The optimized GPR approach’s error histogram of the validation dataset. by Fahd Saeed Alakbari (10701871)

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Cross-plots of (a) the proposed GPR, Ranjbar-Karaml et al. [14], Christaras et al. [52], Feng et al. [16], Gowida et al. [17] models and (b) Khandelwal et al. [13], Brandås et al.... by Fahd Saeed Alakbari (10701871)

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Group error analysis of compressional time for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the validation dataset. by Fahd Saeed Alakbari (10701871)

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(a) Average absolute percentage relative error (AAPRE), and, (b) Coefficient of determination (R²) of the models. by Fahd Saeed Alakbari (10701871)

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P values of Kruskal–Wallis test at 95% significance level. by Fahd Saeed Alakbari (10701871)

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Trend analysis of the ν_s – RHOB in the range of data found in the dataset used for the optimized GPR. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.02 g/cc and DTs =  265.03 us/ft. by Fahd Saeed Alakbari (10701871)

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(a) Taylor diagrams of Khandelwal et al. [13], Brabdas et al, Kumar et al. [12] models. (b) Taylor diagrams of Ranjbar-Karami et al. [14], Christaras et al. [52], Feng et al. [16],... by Fahd Saeed Alakbari (10701871)

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(a, b). The statistical error analyses for the models using the same dataset. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – RHOB trends models with experimental measurements. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.33 g/cc and DTc = 118.67 us/ft. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the training dataset. by Fahd Saeed Alakbari (10701871)

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Methodology flowchart. by Fahd Saeed Alakbari (10701871)

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Trend analysis of the ν_s – DTc in the range found in the dataset used for the optimized GPR. by Fahd Saeed Alakbari (10701871)

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Statistical error analyses of the proposed or optimized GPR model. by Fahd Saeed Alakbari (10701871)

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Comparison of ν_s measured and predicted estimates of the optimized GPR approach for (a) training, (b) validation, and (c) testing datasets. by Fahd Saeed Alakbari (10701871)

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Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…</p>Objective<p>To systematically assess ML-based mortality risk prediction models for SA-AKI patients.…”

Table 4_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…</p>Objective<p>To systematically assess ML-based mortality risk prediction models for SA-AKI patients.…”

Table 3_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…</p>Objective<p>To systematically assess ML-based mortality risk prediction models for SA-AKI patients.…”