https://repositorio.ifpe.edu.br/xmlui/handle/123456789/491 Thu, 03 Sep 2026 02:58:13 GMT 2026-09-03T02:58:13Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2304 Criação de advergame para divulgação institucional: um jogo para divulgação do IFPE Campus Paulista. The search for innovative advertising strategies has led institutions to explore new ways of communicating with their audiences. While traditional methods, such as social media, television commercials, and digital ads, remain in use, digital games are emerging as tools capable of creating interactive and captivating experiences. In this context, the advergame — a game specifically developed to promote brands, products, or institutions — stands out for its ability to engage audiences in a playful and immersive way. This article presents the creation process of an advergame as an institutional advertising tool by the Instituto Federal de Pernambuco (IFPE), Paulista campus. The game was designed to showcase the internal routine to new students and the community, incorporating visual and narrative elements representative of the institution. Through an interactive approach, the advergame aims to expand the reach of institutional communication, strengthening its image and connecting with users in a more playful way. Tue, 15 Jul 2025 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2304 2025-07-15T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2295 Ciência de dados aplicada ao mercado de trabalho de Pernambuco: processamento e visualização dos microdados da PNAD Contínua. This study describes the development of a Data Science-based solution applied to the acquisition, processing, integration, analysis, and visualization of microdata from the Continuous National Household Sample Survey (PNAD Contínua), conducted by the Brazilian Institute of Geography and Statistics (IBGE). The study focuses on exploring the characteristics and structural inequalities of the labor market in the state of Pernambuco, Brazil, from 2019 to 2025. The methodological approach involved the automated collection of microdata from the IBGE portal through a pipeline implemented in Python, as well as the local extraction and management of compressed files and the processing of fixed-width data into structured datasets using the pandas and numpy libraries. The results followed both an analytical and a technological approach. From the analytical perspective, exploratory data analysis revealed sociodemographic and income inequalities within the state. From the technological perspective, an interactive web application was developed using the Streamlit framework and the Plotly library, enabling the dynamic exploration of labor market indicators and access to socioeconomic information about the state. The study demonstrates the effectiveness of technology as an agile social diagnostic tool to support the investigation of the characteristics and inequalities present in the labor market. Wed, 19 Aug 2026 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2295 2026-08-19T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2293 Análise comparativa de modelos de aprendizado de máquina para classificação de corridas rentáveis em plataformas de transporte. The growth of digital urban transportation platforms has increased the amount of data generated about trips, fares, distances, schedules and operational characteristics. Although these data are widely available, drivers and other actors involved in platform-based transportation still face difficulties in converting information into effective decision support, especially when the purpose is to evaluate whether a ride tends to be profitable or not. In this context, this study aims to analyze and compare machine learning (ML) models for classifying profitable and non-profitable rides using two distinct datasets: an Uber/NCR-type ride-booking dataset and a public New York City Yellow Taxi dataset. The methodology was structured according to the CRISP-DM process, covering problem understanding, data understanding, data preparation, modeling and evaluation. Derived variables for estimated cost, estimated profit and profitability were created, together with a methodological discussion of data leakage risks, especially regarding financial variables. Sensitivity analyses were also included to assess the impact of variables directly related to the profitability calculation. The evaluated models included Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, baseline models and a simple TensorFlow/Keras neural network as a complementary experiment. For the Uber/NCR dataset, Gradient Boosting achieved an approximate F1-score of 0.99, a result strongly associated with the presence of booking_value and ride_distance. For the Yellow Taxi NYC dataset, the complementary neural network and Random Forest achieved F1-scores close to 0.93, decreasing to 0.7735 when duration_min and avg_speed_mph were removed. The results indicate that ML models can support the identification of patterns associated with ride profitability, provided that dataset limitations, the estimated nature of the target variable and variable availability at decision time are explicitly considered. Thu, 09 Jul 2026 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2293 2026-07-09T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2292 Inteligência orçamentária e permanência estudantil: um estudo de caso sob a ótica discente no IFPE Campus Paulista (2024-2025). This mixed-method case study (quantitative and qualitative) aimed to analyze how the management and distribution of student support measures affect the retention of students at IFPE Campus Paulista in the years 2024 and 2025. The research was conducted with 33 higher education students through an electronic questionnaire structured into three sections (sociodemographic data, access and use of support, and qualitative perceptions). The quantitative data were analyzed using descriptive statistics and the qualitative data through thematic content analysis. The results showed that 54.5% of respondents received support, while 24.2% applied but did not receive it. Among those who received support, it had a decisive and transformative impact on retention, especially to cover transportation and food costs. Those who did not receive support reported concrete difficulties in maintaining attendance and academic performance. The main weakness pointed out was the lack of transparency in the selection criteria and the lack of individualized feedback to applicants who were not selected. It is concluded that budget management directly affects student retention, but it has gaps in transparency, speed, and value adequacy, suggesting the adoption of budget intelligence practices to optimize public resources. Fri, 03 Jul 2026 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2292 2026-07-03T00:00:00Z