| Titre : | Recognition of Traditional Algerian Dresses Using a Mobile Application |
| Auteurs : | Aya Badi, Auteur ; Dounia Fethallah, Auteur ; Abdessalam Meklid, Auteur |
| Type de document : | Mémoire magistere |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2025 |
| Format : | 1 vol. (77 p.) / ill.couv.ill.encoul / 30cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Résumé : |
Cultural heritage is a crucial element of people’s identity and a reflection of their authenticityand historical antiquity. Traditional clothing is the most prominent element of this heritage, which combines craft, art, and custom passed from generation to generation. Algerian traditional attireexhibits wide diversity reflecting the diversity of the local culture of every region, like the Karako, the Tlemcenian chedda, the Chaoui dress, the Kabylia djebba, and other costumes representingnational traditions and identity.In light of the quick development of technology, it has become crucial to utilize artificial intelligence techniques to record this heritage and present it to new generations. In this regard, our project seeks to conceptualize and implement a mobile application based on deep learning methods(DL), especially convolutional neural networks(CNN), to recognize Algerian traditional dresses using pictures. The application will allow users to take a picture or upload it, and the system will analyze it, recognize what kind of dress it is and provide historical information about it.The application offre features such as browsing by categories, searching, and user accounts(fashion designers, cultural enthusiasts, or regular users). Moreover, users can shop direct within the application and interact with artisan dealing in traditional attire, using Firebase for managing users and data.This project will assist in the promotion and preservation of Algerian traditional dresses in anew digital way, promoting cultural sensitivity and the exhibition of this national heritage in anupdated and appropriate style for the modern age. |
| Sommaire : |
General Introduction 13 Chapter 1: Foundations of Machine Learning and Deep Learning within AI 14 1.1 Introduction . . . 15 1.2 Artificial Intelligence and Its Models 15 1.2.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3 Comparison of Artificial Intelligence, Machine Learning, and Deep Learning . . . . . 38 1.4 Conclusion . . . 40 Chapter 2: Intangible Heritage 41 2.1 Introduction . . . 42 2.2 Intangible Heritage . . 42 2.2.1 According to UNESCO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.2.2 Difference Between Tangible And Intangible Heritage . . . . . . . . . . . . . 43 2.3 Intangible Heritage Domains . . 44 2.4 Importance Intangible Cultural Heritage . . . 46 2.5 Characteristics and Significance of Intangible Cultural Heritage . . . . . . . . . . . 46 2.6 Traditional costumes as part of intangible heritage . 47 2.6.1 The Role of Traditional Clothing in Expressing Cultural Identity . . . . . . . 47 2.6.2 Symbolism of Colors and Embroidery in Algerian Attire . . . . . . . . . . . 47 2.6.3 The Relationship Between Attire and Traditional Occasions . . . . . . . . . 49 2.7 Challenges facing intangible heritage . . .49 2.7.1 Identification of Challenges . . . .50 2.8 Strategies for the preservation of intangible heritage .. 50 2.8.1 Strategie and Response . . .50 2.8.2 The role of cultural institutions in protecting heritage 52 2.8.3 Government and community initiatives to preserve heritage. . . . . . . . . . 52 2.8.4 Using technology to enhance heritage . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Related Work. 54 2.9.1 International Research on Traditional Dress Recognition . . . . . . . . . . . 55 2.9.2 Mobile-Based Clothing Recognition Application . . . . . . . . . . . . . . . . 56 2.9.3 Algerian Contributions to Cultural Heritage and AI . . . . . . . . . . . . . . 58 2.10 Conclusion. 58 Chapter 3: Conceptualization and Structural Design 59 3.1 Introduction . . 60 3.2 Project Overview 3.3 Objectives and Scope of Application . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.3.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.3.2 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.4.1 User Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.4.2 Core Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.5 System design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.5.1 Users part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5.2 Admin part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.5.3 Artisan part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.5.4 Sequence Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Chapter 4: Implementation and Results 76 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.2 Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.2.1 Data Collection Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.2.2 Dress Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.2.3 Data Cleaning and Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3 Development tools and technologies . . .79 4.3.1 Front-end Tools . . .79 4.3.2 Backend Tools . . 80 4.4 Development of recognition model based on deep learning . .82 4.5 Integration of the model of a mobile application.. 83 4.6 Implementation: Features and functionalities . . 85 4.6.1 User interfaces . 85 4.6.2 Admin interfaces . . . . . . . . . . . . 94 4.6.3 Artisan interfaces . . . .96 4.7 Test validation and experimental results . .98 4.7.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.7.2 Training and Validation Performance . . . . . . . . . . . . . . . . . . . . . . 99 4.7.3 Confusion Matrix . . 101 4.7.4 Summary of Results . . . . . . . . . . . . . 103 4.8 Limitation and prospects for improvement . 104 4.8.1 Limitation . 104 4.8.2 Prospects for improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 4.9 Conclusion . . 105 General Conclusion |
| Type de document : | Mémoire master |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| MINF/951 | Mémoire master | bibliothèque sciences exactes | Consultable |
