Chromatography Online

Optimizing HSSE-GC-MS and Machine Learning for Forensic Gender Classification from Human Scent

Researchers have optimized a headspace sorptive extraction (HSSE) method coupled with gas chromatography-mass spectrometry ...
KRDO

‘Richards Rubbish Roundup’ leading by example for ‘Random Acts of Kindness’ day

COLORADO SPRINGS, Colo. (KRDO) - For the volunteers with Richards Rubbish Roundup, their cleanup on Tuesday afternoon is anything but random. "Some of them, their favorite thing to do, is see how many ...
Frontiers

Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, ...
GitHub

Forest Type Classification (Field Level @30m) – Native vs Planted

Input datasets (planted forest records, FSI, satellite imagery) must be preprocessed and clipped to AoI/MWS boundaries. Resolution standardized to 30m. Raster outputs must classify each pixel as ...
IEEE

A Hybrid PSO-Random Forest Framework for Real-Time Vehicle Classification Using Roadside LiDAR

Abstract: Intelligent transportation systems are increasingly reliant on precise and efficient vehicle classification to support traffic management, safety applications, and infrastructure planning.
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 ...
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.
GitHub

Implement Random Forest Algorithm for Encrypted Traffic Classification

@IvanNardi As per our initial discussion: Is your feature request related to a problem? Please describe. Detecting malware and covert communications within encrypted traffic, especially when ...