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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT)
Mansouri, Mohamed
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Improving lithium-ion battery reliability through neural network remaining useful life prediction Zraibi, Brahim; Mansouri, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp83-91

Abstract

The reliable performance of lithium-ion batteries is crucial for the safe and efficient operation of electrical systems, particularly in electric vehicles. To mitigate the risk of battery failure due to degradation, accurate forecasting of the remaining useful life (RUL) is imperative. In this study, we propose employing various recurrent neural network (RNN) methods, including RNN, gated recurrent unit (GRU), and long short-term memory (LSTM), to enhance RUL prediction accuracy for lithium-ion batteries. Our approach aims to provide reliable, accurate, and simple estimates of remaining battery life, facilitating effective management of electric vehicle power systems and minimizing the risk of failure. Performance evaluation metrics such as mean absolute error (MAE), R-squared (R²), mean absolute percentage error (MAPE), and root mean squared error (RMSE) are utilized to assess prediction accuracy. Experimental validation conducted using the NASA lithium-ion battery dataset demonstrates the superiority of LSTM in reducing prediction error and enhancing RUL prediction performance compared to alternative approaches. These findings underscore the potential of neural network methodologies in advancing battery management practices and ensuring the longevity and reliability of lithium-ion battery systems.
Utilizing deep learning algorithms for the resolution of partial differential equations Nouna, Soumaya; Nouna, Assia; Mansouri, Mohamed; Boujamaa, Achchab
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp370-379

Abstract

Partial differential equations (PDEs) are mathematical equations that are used to model physical phenomena around us, such as fluid dynamics, electrodynamics, general relativity, electrostatics, and diffusion. However, solving these equations can be challenging due to the problem known as the dimensionality curse, which makes classical numerical methods less effective. To solve this problem, we propose a deep learning approach called deep Galerkin algorithm (DGA). This technique involves training a neural network to approximate a solution by satisfying the difference operator, boundary conditions and an initial condition. DGA alleviates the curse of dimensionality through deep learning, a meshless approach, residue-based loss minimisation and efficient use of data. We will test this approach for the transport equation, the wave equation, the Sine-Gordon equation and the Klein-Gordon equation.
Two-dimensional Klein-Gordon and Sine-Gordon numerical solutions based on deep neural network Nouna, Soumaya; Nouna, Assia; Mansouri, Mohamed; Tammouch, Ilyas; Achchab, Boujamaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1548-1560

Abstract

Due to the well-known dimensionality curse, developing effective numerical techniques to resolve partial differential equations proved a complex problem. We propose a deep learning technique for solving these problems. Feedforward neural networks (FNNs) use to approximate a partial differential equation with more robust and weaker boundaries and initial conditions. The framework called PyDEns could handle calculation fields that are not regular. Numerical exper- iments on two-dimensional Sine-Gordon and Klein-Gordon systems show the provided frameworks to be sufficiently accurate.
Categorizing hyperspectral imagery using convolutional neural networks for land cover analysis Nouna, Assia; Nouna, Soumaya; Mansouri, Mohamed; Boujamaa, Achchab
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp393-404

Abstract

Categorizing hyperspectral imagery (HSI) is crucial in various remote sensing applications, including environmental monitoring, agriculture, and urban planning. Recently, numerous approaches have emerged, with convolutional neural network (CNN)-based algorithms demonstrating remarkable performance in HSI classification due to their ability to learn complex spatial-spectral features. However, these algorithms often require significant computational resources and storage capacity, which can be limiting in practical applications. In this study, we propose a novel CNN architecture tailored for HSI classification within the spectral domain, focusing on optimizing computational efficiency without compromising accuracy. The architecture leverages advanced spectral feature extraction techniques to enhance classification performance. Experimental evaluations on multiple benchmark hyperspectral datasets reveal that the proposed approach not only improves classification accuracy but also achieves a superior balance between performance and computational demand compared to traditional methods like K-nearest neighbors (KNN) and other deep learning-based techniques. Our results demonstrate the potential of the proposed CNN model in advancing the field of HSI classification, offering a viable solution for real-world applications with constrained computational resources.
Practical application of lithium-ion battery management systems: heating system Zraibi, Brahim; Mansouri, Mohamed; Ezzahi, Abdelghani
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1389-1398

Abstract

This paper presents a lithium-ion battery management system (BMS) aimed at improving battery longevity through hardware and software optimization. The system targets enhancing energy efficiency in heating devices like burners, commonly used in industrial and domestic applications. A key innovation is the modification of the Arduino Pro Mini 8 MHz 3.3 V microcontroller to reduce power consumption during sleep mode. The study evaluates two iterations of the system: an initial manually soldered prototype using the Arduino board and a second iteration with a robust printed circuit board assembly (PCBA). The transition to the PCBA improved system efficiency and eliminated connection issues. The development integrates conventional circuitry and modern software strategies for efficient battery charge/discharge management. Results from both prototypes demonstrate significant improvements in battery life, offering a sustainable solution for energy-efficient applications.
Deep neural network solutions to Newell-Whitehead-Segel equations Nouna, Soumaya; Tammouch, Ilyas; Nouna, Assia; Mansouri, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5172-5182

Abstract

In this work, we use the deep neural network (DNN) approach called NeuroDiffEq, and the unified finite difference exponential approach for obtaining the approximated and exact solutions of Newell-Whitehead-Segel systems that are essential for the biology of mathematics. A unified approach was used to generate several solutions for solitary waves of those systems. The approximated solutions for selected studies are explored using the NeuroDiffEq approach, which is the artificial neural networks (ANN) approach and is based upon trial approximate solution (TAS). The comparison between the obtained approximated solutions and the analytical solutions indicates that the applied method has proved an efficient as well as a highly successful approach to solving various types of the Newell-Whitehead-Segel equations.
Co-Authors Achchab, Boujamaa Boujamaa, Achchab Ezzahi, Abdelghani Nouna, Assia Nouna, Soumaya Tammouch, Ilyas Zraibi, Brahim