首页|New Research on Machine Learning from Imam Khomeini International University (IK IU) Summarized (Comprehensive input models and machine learning methods to impro ve permeability prediction)
New Research on Machine Learning from Imam Khomeini International University (IK IU) Summarized (Comprehensive input models and machine learning methods to impro ve permeability prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting out of Imam Khomeini International University (IKIU) by NewsRx editors, research stated, “This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input lo gs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI).” Our news journalists obtained a quote from the research from Imam Khomeini Inter national University (IKIU): “A total of 57 models were constructed using combina tions of these logs and tested using five machine learning methods: Extreme Lear ning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighb or (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique per meability predictions. RF had the highest correlation coefficient (0.925) and av erage error (0.196), indicating a precision-correlation trade-off. The ELM appro ach had the lowest average error, 0.083, and a correlation value of 0.871. Testi ng on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings.”
Imam Khomeini International University ( IKIU)CyborgsEmerging TechnologiesMachine Learning
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