Browsing by Author "Supervisor: Hichem, Debbi"

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    Healthcare Managment System
    (Mohamed Boudiaf University of M'sila, 2025-06-15) Mounir Islem, Fardjoui; Supervisor: Hichem, Debbi
    In recent years, the healthcare sector has undergone a significant transformation by adopting digital systems instead of paper records. This has improved the quality of care by enabling doctors to access patients' medical files quickly. Patients can also benefit from online services such as booking appointments and securely viewing their health information. With this fast development, there was a need to use more flexible and efficient software architectures to address the challenges of traditional systems. Among these architectures, multi-tenancy allows for a separate database for each doctor, ensuring full isolation and protection of each doctor's data and their patients’ data. This approach provides great value to developers in building flexible and scalable systems. This thesis focuses on designing and implementing an electronic platform for managing clinics and medical services using a multi-tenancy architecture with a separate database for each doctor. The system was developed using modern technologies such as React.js for the frontend, and Node.js with MySQL for the backend. The platform offers advanced features including appointment management, creating medical prescriptions with QR codes, real-time chat between doctors and pharmacists, medical test management, and involving nurses in the medical workflow. The goal is to deliver a complete digital system that improves healthcare quality, ensures data privacy, and facilitates interaction between different stakeholders in the medical field.
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    LLM Powered Intelligent Document Chatbot
    (Mohamed Boudiaf University of M'sila, 2025-06-15) Safa, Chebbih; Sana, Amri; Supervisor: Hichem, Debbi
    In the business world, documents play a foundational role for companies, especially given its influence on the company's future in the term of decision-making, business progress, financial statements, its profits, and its long-term survival in the market. With the company's growth and evaluation, employees found themselves with the need of speed and quality to efficiently handle the documents, requiring their concentration, effort, and considerable time, which occasionally leads to unsuccessful outcomes. As a solution for this problem, we proposed RAGDocAI Chat, which is a chatbot that offers a user-friendly interface that lets the employees or users effectively interact with the internal documents of their company, with comparable performance and accuracy to models such as ChatGPT and Grok, and deepseek using the power of large language models( LLMs) to generate insightful, infered answers based on the documents and the user's query in natural language.RAGDocAI also use Retrieval Augmented Generation technologies such as traditional RAG, Advanced RAG, GraphRAG, and AgenticRAG to build a knowledge domain from these documents, reducing the hallucination of the LLM due to its limited knowledge of those domains or not very familiar with them
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    Network Management Assistance through Large Language Models (LLMs)
    (Mohamed Boudiaf University of M'sila, 2025-06-15) Fatima, Baadji; Supervisor: Hichem, Debbi
    This thesis explores the transformative role of Large Language Models (LLMs) in the domain of network optimization, particularly within the context of 5G communication technologies. It starts by studying deep learning fundamentals and neural network ar chitectures, emphasizing the evolution and impact of Models such as GPT, BERT, and DeepSeek. The study then examines the integration of these models into modern net working workflows, focusing on their applications in network security, task classification as well as answering telecom-domain questions. A core challenge addressed in this work lies in the sheer volume and complexity of technical data in the telecommunications industry—particularly across 3GPP (3rd Gen eration Partnership Project) standards, which has overseen the development of universal standards for Mobile Wireless Networks (MWNs). 3GPP continuously publishes a large number of intricate documents, making it difficult for engineers and researchers to stay updated and extract relevant information efficiently. This creates a need for advanced methods to process, analyze, and understand these documents to ensure network relia bility and performance. To address this, the thesis aiming to be a roadmap for researchers and practitioners to leverage LLMs in solving various telecom tasks. Special attention is given to the appli cation of fine-tuning and Retrieval-Augmented Generation (RAG) techniques to improve technical comprehension and automate knowledge extraction from3GPP specifications. A practical evaluation is conducted using the TSpec-LLM dataset, and chatbot are de veloped to classify telecom tasks and respond to domain-specific queries. Finally, The research demonstrates how LLMs will become increasingly important for telecom-specific operations by enhancing information retrieval accuracy and efficiency and accessibility in complex technical domains.