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Neural Networks for Classification and Regression Applied to Public Lighting Project

Year: 2025

Type: article

Abstract: This paper explores using neural networks to evaluate public lighting projects according to Brazilian Standard 5101/2018. Employing Multilayer Perceptron (MLP) models, the study performs regression to predict illuminance, uniformity, and classification to assess compliance with the standard. The regression model achieved a mean squared error (MSE) of 0.002. The classification model attained an accuracy of 97.26%, with a precision of 97.0%, a recall of 96.5%, and an F1-score of 96.7%. These results underscore the effectiveness of MLP networks in improving compliance evaluation and optimizing public lighting projects.

Source: Journal of Bioengineering, Technologies and Health

DOI: 10.34178/jbth.v8i1.463

Authors: Matheus O.C. Cerqueira, Wild F.S. Santos

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4409201066

FWCI:

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