Isbn: 9786630098167 - leveraging ai for prediction & management of biomedical waste (8 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. LEVERAGING AI FOR PREDICTION & MANAGEMENT OF BIOMEDICAL WASTE | Sukalpaa Chaki (u. a.) | Taschenbuch | Englisch | 2026 | KS OmniScriptum Publishing | EAN 9786630098167 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 R 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. 92 pp. Englisch.…

Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 R 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.…