https://repositorio.ifgoiano.edu.br/handle/prefix/250 Mon, 11 May 2026 13:09:17 GMT 2026-05-11T13:09:17Z https://repositorio.ifgoiano.edu.br/handle/prefix/6557 Título: INSERÇÃO AUTOMATIZADA DE SENSORES POR MEIO DE SIMULAÇÃO EM UMA ARQUITETURA MODEL-VIEW-CONTROLLER (MVC) Autor(es): Jesus Júnior, Ivan Alves de Primeiro Orientador: Bailão, Adriano Soares de Oliveira Abstract: Due to the growing advances in the Internet of Things (IoT), this work presents SimuSensor, a backend system based on the Model-View-Controller (MVC) architecture, developed to simulate the automated insertion of temperature sensor data in an agricultural greenhouse structure. Every second, the system generates and transmits, via HTTP request, random temperature values to the controller, which processes and stores them in an XML file. The backend provides a modular and scalable foundation for the development of monitoring and data logging solutions in Internet of Things (IoT) applications. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso Wed, 11 Mar 2026 00:00:00 GMT https://repositorio.ifgoiano.edu.br/handle/prefix/6557 2026-03-11T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6531 Título: SISTEMA PARA GESTÃO DE VEÍCULOS APREENDIDOS Autor(es): Oliveira Netto, Silvestre Pereira de Primeiro Orientador: Bailão, Adriano Soares de Oliveira Primeiro Membro da Banca: Sardi, Andrea Barboza Proto Segundo Membro da Banca: Damke, Caíke da Rocha Abstract: The management of seized vehicles by the Civil Police is a strategic administrative activity that demands precision, traceability, and security in the handling of information. However, in many police units, this control is still performed manually through scattered spreadsheets and physical records, which leads to inconsistencies, rework, and difficulties in quickly accessing data. This work presents the development of a web-based system for managing seized vehicles, implemented at the 8th Regional Police Station (8th DRP) of the Civil Police of the State of Goiás (PCGO), Brazil. The system was built using the Python programming language and the Django framework, following the Model-View-Template (MVT) architectural pattern, with a PostgreSQL database, a responsive interface built with Bootstrap 5, and PDF document generation through the ReportLab library. The methodology adopted was applied research with a qualitative approach, collecting requirements through informal interviews conducted directly with the police unit's staff. The system encompasses the complete registration of vehicles, owners, and police procedures, as well as full lifecycle tracking of each seized asset — from the initial record through return to the owner, forwarding to auction, or disposal. Additional features include image management with automatic compression, filtered report generation, a real-time dashboard with key indicators, role-based access control, and an audit trail for all operations. The evaluation conducted with the staff of the 8th DRP demonstrated that the tool significantly reduced the time required to locate records, eliminated inconsistencies caused by manual controls, and was considered intuitive and easy to use by the officers themselves. The results indicate that the solution met its proposed objectives, contributing to the modernization of seized asset management and serving as a model for implementation in other Civil Police units. The software was formally registered with the Brazilian National Institute of Industrial Property (INPI) in two modules — backend (process no. BR512025006985-5) and frontend (process no. BR512025006723-2) —, with the IF Goiano as the holder, granting 50-year intellectual property protection to the developed product. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso Tue, 24 Mar 2026 00:00:00 GMT https://repositorio.ifgoiano.edu.br/handle/prefix/6531 2026-03-24T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6508 Título: ANÁLISE DE VULNERABILIDADES DO OWASP TOP 10 RELACIONADAS AO CWE PARA CODIFICAÇÃO SEGURA DE APLICAÇÕES WEB. Autor(es): Morais, Adriano Costa Araujo Primeiro Orientador: Carvalho, Ana Maria Martins Primeiro Membro da Banca: Carvalho, Ana Maria Martins Segundo Membro da Banca: Oliveira, Antônio Neco de Terceiro Membro da Banca: Alves, José Pereira Abstract: The consolidation of web applications and the complexity of modern systems have made information security a critical requirement. Vulnerabilities in source code are one of the main entry points for cyberattacks, since applications are frequently launched with software flaws that result in exploitable security vulnerabilities. The persistence of known flaws highlights a gap between security knowledge and development practice. Therefore, this work aims to demonstrate how the main software flaws impact the security of web applications, establishing a relationship with the first four vulnerability categories of the OWASP Top 10. To this end, static (SAST) and dynamic (DAST) analysis tools were used, adopting a hybrid approach to improve results, reduce false positives, and validate the identified vulnerabilities present in the environment provided by OWASP, Juice Shop. Finally, this work aims to raise awareness about how these flaws can be exploited by malicious users and to reinforce the importance of secure coding as an integral part of the daily lives of developers or those involved in the software lifecycle. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso Wed, 01 Apr 2026 00:00:00 GMT https://repositorio.ifgoiano.edu.br/handle/prefix/6508 2026-04-01T00:00:00Z https://repositorio.ifgoiano.edu.br/handle/prefix/6486 Título: ANÁLISE DE PADRÕES PRODUTIVOS E CLIMÁTICOS DA SOJA EM GOIÁS POR MEIO DE TÉCNICAS DE CLUSTERIZAÇÃO Autor(es): Silva, Lucas Daniel da Primeiro Orientador: Alves, Jesmmer da Silveira Primeiro Membro da Banca: Silva, Leila Roling Scariot da Segundo Membro da Banca: Oliveira, Antônio Neco de Abstract: Soybeans are consolidating their position as the main commodity in Goiás, however, regional analysis of the sector lacks tools that transcend state averages and reveal local patterns. This work applied machine learning techniques to identify productive and climatic clusters in the state, processing historical productivity and meteorological data using the Python language. The methodology included the pre-processing of seasonal data and the application of the K-Means clustering algorithm. The model, configured with five groups (k=5), was validated by metrics such as Silhouette and Caliński-Harabasz Index, showing consistent segmentation. The results highlighted a group with high technological performance, maintaining productivities above 3,470 kg/ha even under restrictive rainfall regimes, suggesting the intensive use of irrigation. The results indicate that the application of clustering algorithms is effective for extracting knowledge from complex databases, allowing automatic distinction between zones of natural suitability and zones of high technological management. Editor: Instituto Federal Goiano Tipo: Trabalho de Conclusão de Curso Wed, 01 Apr 2026 00:00:00 GMT https://repositorio.ifgoiano.edu.br/handle/prefix/6486 2026-04-01T00:00:00Z