ABSTRACT: Riverbank instability poses a mounting global threat, especially across East Africa’s transboundary river systems, where geospatial assessments remain scarce. This study applies advanced ...

Abstract: The goal of this study is to evaluate how well driver drowsiness can be detected using two different machine learning methods: the Decision Tree Classifier and the Novel Random Forest ...

Background: Decisions surrounding involuntary psychiatric treatment orders often involve complex clinical, legal, and ethical considerations, especially when patients lack decisional capacity and ...

This project implements a machine learning-based solution to detect fake Instagram accounts using Random Forest classification. The system analyzes various features of Instagram accounts to determine ...

Abstract: The study aims to improve the accuracy of cyberbullying detection. Compared to the Random Forest classifier, utilize XGBoost to improve accuracy. In this study, two groups were compared. The ...

1 Department of Mathematical and Computer Sciences, Faculty of Science, University of Medical Sciences, Ondo, Nigeria. 2 Department of Computer Science, University of New Haven, West Haven, USA. 3 ...

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment. This is a Machine Learning model ...