MOHAMMED ASSAM Ouali

HAMANI Melak

Etude et modélisation artificielle d'un outil décisionnel pour l'évaluation des indices de la qualité des eaux propres

ABDELDJABAR Fatima

Filtrage du signal electrocardiogramme par la décomposition en modes empiriques et l'exposant de hurst

RAIA Amani , KOURZI

Filtrage du signal electrocardiogramme par le décomposition en ondelettes et l'exposant de hurst

FERAHTIA Chamseddine , Faid Abdelhak

Dispositif de mesure non invasive et de prediction de la glycémie

ALI CHIKOUCHE Younes , KHALFALLAH Ali

Filtrage du signal electrocardiogramme par l'ondelette de HAAR

FERHATI Houssem , HADJI Mohammed

Conception et realisation d'une machine a commande numerique CNC

SASSOUI Abdelmalek , BAALI Mohamed

Conception et realisation d'un systeme d'aide a la decision pour la mesure des indices de qualite des eaux

DEHMECHE BOUTHEYNA , BEN MEKHFI ACHWAQ ABIR

Modelisation et identification hybride des systemes dynamique utilisant la logique flou type1 et l algorithme d optimisation BBO

BRIK Sadjida , DECHOUCHA Ahlem

Contribution a l etude et l evaluation des indices de qualite des eaux par l elaboration d un outil decisionnel intelligent

CHEBABHI YOUNES , LASLEDJ Ahmed

Etude et realisation d'un systeme de tele-surveillance dans les postes-cabines MT-BT de SONELGAZ

ROUMAIR Asma , Kahoul Ranyia

Modelisation et identification hybride des systeme dynamiques en utilisant la logique floue type 1 et l'algorithme d'optimisation PSO

HALITIM IBTISEM , BELAID SOUAAD

Modelisation et identification de series temporelles par les reseaux de neurones et les algorithmes meta-heuristiques

OUALI Marwa , BEN MESLI Amina

Modelisation et identification des systemes dynamiques par les reseaux d neurones artificiels et les algorithmes meta-heuristiques

LAMANI Meriem , HALITIM Rania

Extraction des caracteristiques et classification des signaux ECG

OUDINA Elmoundhir , MEHOUAS Abdallah

Dé-bruitage du signal Électrocardiogramme (ECG) par le filtre de Kalman étendu optimisé par l'algorithme PSO

Predicting Lattice Constant for Complex Cubic Perovskite X2YY’O6 using LSTM machine learning method.

Citation

Hamza BENNACER , MOHAMMED ASSAM Ouali , Inas bouzateur , ,(2023-12-25), Predicting Lattice Constant for Complex Cubic Perovskite X2YY’O6 using LSTM machine learning method.,3rd International Conference on Scientific and Academic Research.,Konya/Turkey.

Perovskites have gained significant attention in recent years due to their unique and versatile material properties. The lattice parameters of the perovskite compounds play a crucial role in the engineering of layers and substrates for heteroepitaxial thin films. As an essential parameter in the cubic perovskite structure, the lattice constant, plays a significant role in the development of materials for specific technological applications and serves as a distinctive identifier of the crystal structure of the material. In the field of materials science, advanced Computational Intelligence (CI)-based techniques have become increasingly important for simulating the relationship between the physicochemical parameters of chemical elements and the physical properties of materials and compounds. Hence, this paper presents efficient techniques based on artificial neural network (ANN) and fuzzy logic to predict the lattice constants of pseudo-cubic and cubic perovskites. The identification of optimized parameters for the ANN and fuzzy logic models is accomplished using innovative metaheuristic algorithms such as, Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO) and Imperialist Competitive Algorithm (ICA). In the first part, the study assessed, the effectiveness of various metaheuristic algorithms (PSO-IWO-ICA) in tuning the parameters of the ANN prediction structure in order to get the optimal parameter of the ANN. Whereas in the second part, once we extracted the best optimization algorithm, we combined it with the fuzzy logic technique and then we compared the effectiveness of the two techniques, ANN and Fuzzy logic. On the basis of root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2), the proposed PSO-ANN and PSO-Fuzzy based models are compared with existing and recent models such as Ubic, Sidey, and Owolabi. The proposed PSO-Fuzzy model performs better than our PSO-ANN model, the Ubic, Sidey, and Owolabi models, with performance improvement of 70.90%, 90.36%, 89.74% 84.46%, respectively on the basis of RMSE. Similarly, it attains performance improvement of 71.26%, 90.31%, 89.58%, and 85.02% on the basis of MAE. Furthermore, the developed PSO-ANN based model outperforms the Ubic, Sidey and Owolabi models with performance improvement of 66.86%, 64.74% and 46.60% respectively, on the basis of RMSE and percentage enhancement of 66.27%, 63.75%, and 47.90% when compared on the basis of MAE. Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.

Citation

Hamza BENNACER , MOHAMMED ASSAM Ouali , Inas bouzateur , , (2023-09-04), Perovskite lattice constant prediction framework using optimized artificial neural network and fuzzy logic models by metaheuristic algorithms, Materials today communications, Vol:37, Issue:, pages:107021, Elsevier

Classification and prediction of water quality index using deep learning techniques

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Hamza BENNACER , Mohamed Ladjal , MOHAMMED ASSAM Ouali , ,(2023-07-10), Classification and prediction of water quality index using deep learning techniques,International Conference on Nonlinear Science and Complexity (ICNSC, 2023),Turkey.

EMD Based Average Wavelet coefficient method for ECG Signal Denoising

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Hamza BENNACER , Zahia nabi , MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2023-04-17), EMD Based Average Wavelet coefficient method for ECG Signal Denoising,International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE),University of M'sila

Sensor Anomaly Detection using Self Features Organizing Maps and Hierarchical-Clustring for Water Quality Assessment

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Hamza BENNACER , Mohamed Ladjal , MOHAMMED ASSAM Ouali , ,(2023-04-17), Sensor Anomaly Detection using Self Features Organizing Maps and Hierarchical-Clustring for Water Quality Assessment,ICATEEE,University of M'sila

Soft Sensing Modeling Based on Support Vector Machine and Self-Organaizing Maps Model Selection for Water Quality Monitoring

Citation

Hamza BENNACER , Mohamed Ladjal , MOHAMMED ASSAM Ouali , ,(2023-04-17), Soft Sensing Modeling Based on Support Vector Machine and Self-Organaizing Maps Model Selection for Water Quality Monitoring,ICATEEE,University of M'sila

Electrocardiogram (ECG) is one of the main tools to interpret and identify cardiovascular disease. ECG signals are frequently submitted to various noises, which alter the original signal and reduce its quality. ECG signal filtering enables cardiologists to assess heart health accurately. The present paper presents a newfound approach for ECG signal denoising built on two techniques which are EMD (Empirical Mode Decomposition) and AWC (Average Wavelet Coefficient method). The basic idea behind the suggested technique initially consists of deconstructing noisy ECG signal data on a restricted number of IMFs (Intrinsic Mode Functions) and then using the AWC technique to compute each IMF’s Hurst exponent. Finally, after a thresholding operation, the clean ECG signal is recovered by adding all IMFs, excluding those considered parts of noise. The suggested approach is assessed over experiments using the MIT-BIH databases. The experimental results reveal that the suggested method efficiently extracts ECG signals from noisy data samples.

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , Hamza BENNACER , ,(2023-04-17), EMD Based Average Wavelet Coefficient Method for ECG Signal Denoising,(ICATEEE),university of m'sila

Double perovskite oxides have received a lot of interest in the last ten years because of their distinctive and adaptable material properties. Among the six parameters in the cubic structure, the lattice constant is the sole changeable parameter, which plays an important role in developing materials for particular technological applications and distinctively identifies the crystal structure of the material. In this paper, the extreme learning machine (ELM) is used to correlate the lattice constant of A+22BCO6 cubic perovskite compounds, such as their ionic radii, electronegativity, oxidation state, and lattice constant. We investigated 147 compounds with lattice constants between 7.700 and 8.890Å. The prediction method has a high level of accuracy and stability and provides accurate estimates of lattice constants.

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , Hamza BENNACER , ,(2023-04-17), Lattice Constant Prediction of Complex Cubic Peroveskite A2BCO6 using Extreme Learning Machine,(ICATEEE),university of m'sila

The electronic band gap energy is an essential photo-electronic parameter in the energy applications of engineering materials, particularly in solar cells and photo-catalysis domains. A prediction model that can correctly predict this band gap energy is desirable. A new approach for predicting a band gap energy is suggested in this paper. The proposed structure is based on artificial neural networks (ANN) and the particle swarm optimization algorithm (PSO); this structure can solve the artificial neural network’s local minima issue while preserving the fitting quality. Our technique will hasten the identification of novel chalcopyrite in photovoltaic solar cells with improved resolution. The suggested model combines two sub-systems in a parallel configuration. A conventional prediction system with a low resolution for the training data being considered makes up the first ANN subsystem. A second ANN sub-system, labelled the error model, is introduced to the primary system to address the resolution quality issue, representing uncertainty in the primary model. The particle swarm optimization algorithm is used to identify the parameters of the proposed neural system. The method’s effectiveness is assessed in terms of several criteria, and the output of our system shows good performance compared to experimental and other calculated results. Several benchmark approaches were compared with the proposed system in detail. Numerous computer tests show that the suggested strategy can significantly enhance convergence and resolution.

Citation

Inas bouzateur , Hamza BENNACER , MOHAMMED ASSAM Ouali , Mohamed Ladjal , , (2023), A new ANN-PSO framework to chalcopyrite’s energy band gaps prediction, Materials Today Communications, Vol:34, Issue:105311, pages:11, Bouzateur inas

Double perovskite oxides have received a lot of interest in the last ten years because of their distinctive and adaptable material properties. Among the six parameters in the cubic structure, the lattice constant is the sole changeable parameter, which plays an important role in developing materials for particular technological applications and distinctively identifies the crystal structure of the material. In this paper, the extreme learning machine (ELM) is used to correlate the lattice constant of A_2^(+2) BCO_6 cubic perovskite compounds, such as their ionic radii, electronegativity, oxidation state, and lattice constant. We investigated 147 compounds with lattice constants between 7.700 and 8.890Å. The prediction method has a high level of accuracy and stability and provides accurate estimates of lattice constants.

Citation

Hamza BENNACER , MOHAMMED ASSAM Ouali , Mohamed Ladjal , Moufdi Hadjab , Inas bouzateur , ,(2022), Lattice Constant Prediction of Complex Cubic Peroveskite A2BCO6 using Extreme Learning Machine,International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE),Msila, Algeria

Water quality control and monitoring is an important concern of countries over the world. We present in this work, the use the self-organizing feature maps of Kohonen (SOFM) as features selection technique and advanced classification techniques, such as: Long Short-Term Memory (LSTM) and Support Vector Machines (SVM). This study involved the advanced assessment of surface water quality from Tilesdit dam in Algeria. Typically, water quality status is determined by comparing collected data with water quality standards. LSTM and SVM have been applied with SOFM-based features selection for water quality classification. In this work, the training step is realized using the mentioned approaches to supervise the water quality from several physicochemical parameters. Eleven of them were collected in 4 seasons during the period (2016-2018) from study area. Experiments step using a mentioned dataset in terms of accuracy (training and test), running time and robustness, is carried out. The performance of our approach is optimized by regulating the parameter values using a SFOM based features selection method. The proposed approach outperforms current conventional methods, as this approach is a combination of strong feature selection and classification techniques. Optimal input features are selected directly from the original datasets, aiming to reduce the computational time and complexity. The impact of this result is significant both technically (lower learning time) and economically (reduced the number of sensors) and can improve obviously the performance of our monitoring system. The accuracy is more than 98% in training and testing steps with features selection process for the LSTM and SVM models. The best results of sensitivity, specificity, precision, and F-score of the two proposed models were ranged all between 96,99 % and 100%. In a nutshell, the two comparative machine learning methods provide very high classification accuracy and make a considerable solution for water quality control and monitoring.

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , , (2022), Self-Organizing Maps-Based Features Selection with Deep LSTM and SVM Classification Approaches for Advanced Water Quality Monitoring, International journal of intelligent engineering and systems, Vol:15, Issue:3, pages:90-102, International journal of intelligent engineering and systems

The objective of this article is the study of conventional techniques, such as Burg algorithm, for parameter estimation of autoregressive moving average ARMA. Then, we introduce optimization techniques based on Genetic Algorithms to improve the parameters estimation of ARMA model. ARMA-Burg procedure was applied and tested on the stator current signatures MCSA in order to detect broken rotor bar of induction motor. It provides a good estimate of the power spectral density (PSD) and refines the parameters estimation of an ARMA model. The test results show the importance and value of the GA in improving performances of parameters estimation of an ARMA model by adjustment.

Citation

MOHAMMED ASSAM Ouali , ,(2022), Application of ARMA model optimized by the GA to detect broken rotor bars faults of induction motor,19 th IEEE International MultiConference on Systems, Signals & Devices SSD'2022,setif-Algeria

A New PSO-ANN Scheme for Composite Materials Properties Prediction

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MOHAMMED ASSAM Ouali , ,(2022), A New PSO-ANN Scheme for Composite Materials Properties Prediction,INTERNATIONAL SYMPOSIUM ON APPLIED MATHEMATICS AND ENGINEERING,Turkey

Soft sensing modeling based on support vector machine and self organizing maps model selection for water quality monitoring

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2022), Soft sensing modeling based on support vector machine and self organizing maps model selection for water quality monitoring,The 2022 international conference of advanced technology in electronic and electrical engineering (ICATEEE)),M'sila-Algeria

Sensor Anomaly detection using self features organizing maps and hierarchical clustring for water quality assessment

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2022), Sensor Anomaly detection using self features organizing maps and hierarchical clustring for water quality assessment,The 2022 international conference of advanced technology in electronic and electrical engineering (ICATEEE)),M'sila-Algeria

An improved ELM framework for dynamical system modeling and identification

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MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2022), An improved ELM framework for dynamical system modeling and identification,INTERNATIONAL SYMPOSIUM ON APPLIED MATHEMATICS AND ENGINEERING,Turkey

An appropriate hybrid technique for ECG signal denoising based on variational mode decomposition and average wavelet coefficient method

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MOHAMMED ASSAM Ouali , ,(2022), An appropriate hybrid technique for ECG signal denoising based on variational mode decomposition and average wavelet coefficient method,1st International Conference on Engineering, Natural and Social Sciences ICENSOS 2022 on December 20 - 23, 2022 in Konya, Turkey,Turkey

The Effect of Bamboo and Hemp Natural Fibers on the Elastic Behavior of Composite Materials Based on PMMA Polymer Matrix

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MOHAMMED ASSAM Ouali , ,(2022), The Effect of Bamboo and Hemp Natural Fibers on the Elastic Behavior of Composite Materials Based on PMMA Polymer Matrix,INTERNATIONAL SYMPOSIUM ON APPLIED MATHEMATICS AND ENGINEERING,Turkey

EMD based average wavelet coefficient method for ECG signal denoising

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2022), EMD based average wavelet coefficient method for ECG signal denoising,The 2022 international conference of advanced technology in electronic and electrical engineering (ICATEEE)),M'sila-Algeria

A New AR-ANN-framework for time series Modeling and Identification enhanced using IWO and CMA-ES metaheuristics approaches: A pilot Study

Citation

MOHAMMED ASSAM Ouali , ,(2021), A New AR-ANN-framework for time series Modeling and Identification enhanced using IWO and CMA-ES metaheuristics approaches: A pilot Study,9th (Online) International Conference on Applied Analysis and Mathematical Modeling,Turkey

An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals.

Citation

MOHAMMED ASSAM Ouali , , (2020), Electrocardiogram Signal Denoising by Hilbert Transform and Synchronous Detection, INT. J. BIOAUTOMATION, Vol:24, Issue:4, pages:323-336, INT. J. BIOAUTOMATION

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-model-based methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

Citation

MOHAMMED ASSAM Ouali , , (2020), TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising, traitement du Signal, Vol:37, Issue:4, pages:541-553, international information and engineering technology association

Optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2020), Optimization of SVM parameters with hybrid PCA-PSO methods for water quality monitoring,The 6th international conference on electrical engineering ICEE,Turkey

This paper presents a novel type-2 fuzzy model for nonlinear dynamical systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. It is based on interval type-2 fuzzy systems and it is comprised of a parallel interconnection of two type-2 sub fuzzy models. The first sub fuzzy model is the primary model, which represents an ordinary model with low resolution for the nonlinear dynamical system under consideration. To overcome resolution quality problem, and obtain a model with higher resolution, we will introduce a second type-2 fuzzy sub model called error model which will represent a model for the error modelling between the primary model and the real nonlinear dynamical system. As the error model represents uncertainty in the primary model, it’s suitable to minimize this uncertainty by simple subtraction of the error model output from the primary model output, which will lead to a parallel interconnection between them, giving then a unique whole final model possessing higher resolution. To apply this approach successfully, the model’s representation and identification are considered in this investigation using type-2 fuzzy auto regressive (T2FAR) and type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithms, like as firefly and biogeography-based optimization algorithms. To evaluate the effectiveness of the proposed method, it will be tested on three types of nonlinear dynamical systems. Computer investigations indicate that the proposed model may significantly improves convergence and resolution.

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , ,(2020), Nonlinear Dynamical Systems Modelling and Identification Using Type-2 Fuzzy Logic: Meta- heuristic Algorithms Based Approach.,2020 International Conference on Electrical Engineering (ICEE) September 25-27, 2020, Istanbul, Turkey,Turkey

A simple and efficient method to remove white Gaussian noises and physiological noises from electrocardiogram (ECG) signals is presented. It is based on simple tools usually used in digital signal processing like moving average filter, median filter, baseline drift removal and peak detection. We show by several simulations that the proposed algorithm outperforms significantly conventional median filter and moving average filter and can be considered as a valid concurrent to the standard wavelet-based method

Citation

MOHAMMED ASSAM Ouali , , (2019), Efficient Filtering Framework for Electrocardiogram Denoising, International Journal Bioautomation, Vol:23, Issue:4, pages:403-420, International Journal Bioautomation

In this investigation a novel type-2 fuzzy model for Time series is presented. It is based on interval type-2 fuzzy systems. The proposed method deals with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserves the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first one is the primary model, which represents an ordinary model with a low resolution for the time series under consideration. To overcome resolution quality problem and obtain a model with higher resolution, we introduce the second model called the error model. This model represents the error modelling between the primary model and the real time series model. The error model characterizes the uncertainty in the primary model which can be minimized by a simple subtraction of the error model output from the primary model output. The result is a parallel interconnection between the two sub models. Thus, a unique and entire final model possessing higher resolution is realized. The model's representation and identification are implemented by using type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithm such as biogeography-based optimization (BBO). The effectiveness of the method is evaluated by testing the proposed model with the reference time series models. In addition, a detailed comparative study with several reference methods will be presented. The results of the experiments that have been conducted confirm that the proposed method can considerably improve convergence, resolution and computation time.

Citation

MOHAMMED ASSAM Ouali , Mohamed Ladjal , DJERIOUI Mohamed , ,(2019), A New BBO-Type-2 Fuzzy scheme for time series Modelling,International Conference on Computational Methods in Applied Sciences (IC2MAS19),Istanbul-Turkey

Water quality monitoring are fundamental tools in the management of water resources and they provide essential information characterizing status of water resources, determining trends and changes over time, and identifying emerging water quality issues. This task consists of collecting quantitative information of different water parameters through a statistical sampling. For each parameters measurement, a sensor or a specific treatment is made. This makes the quality monitoring very expensive in money, time and labor. Therefore, it is important that water quality issues need to be understood in the framework of hydrological processes based on the water quality and hydrological monitoring. To remedy this problem, we propose in this work an efficient system for water quality classification using Minimum Redundancy Maximum Relevance (mRMR) and Extreme Learning Machine (ELM). The mRMR is an algorithm frequently used in a method to accurately identify characteristics to reduce the number of input water parameters introduced in the ELM classifier and is usually described in its pairing with relevant feature selection. These subsets often contain material which is relevant but redundant and mRMR attempts to address this problem by removing those redundant subsets. mRMR has a variety of applications in many areas such as pattern recognition. As a special case, the "correlation" can be replaced by the statistical dependency between variables. Mutual information can be used to quantify the dependency. In this case, it is shown that mRMR is an approximation to maximizing the dependency between the joint distribution of the selected features and the classification variable. The ELM which is a technique for pattern classification has been widely used in many application areas such as water quality monitoring. A multi-class problem using ELM is a typical example for solving the mentioned problem. In this work, Experimental results conducted on real dataset collected from Tilesdit dam of Bouira state (Algeria) were selected for this study. The proposed feature selection method can efficiently reduce the number of water parameters needed to classify its quality, which consequently causes a minimization in number of required sensor/treatment. Therefore, the water quality classification is perfectly insured with the trade-off between the low-cost and a high accuracy. Its performance is more competitive when compared with artificial neural networks. Furthermore, the results demonstrated that the proposed procedure has a great potential in water quality monitoring.

Citation

Mohamed Ladjal , MOHAMMED ASSAM Ouali , DJERIOUI Mohamed , ,(2019), Feature Selection Approach based on Minimum Redundancy- Maximum Relevance for Large and High-dimensional Data Classification,International Conference on Computational Methods in Applied Sciences (IC2MAS19),Istanbul-Turkey

In this investigation a novel type-2 fuzzy model for electrophysiological signals is presented. It is based on interval type-2 fuzzy systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. This approach will significantly reduce the number of type-2 fuzzy rules and simultaneously preserve the fitting quality. The proposed model comprises a parallel interconnection of two type-2 sub-fuzzy models. The first is the primary model, which represents an ordinary model with a low resolution for the electrophysiological signal under consideration, the second is a fuzzy sub-model called the error model, which represents uncertainty in the primary model. Identification is achieved by innovative metaheuristic optimisation algorithms. The method's effectiveness is evaluated through testing on synthetic and real ECG signals. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed method can significantly improve convergence and resolution.

Citation

MOHAMMED ASSAM Ouali , , (2018), A new type-2 fuzzy modelling and identification for electrophysiological signals: a comparison between PSO, BBO, FA and GA approaches, International Journal of Modelling, Identification and Control, Vol:29, Issue:2, pages:403-420, inderscience

aseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The prime cause from which BW arises is the patient's breathing and movement. To facilitate reliable visual interpretation of the ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BW removal method is presented. The hypothesis is based on the observation that ECG signal variation covaries with its BW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition is true: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of one form of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signal recordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numerically evaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibration decomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of the simulation indicate that the proposed technique is most effective in situations where there is a considerable distortion in the baseline wandering.

Citation

MOHAMMED ASSAM Ouali , , (2018), Upper envelope detection of ECG signals for baseline wander correction: a pilot study, Turkish Journal of Electrical Engineering & Computer Sciences, Vol:26, Issue:, pages:803-816, TÜBİTAK Academic Journals

In this paper a combination of Extended Kalman Filter (EKF) and a dynamic model of a synthetic electrocardiogram (ECG) for ECG denoising is proposed. Experimental results show that the proposed algorithm is very efficient for the extraction of the ECG signals from noisy data measurements.

Citation

MOHAMMED ASSAM Ouali , ,(2013), ECG denoising using extended Kalman filter,2013 International Conference on Computer Applications Technology (ICCAT),tunisie

The separation of the maternal and fetal Electrocardi- ograms (ECGs) from skin electrodes located on the mother’s body can be considered as a blind source separation (BSS) problem. In this paper, we propose to apply singular value decomposition (SVD) to separate the maternal and fetal ECG signals.

Citation

MOHAMMED ASSAM Ouali , ,(2013), Separation of composite maternal ECG using SVD decomposition,2013 International Conference on Computer Applications Technology (ICCAT),tuisie

In this paper an efficient filtering procedure based on the Singular Value Decomposition (SVD) has been proposed. SVD, a high resolution spectrum estimation tools, is used to decompose the ECG data matrix into orthogonal subspaces. Due to the energy-preserving orthogonal transformation in the SVD, these subspaces correspond to the signal and noise components contained in the ECG data. Projection of the data onto the desired subspace eliminates the noise and the unwanted signal components.

Citation

MOHAMMED ASSAM Ouali , ,(2013), SVD- based method for ECG denoising,2013 International Conference on Computer Applications Technology (ICCAT),tunisie