HNS-RE2SD

Permanent URI for this communityhttp://dspace.hns-re2sd.dz:4000/handle/123456789/1

Higher National School of Renewable Energies, Environment and Sustainable Development

Browse

Search Results

Now showing 1 - 2 of 2
  • Item
    AI-Powered Platform for Automated Quiz and Question Generation.
    (2025-06-15) AISSAOUI Ayoub; BETTAHAR Akram; BOUREK Khalil; SAIGHI Ahmad Yasser
    This dissertation presents the design, implementation, and evaluation of an AI-powered platform developed for the automated generation of diverse assessment materials from user-provided PDF documents and raw text inputs. Addressing the significant time investment traditionally required for manual quiz creation in educational and training contexts, this work leverages advancements in Natural Language Processing (NLP) to streamline the process. The platform features a modular architecture implemented using Python and the Flask web framework, offering a user-friendly web interface for interaction. The core intelligence for generating Multiple-Choice Questions (MCQs) and Short Answer questions resides in a T5-base transformer model, specifically fine-tuned on the SQuAD v1.1 dataset adapted for question generation. This core model is supplemented by various NLP techniques and libraries (including NLTK, spaCy, and potentially Sense2Vec and Sentence-BERT based on implementation details) to facilitate the generation of Fill-in-the-Blanks questions, Matching tasks, and concise Summaries of the source material. The system allows users to specify the desired types and quantities of questions, providing flexibility in assessment creation. Evaluation of the fine-tuned T5 model demonstrated promising quantitative results, achieving average ROUGE-1 and ROUGE-L scores of 0.5247 and 0.4844, respectively, indicating a strong capability to capture semantic content and structure from the source text. While the average BLEU score was lower at 0.1988, this is often observed in generation tasks where content overlap (measured by ROUGE) is more critical than exact phrasal matching (measured by BLEU). Compared to existing commercial and academic solutions, the developed platform offers a distinct combination of input flexibility (PDF and text), a curated set of pedagogically relevant question types, and the use of an explicitly finetuned, adaptable T5 model. While acknowledging areas for future refinement, particularly concerning the robustness of PDF parsing across diverse formats and enhancing user interaction features, this project successfully demonstrates a viable and versatile approach to automating assessment generation. The platform holds significant potential to support educators, reduce administrative workload, and ultimately contribute to more dynamic and responsive learning environments.
  • Item
    IIoT-Driven AI for Electrifcation Automation
    (2024-06-15) BOUTAA Ali
    In the midst of the AI era, the industrial domain is witnessing the increasing prominence of AI. This project aims to integrate AI into the Siemens system, taking a significant step towards Industry 4.0. By harnessing the power of AI, we will revolutionize electricity consumption prediction, enabling organizations to make informed decisions and optimize their energy usage. Our strategic approach includes analyzing unique business needs, preparing data, developing AI models, deploying them seamlessly, and continuously monitoring and improving their performance. We will leverage AI techniques such as artificial neural networks, genetic algorithms, and expert systems to transform the energy sector and support the growth and stability of Industry 4.0. This integration will empower organizations to make informed decisions, reduce their carbon footprint, and optimize energy usage, ultimately contributing to a more sustainable future. This project will enhance operational performance and productivity, increase competitiveness in the Industry 4.0 landscape, and pave the way for a more sustainable and technologically advanced industrial sector by streamlining decision-making processes and improving energy efficiency.