Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF حسب Jordan D. Chambers (6732224)

SHAP bar plot. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

Sample screening flowchart. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

Descriptive statistics for variables. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

SHAP summary plot. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

ROC curves for the test set of four models. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

Display of the web prediction interface. حسب Meng Cao (105914)

"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"

Proposed model specificity and DSC outcomes. حسب Subathra Gunasekaran (19492680)

"…Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. …"

Bi-directional LSTM Model performance. حسب Muhammad Usman Tariq (11022141)

"…Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. …"

Prediction results of different models. حسب Qinghua Li (398885)

"…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …"

Data_Sheet_1_Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S..pdf حسب Xiao Zhou (122661)

S1 Data - حسب Gaosha Li (20570760)

"…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …"

DataSheet_1_Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach.docx حسب Haoyu Wang (429641)

Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" حسب Ziyu Lin (9151064)

Consort diagram for the study. حسب Nicola Lazzarini (3528197)

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Performance confidence intervals. حسب Nicola Lazzarini (3528197)

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Baseline characteristics of patients in the study cohort. حسب Nicola Lazzarini (3528197)

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Input features. حسب Nicola Lazzarini (3528197)

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Most important predictive features. حسب Nicola Lazzarini (3528197)

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Age feature importance. حسب Nicola Lazzarini (3528197)

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