Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Levy function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Rastrigin function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Rosenbrock function losses for . حسب Shikun Chen (14625352)

"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"

Modular architecture design of PyNoetic showing all its constituent functions. حسب Gursimran Singh (575288)

الموضوعات:

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx حسب Hong Chen (108084)

"…This study aimed to develop and validate a machine learning model to enhance risk prediction of renal function decline in CKD patients, enabling early and personalized interventions.…"

Functional enrichment of DEGs. حسب Man Wang (110466)

"…</p><p>Methods</p><p>The study employed a combination of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and various machine learning algorithms to screen for characteristic genes. …"

Average IOU (dots) and IOU (mistakes) between machine and human drawings. IOU (dots) between human drawings and ground truth is 0.69 and 0.76 for MNIST and Fashion MNIST, respectiv... حسب Tarun Khajuria (12117309)

Table 1_Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures.docx حسب Yufeng Wang (274657)

Different mathematical formalization of model fairness. TPR: True Positive Rate; FPR: False Positive Rate; FNR: False Negative Rate; TNR: True Negative Rate; PPV: Positive Predicti... حسب Marine Hoche (20896647)

Machine learning model construction and accuracy qualification. حسب Lelin Jiang (22127048)

Data Sheet 1_Detecting cognitive motor dissociation by functional near-infrared spectroscopy.pdf حسب Yan Wang (15435)

"…Seven features of hemodynamic responses were extracted during the task and the rest conditions. The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.…"

Data Sheet 2_Detecting cognitive motor dissociation by functional near-infrared spectroscopy.pdf حسب Yan Wang (15435)

"…Seven features of hemodynamic responses were extracted during the task and the rest conditions. The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.…"

Table 1_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx حسب Yuezhou Huang (9998177)

"…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …"

Table 2_SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.xlsx حسب Yuezhou Huang (9998177)

"…Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. …"