Code snippet from “Apache Dubbo” GitHub project. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Overall framework design. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Gamma distribution of reuse. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Code snippet from “Netty/Buffer” Maven artefact. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Top 5 correlated features based on reuse. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Features with the top importance score. حسب Matthew Yit Hang Yeow (20721206)

"…The average F1-score of the ML classification models is 77.19%. …"

Supplementary file 1_Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging feat... حسب Hanlin Wang (240998)

"…The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model.…"

Data Sheet 2_Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging features and... حسب Hanlin Wang (240998)

"…The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model.…"

Data Sheet 1_Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging features and... حسب Hanlin Wang (240998)

"…The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model.…"

Positional shift-induced ICP signal variations. حسب Donatien Legé (20485487)

"…The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61—0.83]). …"

Demographic data of the study cohort. حسب Donatien Legé (20485487)

"…The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61—0.83]). …"

The ten best ranked ICP signal features. حسب Donatien Legé (20485487)

"…The overall one-versus-one classification task was achieved with an averaged AUROC of 0.72 (95%-CI: [0.61—0.83]). …"

ASR accuracy factors of child AAE Speakers (Fletcher et al., 2025) حسب Brittany N. Fletcher (22264800)

"…Acoustic measures from vowels preceding final plosives (including vowel duration, fundamental frequency, average first formant) were extracted using Praat and a custom MATLAB algorithm. …"

Image1_Revisiting Southern Gallo-Romance from a complexity theory standpoint: Occitan.pdf حسب Jean Léo Léonard (20458229)

"…<p>In this paper, the inner structure of the Occitan dialect network is revisited in the light of a range of cumulative (Ward’s method) vs. reductive (Complete linkage, Groupe Average, Weighted Average) hierarchical algorithms provided by Gabmap, an Online dialectometric application for calculating distance/similarities by edit distance (Levenshtein algorithm). …"

Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. حسب Daniel Walke (21680915)

"…For each feature, we plotted the average predictions (average ratio of sepsis classification) made by the trained models across different feature values (i.e., grid values). …"

Effect of post-processing model outputs on biodiversity estimates. حسب Valentine Fleuré (12822137)

Correlation matrix of all twelve features. حسب Toheeb Salahudeen (21368040)

"…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …"

Model 3: Biomarkers only. حسب Toheeb Salahudeen (21368040)

"…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …"

Model 2: Biomarkers + ACE + Age + Gender. حسب Toheeb Salahudeen (21368040)

"…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …"

Class distribution for binary classes. حسب Toheeb Salahudeen (21368040)

"…Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. …"