首页|Reports from University of Pretoria Add New Data to Findings in Artificial Intelligence (Harnessing Explainable Artificial Intelligence for Feature Selection In Time Series Energy Forecasting: a Comparative Analysis of Grad-cam and Shap)
Reports from University of Pretoria Add New Data to Findings in Artificial Intelligence (Harnessing Explainable Artificial Intelligence for Feature Selection In Time Series Energy Forecasting: a Comparative Analysis of Grad-cam and Shap)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - A new study on Artificial Intelligence is now available. According to news reportingoriginating from Pretoria, South Africa, by NewsRx correspondents, research stated, “This study investigatesthe efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted ClassActivation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection processfor national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), bothXAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying andeliminating irrelevant features.”
PretoriaSouth AfricaAfricaArtificial IntelligenceEmerging TechnologiesMachine LearningUniversity of Pretoria
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