Latent state estimations for experimental data with time limit for subject 15. by Samiul Alam (18563323)

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

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

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

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

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

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

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

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

Models and Dataset by 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.…”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Thesis-RAMIS-Figs_Slides by 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 by 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. …”

Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx by 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). …”