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)

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. …"

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

"…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…"

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>. …"

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles حسب Soham Savarkar (21811825)

"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"