https://repositorio.ifpe.edu.br/xmlui/handle/123456789/495 2026-07-29T15:44:28Z 2026-07-29T15:44:28Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2240 2026-07-10T06:02:51Z 2026-05-28T00:00:00Z

O impacto das metodologias cascata e Scrum na criação de aplicações utilizando Outsystems The advancement of low-code platforms, such as OutSystems, has transformed software development by reducing technical complexity and accelerating application delivery. In this context, the choice of development methodology plays a decisive role in project productivity and quality. This study provides a comparative analysis of the impacts of the Waterfall and Scrum methodologies in the creation of low-code applications, based on a literature review and market experiences observed between 2015 and 2026. The results indicate that the Waterfall model is more suitable for projects with a closed scope, regulatory environments, and contexts that require rigorous documentation, benefiting predictability and traceability. In contrast, Scrum enhances the low-code paradigm by fostering iterative cycles, incremental deliveries, and greater adaptability to change, thereby reducing rework and increasing customer satisfaction. It is concluded that the choice of methodology in the context of low-code and the OutSystems platform should consider the degree of requirement stability and organizational maturity, with Scrum being more compatible with dynamic scenarios and better supporting mature teams, while Waterfall proves more efficient in controlled and normative environments, facilitating stakeholder separation and serving as a viable option for less experienced teams.

2026-05-28T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2212 2026-07-10T00:47:50Z 2026-03-09T00:00:00Z

Agente Inteligente baseado em LLM para orientação clinica em profilaxia pós- exposição a materiais biológicos Exposure to biological materials poses significant risks for HIV and other sexually transmitted infections, making the timely initiation and proper management of post-exposure prophylaxis (PEP) essential. Effective PEP, however, depends on accurate risk assessment, clear protocol guidance, and continuous follow-up—processes often limited by healthcare constraints and patient misunderstanding. These challenges may lead to delayed initiation or poor adherence to the 28-day regimen. To address this gap, this study presents a Large Language Model-based conversational Agent integrated with a Retrieval- Augmented Generation (RAG) framework to support patients undergoing PEP in Brazil. The Agent was assessed using a five-point Likert-scale questionnaire applied to eighteen medical specialists, who evaluated clinical accuracy, alignment with official protocols, empathy, and conversational coherence. The Agent’s responses were highly rated, with over 80% strong agreement across all evaluated dimensions. In the overall assessment, 77.8% of evaluators rated the system as high quality, while 16.7% considered it good with minor limitations, supporting its feasibility and potential applicability as a clinical guidance-support tool.

2026-03-09T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2201 2026-05-29T06:01:15Z 2026-03-17T00:00:00Z

Utilização de um LLM local com RAG para auxiliar a fase de extração de dados de uma revisão sistemática da literatura Systematic Literature Review (SLR) is a research methodology that follows specific protocols and is widely used in academic work to summarize and synthesize evidence on a given topic of study, with its application growing in the field of Software Engineering. However, conducting an SLR is laborious, as it requires significant time and human resources. With recent advances in Artificial Intelligence, tools such as Large Language Models (LLMs), Generative Pre-trained Transformer (GPT), for example, and Retrieval-Augmented Generation (RAG) offer opportunities to reduce the manual effort in conducting these reviews. This study aims to investigate whether the use of a local LLM augmented with RAG can assist the data extraction phase of a systematic review. To this end, the Llama 3.2 model was used to extract data from a systematic mapping study containing 22 SLR articles whose contents were provided to the LLM using the RAG technique, and the responses generated by the model were compared with those already extracted by the authors of the mapping. The local LLM augmented with RAG achieved approximately 42% correct answers, demonstrating that it offers limited assistance to the researcher in relation to the data extraction phase of the RSL; however, most of the correct answers concerned bibliographic data from the articles, suggesting that the model can be used to obtain this data more easily.

2026-03-17T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2056 2026-03-17T06:01:36Z 2025-09-16T00:00:00Z

ANS2JSON: uma API baseada em IA, para extração de Informações da avaliação neurológica de pacientes com hanseníase The Simplified Neurological Assessment (SNA) is a mandatory and essential tool for monitoring patients with leprosy in Brazil. However, its physical format and manual completion represent a significant obstacle, as they confine crucial clinical data to paper records and hinder longitudinal analyses, large-scale research, and evidence-based health policymaking. This study introduces ANS2JSON, an innovative application that overcomes this barrier through a hybrid Artificial Intelligence architecture for the complete and automatic digitization of SNA forms. The proposed solution addresses the challenge of interpreting a complex form that combines textual data with visual diagrams by employing a two-stage pipeline: (1) a computer vision model (YOLOv8 + Random Forest) to detect and anatomically map sensitivity assessment points, and (2) an Intelligent Document Processing model to extract textual fields. The approach demonstrated outstanding effectiveness, achieving 97.5% precision in visual data interpretation and over 95% accuracy in extracting key clinical indicators. ANS2JSON, publicly available via the web, provides a robust tool for converting a vast repository of unstructured data into ready-to-use digital information, unlocking the potential of years of clinical records to advance both leprosy treatment and research. The application is accessible at: https://ans2json.dotlabbrazil.com.br .

2025-09-16T00:00:00Z