ZAHIA Nabi
نابي زاهية
ZAHIA.NABI@univ-msila.dz
0562864620
- Departement of ELECTRONICS
- Faculty of Technology
- Grade PHd
About Me
Apport des techniques de traitement de signal dans la localisation spatiale d'un défaut par ultrasons. in Université Mouloud Mammeri Tizi-Ouzou
DomainScience et Technologies
Research Domains
Sciences et technologies
FiliereElectronique
Location
Msila, Msila
Msila, ALGERIA
Code RFIDE- 05-12-2019
-
Apport des techniques de traitement de signal dans la localisation spatiale d'un défaut par ultrasons
Apport des techniques de traitement de signal dans la localisation spatiale d'un défaut par ultrasons - 1995-03-09 00:00:00
-
ZAHIA Nabi birthday
- 2024-06-21
-
2024-06-21
A Novel ANN-ARMA Scheme Enhanced by Metaheuristic Algorithms for Dynamical Systems and Time Series Modeling and Identification
This paper presents a new scheme for dynamical systems and time series modeling and identification. It is based on artificial neural networks (ANN) and metaheuristic algorithms. This scheme combines the strength of ANN with the dexterity of metaheuristic algorithms. This fusion is renowned for its ability to detect complex patterns, which considerably improves accuracy, computational efficiency, and robustness. The proposed scheme deals with the curve fitting and addresses ANN's local minima problem. This approach introduces the identification concept using a fresh novel identification element, referred to as the error model. The proposed framework encompasses a parallel interconnection of two models. The principal sub-model is the elementary model, characterized by standard specifications and a lower resolution, designed for the data being examined. In order to address the resolution limitation and achieve heightened precision, a second sub-model, named the error model, is introduced. This error model captures the disparities between the primary model and considered data. The parameters of the proposed scheme are adjusted using metaheuristic algorithms. This technique is tested across many benchmark data sets to determine its efficacy. A comparative study along with benchmark approaches will be provided. Extensive computer studies show that the suggested strategy considerably increases convergence and resolution.
Citation
Hamza BENNACER , MOHAMMED ASSAM Ouali , Zahia nabi , Mohamed Ladjal , , (2024-06-21), A Novel ANN-ARMA Scheme Enhanced by Metaheuristic Algorithms for Dynamical Systems and Time Series Modeling and Identification, Revue d'Intelligence Artificielle, Vol:38, Issue:, pages:939-956, IIETA
- 2023-04-17
-
2023-04-17
EMD Based Average Wavelet coefficient method for ECG Signal Denoising
EMD Based Average Wavelet coefficient method for ECG Signal Denoising
Citation
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
- 2022
-
2022
An improved ELM-framework for dynamical systems Modeling and Identification
Extreme learning machine (ELM) Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive ELM models for complex systems that are typically found in real-word applications. Extreme learning machine is used in many applications such as image recognition, classification, control and system identification. In this paper, a new hybrid extreme machine-Autoregressive Moving Average (ELMARMA) and Extreme learning machine Autoregressive (ELMAR) scheme applied for dynamical systems modeling is presented. The proposed model comprises a parallel interconnection of tow sub-ELM models. The first sub-ELM model is the primary model, which represents an ordinary model with a low resolution for the dynamical system under consideration. To overcome resolution quality problem and obtain a model with higher resolution, we will introduce a second sub-ELM model called Error model which will represent a model for the error modeling between the primary model and the real nonlinear dynamic system. The method’s effectiveness is evaluated through testing on the three nonlinear dynamical systems described by Narendra in the literature. In addition, a detailed comparative study with several benchmark methods will be given. Intensive computer experimentations confirm that the proposed approach can significantly improve convergence and resolution.
Citation
Zahia nabi , ,(2022), An improved ELM-framework for dynamical systems Modeling and Identification,International Symposium on Applied Mathematics and Engineering (ISAME2022),Turquie
- 2022
-
2022
An appropriate hybrid technique for ECG signal denoising based on variational mode decomposition and average wavelet coefficient method
Electrocardiogram (ECG) signal has a principal role in the diagnosis of diverse kinds of heart diseases. During ECG recording, a variety of noise sources permit to alter the morphology of these signals and result in erroneous interpretations. Denoising of the ECG signal is a great pretreating phase that minimizes the noise for an accurate diagnosis. In this paper, a new hybrid technique based on Variational Mode Decomposition (VMD) and the Average Wavelet Coefficient method (AWC) for ECG signal denoising is presented. The suggested method at first involves the implementation of VMD to the noisy ECG signal for decomposition purposes to gain variational modes, these obtained variational modes are then treated using the AWC technique that permits the computing of the Hurst exponent of all variational modes. Lastly, the denoised ECG signal is reconstructed by summing up all the denoised variational modes, after a thresholding operation, barring parasitical signal elements. Experiments implementing the MIT-BIH databases are used to approve and evaluate the suggested technique. The experimental finding indicate that the proposed approach recovers ECG signals from noisy data samples efficiently.
Citation
Zahia nabi , ,(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,Turquie
- 2022
-
2022
A New PSO-ANN Scheme for Composite Materials Properties Prediction
In this investigation a novel PSO-ANN scheme for composite materials properties prediction is presented. It is based on neural networks which are used in many applications such as image recognition, classification, control and system identification.This approach will deal with local minima problem of the neuronal networks architecture and simultaneously preserve the fitting quality. The proposed scheme comprises a parallel interconnection of tow sub-ANN prediction systems. The first sub-ANN prediction system is the primary system, which represents an ordinary system with a low resolution for the training data under consideration (composite materials properties). To overcome resolution quality problem, and obtain a prediction system with higher resolution, we will introduce a second ANN sub model. ANN scheme Identification is achieved by innovative metaheuristic algorithm such asparticle swarm optimization (PSO). The method’s effectiveness is evaluated through testing on the composite materials to predict their physical properties. Intensive computer experimentations confirm that the proposed approach can significantly improve convergence and resolution.
Citation
Zahia nabi , ,(2022), A New PSO-ANN Scheme for Composite Materials Properties Prediction,International Symposium on Applied Mathematics and Engineering (ISAME2022),Turquie
- 2022
-
2022
Journée Doctorale en Electronique
In this work, I presented the state of progress of my PhD thesis as well as my future goals.
Citation
Zahia nabi , ,(2022), Journée Doctorale en Electronique,Journée Doctorale en Electronique,Msila
- 2022
-
2022
EMD Based Average Wavelet Coefficient Method for ECG Signal Denoising
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
Zahia nabi , ,(2022), EMD Based Average Wavelet Coefficient Method for ECG Signal Denoising,ICATEEE2022,Msila-Algeria