Search alternatives:
significant i.e » significant inter (Expand Search), significant bias (Expand Search), significant gap (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
i.e decrease » we decrease (Expand Search), sizes decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), mean decrease (Expand Search)
significant i.e » significant inter (Expand Search), significant bias (Expand Search), significant gap (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
i.e decrease » we decrease (Expand Search), sizes decrease (Expand Search), use decreased (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), mean decrease (Expand Search)
-
141
-
142
-
143
-
144
Neutrophil Superoxide anion production, Heat map for gene expression from neutrophils of CGD patients, and PCA plot for genome-wide gene expression.
Published 2025“…The cells are colored according to expression level relative to row (i.e., relative within gene). Genes that responded to IFN-γ with increased expression are at the bottom and those with decreased expression are at the top. …”
-
145
-
146
-
147
-
148
-
149
-
150
-
151
-
152
-
153
-
154
Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML
Published 2025“…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”
-
155
-
156
-
157
-
158
Variation law of UCS.
Published 2025“…The initial stage of internal crack propagation in sandstone is accompanied by a significant increase in AE events rate and drastic fluctuations in <i>b</i>-value. …”
-
159
-
160