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!

Abstract: Text classification is fundamental in Natural Language Processing (NLP), and the advent of Large Language Models (LLMs) has revolutionized the field. This paper introduces an adaptable and ...

Three NLP techniques were identified in the included studies: sentiment analysis (n=32), topic modelling (n=15) and text classification (n=7). Sentiment analysis was applied to explore associations ...

This is a Natural Language Processing (NLP) application that provides comprehensive analysis of text input, including various statistics and visualizations. The application is available both as a ...

Search engines have come a long way from relying on exact match keywords. Today, they try to understand the meaning behind content — what it says, how it says it, and whether it truly answers the ...

Background: Free-text comments in patient-reported outcome measures (PROMs) data provide insights into health-related quality of life (HRQoL). However, these comments are typically analysed using ...

We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community ...

Abstract: The advancement in information technology has been on the increase in the recent past with the expansion of text data dissemination in the form of news, medical reports, product reviews, and ...

In recent decades, medical short texts, such as medical conversations and online medical inquiries, have garnered significant attention and research. The advances in the medical short text have ...