Gilson Adamczuk Oliveira

João Henrique Pereira Machado, Gilson Adamczuk Oliveira, Érick Oliveira Rodrigues

In this work, we utilize image segmentation to visually identify blood vessels in retinal examination images. This process is typically carried out manually. However, we can employ heuristic methods and machine learning to automate or at least expedite the process. In this context, we propose a cross-platform, open-source, and responsive software that allows users to manually segment a retinal image. The purpose is to use the user-segmented image to retrain machine learning algorithms, thereby enhancing future automated segmentation results. Moreover, the software also incorporates and applies certain image filters established in the literature to improve vessel visualization. We propose the first solution of this kind in the literature. This is the inaugural integrated software that embodies the aforementioned attributes: open-source, responsive, and cross-platform. It offers a comprehensive solution encompassing manual vessel segmentation, as well as the automated execution of classification algorithms to refine predictive models.

Eduardo Luiz Alba, Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk et al.

Educational institutions are essential for economic and social development. Budget cuts in Brazil in recent years have made it difficult to carry out their activities and projects. In the case of expenses with water and electricity, unexpected situations can occur, such as leaks and equipment failures, which make their management challenging. This study proposes a comparison between two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), for water and electricity consumption forecasting at the Federal Institute of Paraná-Campus Palmas, with a 12-month forecasting horizon, as well as evaluating the influence of the application of climatic variables as exogenous features. The data were collected over the past five years, combining details pertaining to invoices with exogenous and endogenous variables. The two models had their hyperpa-rameters optimized using the Genetic Algorithm (GA) to select the individuals with the best fitness to perform the forecasting with and without climatic variables. The absolute percentage errors and root mean squared error were used as performance measures to evaluate the forecasting accuracy. The results suggest that in forecasting water and electricity consumption over a 12-step horizon, the Random Forest model exhibited the most superior performance. The integration of climatic variables often led to diminished forecasting accuracy, resulting in higher errors. Both models still had certain difficulties in predicting water consumption, indicating that new studies with different models or variables are welcome.