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El Fahssi, Khalid
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Comparing Leach protocol and its descendants on transferring scalar data Bennani, Mohamed Taj; Zbakh, Abdelali; El Far, Mohamed; Lamrini, Mohamed; El Hichami, Outman; El Fahssi, Khalid; Satori, Hassan
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.pp255-262

Abstract

In the last years, The CMOS was developed and miniaturized rapidly, which, made sensors very fast, small and accurate. Hence, the creation of wireless sensor network (WSN) which are a network of nodes that exchange the data between them until it reaches the sink (base station). It is responsible for treating the data and transfer them to other servers linked to the internet for further treatment or storage. Therefore, everything related to WSN is a big topic of research for scientific community, especially transferring scalar data. In fact, many factors enter into account when it comes to send data like a radio, range of transmission, energy consumption and routing protocol. Routing protocols are very important in transferring data. They also have a big impact on energy consumption by nodes. Many categories of routing protocols exist: planning and level routing. Each type has its strength and weakness points. So, using a routing protocol in high-density environments is very challenging in energy consumption and data delivery. In addition, since level routing protocols like Leach are known for their energy efficiency. We choose three level routing protocol (Leach, MLD-Leach and MRE-Leach) to put them in a harsh environment to test their energy consumption and data transferring. We found that MLD-Leach has better energy consumption and data delivery.
Exploring parents’ perceptions of sex education pedagogy in Moroccan schools using an association rules mining-based algorithm Ben Azza, Chaymae; El Hamdani, Sara; Bennani, Mohamed Taj; El Fahssi, Khalid; Lamrini, Mohamed; Elfar, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1124-1136

Abstract

Sex education is vital for promoting healthy relationships and preventing sexual exploitation by teaching boundaries, consent and abuse recognition. Customized strategies are needed for children, balancing age-appropriate content with parental and community perspectives. Our study assessed Moroccan parents’ views on sex education’s adoption in schools. Conducted in Taza city, the survey targeted 1946 parents of students over 7 years old. Using association rule mining (ARM), we analyzed their responses. Therefore, Apriori algorithm was implemented to discover strong association rules within parents’ selected responses. Results showed that 74.53% of parents aged 19-30 support sexual education, citing its absence as a factor in child abuse. Meanwhile, 60.48% of those aged 31-59 with university education believe psychological disorders contribute to assaults. While some fathers (32.48%) and some mothers (67.52%) support sexual education, others don’t, but all agree on restricting children’s internet use until age 16 to avoid harmful content. These findings can inform comparative studies, aid decision-makers and enhance AI-based EdTech systems by offering insights into sex education perceptions.
Boosting stroke prediction with ensemble learning on imbalanced healthcare data Labaybi, Outmane; Taj, Mohamed Bennani; El Fahssi, Khalid; El Garouani, Said; Lamrini, Mohamed; El Far, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1137-1148

Abstract

Detecting strokes at the early day is crucial for preventing health issues and potentially saving lives. Predicting strokes accurately can be challenging, especially when working with unbalanced healthcare datasets. In this article, we suggest a thorough method combining machine learning (ML) algorithms and ensemble learning techniques to improve the accuracy of predicting strokes. Our approach includes using preprocessing methods for tackling imbalanced data, feature engineering for extracting key information, and utilizing different ML algorithms such as random forests (RF), decision trees (DT), and gradient boosting (GBoost) classifiers. Through the utilization of ensemble learning, we amalgamate the advantages of various models in order to generate stronger and more reliable predictions. By conducting thorough tests and assessments on a variety of datasets, we demonstrate the efficacy of our approach in addressing the imbalanced stroke datasets and greatly enhances prediction accuracy. We conducted comprehensive testing and validation to ensure the reliability and applicability of our method, improving the accuracy of stroke prediction and supporting healthcare planning and resource allocation strategies.
Co-Authors Ben Azza, Chaymae bennani, Mohamed Taj El Far, Mohamed El Garouani, Said El Hamdani, Sara El Hichami, Outman Elfar, Mohamed Labaybi, Outmane Lamrini, Mohamed Satori, Hassan Taj, Mohamed Bennani Zbakh, Abdelali