Implementing the Random Forest algorithm within the nDPI (nDPI) framework can enhance the classification of encrypted traffic, enabling more accurate detection of malicious patterns. Future ...

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 ...

Lin, J. and He, J. (2022) Parallel Random Forest Prediction Algorithm Based on PCA Stratified Sampling in the Big Data Environment. China Management Informationization, 25, 172-176.

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 ...

Ange Postecoglou tries to explain a 1-0 loss to Nottingham Forest. Tottenham lost for the fifth time in eight Premier League games, and the pressure is growing as they lie at the bottom half of ...

In this post, we’ll provide examples of how bias can influence AI results and how AI algorithms are used, define sources of bias that can be associated with AI, and discuss mechanisms for legal ...

The Random Forest algorithm proves to be a powerful tool for ToF-SIMS data, but there are some points of attention. Even though RF can handle outliers and noise in the data efficiently due to random ...

With the continuous expansion of the scale of electric power system and the diversification of construction, the difficulty of electric power engineering cost analysis is gradually increasing. There ...

As opposed to random forest algorithms, the objective of this study is to increment expectation rate involving a clever model of bidirectional encoder portrayals for transformers (BERT). The viability ...