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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Hamid Bouyghf
Hassan II University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Unloaded quality factor optimization of substrate integrated waveguide resonator using genetic algorithm Souad Akkader; Hamid Bouyghf; Abdennaceur Baghdad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2857-2864

Abstract

The main objective purpose of this paper is to study the enhancement techniques of the unloaded quality factor of substrate integrated waveguide (SIW) resonator, given that the quality of filters depends first on the quality of the resonators that compose it. Performance enhancement is achieved by employing a MATLAB-based genetic algorithm to optimize the geometrical parameters of the SIW resonator by iterative convergence to the target frequency (10 GHz frequency). On the other hand, the Ansys HFSS tool is used to model and optimize the SIW resonator with the suitable transition and plot the S-parameters for a frequency sweep range to validate its property. The results obtained allow increasing the unloaded Q-factor to be more than 1609 and reducing not only insertion and return losses but also reducing the size of the resonator. The proposed SIW resonator with its small size and low loss is directly useful for microwave and millimeter-wave applications.
Metaheuristic optimization for atrial fibrillation detection: feature extraction, selection, and hyperparameter tuning Zaid Nouna; Hamid Bouyghf; Mohammed Nahid; Issa Sabiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3878-3887

Abstract

Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and intervention. This study presents a multi-objective optimization approach for AF detection, focusing on feature extraction, selection, and neural network hyperparameter tuning. The methodology uses cross-validation during the training of the two concatenated ECG dataset features and simultaneously minimizes the error rate on the separate validation folds of each dataset and reduces the number of selected features, enhancing model generalization and efficiency. Particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were implemented to navigate this multi-objective space. While all three algorithms were explored, the final solution, demonstrating a superior trade-off between accuracy and feature reduction, was obtained using DE. This approach effectively identifies optimal feature subsets and neural network configurations, yielding a robust and compact AF detection model. The proposed approach has shown promising results, with the model achieving accuracies of 96.38% and 90.69%, and corresponding area under the curve (AUC) values of 0.99 and 0.96, for the first and second datasets, respectively, using 10 optimally selected features.
Co-Authors Abdennaceur Baghdad Issa Sabiri Mohammed Nahid Souad Akkader Zaid Nouna