TCC Sistemas de Informação

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Navegando TCC Sistemas de Informação por Tipo de Acesso "Attribution 3.0 Brazil"

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    Letramundo: um jogo sério para alfabetização de crianças com Transtorno do Espectro Autista (TEA) em ambiente escolar
    (Instituto Federal de Educação Ciência e Tecnologia de Alagoas, 2025-07-03) Silva, Arthur Henrique Souza da; Cunha, Mônica Ximenes Carneiro da; http://lattes.cnpq.br/1775024859845111; Kamei, Fernando Kenji; http://lattes.cnpq.br/5033020411757389; Calado, Ivo Augusto Andrade Rocha; http://lattes.cnpq.br/5748220882915553
    This work presents the development, implementation, and testing of a Serious Games (SG) web application for literacy aimed at children with Autism Spectrum Disorder (ASD) to assist special education and promote social inclusion in the educational environment. The innovative aspects of this study lie in the use of hyperfocus as a strategic tool to enhance student learning and engagement, the use of TEACCH principles, and the use of the phonics method as a literacy model. The tool combines playful elements, specific educational goals, and reading teaching methods based on the latest studies on verbal language, especially in the areas of psycholinguistics and neuroscience. The prototype of this application was tested by special education professionals, and the test results indicated the need for a redistribution of levels and content. The modifications were made during the implementation phase, and the platform was tested with special education professionals from the early years of elementary school and with children with ASD aged 6 to 12, in the first grades of elementary school. The results showed high student interest in the platform, as well as strong adherence to the most current teaching methods for children with ASD.
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    Predição de crimes violentos contra o patrimônio em Maceió e Arapiraca usando o ConvLSTM
    (2026-05-02) Ferreira, Leonardo Victor Fernandes; Medeiros, Leonardo Melo de; http://lattes.cnpq.br/1080593968001453; Cruz, Jailton Cardoso da; http://lattes.cnpq.br/9366016044068759; Costa, Breno Jacinto Duarte da; http://lattes.cnpq.br/7418697922506495
    Spatiotemporal forecasting of violent property crimes is a relevant problem for the planning of preventive actions and for the allocation of public security resources. This work presents a methodological replication, in the context of the cities of Maceió and Arapiraca, of the fine-grained crime forecasting pipeline proposed by Albors Zumel, Tizzoni, and Campedelli (2025), based on a ConvLSTM architecture. From georeferenced records of violent property crimes provided by the Military Police of Alagoas, covering the period from 2012 to 2022, the work organizes the data into regular grids with cells of approximately 0.2 km2, aggregates occurrences in 12-hour windows, binarizes the cell-level signal, and trains a ConvLSTM with explicit imbalance treatment via weighted BCEWithLogitsLoss. Evaluation is performed by sweeping the classification threshold, reporting precision, recall, and F1 both in the traditional variant (cell-by-cell) and with spatial tolerance (Chebyshev neighborhood ≤ 1). The results show that, in both cities, the model achieves performance in the spatial variant significantly higher than in the traditional variant (spatial F1 of approximately 0.24 in Maceió and 0.21 in Arapiraca, against values below 0.05 in the traditional variant), evidencing that the network learns to correctly locate risk neighborhoods even when missing the exact cell. The work also discusses the main limitations of the approach, particularly the extreme sparsity of the data, and proposes directions for future developments, including the incorporation of sociodemographic and human mobility channels and comparison against baseline models.