AU-NN: ANFIS Unit Neural Network

1 code implementation • 21 Apr 2022 • Tonatiuh Hernández-Del-Toro, Carlos A. Reyes-García, Luis Villaseñor-Pineda

Two use cases of this network are shown to test the capability of the network.

Classification Incremental Learning

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

1 code implementation • 13 Apr 2021 • Tonatiuh Hernández-Del-Toro, Carlos A. Reyes-García, Luis Villaseñor-Pineda

An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech.

Brain Computer Interface EEG

Semantically-informed distance and similarity measures for paraphrase plagiarism identification

no code implementations • 29 May 2018 • Miguel A. Álvarez-Carmona, Marc Franco-Salvador, Esaú Villatoro-Tello, Manuel Montes-y-Gómez, Paolo Rosso, Luis Villaseñor-Pineda

Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques.

A visual approach for age and gender identification on Twitter

no code implementations • 28 May 2018 • Miguel A. Alvarez-Carmona, Luis Pellegrin, Manuel Montes-y-Gómez, Fernando Sánchez-Vega, Hugo Jair Escalante, A. Pastor López-Monroy, Luis Villaseñor-Pineda, Esaú Villatoro-Tello

The goal of Author Profiling (AP) is to identify demographic aspects (e. g., age, gender) from a given set of authors by analyzing their written texts.

Marketing

Early text classification: a Naive solution

no code implementations • 20 Sep 2015 • Hugo Jair Escalante, Manuel Montes-y-Gómez, Luis Villaseñor-Pineda, Marcelo Luis Errecalde

A document is processed as a sequence of terms, and the goal is to devise a method that can make predictions as fast as possible.

General Classification text-classification +1