A
Adriano C. M. Pereira
Researcher at Universidade Federal de Minas Gerais
Publications - 131
Citations - 1684
Adriano C. M. Pereira is an academic researcher from Universidade Federal de Minas Gerais. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 15, co-authored 121 publications receiving 1094 citations. Previous affiliations of Adriano C. M. Pereira include Centro Federal de Educação Tecnológica de Minas Gerais.
Papers
More filters
Proceedings ArticleDOI
Stock market's price movement prediction with LSTM neural networks
TL;DR: This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators, and the results are promising.
Journal ArticleDOI
Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review.
João Gustavo Claudino,Daniel de Oliveira Capanema,Thiago Vieira de Souza,Júlio Cerca Serrão,Adriano C. M. Pereira,George P. Nassis,George P. Nassis +6 more
TL;DR: A prevalent application of AI methods in team sports based on the number of published studies is suggested and a promising future with regard to AI use in team sports is proposed.
Proceedings ArticleDOI
An evaluation of machine translation for multilingual sentence-level sentiment analysis
TL;DR: Evaluating existing efforts proposed to do language specific sentiment analysis for English suggests that simply translating the input text on a specific language to English and then using one of the existing English methods can be better than the existing language specific efforts evaluated.
Journal ArticleDOI
A customized classification algorithm for credit card fraud detection
TL;DR: Fraud-BNC presented itself as the best algorithm to provide a good trade-off between both perspectives, improving the current company’s economic efficiency in up to 72.64%.
Journal ArticleDOI
A comparative study of machine translation for multilingual sentence-level sentiment analysis
TL;DR: This work evaluates existing efforts proposed to do language specific sentiment analysis with a simple yet effective baseline approach and suggests that simply translating the input text in a specific language to English and then using one of the existing best methods developed for English can be better than the existing language-specific approach evaluated.