Comparison of average performance across 10 runs on different datasets. by André G. Coimbra (21354135)

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Comparison of and <i>F</i>2 in the positive class. by André G. Coimbra (21354135)

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GA optimization + Naive Bayes classification. by André G. Coimbra (21354135)

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PSO optimization + Naive Bayes classification. by André G. Coimbra (21354135)

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Patient characteristics in the dataset. by André G. Coimbra (21354135)

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MLP classification. by André G. Coimbra (21354135)

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GA parameters and objective function. by André G. Coimbra (21354135)

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MLP parameters. by André G. Coimbra (21354135)

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Comparison of in the negative class. by André G. Coimbra (21354135)

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Comparison of recall (R) and precision (P) in the negative class. by André G. Coimbra (21354135)

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Accuracy comparison. by André G. Coimbra (21354135)

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Comparison of recall and precision for the negative class. by André G. Coimbra (21354135)

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AUC-ROC comparison. by André G. Coimbra (21354135)

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Naive Bayes classification. by André G. Coimbra (21354135)

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Consensus network inferred on the healthy individuals (n = 102 samples, p = 151 species) from the liver cirrhosis dataset, followed by CORE-clustering algorithm to identify the mic... by Camille Champion (15238934)

U-Net feature learning module. by Mohan Yin (20721395)

“…This paper proposes a low-light image enhancement network based on a combination of iterative learning mechanisms and Retinex theory (defined as ILR-Net) to enhance both detail and color features simultaneously. …”

Table 1_A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.xlsx by Lingfeng Zhang (11559723)

“…Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. …”

Data Sheet 1_Effect and prediction of long-term weather and pollutant exposure on hemorrhagic fever with renal syndrome: based on statistical models.doc by Weiming Hou (16641637)

“…The support vector machine (SVM)-radial algorithm outperformed other algorithms when pollutants are used as predictor variables.…”

Table 3_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Table 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Subsequent analysis of subtype feature genes was conducted using the weighted gene co-expression network analysis (WGCNA). Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”