LEVERAGING AI FOR PREDICTION & MANAGEMENT OF BIOMEDICAL WASTE
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Beschrijving
Bol
This study utilized AI models, including SVM, ET, and GPR, to predict BMW generation across Indian states. PCA, a key feature extraction method, was employed to enhance prediction accuracy, resulting in a PCA-based hybrid model derived from the best-performing approach. Model performance was assessed using RMSE and R² metrics. Pearson correlation analysis, along with two-tailed significance testing, validated the statistical relationships between variables and the proposed BMW generation features. Sensitivity analysis, conducted with non-linear techniques, and the evaluation of eight distinct models provided valuable insights.
Beschrijving
(2)
This study utilized AI models, including SVM, ET, and GPR, to predict BMW generation across Indian states. PCA, a key feature extraction method, was employed to enhance prediction accuracy, resulting in a PCA-based hybrid model derived from the best-performing approach. Model performance was assessed using RMSE and R² metrics. Pearson correlation analysis, along with two-tailed significance testing, validated the statistical relationships between variables and the proposed BMW generation features. Sensitivity analysis, conducted with non-linear techniques, and the evaluation of eight distinct models provided valuable insights.
AmazonPagina's: 92, Paperback, KS OmniScriptum Publishing
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