Latent state estimations for experimental data with time limit for subject 4. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 19. حسب Samiul Alam (18563323)

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

Latent state estimations for experimental data with time limit for subject 18. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 8. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 6. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 1. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 15. 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)

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. …"

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment حسب Jianfang Cao (1881379)

"…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"

Sample image for illustration. حسب Indhumathi S. (19173013)

"…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …"

Comparison analysis of computation time. حسب Indhumathi S. (19173013)

"…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …"

Process flow diagram of CBFD. حسب Indhumathi S. (19173013)

"…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …"

Precision recall curve. حسب Indhumathi S. (19173013)

"…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …"

Quadratic polynomial in 2D image plane. حسب Indhumathi S. (19173013)

"…<div><p>Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. …"

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf حسب Cecilia Lindig-León (7889777)

"…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …"

Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods حسب Jiacong Du (12035845)

"…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …"