Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Warning dialog box of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Feature extraction of proposed NIDS. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Performance comparison analysis. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Trained dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Environmental setup. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Data repository. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Proposed architecture of fast R–CNN. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Test dataset after preprocessing. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Accuracy comparison with various datasets. by Parthiban Aravamudhan (15338781)

“…To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

DataSheet1_Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images.PDF by Niccolò Marini (11247936)

“…<p>Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. …”

Generalized Internal Coordinates for Creative Exploration of Interatomic Geometries by Aleksandr V. Marenich (1283298)

“…Our algorithm allows the user to create compound internal coordinates that are functions of other coordinates, as well as special-purpose coordinates for specific classes of problems. …”

Generalized Internal Coordinates for Creative Exploration of Interatomic Geometries by Aleksandr V. Marenich (1283298)

“…Our algorithm allows the user to create compound internal coordinates that are functions of other coordinates, as well as special-purpose coordinates for specific classes of problems. …”