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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Bahatti, Lhoussain
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Computer Science & IT Engineering

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MODIS-NDVI and wheat yield patterns and predictions in Taounate, Morocco Bouskour, Sara; Zaggaf, Mohamed Hicham; Bahatti, Lhoussain; Zayrit, Soumaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp648-659

Abstract

This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the non-linear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region.
Sampled-data observer design for sensorless control of wind energy conversion system with PMSG Zaggaf, Mohammed Hicham; Mansouri, Adil; El Magri, Abdelmounime; Watil, Aziz; Lajouad, Rachid; Bahatti, Lhoussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp52-61

Abstract

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.
Hypovigilance detection based on analysis and binary classification of brain signals El Hadiri, Abdeljalil; Bahatti, Lhoussain; El Magri, Abdelmounime; Lajouad, Rachid
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.pp984-991

Abstract

Road safety has now become a priority for drivers and citizens alike, given its considerable impact on the economy and human life, which is reflected in the increase in the number of accidents worldwide. This increase is linked to a number of factors, drowsiness being one of the main causes that can lead to tragic consequences. Various systems have been developed to monitor the state of alertness. The main idea adopted in this paper is based on the integration of a biosensor to acquire the cerebral signal, then the processing and analysis of the characteristics required to detect the two states of the driver using intelligent machine learning algorithms. Two models were chosen to carry out this binary classification: The K-nearest neighbour (KNN) and logistic regression (LR) classifiers. The experimental simulation results show that the first model outperforms the second in terms of accuracy, with a percentage of 97.83% for k=3. This could lead to the development of a new safety machine brain system based on classification to control vehicle speed deceleration or activate self-driving mode in the event of hypovigilance.
Co-Authors Bouskour, Sara El Hadiri, Abdeljalil El Magri, Abdelmounime Lajouad, Rachid Mansouri, Adil Watil, Aziz Zaggaf, Mohamed Hicham Zaggaf, Mohammed Hicham Zayrit, Soumaya