Uma arquitetura de processamento distribuído para análise de propagação de doenças na PDSA-RS

• Ano de defesa: 2024
• Autor(a) principal: Ebling, Denílson dos Santos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/0013000018zvz
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Ciência da Computação; UFSM; Programa de Pós-Graduação em Ciência da Computação; Centro de Tecnologia
• Palavras-chave em Português: Sistemas Inteligentes; Sistemas de suporte à decisão; Controle de doenças; Controle de surto; Intelligent systems; Decision-support systems; Diseases control; Outreabk control; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/34137

In today’s world, machine learning systems have permeated various domains, from object detection to disease spread prediction, playing pivotal roles in decision-making processes. Amid the COVID-19 pandemic, the utilization of machine learning methods like artificial neural networks and LSTM networks has significantly enhanced forecasting accuracy for disease outbreaks. This paper delves into the development of an intelligent system proposed by (CARDENAS; LOPES; MACHADO, 2022), focusing on simulating disease spread in animals and facilitating control measures through a stochastic model. Leveraging Docker containers for deployment, this system offers valuable insights for public health interventions, enabling swift responses to disease outbreaks. The primary objective of this work is to provide veterinarians with a user-friendly tool that integrates a stochastic model through an intuitive interface, aiding in critical decision-making processes in a scalable manner. The paper outlines the background of the stochastic model, introduces the proposed system for integrating and addressing the identified problem, presents an evaluation scenario to validate the system’s efficacy, and concludes with insights drawn from this research endeavor.