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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Applied Data Sciences
Abouelmehdi, Karim
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Intelligent agriculture system using low energy and based on the use of the internet of things Elhattab, Kamal; Abouelmehdi, Karim
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6346

Abstract

The field of smart agriculture is ranked among the top areas that uses the internet of things (IoT), whose goal is to increase the quantity and quality of agricultural productivity. The aim of this work is to realize a new device that will be cost-effective, reliable, and autonomous using a solar panel to provide electricity in large-scale agricultural fields, ESP32 to interconnect IoT sensors and the long range (LoRa) data transmission protocol to guarantee connectivity in places where there is no internet, whose objective is to monitor and irrigate agricultural fields only when there is a need for water. The data received by the sensors is sent to mobile app users via the Blynk cloud. The performance of our new approach is measured in terms of energy savings. This new model of irrigation and smart monitoring will improve the efficiency of farming techniques.
Student Engagement in E-Learning During Crisis: An Unsupervised Machine Learning and Exploratory Data Analysis Approach Daoud, Rachid Ait; Amine, Abdellah; Abouelmehdi, Karim; Razouk, Ayoub
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.458

Abstract

The lockdown caused by COVID-19 has forced educational institutions to rapidly adopt e-learning, which has revealed many significant challenges related to student engagement. Following the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, the present work aims to provide teachers and university administrators with a framework based on unsupervised machine learning and exploratory data analysis to identify engagement levels and understand the potential reasons for low engagement. Various data sources, including Microsoft Teams logs, demographic, and educational data, were merged to create a comprehensive dataset with the most relevant and useful measures for the success of our approach. This study was structured around three main research questions to achieve our goal. First, we sought to identify the most effective Microsoft Teams measures for identifying students' engagement levels. Then, our analysis focused on comparing different clustering models (two-level, three-level, and four-level models) to determine which one is most accurate in identifying low-engaged students. Finally, we examined the demographic and educational factors influencing low student engagement. The results revealed that: by applying the Sequential Forward Selection (SFS) technique, ScreenShareTime, VideoTime, NbrViewedVideos, Recency, and AvgTeamsSessionDay are the most relevant Microsoft Teams engagement metrics, improving the silhouette width from 0.37 to 0.70 when using these selected measurements. The four-level clustering model (Low, Medium, High, and Super) proved most effective in identifying low-engaged students. Analysis of factors showed that low engagement is primarily related to limited living conditions, with 66% of low-engaged students having low incomes. In addition, 50% do not use online services and 62% of low-engaged students took more than three years to reach their final year, indicating pre-existing academic difficulties. These findings provide educational institutions with valuable insights to enhance student engagement in distance learning, particularly during crisis periods such as the COVID-19 pandemic.
The future of healthcare: exploring internet of things and artificial intelligence applications, challenges, and opportunities Elhattab, Kamal; Naji, Driss; Ait ider, Abdelouahed; Abouelmehdi, Karim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3075-3083

Abstract

The internet of things (IoT) refers to a network of physical devices embedded with sensors, software, and communication tools, which allow for seamless exchange and collection of data. This technology enables automation, continuous monitoring, and data-driven decision-making across a variety of fields. In the healthcare sector, the integration of IoT with artificial intelligence (AI) is transforming how patient care is delivered, providing real-time health monitoring, personalized treatment options, and more efficient management of healthcare resources. This study investigates the significant influence of the IoT and AI on the healthcare system, focusing on how these technologies improve patient outcomes and streamline healthcare operations. It also highlights emerging challenges in the adoption of these technologies and suggests potential solutions to address these obstacles and enhance healthcare delivery. The research is based on an in-depth review of AI and IoT applications in healthcare, uncovering advancements in patient monitoring, disease management, and operational efficiency, while also identifying key challenges such as data privacy concerns and issues with system interoperability.
Co-Authors Ait ider, Abdelouahed Amine, Abdellah Daoud, Rachid Ait Driss Naji Elhattab, Kamal Razouk, Ayoub