Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

Browse

Subject: search.filters.subject.Dataset

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    ChatGPT backend: A comprehensive analysis
    (Institute of Electrical and Electronics, 2023) Belgacem, Ali; Bradai, Abbas; Beghdad-Bey, Kadda
    Artificial intelligence (AI) has transformed the field of natural language processing, enabling substantial advances in understanding, interpreting, and producing human language. The ability of AI to find new solutions to difficult linguistic expressions has led to the birth of sophisticated language models such as ChatGPT. This model uses cutting-edge deep learning algorithms to produce high-quality, human-like writing in response to natural language inputs. ChatGPT has an amazing capacity to recognize context, evaluate sentiment, and provide coherent and appropriate replies. It has a wide range of applications, from virtual assistants and customer support bots to language translation and content development. Therefore, understanding its backend has become essential. In this paper, we summarize the key principles underlying the operation of ChatGPT's back-end. This study is required reading for ChatGPT researchers because it covers critical aspects of the ChatGPT backend. It includes essential information for researchers looking to improve ChatGPT's performance or create new language models based on its architecture
  • No Thumbnail Available
    Item
    Prediction of shear wave velocity in the williston basin using big data analysis and robust machine learning algorithms
    (2022) Laalam, A.; Mouedden, N.; Ouadi, H.; Chemmakh, A.; Merzoug, A.; Boualam, A.; Djezzar, S.; Aihar, A.; Berrehal, B. E.
    The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir characterization. However, the sonic log is not acquired in every drilled well. We analyzed the log data of more than 35000 wells in the Williston Basin, and we found that only very few wells had sonic logs. For this reason, several studies attempted to correlate the shear velocity (or slowness) to other easily accessible properties; these will be presented in the literature review, with their pros and cons. The focus of this paper is to apply machine learning algorithms to synthesize the shear slowness log. Our models are trained and tested with log data from 27 wells drilled in the Bakken petroleum system, Williston Basin. Logging data include Gamma Ray, Deep Resistivity, Density, Neutron Porosity, and Shear Slowness. Five different algorithms were developed and tested against blind data including Xtreme Gradient Booster, Random Forest Regressor, Linear Regression, Ada Boost Regression, and Bayesian Ridge Regression. Overall, the R2-score varied from 0.55 to 0.92, with the XGBoost outperforming the other algorithms