Search
Now showing items 1-6 of 6
Método para classificação de padrões da Lagarta do cartucho (Spodoptera frugiperda) na cultura do milho baseado em processamento de imagens digitais e aprendizado de máquina
(Universidade Federal de São Carlos, 2021-12-29)
The detection, identification, and control of the Fall Armyworm (Spodoptera frugperda) pest
into the maize culture (Zea mays) are greatly dependent on the human factor. Currently, such
control occurs mainly through the ...
Classificação de retornos utilizando dados de alta frequência no mercado de bitcoins
(Universidade Federal de São Carlos, 2020-03-10)
In the cryptocurrency market, Bitcoin stands out as the most accepted traded in the world. However, due to its high volatility, the prediction of price behaviors, in special, the trend classification, becomes a challenge ...
Análise da predição da violência infantil por meio de árvores de decisão e regras de associação
(Universidade Federal de São Carlos, 2020-06-02)
According to the United Nations International Children's Emergency Fund (UNICEF), currently around 300 million children around the world suffer from various types of abuse, including: psychological, physical, sexual or ...
On the advances in pattern recognition using Optimum-Path Forest
(Universidade Federal de São Carlos, 2020-09-24)
Pattern recognition (PR) techniques have been paramount to solve different and complex problems in many fields of study. The basic idea behind PR techniques is to compute a model capable of classifying unknown samples. ...
Aprendizado de subcategorias para Never-ending Language Learning: uma abordagem baseada em perguntas e respostas
(Universidade Federal de São Carlos, 2021-01-08)
In recent years, ontologies have been used in information systems to index large corpora of documents or collections of facts and directly support user interaction with the system through functionalities such as navigation ...
Data preparation pipeline recommendation via meta-learning
(Universidade Federal de São Carlos, 2021-05-26)
Data preparation is a essential stage in the machine learning pipeline, aiming to convert noisy and disordered data into refined data compatible with the algorithms. However, data preparation is time-consuming and requires ...