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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Raihani, Abdelhadi
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Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Improving skin diseases prediction through data balancing via classes weighting and transfer learning El Gannour, Oussama; Hamida, Soufiane; Lamalem, Yasser; Mahjoubi, Mohamed Amine; Cherradi, Bouchaib; Raihani, Abdelhadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5999

Abstract

Skin disease prediction using artificial intelligence has shown great potential in improving early diagnosis and treatment outcomes. However, the presence of class imbalance within skin disease datasets poses a significant challenge for accurate prediction, particularly for rare diseases. This study proposes a novel approach to address class imbalance through data balancing using classes weighting, coupled with transfer learning techniques, to enhance the performance of skin disease prediction models. Two experiments were conducted using a tuned EfficientNetV2L based classifier. In the first experiment, a default dataset structure was utilized for training and testing. The second experiment involved employing classes weighting approach to balance the dataset. The effectiveness of the proposed approach is evaluated using the ISIC 2018 dataset, which comprises a diverse collection of skin lesion images. By assigning appropriate weights to different classes based on their prevalence, the proposed method aims to balance the representation of rare disease classes. To evaluate the performance of the proposed methodology, several performance evaluation metrics, including accuracy, precision, and recall, were employed. These findings revealed that the balanced dataset achieved enhanced generalization, mitigating the biases associated with class imbalance. As a result, the efficacy of artificial intelligence models is enhanced.
Nonlinear control of three level NPC inverter used in PV/grid system: comparison of topologies and control methods Atifi, Youness; Raihani, Abdelhadi; Kissaoui, Mohammed; Lajouad, Rachid; Errakkas, Khalid
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7122

Abstract

With the passage of time, the importance of using renewable energy systems to overcome energy consumption and improve the quality of the grid has emerged through the use of nonlinear control techniques and reliance on advanced types of inverters such as multi-level inverters. This research is focused on comparing two grid-connected converter topologies in a photovoltaic (PV) generation system connected to a three-phase grid that serves a non-linear load. Additionally, the study explores two different control techniques applied to this converter, evaluating their effects on the total harmonic distortion coefficient. A comparison has been made between the traditional inverter and the three-level inverter type neutral point clamped (NPC) inverter, with the use of integral backstepping (IBS) technique which was also compared with the proportional integral (PI) controller. The simulation results in MATLAB/Simulink are presented illustrating the performances and the strong effectiveness of the three-level NPC inverter controlled by the proposed technique (IBS).
Co-Authors Atifi, Youness Bellat, Abdelouahed Cherradi, Bouchaib El Gannour, Oussama El Harrouti, Taoufiq Errakkas, Khalid Hamida, Soufiane Jebbari, Mohammed Kissaoui, Mohammed Lajouad, Rachid Lamalem, Yasser Mahjoubi, Mohamed Amine Mansouri, Khalifa Ouassil, Mohamed Amine Tyass, Ilham