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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Communications in Science and Technology
Garces-Gomez, Yeison Alberto
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Predictive model for acute myocardial infarction in working-age population: a machine learning approach Urbano-Cano, Astrid Lorena; López-Mesa, Diana Jimena; Alvarez-Rosero, Rosa Elvira; Garces-Gomez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp854-860

Abstract

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population.
Methodology for the selection of an optimal optical sensor for a 6U CubeSat constellation Chirán-Alpala, William Efrén; Cárdenas-Espinosa, Lorena Paola; Garces-Gomez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5297-5307

Abstract

The payload, in defining the central objective of a satellite mission, plays a critical role in determining the overall efficiency of the satellite. Consequently, the satellite's effectiveness is strongly influenced by both the payload itself and its configuration. Given the essential importance of choosing an optimal payload and aware of the direct impact it has on the success of a space mission, this article presents a methodology for selecting an optical sensor intended for the 6U CubeSat constellation of the FACSAT-3 mission and future space missions of the Colombian Aerospace Force (FAC). The methodology includes the definition of mission objectives, definition of key parameters, performance modeling, risk and reliability assessment, and other critical aspects that influence mission efficiency and success.
Assessing the performance of random forest regression for estimating canopy height in tropical dry forests Pinza-Jiménez, Christian Javier; Garces-Gomez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6787-6796

Abstract

Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts.
Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp732-740

Abstract

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.
Thematic review of light detection and ranging and photogrammetric technologies in unmanned aerial vehicles: comparison, advantages, and disadvantages Gómez-Moya, Diego Alexander; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3748-3758

Abstract

The development of unmanned aerial vehicles (UAVs) has positively influenced various remote sensing techniques, making them more accessible to different types of users. Among these, photogrammetry and light detection and ranging (LiDAR) stand out for their versatility and possibilities in terrain modeling. This study evaluates the advantages of each one in various fields of knowledge and industry, comparing their possibilities in terms of positional accuracy, completeness, and efficiency in terrain modeling. It is evident that the use of these techniques in different areas generates an opportunity to implement algorithms or processes in mapping and cartography. Regarding their use, the advantage of the LiDAR sensor is identified in inhospitable and inaccessible areas covered by vegetation and with problems in the geodetic network. On the other hand, the versatility of photogrammetry is shown in small areas with exposed soil. The advantage of point cloud fusion or the combination of techniques in the construction industry and in archaeological and architectural surveys is also noted. Finally, emphasis is placed on variables to consider, such as georeferencing techniques, the ground control point (GCP) network, algorithms and software, and flight plan reviews, in order to improve their accuracy.
Optimization of water resource management in crops using satellite technology and artificial intelligence techniques Reyes-Galván, Erick Salvador; Bolivar-Gomez, Fredy Alexander; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5847-5853

Abstract

This study aims to optimize water consumption in avocado crops through the application of satellite technology, machine learning algorithms, and precise climate data from the climate hazards group infrared precipitation with stations (CHIRPS) system. Crop classification in satellite images is conducted using the random forest algorithm, enabling detailed categorization of cultivated areas, urban land, soil, and vegetation, with a specific focus on avocados due to their high-water demand. Given its economic importance and status as one of the most water-intensive crops, avocado cultivation presents a critical challenge for agricultural sustainability. To validate predictive models and ensure classification accuracy, advanced evaluation methodologies such as the confusion matrix and Cohen's kappa index are utilized, quantifying the precision and reliability of the results. This estimation of water consumption under deficit and surplus conditions offers key insights for efficient water management in avocado cultivation. The results generated can enhance agricultural efficiency by aligning water use with the crop’s actual requirements, thereby contributing to the reduction of its water footprint.
Image-based assessment of cattle manure-induced soil erosion in grazing systems Gómez-Guzmán, Cristian; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5360-5370

Abstract

Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
Co-Authors Alvarez-Rosero, Rosa Elvira Alzate, Valentina Cadavid Bolivar-Gomez, Fredy Alexander Cárdenas-Espinosa, Lorena Paola Cardona-Morales, Oscar Chirán-Alpala, William Efrén Corpas-Iguarán, Eduardo Corpas-Iguarán, Eduardo Javid Giraldo-García, Jaime Gómez-Guzmán, Cristian Gómez-Moya, Diego Alexander Henao-Cespedes, Vladimir Hernandez-Jorge, Freddy Eliseo Lara Escobar, Rubén Darío López-Mesa, Diana Jimena López-Naranjo, Oscar Montoya-Estrada, Claudia Nohemy Pinza-Jiménez, Christian Javier Reyes-Galván, Erick Salvador Rodríguez Ortiz, Angélica María Tapasco-Alzate, Omar Triviño-Valencia, Jessica Urbano-Cano, Astrid Lorena