https://repositorio.uema.br/jspui/handle/123456789/2201 Mon, 21 Sep 2026 23:36:23 GMT 2026-09-21T23:36:23Z http://repositorio.uema.br:80/jspui/retrieve/3deaa99b-7680-4af9-890d-fe5119e240f2/ENGECOMP.jpg https://repositorio.uema.br/jspui/handle/123456789/2201 https://repositorio.uema.br/jspui/handle/123456789/6250 Título: Automatização do reconhecimento de células germinativas em imagens histológicas de ovários de peixes: estudo de caso de peixes encontrados no estado do Maranhão Abstact: The state of Maranhão has significant fishing activity, making it essential to monitor the reproductive biology of fish for sustainable management. This work presents an automated approach for recognizing germ cells in histological images of fish ovaries, using digital image processing and supervised learning. The objective is to develop a tool that aids in the efficient analysis of gonads, contributing to knowledge in the field and better management of fishing resources. The methodology uses the Canny edge detection algorithm to segment cells and supervised learning techniques to classify them, overcoming the limitations of traditional manual methods. The results show gains in batch processing speed (50 images/minute) and precision (56% accuracy) with STERapp. The solution aims to increase the precision and reproducibility of analyses, positively impacting fishing sustainability in Maranhão and similar regions Mon, 17 Feb 2025 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6250 2025-02-17T00:00:00Z https://repositorio.uema.br/jspui/handle/123456789/6249 Título: Sistema Web para delegacias com geração de imagens fotorrealistas baseadas em Inteligência Artificial a partir de relatos textuais Abstact: This work presents the development of a web application designed for police station management, focusing on integrating artificial intelligence (AI) to generate photorealistic images from textual descriptions to enhance criminal investigations. Built on a client-server architecture, the system employs Next.js for the front-end, Flask for the back-end, and the Stable Diffusion model for image generation. The methodology combined applied, exploratory, and experimentais approaches with incremental prototyping to ensure scalability and usability. Requirements analysis identified gaps in virtual police station systems, such as the lack of advanced visual tools, guiding the development of features like creation, management, and finalization of incident reports, secure JWT authentication, and AI integration. System modeling utilized UML, including use case, class, sequence, activity, and deployment diagrams. Integration and functional tests, conducted with tools like Postman and Cypress, validated the application's efficiency and robustness. The results demonstrate that the system meets its objectives, providing an innovative solution that streamlines suspect identification and improves incident reporting efficiency, with potential for future integration with biometric databases and further AI model adaptation Thu, 01 Jan 2026 00:00:00 GMT https://repositorio.uema.br/jspui/handle/123456789/6249 2026-01-01T00:00:00Z