the core values with good accuracy. This approach provides significantly a robust computation method and reduces dependency on prior domain knowledgeItem A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm(Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Thanh, Cuong Le; Khatir, Samir; Abdel Wahab, Magd M.In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer.Item Computer numerical control machine tool wear monitoring through a data-driven approach(SAGE, 2024) Gougam, Fawzi; Afia, Adel; Ait Chikh, Mohamed Abdessamed; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, DjamelThe susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methodsItem ArabAlg: A new Dataset for Arabic Speech Command Recognition for Machine Learning Applications(University of Bahrain, 2024) Oukas, Nourredine; Haboussi, Samia; Maiza, Chafik; Benslimane, NassimAutomatic Speech Recognition (ASR) systems have witnessed significant advancements in recent years, thanks to the emergence of deep learning techniques and the availability of large speech datasets in various languages. With the increasing demand for Arabic voice-enabled technologies, the availability of a high-quality and representative dataset for the Arabic language becomes crucial. This paper presents the development of a new dataset called ArabAlg, specifically designed for Arabic Speech Command Recognition (ASCR), to support the integration of Arabic voice recognition systems into smart devices in the Internet of Things (IoT). This research focuses on collecting and annotating a diverse range of Arabic speech commands, encompassing various domains and applications. The dataset construction process involves recording and preprocessing several utterances from native Arabic speakers. To ensure precision and reliability, quality control measures are implemented during data collection and annotation. The resulting dataset provides a valuable resource for training and evaluating ASCR systems tailored for Arabic speakers using Machine Learning and Deep Learning.Item Enhancing air compressors multi fault classification using new criteria for Harris Hawks optimization algorithm in tandem with MODWPT and LSSVM classifier(SAGE, 2023) Rahmoune, Chemseddine; Amine Sahraoui, Mohammed; Gougam, Fawzi; Zair, Mohamed; Meddour, IkhlasThe evolution of industrial systems toward Industry 4.0 presents the challenge of developing robust and accurate models. In this context, feature selection plays a pivotal role in refining machine learning models. This paper addresses the imperative of accurate fault diagnosis in industrial systems, focusing on air compressors. These systems, vital for efficient operations, demand early fault detection to prevent performance degradation. Conventional methods often encounter challenges due to the occurrence of similar failure patterns under comparable conditions. To address this limitation, our approach delves into a more complex scenario, where air compressors operate under diverse fault conditions. This study introduces novel feature selection criteria achieved through a fusion of the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT), the Harris Hawks Optimization (HHO) algorithm, and the Least Squares Support Vector Machine (LSSVM) classifier. The synthesis of these components aims to bolster the multi-fault diagnosis accuracy and stability for each fault class. The evaluation focuses on key statistical metrics—minimum, maximum, mean, and standard deviation. Experimental outcomes underscore the method’s superiority over traditional feature selection techniques. The approach excels in accuracy and stability, particularly across various fault categories, affirming the efficacy and resilience of the new criteria. The symbiotic integration of MODWPT, HHO, and LSSVM within our framework highlights its potential to elevate classification performance in the realm of industrial fault diagnosis.