IEEE

Emotion Classification from EEG Using CNN-LSTM Architectures with Continuous Intensity Modeling

Abstract: Emotion recognition from Electroencephalography (EEG) signals is a critical component of affective computing, enabling intuitive human-computer interaction that can be used for ...
The Hacker News

GlassWorm Attack Uses Stolen GitHub Tokens to Force-Push Malware Into Python Repos

The GlassWorm malware campaign is being used to fuel an ongoing attack that leverages the stolen GitHub tokens to inject malware into hundreds of Python repositories. "The attack targets Python ...
GitHub

EEG CNN-LSTM-Transformer (EEG-CLT) for motor imagery decoding

To improve the classification accuracy of motor imagery EEG signals, In this research I propose the CLT model, which combines three architectures sequentially: Convolutional Neural Network (CNN), Long ...
IEEE

EEG-Based Four Level Fear Classification using Graph Neural Network and LSTM Fusion Model: Comparing Raw and DE Features with k-Fold and Leave-One-Subject-Out

Abstract: Electroencephalography (EEG)-based four-level fear classification is important for mental-health assessment and emotion-aware systems, yet robust generalization to unseen subjects remains ...
Virtualization Review

How to Create a Free Web Page Using GitHub Pages

Tom Fenton used AI-assisted vibe coding to create and deploy a free, cloud-hosted static web page. GitHub Pages provided a no-cost way to host static HTML content without servers, databases, or paid ...
GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
Scientific Research Publishing

Liang, M. and Niu, T. (2022) Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs. Procedia Computer Science, 208, 460-470.

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...