http://repositorio.ifpe.edu.br:80/xmlui The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. 2026-08-16T07:57:22Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/1280 Guia de Impressão synesthesia vision : iluminando vidas 2024-04-11T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2141 Desenvolvimento de Aplicativo Móvel para Solicitação de Atendimento de Urgência Pre-hospital care is essential to reduce morbidity and mortality in urgent and emergency situations. However, agencies responsible for this service face operational challenges, such as prank calls and failures in the initial triage process. This study aims to develop and evaluate UrgenciaSeguraApp, a mobile application designed for the public to request emergency services, and Urgencia-Segura-Web, a web system that enables Municipal Guard and Civil Defense professionals to view and manage Instituto Federal de Pernambuco. Campus Palmares. Curso de Análise e Desenvolvimento de Sistemas. 28 de julho de 2025. incidents integrated with Firebase Realtime Database. The methodological approach combined quantitative research, through a questionnaire applied to Civil Defense agents, and qualitative research, through indirect interviews conducted by the advisor with Municipal Guard and Civil Defense agents, to gather perceptions regarding the solution’s feasibility and effectiveness. In the results, the responding agent rated the application and the portal with the highest score for clarity and practicality, highlighting ease of use, proper functioning of the features, and absence of difficulties in the tested functionalities. The interviews reinforced the suitability of the interface and the usefulness of the system in triage. It is concluded that the proposed solution has effective potential to optimize triage, reduce response time, and minimize waste of public resources, with the possibility of future expansion through integration with other agencies, such as SAMU. 2025-07-28T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2140 Iomt: inovação e aplicação na saúde – uma revisão sistemática da literatura The Internet of Medical Things (IoMT) is a healthcare technology that enables the integration of devices, sensors, and intelligent systems, allowing real-time monitoring, data collection, and analysis. This technology has brought significant results to healthcare services by streamlining processes, improving patient management, and enhancing various medical procedures. The main objective of this study is to conduct systematic literature reviews in order to identify the applications, innovations, challenges, and trends of IoMT in the healthcare context. The methodology employed is based on the PRISMA protocol, with research carried out in reliable scientific databases such as IEEE Xplore, PubMed, Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The selection criteria included peer-reviewed scientific articles addressing the use of IoMT. 2025-07-31T00:00:00Z https://repositorio.ifpe.edu.br/xmlui/handle/123456789/2133 Aprendizado de máquina aplicado à detecção de ataques DDOS com abordagens de inteligência artificial explicável (XAI) The accelerated growth of cyberattacks in recent years has posed a significant threat to the security of computer systems worldwide, especially due to their increasing complexity. Among these threats, Distributed Denial of Service (DDoS) attacks stand out for aiming to make services unavailable by overloading systems with malicious traffic. Given the limitations of traditional signature-based detection methods, this study aims to analyze the application of machine learning techniques for detecting DDoS attacks using Instituto Federal de Pernambuco. Campus Palmares. Curso de Análise e Desenvolvimento de Sistemas. 30 de julho de 2025. a public dataset, as well as to employ explainable artificial intelligence approaches to make the results more transparent and interpretable. The methodology involved the use of the DDoS SDN dataset, with preprocessing steps that included removing null values and irrelevant columns, followed by data normalization using Standard Scaler and One Hot Encoder. The models applied were K-Nearest Neighbors (KNN), Decision Tree, and Random Forest, and were evaluated using metrics such as accuracy, precision, recall, and F1-Score. The results showed high performance from all models, with Decision Tree and Random Forest achieving 100% in all evaluated metrics, while KNN achieved 97.22% accuracy and a 96.43% F1-Score. These findings highlight the effectiveness of machine learning models in identifying malicious patterns and demonstrate their potential to enhance precision, adaptability, and interpretability in the detection of DDoS attacks. 2025-07-30T00:00:00Z