http://repositorio.ufc.br/handle/riufc/23979 Wed, 08 Apr 2026 09:13:39 GMT 2026-04-08T09:13:39Z http://repositorio.ufc.br/handle/riufc/85473 Título: ENADEXP: uma aplicação web para auxiliar estudantes na preparação para o ENADE Autor(es): Lustoza, Caio Vinícius Magalhães Abstract: The National Student Performance Exam (ENADE) is a standardized assessment composed of multiple-choice and discursive questions that evaluate general knowledge and specific components across a wide range of undergraduate programs in Brazil, including bachelor’s degrees and teaching licenses. Considering the relevance of this assessment and the lack of tools that adequately facilitate students’ preparation, this work proposes the development of a web application, entitled ENADExp, aimed at assisting students in the review process for the ENADE. Among the proposed application’s features are a question bank, advanced exercise filters, the generation of personalized and random lists based on selected preferences, as well as mock exams prepared by professors. In addition to creating mock exams, professors will also be able to manage the question repository that serves as the core of the application, with permissions to create, update, delete, and retrieve items. Therefore, this system aims to provide both professors and students with tools that support the ENADE preparation process. Finally, the application was assessed by both students and professors using the Technology Acceptance Model (TAM), and the results indicated a positive overall perception regarding the platform’s usefulness and ease of use, with minor limitations related to the OCR module reported by professors. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85473 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85296 Título: Assistente Virtual Embarcada Autor(es): Rodrigues, Francisco Caioã de Aragão Abstract: The Embedded Virtual Assistant (EVA) was researched, designed, implemented, and validated with the aim of offering a low-cost home automation solution that prioritizes local data processing and direct control of domestic devices, minimizing cloud dependency and user privacy risks. The system’s architecture follows an edge computing paradigm, dividing processing between an acquisition module (based on the ESP-32-WROOM-32 microcontroller) and a processing module installed on a local computer network. The acquisition module is responsible for audio capture via microphone and sound feedback reproduction, activating only when a button is pressed and communicating via Wi-Fi. Collected data is sent to the edge server via TCP/IP. The processing module, implemented in Python, performs computationally intensive tasks such as audio-to-text transcription using an artificial neural network framework (Vosk) and natural language processing for intention identification and device management. After processing, results are returned to the microcontroller in JSON format for action execution and user feedback. The methodology included a bibliographic study of Artificial Neural Networks for audio, comparative analysis of similar projects, and validation through manual prototype testing. The system is expected to interpret simple voice commands, such as lighting automation or answering questions, with high accuracy. This work demonstrates EVA’s technical and economic feasibility, combining energy efficiency with advanced processing capabilities. The modular architecture and focus on local processing optimize resources, meet demands for privacy and autonomy, and facilitate expansion and customization for various application scenarios, with security protocols ensuring reliable operation. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85296 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85107 Título: Aplicação web de um sistema de informação geográfica (SIG) com OpenLayers: um estudo de caso e aprimoramento na plataforma Sobral em Mapas Autor(es): Vasconcelos, Francisco Antônio Abstract: Publishing geospatial data on public portals demands solutions that balance performance, reliability, and low coupling across system layers. Focusing on the Municipality of Sobral, this study evaluates and enhances the implementation of the OpenLayers library within “Sobral em Mapas,” a municipal WebGIS whose front end is implemented with this library and interacts with a Laravel back end integrated with GeoServer for OGC services and PostGIS data. The refined architecture comprises thematic layers, vector drawing and editing tools, and a custom map export module with server-side request intermediation. In an applied evaluation, the system delivers smooth navigation, responsive spatial queries, and cartographic consistency across client and server, evidencing the efficiency of OpenLayers for municipal geospatial data consumption and interaction. The results support the feasibility of an open-source stack for government WebGIS, highlighting flexibility, cost reduction, and standardized integrations. Future work includes scale-aware cartographic printing, expanded spatial analytics, and usability optimization for mobile devices. Tipo: TCC Wed, 01 Jan 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85107 2025-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/84920 Título: Comparativo de desempenho e acurácia de técnicas de Machine Learning aplicadas a ECG em infraestruturas de computação em nuvem Autor(es): Pinto, Akyla de Aquino Abstract: The application of Artificial Intelligence (AI) techniques to support medical diagnosis has emerged as a promising approach for the automated analysis of biomedical signals, particularly in scenarios that require fast and reliable responses. In this context, cloud computing represents a viable solution to overcome the processing and storage limitations of resource-constrained devices, such as those employed in Internet of Things (IoT)-based remote health monitoring systems. This work aims to analyze the quality of cloud computing services in the execution of AI models applied to the processing of electrocardiogram (ECG) signals, considering criteria such as response time, latency, operational cost, and ease of deployment. Clinical data from the Heart Disease Dataset, provided by the UCI Machine Learning Repository and widely used as a benchmark in cardiovascular risk prediction studies, were employed in the experiments. The proposed methodology includes data preprocessing and exploratory analysis, machine learning model development and evaluation, and the implementation of an API to assess performance across different computational infrastructures. The models evaluated were KNN, Neural Networks, Support Vector Machine (SVM), and Logistic Regression. Each model was executed five times using independent data splits, and the results were aggregated using the arithmetic mean. Performance was assessed using precision, recall, F1-score, accuracy, response latency, and operational cost. The results show that the Logistic Regression model achieved the best overall performance, presenting high average accuracy and greater stability across executions. Furthermore, the comparative analysis demonstrated that cloud-based infrastructures outperform local processing in terms of latency and scalability, with AWS standing out for lower response times and Magalu Cloud for its cost-effectiveness. These findings indicate that cloud computing services are a suitable and efficient solution for AI-based diagnostic support systems, especially for time-sensitive healthcare applications. Tipo: TCC Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/84920 2026-01-01T00:00:00Z