IEEE

Detecting HLS Hardware Trojans using Random Forest Classifier

Abstract: High-level Synthesis (HLS) generated IP designs are widely and effectively used in several image/video processing applications, consumer electronics applications as well as multimedia ...
IEEE

Android Malware Detection Using API Calls and Permissions with Random Forest Classifier

Abstract: Android malware poses a persistent and evolving threat to mobile security, considering its capability to compromise sensitive user data and evade traditional detection methods. While ...
GitHub

Credit-Card-Fraud-Detection-with-Random-Forest-Classifier

The goal of this project is indentify fraudulent transactions while minimizing false positives (non-fraudulent transactions flagged as fraud) and false negatives (missed fraudulent transations). The ...
Frontiers

Machine learning models for predicting the risk of depressive symptoms in Chinese college students

1 Department of Psychology/Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, Guangdong, China 2 School of Psychology, South China Normal ...
Frontiers

Machine learning approach for personalized vancomycin steady-state trough concentration prediction: a superior approach over Bayesian population pharmacokinetic model

Introduction: Appropriate vancomycin trough levels are crucial for ensuring therapeutic efficacy while minimizing toxicity. The aim of this study is to identify clinical factors that influence the ...
EurekAlert!

Smarter skies: A new AI model turns street cameras into rainfall sensors

A novel framework integrates urban surveillance video data with a two-stage AI pipeline: an enhanced random forest classifier detects rain streaks and selects key image regions, while a hybrid deep ...
Scientific Research Publishing

Kirasich, K., Smith, T. and Sadler, B. (2018) Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets. SMU Data Science Review, 1, Article 9.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Scientific Research Publishing

Adugna, T., Xu, W., & Fan, J. (2022). Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images ...

ABSTRACT: Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion, especially in Tiaty, Baringo, Kenya. These issues create mixed opportunities for pastoral ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.