A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

The machine learning algorithms used were Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Generally, a Decision Tree classifier is non-parametric, not requiring prior ...

This paper proposes a Random Forest (RF) machine learning algorithm-based prediction model for the state of charge (SoC) level of lithium-ion batteries for electric vehicles. To show the effectiveness ...

This study explores the application of machine learning algorithms, specifically decision tree and random forest models, to predict house prices within the Indian real estate market. Utilizing a ...

Machine Learning Models Random Forest Random Forest is a versatile machine learning algorithm that is effective in handling large datasets with multiple features. By constructing a multitude of ...

In the second part of the analysis, three machine learning models—Logistic Regression, Random Forest, and XGBoost—were implemented for predictive performance. Logistic Regression outperformed others ...

This paper proposes a Random Forest grid fault prediction model based on Genetic Algorithm optimization (GA-RF) to classify the grid fault types, which improves the distribution network fault ...

It also evaluates AI’s role in automation and prediction through portfolio management, predictive analysis, and risk mitigation, emphasizing advanced machine learning techniques, including deep ...