http://repositorio.ufc.br/handle/riufc/23978 Wed, 10 Jun 2026 01:15:42 GMT 2026-06-10T01:15:42Z http://repositorio.ufc.br/handle/riufc/86624 Título: Uma análise comparativa do desempenho de frameworks back-end para APIs RESTful em ambientes controlados Autor(es): Furtado, Mauro Lúcio Lopes Abstract: The increasing demand for scalable web applications has driven the adoption of different backend frameworks for the development of RESTful APIs. In this context, the choice of framework can directly impact application performance, stability, and resource consumption. This work presents a comparative performance analysis of REST APIs implemented using Node.js with Express.js, Django REST Framework, and ASP.NET Core under controlled load conditions. Three functionally equivalent REST APIs were developed based on a room reservation system, ensuring identical business rules, data structures, and CRUD operations. The applications were subjected to stress, spike, and endurance tests using Apache JMeter to simulate concurrent users, while Prometheus and Grafana were employed to monitor CPU and memory usage. All experiments were conducted in a containerized environment using Docker, ensuring reproducibility and standardized execution conditions. The results revealed significant performance differences among the evaluated frameworks. ASP.NET Core consistently achieved the best overall performance across all test scenarios, exhibiting higher throughput and lower response times. In the stress test, ASP.NET Core processed up to 280.4% more requests per second than Node.js and 1137.7% more than Django REST Framework, while reducing the average response time by up to 94.3%. Node.js demonstrated intermediate performance, standing out for its efficient resource utilization, whereas Django REST Framework showed considerable limitations under high concurrency, with higher resource consumption and reduced stability.These findings indicate that ASP.NET Core is more suitable for high-concurrency RESTful applications, while Node.js represents a balanced alternative in environments with resource constraints. Django REST Framework is better suited for scenarios with lower concurrency requirements. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86624 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86038 Título: Métodos de aprendizado de máquina aplicados ao monitoramento ambiental Autor(es): Aragão Neto, Pedro Sousa de Abstract: In recent years, the world has been undergoing enormous environmental transformations and where there is a constant occurrence of major technological innovations, the need arises to understand how technology can be beneficial to the environment. Neural Networks have emerged as powerful tools for classifying complex data, offering solutions for identifying patterns and anomalies. This article explores the application of three neural networks – Artificial Neural Networks (ANN), Multilayer Perceptron (MLP) and Radial Basis Function Networks (RBF) – in the context of urban environmental data classification. We present a comparative analysis of their performance using real datasets, highlighting their strengths and limitations in accurately categorizing environmental parameters. Our results demonstrate the effectiveness of these neural network models in contributing to environmental monitoring, providing valuable insights. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86038 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86008 Título: Análise comparativa de ferramentas de CI/CD: um estudo de caso da migração entre GitHub Actions e GitLab CI no sistema Adote Fácil Autor(es): Gomes, Márcio Bruno Loiola Abstract: The adoption of DevOps practices and the implementation of Continuous Integration and Continuous Delivery (CI/CD) pipelines have become fundamental pillars for ensuring agility and reliability in modern software development. However, the diversity of tools available on the market imposes significant interoperability challenges. Choosing between leading solutions, such as GitHub Actions and GitLab CI/CD, involves technical trade-offs that directly impact team productivity. This work aims to conduct a practical comparative analysis between these tools, documenting the pipeline migration process of the “Adote Fácil” application, a microservices-based pet adoption platform. To this end, a complete portability of the automation flow was performed, identifying critical challenges such as syntactic differences (workflow-centric versus stage-centric approach) and the complexity of Docker-in-Docker (DinD) orchestration. Quantitative results demonstrated that migrating to GitLab CI resulted in a 2.6-fold increase in configuration code verbosity and required 7 correction iterations for complete stabilization. Qualitatively, it is evidenced that while GitHub Actions prioritizes ease of use through abstractions, GitLab CI offers greater granular control over the execution environment, albeit requiring a steeper learning curve. The study contributes a technical guide for developers facing transition challenges between automation tools. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86008 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86007 Título: BuriedBrains: um ambiente multiagente inspirado em roguelike para avaliação de memória e tomada de decisão Autor(es): Silva, Ismael Soares da Abstract: This work presents the development and validation of BuriedBrains, a multi-agent simulation environment based on procedural generation and partial observability, structured through hybrid dynamic graphs. The primary objective is to measure the performance of Reinforcement Learning (RL) agents in solving long-term planning and generalization problems under conditions of permadeath and resource constraints. The environment’s topology, inspired by roguelike mechanics — specifically the title Buriedbornes (Nussygame, 2016) —, utilizes Directed Acyclic Graphs (DAGs) for Progression Zones and cyclic graphs for Sanctuaries. These structures demand inventory and skill management, as state transitions result in persistent changes to variables such as Karma and the agent’s survival probability. The methodology consisted of a comparative study to isolate the impact of temporal recurrence, contrasting a reactive architecture (PPO) with a recurrent one (LSTM). Both models were refined through Hyperparameter Optimization (HPO) and integrated with Self-Attention mechanisms. Data suggests that the characteristics of BuriedBrains restrict the performance of reactive models under the tested conditions, as reflected by PPO’s stabilization at a 65% Explained Variance (EV) plateau, which remained constant despite increases in network complexity or sampling horizon. The recurrent agent demonstrated 24% superior performance in HPO tests and reached 80% explained variance. Results suggest the architecture was capable of sustaining policies focused on attribute specialization and exhibiting multi-agent interaction patterns. Visualization-based analysis identified convergence toward reciprocal non-aggression states, where memory enabled the optimization of episode longevity over zero-sum interactions. BuriedBrains thus serves as a benchmark for evaluating long-term temporal dependency resolution and decision-making in autonomous systems. Tipo: TCC Wed, 01 Jan 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86007 2025-01-01T00:00:00Z