Publications Scientifiques
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Item Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration(Sciendo, 2025) Hammachi, Radhouane; Messaoudi, Noureddine; Belkacem, Samia; Pasetto, Edoardo; Delilbasic, AmerIntroduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromuscular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificial intelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for various medical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of this study is to employ both quantum and classical support vector machines (SVMs) to classify EMG signals into two classes, healthy and myopathy, and compare their performance. Methods: Various approaches were tested; classical SVM and quantum-kernel-based SVM, both with manually extracted features, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evaluation of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantum methods. Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparable to classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep feature extraction approach, which delivered a high training and testing accuracies of up to 96.7% and 85.1%, respectively. Conclusion: These findings encourages the necessity for more advanced QML research, particularly in medical applications as quantum technology progressesItem Classical and Quantum SVM for Electromyography-Based Myopathy Detection: A Comparative Exploration(Polish Society of Medical Physics, 2025) Hammachi, Radhouane; Messaoudi, Noureddine; Belkacem, Samia; Pasetto, Edoardo; Delilbasic, AmerIntroduction: Electromyography (EMG) analysis is one of the most fundamental approaches for diagnosing neuromuscular diseases. Current advancements in technology have the potential to improve diagnosis accuracy using artificial intelligence (AI). Quantum machine learning (QML), while still in its early stages, offers promising potential for various medical applications, but its effectiveness in real-world diagnostic tasks needs further exploration. Thus, the aim of this study is to employ both quantum and classical support vector machines (SVMs) to classify EMG signals into two classes, healthy and myopathy, and compare their performance. Methods: Various approaches were tested; classical SVM and quantum-kernel-based SVM, both with manually extracted features, and convolutional neural network (CNN)-based deep features extraction techniques. This allows for an evaluation of the strengths and limitations of this new technology, acknowledging the potential of both classical and quantum methods. Results: The obtained results showed that the proposed quantum methods yielded promising outcomes and comparable to classical methods. Particularly, the competitive results of the quantum SVM (QSVM) with the CNN-based deep feature extraction approach, which delivered a high training and testing accuracies of up to 96.7% and 85.1%, respectively. Conclusion: These findings encourages the necessity for more advanced QML research, particularly in medical applications as quantum technology progresses.Item Deep Learning Classification of Simulated Surface EMG Signals across Maximum Voluntary Contraction Levels(Institute of Biophysics and Biomedical Engineering at the Bulgarian Academy of Sciences, 2025) Hammachi, Radhouane; Belkacem, Samia; Messaoudi, Noureddine; Bekka, Raïs El’hadiElectromyography (EMG) is a fundamental tool in diagnosing neuromuscular disorders (NMD). Due to the complex nature of EMG signals, different approaches, based on artificial intelligence and machine learning, were developed for EMG signal analysis and NMD diagnosis. Considering the critical role of maximum voluntary contraction (MVC) as a fundamental metric in assessing muscle fatigue, in this work, classification of simulated surface EMG (sEMG) into MVC levels is performed. Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. The obtained results demonstrate that the model achieved high performance in differentiating between the ten classes (MVC levels), with an accuracy, F1-score, recall, and precision of 88.88%, 88.75%, 88.80% and 88.86%, respectively. These findings reveal that the model can accurately differentiate across MVC levels, indicating a potential method for accurate assessment of muscle fatigue intensity.Item Crack growth diagnostic of ball bearing using vibration analysis(Sciendo, 2022) Belaid, S.; Lecheb, Samir; Chelil, A.; Mechakra, H.; Safi, Brahim; Kebir, H.It is known that supported ball bearings have great effects on the vibrations of the gear transmission system, above in all the presence of local faults as well as the crack growths. For this purpose, this paper focuses on shock and vibration crack growth diagnostic of ball bearing using vibration analysis. Our work is devoted first to a study the static behaviour of the ball bearing by determining the stress, strain and displacement, then its dynamic behaviour by determining the first four natural frequencies. Secondly, a dynamic analysis study of the bearing was carried with defects as a function of crack size and location. The obtained results clearly show that the natural frequencies decrease in a non-linear way with the growth of the length of the crack, on the other hand the stress increases with the presence of the singular points of the crack. Finally, this residual decrease in natural frequencies can be used as an indicator of the state of failure, as well as a parameter used for the diagnosis and screening, and to highlight the fatigue life of the bearingItem Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals(SAGE, 2023) Moussaoui, Imane; Rahmoune, Chemseddine; Benazzouz, DjamelThe 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 conditionsItem Robust Fault Diagnosis using Uncertain Hybrid Bond Graph Model: Application to Controlled Hybrid Thermo-Fluid Process(2019) Lounici, Yacine; Touati, Youcef; Adjerid, Smail; Benazzouz, DjamelThe continuous increase in engineering systems complexity and industrial safety requirements has led to a rising interest in the development of new Fault diagnosis algorithms. This paper addresses the fault diagnosis problem of uncertain hybrid systems containing both discrete and continuous modes using a hybrid bond graph (HBG) approach. The latter provides through its properties, an automatic Global Analytical Redundancy Relations (GARRs) generation. The numerical evaluation of GARRs yields fault indicators named residuals, which are used to verify the coherence between the real system behavior and reference behavior for real-time diagnosis. In fact, the residual is compared to its adaptive thresholds to detect the actual faults. In addition, the Global Fault Signature matrix (GFSM) allows making a decision on fault isolation. The main scientific interest of the proposed method remains in integrating the benefits of the HBG with the approach for adaptive thresholds generation for systems having uncertain parameters and measurements. For this task, first, the HBG model is obtained to model the hybrid system using the controlled junctions taken into consideration discrete modes changes. Secondly, the parameter and measurement uncertainties are modelled directly on the HBG in preferred derivative causality for residuals and adaptive thresholds generation. The proposed methodology is studied under various scenarios via simulation over a controlled hybrid thermo-fluid two-tank system.Item Segmentation and extraction of GBM tumor in brain MRI medical images : comparative study(Springer, 2022) Doghmane, Mohamed Zinelabidine; Driss, M.; Eladj, S.In this study, a comparison between two image segmentation methods has been discussed; the first method is based on normal brain's tissue recognition then tumor extraction using Thresholding method. The second method is classification based on fuzzy EM segmentation, which is used for both brain recognition and tumor extraction. Medical image examinations often use more information that are acquired from multiple imaging modalities, this use is increasing due to the complementary information that can be obtained from data fusion. The later improves the quality of the diagnosis; for instance, the image fusion can be developed for data from different modalities or different individuals, it may also concern fusion of image data with an external model, this can express prior knowledge about the problem at hand. The image data used in this comparative study belong to Algerian patients with Glioblastoma multiform. Since the goal of these methods is to detect, segment, extract, classify and measure properties of the brain normal and abnormal (tumor) tissues, the results of comparative study provided a guide tool of which method is more accurate in term of GBM volume estimation for the studied samples of the patientsItem Artificial Neuron Network Based Faults Detection and Localization in the High Voltage Transmission Lines with Mho Distance Relay(IETA, 2020) Boumedine, Mohamed Said; Khodja, Djalal Eddine; Chakroune, SalimThis study offers the opportunity to extend the functioning of the most advanced protection systems. The faults which can arise on the power transmission lines are numerous and varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the conventional sensor as the Mho distance relay also known as the admittance relay is generally used. This relay will be discussed later in this study. By taking into account the preventive risks of the Mho relay and discover the new techniques of artificial intelligence, namely the neural network which can contribute to the precise and rapid detection of all types of short-circuit faults. The results of the simulation tests demonstrate the effectiveness of the methods proposed for the automatic diagnosis of faults.Item New gear fault diagnosis method based on MODWPT and neural network for feature extraction and classification(ASTM International, 2019) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem; Fedala, SemchedineGear fault diagnosis using vibration signals has become the subject of intensive studies to detect any sudden failure. However, these signals exhibit nonlinear and nonstationary behaviors when the rotating machine operates under multiple working conditions. Furthermore, fault features extraction and classification of multiple gear states are always unsatisfactory and considered as a huge task. This is the main reason that motivates us to develop a new intelligent gear fault diagnosis method in order to automatically identify and classify several kinds of gear defects under different work conditions. So in this article, we propose a combination between the maximal overlap discrete wavelet packet transform (MODWPT), entropy indicator, and a multilayer perceptron (MLP) neural network as a new automatic fault diagnosis approach. MODWPT decomposes the data signal into several components using a uniform frequency bandwidth. Each decomposed component is selected to extract feature vector using entropy indicator. Finally, MLP provides a powerful automatic tool for identifying and classifying the aforementioned extracted features. Experimental vibration signals of healthy gear; gear with general surface wear; gear with chipped tooth in length; gear with chipped tooth in width; gear with missing tooth; and gear with tooth root crack are recorded under fifteen different work conditions to test the effectiveness of the suggested technique. Experimental results affirm that our proposed approach can successfully detect, identify, and classify the gear fault pattern in all casesItem Bond Graph Model-Based Methods for Fault Diagnosis: A Comparative Study(2019) Lounici, Yacine; Touati, Youcef; Adjerid, SmailAdvanced methods of fault diagnosis become increasinglysignificant for improving the safety, reliability and efficiently of dynamic systems in various domains of industrial engineering. This paper reviews and comparesthree bond graph model-based methods for fault diagnosis. These methods are causality inversion method, augmented Analytical redundancy relation method, and fault estimationmethod. Thesemethods are applied toa simulation model of an electricalsystem. This latteris used to simulate the system variables in both normal and faulty situationsand to generate residuals for fault detection and isolation. The results of the case study are compared for highlighting the fault diagnosisperformanceand capability of a method over another.The result showsthat the faultestimationmethodhas a better diagnosis performance when compared to the other methods