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All Journal International Journal of Electrical and Computer Engineering
El Bouhdidi, Jaber
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Computer Science & IT Electrical & Electronics Engineering

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Proposal of a similarity measure for unified modeling language class diagram images using convolutional neural network Jebli, Rhaydae; El Bouhdidi, Jaber; Yassin Chkouri, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1979-1986

Abstract

The unified modeling language (UML) represents an essential tool for modeling and visualizing software systems. UML diagrams provide a graphical representation of a system's components. Comparing and processing these diagrams, for instance, can be complicated, especially as software projects grow in size and complexity. In such contexts, deep learning techniques have emerged as a promising solution for solving complex problems. One of these crucial problems is the measurement of similarity between images, making it possible to compare and calculate the differences between two given diagrams. The present work intends to build a method for calculating the degree of similarity between two UML class diagrams. With a goal to provide teachers a helpful tool for assessing students' UML class diagrams.
Enhancing supply chain agility with advanced weather forecasting Zeroual, Imane; El Bouhdidi, Jaber
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5904-5913

Abstract

This article presents a solution that leverages artificial intelligence techniques to enhance urban freight transportation planning and organization through the integration of weather forecasting data. We identify key challenges in the current urban logistics landscape and introduce a range of machine learning models designed to predict delivery delays. Logistic regression serves as the foundational model, analyzing historical delivery data in conjunction with weather conditions to assess the likelihood of delays, thus enabling informed decision-making for companies. Additionally, we evaluate two other machine learning models to determine the most effective approach for our specific context, assessing their accuracy and capacity to deliver actionable insights. By improving the predictive capabilities of urban freight systems, this research aims to streamline operations, reduce costs, and enhance overall service reliability, contributing to more efficient and resilient urban transportation networks.
Co-Authors Jebli, Rhaydae Yassin Chkouri, Mohamed Zeroual, Imane