Machine learning often feels difficult at the beginning, especially when everything stays theoretical. That changes once you ...

AI cyberattacks are rapidly transforming the cybersecurity landscape, enabling attackers to automate and scale operations with unprecedented speed. Through machine learning hacking, adversaries can ...

This project fine-tunes the superb/wav2vec2-large-superb-er model on custom audio data for emotion recognition. The model achieves robust performance across four emotion classes using a manual ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

Abstract: Emotion recognition from speech is an emerging field within machine learning, aimed at improving human-computer interaction by enabling systems to understand and respond to human emotions.

Abstract: The aim of the paper is to detect the emotions which are elicited by the speaker while speaking. Emotion Detection has become a essential task these days. The speech which is in fear, anger, ...

Mental disorders have a significant impact on many areas of people’s life, particularly on affective regulation; thus, there is a growing need to find disease-specific biomarkers to improve early ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Google is testing a machine learning-powered tech in the U.S. to determine the age of users and filter content across all its products accordingly. The company said it will consider data from Google ...