01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
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Resultados da Pesquisa
Item Predição do consumo energético de dispositivos LoRa usando aprendizagem de máquina(2024-12-10) Pimentel, Henrique Pablo Pinheiro dos Santos; Araújo, Danilo Ricardo Barbosa de; http://lattes.cnpq.br/2708354422178489; http://lattes.cnpq.br/0078523045227122A Internet das Coisas (IoT) é um conceito em constante evolução que tem conquistado destaque tanto na comunidade acadêmica quanto na indústria. Dentro dela, o consumo energético é um fator fundamental para determinar o tempo de funcionamento dos dispositivos e a frequência necessária para realizar a manutenção deles. Este artigo investiga a aplicação de algoritmos de aprendizado de máquina para predição do consumo energético de dispositivos IoT-LoRa, permitindo estimar a duração da bateria dos dispositivos e sua autonomia. A metodologia considerou a criação de um conjunto de dados a partir de experimentos com placas de desenvolvimento Event stream processing (ESP32), capturando métricas como tempo de hibernação, tipo de conexão e consumo energético. Técnicas de Inteligência Artificial (IA) são então aplicadas para prever o consumo energético com base nessas variáveis. De acordo com os resultados obtidos, a melhor técnica para prever o consumo energético é a Decision Tree, com um coeficiente de determinação superior a 96%. O estudo contribui para processos decisórios que visam selecionar dispositivos IoT considerando a autonomia projetada para as baterias de tais dispositivos.Item Development of machine learning models for the prediction of dissolved oxygen in aquaculture 4.0(2021-02-24) Freitas, Fábio Alves de; Nóbrega, Obionor de Oliveira; Lins, Fernando Antonio Aires; http://lattes.cnpq.br/2475965771605110; http://lattes.cnpq.br/8576087238071129; http://lattes.cnpq.br/5725435192607619The world faces the problem of feeding a growing population, which will reach more than 9 billion people by 2050. Thus, there is a need to develop activities that promote food production, within the dimensions of sustainability (social, technicaleconomic, and environmental). In this context, IoT systems focused on aquaculture 4.0 stand out, which allows the cultivation of high productions per unit of volume, with low environmental impact. However, these systems need to be extremely controlled, requiring sensors to perform realtime readings of water metrics, with emphasis on the dissolved oxygen (DO) sensor, which plays an essential role in determining the quality and quantity of available habitat for the organisms present in the system. Even with this importance, this sensor is often not used, due to its high associated cost. As an alternative solution to this problem, machine learning models have been proposed to predict DO, using temperature and pH readings as inputs. Experiments were carried out comparing different data scaling techniques and the prediction performance in different seasons of the year and regression metrics were used to evaluate the implemented models. The results showed that the proposed LSTM model is capable of making OD predictions and being applied in IoT and aquaculture 4.0 systems.Item Estudo de técnicas preditivas para o auxílio a gestores na pandemia de COVID-19(2022-05-27) França, Eliana Maria Silva de; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2782168150783950The main objective of this work is to propose an alternative to exploratory statistical surveys, to support the decision-making of managers, during the confrontation of the COVID-19 pandemic. To this end, a methodology was created, using machine learning to provide a new tool for predicting deaths caused by COVID-19, from open data that contain sanitary, demographic and population characteristics. In such a way that, from this study, an artificial intelligence model can be developed capable of helping to face the COVID-19 pandemic. Of the 3 artificial intelligence algorithms used (Decision Tree, Support Vector Machine and Multilayer Perceptron), the model based on Support Vector Machine showed the best performance, because it has the lowest Mean Absolute Error, a metric used to measure the quality of regression-based artificial intelligence models.