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محاضرات في مقياس القانون الدستوري / السداسي الثاني
(جامعة غرداية, 2025) عبد الحكيم, مولاي براهيم
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On Limit cycles for Bernoulli and Riccati differential equations
(university OF ghardaia, 2025) Dahou, Baya; KINA, Abdelkrim
This thesis presents a comprehensive study of planar polynomial differential systems, which are fundamental in the qualitative analysis of differential equations. Among the dynamic behaviors of interest are limit cycles closed periodic solutions that characterize long term system behavior and stability. The central problem lies in proving the existence, number, and stability of such cycles, especially in specific cases such as Bernoulli and Riccati equations .Within this framework, the thesis reviews key preliminary notions such as vector fields, equilibrium points, invariant curves, and Darboux integrability, alongside analytical tools like the Poincaré map and the Hartman Grobman theorem for classifying behavior near critical points. The core contribution consists in studying and interpreting the results from Clàudia Valls’ article [44], where I reformulated and simplified the theoretical proofs concerning rational limit cycles, and enriched them with illustrative examples and diagrams aimed at enhancing understanding
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Effet de la méthode d’extraction aqueuse « macération et décoction » sur les différents composés et l'activité antioxydante des cladodes de l'Opuntia ficus-indica de la région de Ghardaïa
(Faculté Science de la Nature et de la Vie et Sciences de la Terre - Université de Ghardaïa, 2025) HOUDJEDJE, Asma; BAAMMOUR CHIKH, Hanan
The young cladodes of Opuntia ficus-indica, consumed as a vegetable, are a valuable source of bioactive compounds and nutrients, and are the subject of our study on their antioxidant activity and nutritional value by analyzing primary and secondary metabolites as well as mineral content. Moisture content, phytochemical composition, mineral analysis, and quantitative determinations: primary metabolites, total polyphenols, total flavonoids, and antioxidant capacity were carried out following established experimental protocols on the dry matter and/or the two aqueous extracts prepared by maceration (Ex M) and decoction (Ex D). The results revealed the presence of flavonoids, polyphenols, alkaloids, free quinones, tannins, terpenes, and reducing compounds, while anthraquinones and saponins were absent. Nutritional analysis showed moisture, fiber, lipid, and ash contents of 92 ± 0,03%, 7,135 ± 0,3%, 1,763 %, and 26,69 ± 0,37%, respectively. Protein content was higher in the maceration extract (14,46 ± 0,01%) than in the decoction extract (10,02 ± 0,02%). Carbohydrates were 56.47% in the dry powder, 49.65% in Ex D, and 55.66% in Ex M. The energy value was low in fresh material (22 kcal/100 g), while the dry matter extracts were much more energetic (273–281 kcal/100 g DM). Total polyphenols and flavonoids were also more abundant in the maceration extract (53,53 ± 0,04 μg GAE/mg and 150,4 ± 0,03 μg RE/mg, respectively) than in the decoction extract. The maceration extract also contained higher levels of minerals, particularly Zn²⁺, Mg²⁺, P, Ca²⁺, and K⁺. Antioxidant activity assessed by the DPPH assay showed a strong IC₅₀, comparable to ascorbic acid, with 42,731 ± 0,46 mg/ml for the maceration extract and 49,643 ± 1,9 mg/ml for the decoction extract. Total antioxidant activity was 35,08 ± 0,06 μg AAE/mg for Ex M and 30,9 ± 0,00 μg AAE/mg for Ex D. According to our study, higher extraction temperatures have a negative impact on the levels of secondary metabolites and nutritional compounds in the aqueous extracts of the cladodes, consequently reducing their antioxidant activity.
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Compression d'images par la méthode K-moyennes (K-means)
(university OF ghardaia, 2022) BOUCHELAGHEM, Nacer; AOUF, Hassen; BOUMEDIENE, Ladjal
Évolutions technologiques importantes Informatisation et diversité des applications multimédias ces dernières années consiste à développer des techniques de compression d'image plus efficaces Son but est d'augmenter la capacité de transmission et le stockage des données. Dans cet article, nous allons étudier l'un d'entre eux, qui est le plus populaire et le plus facile parmi les algorithmes d'apprentissage non supervisé : K Means L'algorithme K-means identifie plusieurs centroïdes dans un ensemble de données, où le centroïde est la moyenne arithmétique de tous les points de données appartenant à un cluster particulier. L'algorithme attribue ensuite chaque point de données au cluster le plus proche, en essayant de garder les clusters aussi petits que possible (le terme "moyenne" dans K-means fait référence à la tâche de faire la moyenne des données ou de trouver le centroïde). Dans le même temps, K-means essaie de garder les autres clusters aussi distincts que possible.
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Approches profondes pour la détection de communautés dans le Big Data
(university OF ghardaia, 2025-12-01) BEKKAIR, Abdelfateh; BELLAOUAR, Slimane Encadreur; OULED-NAOUI, Slimane co-encadreur
The intricate relationships and organizational principles inherent in complex systems are effectively deciphered through network analysis, an indispensable tool in this regard. Within this field, this thesis addresses the fundamental challenge of community detection in complex networks, particularly focusing on attributed graphs where both topological structure and node features are available. Effectively integrating this rich information to identify cohesive communities remains a critical task in understanding complex systems. This work contributes the field by offering both a systematic understanding of existing methodologies and novel algorithmic contributions. We first establish a comprehensive taxonomy that categorizes community detection techniques across classical, traditional machine learning, and deep learning paradigms, providing a structured state-of-the-art review. Building on this, rigorous comparative studies on prominent graph neural network models, including CNN-based and Graph Autoencoder (GAE)- based approaches, identify current performance limitations. Our methodological contributions include AA-LPA, a classical heuristic algorithm that enhances the Label Propagation Algorithm (LPA). By leveraging the Adamic-Adar index and incorporating deterministic mechanisms for node prioritization and tie resolution, AA-LPA significantly improves stability and robustness against randomness in non-attributed graphs. The primary contribution is G2ACO, a novel deep graph autoencoder for attributed networks. G2ACO integrates a K-means clustering objective with reconstruction, with the aim of maximizing inter-community and minimizing intra-community distances. A key innovation is its unique optimization strategy, which decouples K-means centroid updates from gradient propagation to effectively handle non-differentiability, ensuring stable training and robust performance in learning clustering-oriented node representations via multi-head attention. Empirical evaluations on various benchmark datasets demonstrate that our methods provide effective solutions. AA-LPA is tested on classical social network datasets, where it significantly enhances stability compared to traditional LPA. Moreover, G2ACO is rigorously evaluated in both citation network datasets and social networks, consistently outperforming state-of-the-art baselines. This thesis contributes to valuable insights and tools for network analysis, providing a comprehensive solution for community detection in complex and attributed graphs.