https://repositorio.ifpe.edu.br/xmlui/handle/123456789/489 Thu, 09 Apr 2026 02:07:23 GMT 2026-04-09T02:07:23Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2049 Guia 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. Wed, 01 Feb 2023 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2049 2023-02-01T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1974 Classificação 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. Wed, 26 Nov 2025 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1974 2025-11-26T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1934 Justiça 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. Fri, 11 Jul 2025 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1934 2025-07-11T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1933 Armazenamento escalável de dados com Object Storage e tolerância a falhas: um estudo de caso em ambiente on-premise This article explores scalable data storage using Object Storage, with a specific focus on fault tolerance. The theoretical analysis covers storage paradigms, emphasizing the significance of Object Storage for efficient scalability. A local case study illustrates practical implementation, showcasing the system's resilience in the case of faults. Results highlight the viability and benefits of Object Storage in environments requiring reliability and flexibility, contributing to both practical and theoretical understanding of data storage. Tue, 11 Feb 2025 00:00:00 GMT https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1933 2025-02-11T00:00:00Z