Publications Scientifiques

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Subject: search.filters.subject.Random forest

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    New criteria for wrapper feature selection to enhance bearing fault classification
    (SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Meddour, Ikhlas; Bettahar, Toufik; Zair, Mohamed
    Classification is a critical task in many fields, including signal processing and data analysis. The accuracy and stability of classification results can be improved by selecting the most relevant features from the data. In this paper, a new criterion for feature selection using wrapper method is proposed, which is based on the evaluation of the classification results according to the accuracy and stability (standard deviation) of each class and the number of selected features. The pro- posed method is evaluated using Random Forest (RF) and Ant Colony Optimization (ACO) algorithms on a benchmark dataset. Results show that the proposed method outperforms classical feature selection methods in terms of accuracy and stability of classification results, especially for the difficult-to-classify combined damage class. This study demon- strates the effectiveness of the proposed new wrapper feature selection criterion to improve the performance of classifi- cation algorithms with higher stability (STD: C1 = 0.5, C2 = 0.8, C3 = 0.6, C4 = 1.8) and better accuracy (average C1 = 98.5%, C2 = 96.6%, C3 = 9.5%, C4 = 93) for the both; the statoric current and the vibration signal compared to other techniques. Machine learning methods had proven their efficiency in time-varying machines fault diagnosis when taking vibration signals and statoric currents extracted features as inputs. However, the use of the both demonstrated a higher robustness and a remarkable superiority.
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    Detection of knee osteoarthritis based on wavelet and random forest model
    (2021) Messaoudene, Khadidja; Harrar, Khaled
    The most recurrent kind of osteoarthritis is Knee osteoarthritis (KOA). Doctors encounter difficulties for a precise diagnosis through its features and to the naked eye. In this paper, we propose a new approach for the classification of KOA by combining the discrete wavelet decomposition (DWT) and random forest classifier from knee X-ray images. A total of 50 images from patients suffering or not from osteoarthritis were used in this study. The suggested technique includes image enhancement using the Gaussian filter followed by Haar wavelet transform. Five texture features namely, contrast, entropy, correlation, energy, and homogeneity were extracted from the transformed image, and these attributes were used to differentiate the radiographs into two groups: normal (KL 0) or affected with osteoarthritis (KL2). Four classifiers including random forest, SVM, RNN, and Naïve Bayes were tested and compared. The results obtained reveal that random forest achieved the highest performance in terms of accuracy (ACC = 88%) on X-Ray images of the Osteoarthritis Initiative (OAI) dataset.
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    Computerized diagnosis of knee osteoarthritis from x-ray images using combined texture features: Data from the osteoarthritis initiative
    (Wiley-Blackwell, 2024) Messaoudene, Khadidja; Harrar, Khaled
    The prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early-stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K-Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x-ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10-fold cross-validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.
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    Enhancing fault diagnosis of undesirable events in oil & gas systems: A machine learning approach with new criteria for stability analysis and classification accuracy
    (SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Zair, Mohamed; Gougam, Fawzi; Damou, Ali
    Petroleum serves as a cornerstone of global energy supply, underpinning economic development. Consequently, the effective detection of faults in oil and gas (O&G) wells is of paramount importance. In response to the limitations observed in prior research, this study presents an innovative fault diagnosis system, rooted in machine learning techniques. Our approach encompasses a comprehensive analysis, incorporating stability assessment via standard deviation (STD), and a meticulous evaluation of accuracy and stability for distinct fault scenarios. By integrating data preprocessing, feature selection methods, and deploying a robust random forest classifier, our model achieves a substantial enhancement in fault classification accuracy and stability. Extensive experimentation substantiates the superiority of our approach, surpassing the performance of previous studies that predominantly emphasized overall accuracy while disregarding stability analysis. Notably, our model attains remarkable accuracies, notably achieving a flawless 100% accuracy for scenario 3 faults. Detailed examination of mean accuracies and STDs further reinforces the precision and consistency of our model's predictive capabilities. Additionally, a qualitative assessment underscores the practical utility and reliability of our model in accurately identifying critical fault types. This research significantly advances fault detection methodologies within the O&G industry, providing valuable insights for decision-making systems in oil well operations.
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    Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals
    (SAGE, 2023) Moussaoui, Imane; Rahmoune, Chemseddine; Benazzouz, Djamel
    The precise identification of faults is vital for ensuring the reliability of the bearing’s performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings’ health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions
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    Topological analysis for osteoporosis assessment in X-Ray images using random forest model
    (Springer, 2021) Harrar, Khaled
    This paper deals with the characterization of the texture of the bone. The objective is to combine clinical parameters and topological attributes extracted from X-Ray images for osteoporosis assessment. A total of 120 women are included in this study, divided into two populations, composed of 60 healthy subjects, and 60 osteoporotic patients. Nine features are involved and a random forest model is used to differentiate the patients. Different configurations are tested, trained, and validated using k-fold cross-validation technique with different values of k. The aim is to seek the combination giving the best accuracy for discriminating between the populations. Several classifiers are also tested and compared. The obtained results affirm that the tenfold cross-validation technique with the random forest model combining the topological and the clinical parameters outperform the other classifiers, and provide high accuracy (ACC = 87,5%) demonstrating the efficiency of this model