Navegando por Autor "Nascimento, Tuany Mariah Lima do"
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TCC A Comparative Analysis of Features Selection Techniques Using Genetic Algorithm in Keystroke Dynamics(Universidade Federal do Rio Grande do Norte, 2019-06-10) Nascimento, Tuany Mariah Lima do; Oliveira, Laura Emmanuella Alves dos Santos Santana de; Márjory Da Costa Abreu; Oliveira, Josenalde Barbosa de; Araújo, Daniel Sabino Amorim deDue to the continuous use of social networks, users can be vulnerable to situations such as paedophilia treats. One of the ways to do the investigation of an alleged paedophile is to verify the legitimacy of the genre that it is said to be. One possible technique to adopt is keystroke dynamics analysis. However, this technique can extract many attributes, causing a negative impact on the accuracy of the classifier due to the presence of redundant and irrelevant attributes. Therefore, the present work presents a comparative analysis between two attribute selection approaches, wrapper and hybrid (wrapper + filter), using the metaheuristic genetic algorithm, as KNN, SVM, and Naive Bayes classifiers and as Correlation and Relief filter. Bringing the best SVM classifier using the wrapper approach, for both databases.Dissertação Using semi-supervised learning models for creating a new fake news dataset from Twitter posts: a case study on Covid-19 in the UK and Brazil(Universidade Federal do Rio Grande do Norte, 2022-01-14) Nascimento, Tuany Mariah Lima do; Abreu, Marjory Cristiany da Costa; Oliveira, Laura Emmanuella Alves dos Santos Santana de; 05069886436; http://lattes.cnpq.br/8996581733787436; http://lattes.cnpq.br/2234040548103596; Cavalcante, Everton Ranielly de Sousa; http://lattes.cnpq.br/5065548216266121; Souza Neto, Placido Antônio de; http://lattes.cnpq.br/3641504724164977Fake News has been a big problem for society for a long time. It has been magnified, reaching worldwide proportions, mainly with the growth of social networks and instant chat platforms where any user can quickly interact with news, either by sharing, through likes and retweets or presenting hers/his opinion on the topic. Since this is a very fast phenomenon, it became humanly impossible to manually identify and highlight any fake news. Therefore, the search for automatic solutions for fake news identification, mainly using machine learning models, has grown a lot in recent times, due to the variety of topics as well as the variety of fake news propagated. Most solutions focus on supervised learning models, however, in some datasets, there is an absence of labels for most of the instances. For this, the literature presents the use of semi-supervised learning algorithms which are able to learn from a few labeled data. Thus, this work will investigate the use of semi-supervised learning models for the detection of fake news, using as a case study the outbreak of the Sars-CoV-2 virus, the COVID-19 pandemic. Our results have shown that we have an interesting methodology which can be used to built a new social media dataset and automatic label the samples using semi-supervised learning models. We also have as an important contribution a new fake news dataset.