Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

Australia’s tropical forests are the world’s first to flip a worrisome switch. The forests are now putting more carbon into the atmosphere than they are taking out, researchers report in the Oct. 16 ...

Postdoctoral Researcher in Tropical Forest Ecology, Hawkesbury Institute for the Environment, WSU, Australian National University Hannah Jayne Carle is affiliated with the Queensland Permanent ...

This repository contains the solution for Task 5 of the Elevate AI & ML Internship. The goal of this task was to learn and implement tree-based models for both classification and regression, analyze ...

A pioneering study reveals how archaeologists' satellite tools can be repurposed to tackle climate change. By using AI and satellite LiDAR imagery from NASA and ESA, researchers have found a faster, ...

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split, cross_val_score from sklearn.tree import DecisionTreeClassifier, plot_tree from sklearn.ensemble import ...

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 ...

Abstract: This paper discusses computer vision-based sign language by utilizing landmark detection and Scikit-Learn. The objective of this research is to develop a model that proficient in the ...

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.