Campus Jaboatão dos Guararapeshttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/4892026-04-29T14:20:07Z2026-04-29T14:20:07ZAnálise da participação feminina nos cursos da área de computação da rede federalhttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/21092026-04-17T06:01:29Z2026-01-05T00:00:00ZAnálise da participação feminina nos cursos da área de computação da rede federal
This study analyzes female participation in Computing courses at the Federal
Education Network of Brazil, based on the microdata provided on the Nilo Peçanha
Platform from 2017 to 2023. The analysis was conducted in two stages. Initially, a
descriptive quantitative approach was adopted, using Power BI to examine the data
from all Computer Science courses in the Federal Network. Next, only the data from the Federal Institute of Pernambuco (IFPE) was considered, in order to conduct a
predictive analysis using Machine Learning techniques, with the aim of identifying
variables capable of predicting student dropout in the Computing courses of this
institution. The results indicate a continuous growth in female participation, reaching
37.64% at IFPE in 2023, a percentage higher than the national average. However,
dropout rates are critical among low-income and brown students. The Random Forest
algorithm showed the best performance in predicting dropout risk, with an accuracy of
70.32% and a recall of 84.67%. It is concluded that socioeconomic and racial factors
have a greater predictive weight on dropout rates than gender alone. Thus, retention
policies should prioritize social vulnerability, aiming to ensure the continued presence
of women in the field of Computing.
2026-01-05T00:00:00ZGuia inteligente: uma ferramenta de acessibilidade para pessoas cegas ou com baixa visãohttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/20492026-03-12T06:01:56Z2023-02-01T00:00:00ZGuia inteligente: uma ferramenta de acessibilidade para pessoas cegas ou com baixa visão
This course conclusion work, proposes the construction of
fundamental bases for the creation of an accessibility tool, also known as assistive
technology or adaptive technology, for people with visual impairments, whether blind
or with low vision, providing for these people a means of facilitating social integration,
especially with regard to their academic training, with the objective of helping them
during their displacements, in the internal dependencies of the Federal Institute of
Education, Science and Technology of Pernambuco, Jaboatão dos Guararapes
campus, and, To fulfill this objective, it is necessary to integrate various knowledge,
techniques and methods, facilitating accessibility for people with visual impairments,
namely: Orientation and mobility, audio description of scenarios, Assistive
technologies, working in an integrated way with the most current techniques of
Computer Vision and Natural Language Processing, s being these, subfields of
artificial intelligence, finally, this work will be built based on the Design Science
Research methodology of research and development.
2023-02-01T00:00:00ZClassificação de vocalizações em indivíduos com TEA: avaliação de modelos de machine learninghttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/19742026-03-04T13:53:57Z2025-11-26T00:00:00ZClassificação de vocalizações em indivíduos com TEA: avaliação de modelos de machine learning
This study investigates the application of machine learning techniques for classifying nonverbal vocalizations of minimally verbal individuals with Autism Spectrum Disorder (ASD). Based on the American ReCANVo dataset, six categories of vocalizations were explored: delight, dysregulated, frustrated, request, selftalk, and social. The process involved acoustic feature extraction, class balancing, and training multiple models. The experimental evaluation demonstrated that models trained exclusively on the ReCANVo dataset are ineffective at generalizing to the Portuguese-speaking context. In a test with 53 vocalizations from a Brazilian individual, the 75.47% accuracy achieved by the best model (SVC) proved to be misleading, as it was concentrated on a single over represented class while failing on the minority classes. These results demonstrate the lack of effective cross-cultural generalization and highlight the critical need for data aligned with the local linguistic context. To address this gap, the mobile application VocalizeAI was developed to enable the creation of a Brazilian dataset, which is essential for advancing assistive communication technologies for individuals with ASD.
2025-11-26T00:00:00ZJustiça em modelos de inteligência artificial: um estudo comparativo de técnicas de mitigação e suas implementações no contexto educacionalhttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/19342025-12-05T06:00:51Z2025-07-11T00:00:00ZJustiça em modelos de inteligência artificial: um estudo comparativo de técnicas de mitigação e suas implementações no contexto educacional
Machine learning models are increasingly being used to influence decision-making
that directly and indirectly impacts people's lives. Specifically, decision-making
related to education can have a profound influence, both in the present and in the
future, on the repercussions of the results generated by these models. In this context,
there has been growing concern about the fairness of models, that is, whether they
have biases that are incompatible with the equity of socially stigmatized groups, such as gender, race, age, income, etc. The objective of this work is to compare the main
libraries that implement fairness metrics and mitigation techniques, and to provide
data that help choose the most appropriate mitigation implementation for similar
cases, in addition to demonstrating the impact of sensitive variables on the output of
the models.
2025-07-11T00:00:00Z