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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Helbert Eduardo Espitia
Universidad Distrital Francisco José de Caldas
Computer Science & IT Electrical & Electronics Engineering

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Prediction of atmospheric pollution using neural networks model of fine particles in the town of Kennedy in Bogota Juan Camilo Pedraza; Oswaldo Alberto Romero; Helbert Eduardo Espitia
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6574-6581

Abstract

This work shows an application based on neural networks to determine the prediction of air pollution, especially particulate material of 2.5 micrometers length. This application is considered of great importance due to the impact on human health and high impact due to the agglomeration of people in cities. The implementation is performed using data captured from several devices that can be installed in specific locations for a particular geographical environment, especially in the locality of Kennedy in Bogotá. The model obtained can be used for the design of public policies that control air quality.
Detection of uveal melanoma using fuzzy and neural networks classifiers Daniel F. Santos; Helbert Eduardo Espitia
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14228

Abstract

The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.
Co-Authors Daniel F. Santos Juan Camilo Pedraza Oswaldo Alberto Romero