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

Subjects:

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

Subjects:

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. The Dewesoft FFT DAQ system is designed to extract the high-quality data from the CPM based on sensor fusion technology. …”

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

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

Subjects:

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

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

Table 1_WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer.docx by Jing Lv (128179)

Image 1_WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer.tif by Jing Lv (128179)

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)

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)

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)

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)

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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Correlation between TMscore and Length of the RNAs for RNAs longer than 100 nucleotides. by Akash Bahai (20485919)

Correlation between RMSD and Length of the RNAs for RNAs longer than 100 nucleotides. by Akash Bahai (20485919)

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)

Global and local feature (predictor variable) explanations from the XGBoost trained with Quebec data (2014–2021). by Hamid Ghanbari (6821096)