Abstract: Feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for ...

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!

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

Getting input from users is one of the first skills every Python programmer learns. Whether you’re building a console app, validating numeric data, or collecting values in a GUI, Python’s input() ...

JSON Prompting is a technique for structuring instructions to AI models using the JavaScript Object Notation (JSON) format, making prompts clear, explicit, and machine-readable. Unlike traditional ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

Abstract: We consider a human-automation team jointly solving binary classification tasks over multiple time stages. At each stage, the automation observes the data for a batch of classification tasks ...

This repository provides an efficient binary video classification pipeline using PyTorch, optimized for local GPU-enabled PCs. It includes preprocessing and model inference tools for classifying ...