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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Adil El Makrani
Ibn Tofail University
Computer Science & IT Electrical & Electronics Engineering

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Comparative study of proactive and reactive routing protocols in vehicular ad-hoc network Oussama Sbayti; Khalid Housni; Moulay Hicham Hanin; Adil El Makrani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5374-5387

Abstract

In recent years, the vehicular ad-hoc network (VANET), which is an ad-hoc network used by connected autonomous vehicles (CAV) for information processing, has attracted the interest of researchers in order to meet the needs created by the accelerating development of autonomous vehicle technology. The enormous amount of information and the high speed of the vehicles require us to have a very reliable communication protocol. The objective of this paper is to determine a topology-based routing protocol that improves network performance and guarantees information traffic over VANET. This comparative study was carried out using the simulation of urban mobility (SUMO) and network simulator (NS-3). Through the results obtained, we will show that the choice of the type of protocol to use depends on the size of the network and also on the metrics to be optimized.
Automatic facial expression recognition under partial occlusion based on motion reconstruction using a denoising autoencoder Abdelaali Kemmou; Adil El Makrani; Ikram El Azami; Moulay Hafid Aabidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp276-289

Abstract

Automatic facial expression recognition (FER) plays a valuable role in various fields, including health, road safety, and marketing, where providing feedback on the user’s condition is crucial. While significant progress has been made in controlled environments (such as frontal, unconcluded, and well-lit conditions), recognizing facial expressions in unconstrained environments (natural settings) remains challenging. The presence of occlusions poses a particular difficulty as they obscure parts of the facial information captured in the image. To address this issue, researchers have proposed different solutions, broadly categorized into two approaches: those focusing on visible regions of the face and those attempting to reconstruct hidden parts. Currently, most solutions rely on texture or geometry-based methods, with only a few utilizing motion-based approaches. However, incorporating motion appears to be particularly promising in adapting to occlusions due to its unique characteristics, such as close-range propagation and local coherence. In this paper, our focus lies on leveraging motion to overcome the challenges posed by occlusions in FER tasks.
S&P 500 forecasting with financial news sentiment using FinBERT-CNN-LSTM Zakaria Al Bakkari; Ikram El Azami; Adil El Makrani
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.11750

Abstract

Accurate stock market forecasting remains challenging due to the nonlinear, noisy, and volatile nature of financial markets. Beyond historical prices, market movements are influenced by financial news and investor sentiment. This study proposes a hybrid financial bidirectional encoder representations from transformers with convolutional neural network and long short-term memory (FinBERT-CNN-LSTM) model for forecasting the Standard & Poor’s 500 (S&P 500) index by integrating financial news sentiment with historical market data. FinBERT generates daily aggregated sentiment scores, which are combined with market features and processed through a CNN-LSTM architecture. The CNN captures local patterns and short-term interactions, while the LSTM models temporal dependencies in financial time series. Experimental results show that the proposed model outperforms benchmark models, achieving a root mean square error (RMSE) of 0.0119, mean absolute error (MAE) of 0.0091, mean absolute percentage error (MAPE) of 2.10%, and directional accuracy (DA) of 63.4%. It also achieves a cumulative return of 15.8% and a Sharpe ratio of 1.35. Rolling-window validation and Diebold–Mariano (DM) testing further support the statistical significance of the improvements. These findings demonstrate the effectiveness of combining financial sentiment and deep learning (DL) for stock market forecasting.
Co-Authors Abdelaali Kemmou Ikram El Azami Khalid Housni Moulay Hafid Aabidi Moulay Hicham Hanin Oussama Sbayti Zakaria Al Bakkari