AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology by Jessica Valero-Rojas (19992485)

Subjects:

AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology by Jessica Valero-Rojas (19992485)

Subjects:

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…IUTF Dataset: A Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems<h2>Description</h2><p dir="ltr">The Integrated Urban Traffic-Flood (IUTF) dataset is a comprehensive, open-access resource that addresses a critical gap in urban transportation and climate resilience research. …”

Stage 1 Surveys: Survey of Australian Professionals Using Educational Technology by Glen Shearer (9834266)

Cross-tabulation of study participants by gender. by Ya Shi (3407390)

“…Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. …”

ROC curve performance of SWB prediction. by Ya Shi (3407390)

“…Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. …”

Predictors of SWB. by Ya Shi (3407390)

“…Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. …”

Supplementary Fig 1. by Ya Shi (3407390)

“…Our study selected a wide range of predictors, including sociodemographic characteristics, leisure activities, social class identification, and preferences in socialization interaction patterns. Eight common ML algorithms were utilized to construct prediction models. …”

Overview of LWLCM. by Shahnwaz Afzal (22184546)

“…<div><p>The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. …”

Key initialization. by Shahnwaz Afzal (22184546)

“…<div><p>The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. …”

Decryption Time(sec). by Shahnwaz Afzal (22184546)

“…<div><p>The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. …”

Key-Keystream Avalanche effect. by Shahnwaz Afzal (22184546)

“…<div><p>The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. …”