Palestrante: Jonathan Mendes de Almeida(defesa mestrado em 26/02)
Orientadores: Profa Célia Ghedini Ralha e Prof Marcelo Marotta
Título: Ecology, Bioinformatics, Artificial Intelligence, Statistics, and Wireless Networks: A journey from the undergraduate degree to the PhD
Resumo: In this lecture I will present my academic journey as an undergraduate and graduate student at UnB. The main goal is tomotivate more students to pursue a career in the academy and present some opportunities to do research during the undergraduate degree. Concluding the lecture, I will present the main requiremetns for most of the (funded) PhD programs from American and Japanese universities.
Horário: 15h
Palestrante: Jefferson Viana Fonseca Abreu (mestrando)
Orientadora: Profa Célia Ghedini Ralha
Título: Twitter Bot Detection with Reduced Feature Set
Evento: IEEE Int. Conf. on Intelligence & Security Informatics (ISI 2020)
Resumo:Online social networks provide a novel channel to allow interaction between human beings. Its success has attracted interest in attacking and exploiting them through a wide range of unethical activities, such as malicious actions to manipulate users. One of the methods to carry out these abuses is the use of bots on Twitter. Recent examples of bots influencing public opinion in the election process demonstrate their potential harm to the democratic world. Such malicious behavior needs to be checked and its effects should be diminished. Recently, machine learning (ML) classifiers to distinguish between real and bot accounts have proven advances. Thus, in this work four ML algorithms were tested using a public dataset and a few expressive features based on simple user profile counters for the classification of bots on Twitter. We measured their performance compared to one state-of-the-art bot detection work. The classifier accuracy was considered homogeneous with a mean of 0.854 and 0.1889 of standard deviation. Besides, all multiclass classifiers obtained AUCs greater than 0.9 indicating a practical benefit for bot detection on Twitter.
Horário: 15h30
Palestrante: Yuri Barcellos Galli (mestrando)
Orientador: Prof Bruno Macchiavello
Título: Deep Neural Networks as an Aid in Detecting Signsof Osteoporosis by Analyzing Panoramic Radiographs
Resumo: Osteoporosis is a synonym to bone fragility, and it is a silent disease that is only detected commonly after it has already caused damages to the person that has it. This bone fragility disease makes fracture more common and more damaging to its holders, and for that reason is a matter of public health. Identifying the disease in an early stage is essential to help avoid its damages, and in that task artificial intelligence has proven to be of great aid in recent years. Machine learning algorithms can predict the risk of osteoporosis by analyzing patient’s images coming from routine exams such as panoramic radiographs. This work proposes a Convolutional Neural Network (CNN) architecture that aims to identify signs of osteoporosis in that type of image using machine learning techniques.
Local: Teams MS - Equipe PPGI-316415 Seminário, Canal Seminários 2-2020
https://teams.microsoft.com/l/channel/19%3af51d2e3473d64c379d2b885d659a05dc%40thread.tacv2/Semin%25C3%25A1rios%25202-2020?groupId=93b66213-b249-467a-bcbe-dcd4255edf95&tenantId=ec359ba1-630b-4d2b-b833-c8e6d48f8059
Profa Célia Ghedini Ralha (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Coordenadora Seminários de Pós-Graduação em Informática 2-2020