http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/16 Sun, 19 Apr 2026 20:59:18 GMT 2026-04-19T20:59:18Z http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1414 Titre: Segmentation automatique des régions d'intérêt dans des images réelles Auteur(s): BOUDAIEB, Ahmed Résumé: Cette thèse porte sur la segmentation automatique des régions d’intérêt dans des images réelles, avec une application spécifique à la détection des lésions cutanées. Nous proposons une architecture de segmentation basée sur U-Net améliorée par un encodeur ResNet50V2, une stratégie d’augmentation avancée et une fonction de perte hybride Dice + BCE. Le modèle a été évalué sur le dataset PH2 puis testé sur un jeu de données externe (ISIC 2016) afin d’analyser sa robustesse et sa capacité de généralisation. Les résultats obtenus montrent des performances élevées en segmentation, surpassant plusieurs méthodes traditionnelles et modèles profonds de référence. Les analyses menées, incluant une étude d’ablation et une comparaison complète avec l’état de l’art, confirment l’efficacité du modèle proposé pour la segmentation précise des lésions cutanées dans des conditions réelles. Tue, 24 Mar 2026 00:00:00 GMT http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1414 2026-03-24T00:00:00Z http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1316 Titre: Medical Image Segmentation Based on Artificial Intelligence Approaches Auteur(s): Mokhtari, chakir Résumé: Medical image segmentation is a critical preprocessing step in computer-aided diagnosis, treatment planning, and biomedical research. While Fuzzy C-Means (FCM) clustering is a widely adopted technique for this task due to its ability to handle inherent ambiguities in medical data, its performance is highly sensitive to initial parameters and is prone to convergence to local optima. This thesis presents a comprehensive approach to overcoming these limitations through two primary contributions. First, we provide an overview of medical imaging and the fundamental challenges of segmentation. We then detail traditional clustering-based methods, with a specific focus on the FCM algorithm, outlining its strengths and well-documented limitations. To address these limitations, we explore bio-inspired optimization metaheuristics as a powerful strategy for guiding the clustering process. The core contribution of this work is the novel hybridization of the Artificial Bee Colony (ABC) algorithm with FCM. The proposed method focuses on the simultaneous optimization of the crucial FCM parameters: primarily the number of cluster centers and their values and the optimization of the objective function by escaping to the local optima, to achieve a superior and more robust segmentation outcome. The effectiveness of this hybrid ABC-FCM approach is rigorously validated through experiments on both simulated brain MRI and real clinical MRI brain images. Results demonstrate a significant improvement in segmentation accuracy and convergence behavior compared to standard FCM and other optimization-enhanced variants. The second major contribution is the development of a new cluster validity index (CVI) to automatically determine the optimal number of segments. This index is designed to enhance the separation metric of the IMI index by incorporating a measure based on Kullback-Leibler (KL) divergence, which better captures the statistical distance between fuzzy clusters. Experimental results confirm that the proposed KL-based CVI outperforms existing indices in accurately identifying the true number of clusters in both synthetic and complex medical imagery. This thesis offers significant advancements in AI-driven medical image segmentation by introducing an optimized clustering framework and a more robust validation metric, both contributing to higher diagnostic reliability. Wed, 15 Oct 2025 00:00:00 GMT http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1316 2025-10-15T00:00:00Z http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1293 Titre: Contribution à la reconnaissance hors ligne de l'écriture arabe manuscrite Auteur(s): Chadli, Mohamed Amine Résumé: Offline Arabic Handwritten Text Recognition (HTR) is an important research area. Contributing to its development may have significant and exciting implications for many Arabic-speaking countries, where Arabic is the official spoken and written language. This thesis has examined the Arabic handwritten language, its characteristics, and the challenges of recognizing its written script. We conducted thorough literature reviews on classical and modern deep learning methods for recognizing Arabic script. We delve deep into the resources used to train the Arabic HTR systems, trying to extract all the known databases of Arabic handwritten text. We also investigated the data augmentation approaches used to enrich the handwritten Arabic text databases with new data. Later, we proposed a new deep learning architecture based on Convolutional Recurrent Neural Network (CRNN) to recognize offline Arabic scripts. As a final contribution, we proposed a new data augmentation technique based on Moving Least Squares (MLS), specifically designed to generate new images of Arabic handwritten text to serve as training data for deep learning systems. Thu, 17 Jul 2025 00:00:00 GMT http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1293 2025-07-17T00:00:00Z http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1292 Titre: Energy-Constrained Resource Allocation and Scheduling in Cloud Computing Auteur(s): Mehor, Yamina Résumé: In virtualized cloud computing systems, energy reduction is a major concern since it can provide many major benefits, such as reducing operating costs, increasing system efficiency, and protecting the environment. Typically, customers submit their appli- cations with millions of tasks executed in cloud data centers by thousands of high- performance servers installed. The cloud offers a variety of services through virtual machines (VMs). These latter usually consume a large amount of energy. Such energy consumption increases the cost of electricity and has a negative environmental effect. To maintain a better performance of the services offered by data centers and a reason- able energy consumption. A detailed study of the behavior of these systems is essential for the design of efficient optimization solutions to reduce energy consumption. This thesis work focuses on the development of a task scheduling model in order to minimize the energy consumption of data center resources while meeting customer requirements for quality of services. Thu, 17 Jul 2025 00:00:00 GMT http://dspace.univ-mascara.dz:8080/jspui/handle/123456789/1292 2025-07-17T00:00:00Z