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 ...
In the middle of a hemlock grove along the Connecticut River in Norwich is an old landline phone set up in a wooden enclosure. It doesn’t work; it’s not connected to any phone line. But you can still ...
Background: Decisions surrounding involuntary psychiatric treatment orders often involve complex clinical, legal, and ethical considerations, especially when patients lack decisional capacity and ...
1 Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka 2 Department of Radiography/Radiotherapy, Faculty of Allied Health ...
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 ...
ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...
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