Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by ...
Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...
Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...
Abstract: A precise change detection in the multi-temporal optical images is considered as a crucial task. Although a variety of machine learning-based change detection algorithms have been proposed ...
Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...
Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level ...
Abstract: This paper presents the design of a predictive model based on a combination of ARIMA and Random Forest algorithms, specifically tailored for forecasting medal scenarios. By establishing a ...
Background: Neovascular glaucoma (NVG) is one of the most severe complications of proliferative diabetic retinopathy (PDR), carrying a high risk of blindness. Establishing an effective risk prediction ...
ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...
ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results