Latent state estimations for experimental data with time limit for subject 22. by Samiul Alam (18563323)

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

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

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

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

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

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

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

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

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX by Umesh C. Sharma (10785063)

“…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by Jun Zhang (48506)

“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”

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

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<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.…”

Fig 12 - by Nisha Yadav (366131)

Symmetry test result. by Jiyong Moon (16819302)

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”