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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Soumia Ziti
Mohammed V University in Rabat
Computer Science & IT Engineering

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An automatic alignment of the business process and business value models: a novel MDA method Nassim Kharmoum; Sara Retal; Karim El Bouchti; Wajih Rhalem; Soumia Ziti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp501-509

Abstract

With the massive development of end-users requirements, the model alignment has become an essential stage in software engineering, especially in the model driven architecture (MDA) approach, to absorb the end-user’s need. The purpose is to facilitate the alignment of new models from existing ones. Our contribution in this paper is to deal with the MDA higher abstraction lever by focusing on the automatic alignment of the business value with the business process models for the information system (IS). For our case, the data-flow diagram (DFD) illustrates the business process model, and the E3value model illustrates the business value model. However, the ATLAS-transformation language (ATL) ensures automatic alignment. The main goal is to facilitate and accelerate IS implementation while enhancing its quality.
Model-driven architecture: generating models from Symfony framework M'hamed Rahmouni; Chaymae Talbi; Soumia Ziti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1659-1668

Abstract

The web application development industry is constantly growing due to the extensive use of web applications in different devices, most of them run on Android, iOS, and Windows Phone operating systems. However, the development of applications designed for platforms requires more concerns such as code efficiency, interaction with devices, and speed of market penetration. The model-driven approach (MDA) combined with unified modeling language (UML) could provide abstraction and automation for software developers. This paper presents an MDA approach for the development of web applications based on the Symfony framework, UML modeling, model transformation, and then automatic code generation in order to facilitate and accelerate the development of web applications. The first step of this work is to establish the metamodel of Symfony framework and the metamodel of UML class diagram. In the second step, the various transformation rules between the source and target metamodels are first defined. Atlas transformation language (ATL) implements these rules. The result of this transformation is a platform-specific model (PSM) represented by Ecore language. The generated PSM model represents the input model of model-to-code (M2C) transformation for generating the code of web applications. To validate this work, we have implemented a case study.
Brain tumor detection using VGG-16 model Aicha Oussous; Abderrahmane Ez-zahout; Soumia Ziti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2337-2346

Abstract

Research in medical image analysis, specifically through deep convolutional networks, addresses the challenges of manually analyzing large magnetic resonance imaging (MRI) image volumes for brain tumor detection. The manual analysis is time-consuming, tedious, and prone to inaccuracies due to subtle visual similarities between normal tissue and tumor cells. This research aims to automate tumor detection, increasing accuracy and efficiency in medical treatments. This study aimed to develop a model capable of classifying brain tumors 2D MRI images, and the convolutional neural network (CNN)-based model successfully achieved an accuracy of 99.21% but suffered from noticeable Overfitting. Implementing the independent tests set and early stopping mitigated this issue, making the model more reliable for production deployment and demonstrating its potential in supporting physicians in detecting brain tumors, thereby enhancing treatment efficiency. The use of Python, TensorFlow, and Keras facilitated the development of the proposed solution, focusing on a diverse set of MRI images with varying tumor sizes, locations, shapes, and intensities.
Co-Authors Abderrahmane Ez-Zahout Aicha Oussous Chaymae Talbi Karim El Bouchti M'hamed Rahmouni Nassim Kharmoum Sara Retal Wajih Rhalem