Performance comparison of ML models. by Gourab Saha (8987405)

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Evaluation metrics of selected ML models. by Gourab Saha (8987405)

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Example of accuracy plots for soil organic carbon (oc_usda.c729_w.pct) content and soil pH in water (ph.h2o_usda.a268_index) with Lin’s CCC gradually decreasing from MIR to NIR Neo... by José L. Safanelli (20552654)

Performance Table for ML Models. by Gaurav Sandeep Dave (21519884)

“…The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. …”

Boxplot comparing Ct values for HD in flies and clothes. by Philippe Ndzomo (17706039)

Decision curve analysis of PIERS-ML model on day 0 (a), day 4 (b), day 8 (c), and day 13 (d). by Guiyou Yang (10259597)

Decrease in loss during the training process. by Muhammad Idrees (92773)

Loss of MAP4K4 expression causes a decrease in the number of neutrophils. by Guihua Wang (286390)

Crop yield prediction accuracy using different ML algorithms. by Gourab Saha (8987405)

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Table 2_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

Table 3_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

Table 1_Identify predictive factors for the emergence of self-reported oropharyngeal dysphagia in older men and women populations: a retrospective cohort analysis.xlsx by Shuang Liu (8449)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power.... by Medhat A. Tawfeek (22522087)

“…The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. …”

Daily number of adverse outcomes (bars) and area under the receiver operating characteristic curve per day of the PIERS-ML model. by Guiyou Yang (10259597)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

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Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

The motor torque curves of load increase/decrease (A) the load of 10 N • m (B) the load of 20 N • m. by Taochang Li (20642935)

The motor speed curves of load increase/decrease (A) the load of 10 N • m (B) the load of 20 N • m. by Taochang Li (20642935)

Skin marks is the S1 Fig title. by Mercedes Soto-González (20422078)