Publications Scientifiques

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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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    Local feature selection using the wrapper approach for facial-expression recognition
    (Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT), 2024) Boukhobza, Fatima Zohra; Gharbi, Abdenour Hacine; Rouabah, Khaled; Ravier, Phillipe
    Automatic Facial Expression Recognition (FER) systems provide an important way to express and interpret the emotional and mental states of human beings. These FER systems transform the facial image into a set of features to train a classifier capable of distinguishing between different classes of emotions. However, the problem often posed is that the extracted feature vectors sometimes contain irrelevant or redundant features, which decreases the accuracy of the induced classifier and increases the computation time. To overcome this problem, dimensionality must be reduced by selecting only the most relevant features. In this paper, we study the impact of adding the "Wrapper" selection approach and using the information provided by different local regions of the face such as the mouth, eyes and eyebrows, on the performance of a traditional FER system based on a local geometric feature-extraction method. The objective here is to test and analyze how this combination can improve the overall performance of the original traditional system. The obtained results, based on the Multimedia Understanding Group (MUG) database, showed that the FER system combined with the proposed feature-selection strategy gives better classification results than the original system for all four classification models; namely, K-Nearest Neighbor (KNN) classifier, Tree classifier, NB classifier and Linear Discriminant Analysis (LDA). Indeed, a considerable reduction (up to 50%) in the number of features used and an accuracy of 100%, using the LDA classifier, were observed, which represents a significant improvement in terms of computation time, efficiency and memory space. Furthermore, the majority of relevant features used are part of the "eyebrows’ region", which proves the importance of using information from local regions of the face in emotion recognition tasks.
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    Integration of generalized discriminant analysis and classification technique for identification well test interpretation model
    (IJISAE, 2023) Saifi, Redha; Zeraibi, Nourreddine
    This paper presents a hybrid method that combines generalized discriminant analysis and machine learning technique for identifying well test model. The proposed method consists of three stages: (1) nonlinear combination of features spaces to maximize the separability among the class models through generalized discriminant analysis. (2) Construction a set of classifier and classify the new data points by a plurality vote of their prediction. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 70% for training, 15% for validation, and 15% for testing. We notice that the generalized discriminant analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 99%.
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    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, Hocine
    Rapid 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.
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    Artificial Intelligence Based Detection of COVID-19 Pneumonia Using CT Scan and X-ray Images: A Comparative study
    (Institute of Electrical and Electronics Engineers Inc, 2023) Ilyas, Muhammad; Cherifi, Dalila
    According to a new study, a computer program that was trained to see patterns by analyzing thousands of chest X-rays was able to predict with up to 95% accuracy which patients with coronavirus disease (COVID-19) would develop life-threatening complications within four days. In order to quickly identify patients with COVID-19 whose condition is most likely to deteriorate, hospital physicians and radiologists require tools like our program.Unfortunately, we are fighting one of the worst epidemics ever known to mankind called COVID-2019, a coronavirus-derived pathogen. We see ground-glass opacity in the chest X-ray and CT scan images as a result of fibrosis in the lungs when the virus has reached the lungs. The artificial intelligence techniques can be used to identify and quantify the infection because of the significant differences between infected and non-infected X-ray images. A classification model for interpreting chest X-rays and CT scan images is proposed, which may lead to improved COVID-19 diagnosis. Classifying the chest X-rays into three categories, normal, viral pneumonia, and COVID-19, is our method of classification. Additionally, COVID-19 using CT scan images has higher classification accuracy as compared to x-ray images.
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    Classification of Left/Right Hand and Foot Movements from EEG using Machine Learning Algorithms
    (Institute of Electrical and Electronics Engineers Inc, 2023) Cherifi, Dalila; Berghouti, Baha Eddine; Boubchir, Larbi
    In recent years, there has been growing interest in utilizing Electroencephalography (EEG) data and machine learning techniques to develop innovative solutions for individuals with disabilities. The ability to accurately classify hands and foot motion based on EEG signals holds great potential for enabling individuals to regain control and functionality of their disabled parts, improving their quality of life and independence. Making a better solution than the traditional ones that often require physical contact or can be challenging to operate. In our study, we have focused on hands (right/left) and foot motion disabilities, using supervised Machine Learning algorithms for the classification of EEG data related to left/right hand and foot movements; aiming to reach accurate results that can contribute to providing a solution for people with this kind of motion disabilities. Three supervised machine learning algorithms are considered for the EEG classification, namely Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), using Common Spatial Patterns (CSP) algorithm and logarithm of the variance (logvar) for feature extraction. In our experiments, we adopted these algorithms to classify the Motor Imagery EEG dataset for hands and foot movements given in BCI Competition IV. The data we used went through different steps before fitting into the models such as filtering, feature extraction, and discrimination. We achieved significant success in accurately classifying hand movements in the initial experiment, attaining an impressive classification accuracy of up to 97.5% with SVM and LDA. Furthermore, in the multi-classification task involving both hand (right/left) and foot movements, KNN and SVM classifiers yielded commendable results up to 87%. These models can be further used and developed, where a hardware implementation will be done as a further work for this study.
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    Performance Evaluation of Machine Learning Algorithms for Smart Irrigation Systems
    (Institute of Electrical and Electronics Engineers Inc, 2024) Yahia, Amina; Menasri, Wahiba; Cherifi, Dalila; Gacemi, Abderrezak
    Smart irrigation systems have revolutionized the farming industry by utilizing modern innovations to optimize water usage and handle water scarcity issues. The performance of two machine learning algorithms, Decision Tree and Support Vector Machines (SVM), in categorizing irrigation status in smart irrigation systems is evaluated in this research. The goal is to detect whether or not specified areas or plants have been watered, which is critical for proper irrigation management. Sensors in smart irrigation systems detect environmental data such as temperature, humidity, soil moisture, and rainfall. The obtained data is processed using machine learning methods to classify the irrigation status. For training and evaluation, this study makes use of a large dataset from a real-world smart irrigation system. The results demonstrate the effectiveness of both the Decision Tree and SVM algorithms. Decision Tree excels in terms of precision and recall, allowing for the accurate detection of hydrated and non-watered areas or plants. SVM achieves good accuracy and F1-score, allowing for a complete evaluation of irrigation status. These findings promote smart irrigation systems by emphasizing the importance of machine learning methods for accurate irrigation status classification. The findings help stakeholders choose appropriate algorithms for efficient water management and support sustainable agriculture practices.
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    EEG signal feature extraction and classification for epilepsy detection
    (Slovene Society Informatika, 2022) Cherifi, Dalila; Falkoun, Noussaiba; Ouakouak, Ferial; Boubchir, Larbi; Nait-Ali, Amine
    Epilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection)
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    Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study
    (Elsevier, 2022) Ammar, Mohammed; Lamri, Mohamed Amine; Mahmoud, Saïd; Laid, Amel
    Every day, a large number of people with brain injury are received in the emergency rooms. Due to the large number of slices analyzed by the doctors for each patient and to accelerate the diagnosis, the development of a precise computer-aided diagnosis system becomes very recommended. The aim of our work is developing a tool to help radiologists in the detection of intracranial hemorrhage (ICH) and its five (05) subtypes in computed tomography (CT) images. Five deep learning models are tested: ResNet50, VGG16, Xception, InceptionV3 and InceptionResNetV2. Before training these models, preprocessing operations are performed like normalization and windowing. The experiments show that VGG-16 architecture provides the best performances. The model achieves an accuracy of 96%.
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    Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier
    (SAGE Publications, 2021) Moussaoui, Imane; Rahmoune, Chemseddine; Zair, Mohamed; Benazzouz, Djamel
    Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency