Latent state estimations for experimental data with no stressors for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 10. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 9. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 12. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 20. حسب Samiul Alam (18563323)

Models and Dataset حسب M RN (9866504)

"…The algorithm does not rely on predefined control parameters like crossover or mutation rates, which makes it lightweight and easy to implement for various feature selection and optimization tasks.…"

Thesis-RAMIS-Figs_Slides حسب Felipe Santibañez-Leal (10967991)

"…In the context of facies recovery using simulations, the task of optimal sampling is formalized and addressed using a maximum information extraction criterion. …"

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

"…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …"

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... حسب Uttam Khatri (12689072)

"…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …"

Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx حسب Yanbo Sun (2202439)

"…Five machine learning models were deployed for the binary classification task of DM, and their performance was evaluated using the area under the curve (AUC). …"