Publications Scientifiques

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Author: search.filters.author.Belkacem, SamiaHas files: No

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    Efficient image encryption scheme using a nonlinear shift register and chaos
    (TARU PUBLICATIONS, 2024) Bourekouche, Hadjer; Belkacem, Samia; Messaoudi, Noureddine
    Powerful cryptographic systems require a qualified random number generator. This research purposes to provide a comprehensive comparative analysis done on several of the well-known pseudo-random number generators (PRNGs) regarding their efficiency and resilience against crypto-analytical threats. These generators consist of the basic 8-bit Non- Linear Feedback Shift Register (NLFSR), the logistic map (LM), and our proposed hybrid random number generator named NLFSR-LM, which combines through XOR operation the sequences of the NLFSR with the LM to achieve a high quality of randomness. The performance of the created generator is examined and subsequently compared according to statistical tests of randomness alongside cryptographic features in terms of key space, key sensitivity and resistance to numerous attacks. The proposed generator produced good results and exhibited several interesting properties, such as a high degree of security, a sufficiently large key space, and it provided better randomness than other frequently used PRNGs.
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    Simulated surface electromyographic (semg) signal generation and detection model
    (Sciendo, 2023) Messaoudi, Noureddine; Belkacem, Samia; Bekka, Rais El’hadi
    For didactic purposes, the aim of this work was to improve a simulation model of surface electromyographic (sEMG) signal by taking into consideration the shortcomings of previously developed models. This model started with the simulation of the single fibre action potential (SFAP), then the model of the single motor unit action potential (MUAP), afterwards the imitation of the train of MUAP and finally the modellig of the resultant sEMG signal which is the sum of the MUAPs trains. SFAP simulation was based on: i) the description of the volume conductor model which is composed of four layers (bone, muscle, fat and skin), ii) the description of the electrodes shapes and sizes as well as spatial filters, iii) and the transmebrane current. The proposed model shows its effectiveness in the possibility of carrying out practical work by simulation on the modelling of SFAP, MUAP, MUAPT and the sEMG signal. The most important result of this model is that signal processing tools can be applied to analyze and interpret real-world phenomena such as the effects of physiological , non physiological and sensing system parameters on the shape of the simulated sEMG signal.
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    ECG beats classification with interpretability
    (IEEE, 2022) Hammachi, Radhouane; Messaoudi, Noureddine; Belkacem, Samia
    Recently, a lot of emphasis has been placed on Artificial Intelligence (AI) and Machine Learning (ML) algorithms in medicine and the healthcare industry. Cardiovascular disease (CVD), is one of the most common causes of death globally, and Electrocardiogram (ECG) is the most widely used diagnostic tool to investigate this disease. However, the analysis of ECG signals is a very difficult process. Therefore, in this work, automated classification of ECG data into five different arrhythmia classes is proposed, based on MIT-BIH dataset. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Deep Learning (DL) models were used. The black-box nature of these complex models imposes the need to explain their outcomes. Hence, both Permutation Feature Importance (PFI) with Gradient-Weighted Class Activation Maps (Grad-CAM) interpretability techniques were investigated. Using the K-Fold cross-validation method, the models achieved an accuracy of 97.1% and 98.5% for CNN and LSTM, respectively
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    Ability of spatial filters to distinguish between two MUAPs generated from MUs with different locations, sizes and fibers pennation
    (IOP Publishing, 2023) Messaoudi, Noureddine; Belkacem, Samia; Bekka, Raïs El’hadi
    In this study, we investigated the effects of the motor unit (MU) location and size and the fibres pennation on the ability of anisotropic and almost isotropic spatial filters used to detect surface electromyographic (EMG) signals to make a distinction between motor unit action potentials (MUAPs) generated from two MUs. The study was based on simulated MUAPs. The fibres orientation was performed by varying the fibres pennation angle (FPA). The root mean square error (RMSE) between MUAPs generated from two MUs was used as a criterion to evaluate the ability of the investigated filters to distinguish between two generated MUAPs. The location of a MU was fixed and the second MU moved away from the first MU in the transversal direction for the first case and in the depth direction in the second case to take five different locations in every case. We showed that the capability of the studied filters to more separate two MUAPs strongly depended on MU location, MU size and FPA. This capability of separation was best with large distances between the two MUs and with large sizes of them. Furthermore, the main survey of this work was that the BiTDD filter has the best ability of separation of two MUAPs than the other filters in a given FPA interval. The number of pennation angles in this interval is related to the location and size of the moved MU
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    Influence of Fibres Inclination on the Degree of Gaussianity of Simulated Surface EMG Signals
    (ACM DIGITAL LIBRARAY, 2020) Messaoudi, Noureddine; Bekka, Raïs El’hadi; Belkacem, Samia
    The main purpose of this simulation study was to estimate the effect of muscle fibres inclination on the degree of Gaussianity of the surface electromyographic (sEMG) signals generated in a cylindrical multilayer volume conductor constituted by bone, muscle, fat and skin layers and detected by the longitudinal single differential (LSD), inverse binomial of order two (IB2), maximum kurtosis filter (MKF) and three rings (3RGs) systems. This estimation was based on the computation of the Kurtosis of simulated sEMG signals when the fibres inclination angle (FIA) varied from 0° to 180° in a step of 2.5°. For each FIA, the effects of the motor units (MUs) recruitment range (RR) and the level of muscular voluntary contraction (MVC) were also assessed. The results showed that with the same detection system, the degree of Gaussianity of the EMG signal is highly influenced by the fibres inclination with respect to the electrodes arrangement. Indeed, the classification of the studied detection systems according to the degree of Gaussianity of sEMG signals detected by them is different from a FIA interval to another i.e. a surface EMG signal detected by any detection system may be the most Gaussian with an FIA interval as it may be the least Gaussian in another FIA interval.