Great machine learning performance with multiple classification algorithms. حسب Sydney C. Weiser (15383531)

<i>C</i>. <i>difficile</i> toxin production is regulated by multiple metabolic signals. حسب Deborah A. Powers (15361394)

Prediction to belong to Cluster 1 or Cluster 2 using random forest algorithm. حسب Olivier Mascle (14164997)

Data Sheet 1_Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms.csv حسب Hongjun Tao (21448853)

"…Among the 6 predictive models, the random forest algorithm demonstrated the highest performance (AUC = 0.865), significantly outperforming the others. …"

Direct estimator used in Classifier 1. حسب Marinara Marcato (16412686)

"…Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. …"

Categories of behaviour analysed. حسب Marinara Marcato (16412686)

"…Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. …"

The structure of the datasets. حسب Gang Li (34549)

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives حسب Carlos Santiago (13227143)

"…A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81–98%) in training and validation series. …"

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives حسب Carlos Santiago (13227143)

"…A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81–98%) in training and validation series. …"

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives حسب Carlos Santiago (13227143)

"…A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81–98%) in training and validation series. …"

ER Signaling pathway endpoints detected using the GFP-ER⍺:PRL-HeLa cell model. حسب Rajib Mukherjee (532424)

Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx حسب Haohan Xue (17892128)

DataSheet1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.ZIP حسب Haohan Xue (17892128)

Differentiating effects of salvage logging and recovery patterns on post-fire boreal forests in Northeast China using a modified forest disturbance index حسب Kewei Li (706223)

"…Furthermore, uncertainty remains about post-disturbance vegetation dynamics and the effects of forest recovery under the interaction of burn severity, biological-legacy management, and active forest restoration (i.e. artificial regeneration and assisted natural regeneration). …"

Table_1_Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based... حسب Gabriel Idakwo (7216151)

"…Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. …"

Unmixing marsh vegetation species across multiple sensors and spatial scales حسب Zhicheng Yang (216080)

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP حسب Xiaoyuan Wang (492534)

"…Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. …"

Forest canopy height mapping considering the seasonal rhythm of different dominant tree species حسب Yali Zhang (206227)

"…The validation <i>R</i>² of the FCH model using active and passive remote sensing variables with the random forest algorithm was only 0.37, while the <i>R</i>² value increased to 0.57 when the seasonal rhythm information was included. …"

Table_1_Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule.XLSX حسب Lei Chen (54296)

Table_3_Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule.XLSX حسب Lei Chen (54296)