S1 Dataset - by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

S2 Dataset - by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

S5 Dataset - by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

Minority samples division. by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

S3 Dataset - by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

S1 Data - by JiaMing Gong (20427837)

“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”

Optimizing Neuronal Calcium Flux Analysis: A Python Framework for Alzheimer's and TBI Studies by Huiying Huang (490768)

“…Dead cells are identified via watershed algorithms, and all cells are segmented using Cellpose, an AI-based tool. …”

Sample clustering and subtyping analysis. by Ziyang Liu (242055)

Average accuracy by feature extraction layer. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Performance analysis by feature extraction layer. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Best model class-wise performance on SMIDS. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Average accuracy by feature selection method. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Classifier performance overview. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Best model class-wise performance on HuSHeM. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches by Lisa Lombardo (7301969)

“…In this project, we developed three distinct machine learning (ML) approaches based on classification, regression, and multiclassification models to predict the activity and selectivity profiles of SR ligands. …”

Results of each step in all data partitions. by Samer Elsheikh (6439604)

“…We used accuracy measures to assess detection and classification results and intraclass correlation coefficient to assess the quantification of the drain coverage by the intracerebral hemorrhage.…”

Landscape Change Monitoring System (LCMS) CONUS Change Attribution (Image Service) by U.S. Forest Service (17476914)

“…Continuous change detection and classification of land cover using all available Landsat data. …”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Continuous change detection and classification of land cover using all available Landsat data. …”

Gradient Boosting Trees Machine Learning Approach to Predict the Mutation Types in CaSR. by Aylin Bircan (20176049)

Table 4 - by Muhammad Tayyab Zamir (20455240)

“…<p>Statistical performance indicators for deep learning algorithms in classification can be evaluated through (a) overall metrics and (b) classification based on macro and weighted indicators.…”