Khalifa Mansouri
Hassan II University

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Integrated framework studying contribution of information system to firm performance Ansar Daghouri; Khalifa Mansouri
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp375-385

Abstract

The main purpose of this paper is to study the correlation between information system (IS) success and firm performance based on two evaluation models already construct. The first model allowed to define the criteria and sub-criteria for evaluating the firm performance and the second model consisted of evaluating the IS success. Our contribution is to formalize a decision-making process based on the criteria of the two models as well as the weights generated by the implementation of the analytic hierarchy process (AHP) method to construct the influence diagrams that will allow us to trace the causal links between the two models. This approach has been implemented in three sectors chosen according to their use of information systems. The results obtained confirmed that the evaluation models are sectorial and therefore even the influence diagrams, hence the difficulty of studying the contribution of the IS success in achieving the firm performance with a general and generic approach.
Towards an automatic classification of welding defect by convolutional neural network and robot classifier Nissabouri Salah; Ennadafy Hamza; Jammoukh Mustapha; Khalifa Mansouri
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1768-1774

Abstract

The control process of welding requires manual operations, and this consumes time. Robot classifier can help by automatic detection of welding defect and by taking rapid actions to correct in situ the defect. This paper presents a convolutional neural network (CNN) model developed to classify the welding defect like splash, twisty, overlap, edge and copper adhesion based on machine vision. Using a resistance spot welding (RSW) dataset the CNN model was trained and evaluated to achieve the best performance. The batch size was varied to quantify its effect on the precision of the model. The model can predict the type of welding surface by confidence of 99.86%.
Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms Lahcen Amhaimar; Younes Nadir; Bakhouyi Abdellah; Khalifa Mansouri; Mohamed Bayjja; Abderrahim Khalidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp926-935

Abstract

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.