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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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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.
Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto; Cardona-Morales, Oscar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7441

Abstract

This article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an ?2 = 0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s ? = −0.599 (Fisher’s ? = −0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s ? = 0.165 (Fisher’s ? = 0.167) was obtained.
Evaluation of viability and survival of free and maltodextrin microencapsulated Bifidobacterium animalis subsp. animalis through spray-drying process Corpas-Iguarán, Eduardo Javid; Triviño-Valencia, Jessica; Tapasco-Alzate, Omar; Garcés-Gómez, Yeison Alberto
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1239

Abstract

Bifidobacterium animalis subsp. animalis is a microorganism integrated into the human intestinal microbiota and performs a probiotic function through mechanisms that promote the absorption of nutrients, the modulation of the immune system, and the production of lactic acid, among other aspects. Microencapsulation using maltodextrin promotes the protection of microorganisms against physical and chemical factors, improving viability over time. Bifidobacterium animalis subsp. animalis was microencapsulated through spray-drying using maltodextrin. Survival under pH conditions, bile salts, and temperature were evaluated as well as its viability during storage conditions. The viability of the encapsulated agent stored at 25 °C remained high and constant during the first three weeks. The results for free and microencapsulated thermal tolerance showed an important difference among survival percentages of each tested temperature, and microencapsulation showed a protective effect against temperatures like or lower than 55 °C. Regarding pH 2.5 exposure for 3h, there is a survival of 5.38% for free microorganisms in contrast to 11.87% for encapsulated, whereas in a pH 3.5 for 3h, the encapsulated agent showed a survival of 23%. The results obtained from encapsulated cells stressed with a 1g/L concentration of bile salts showed a survival of 19%, while free cells presented a total loss of viability when subjected for 3h at the same concentration. Microencapsulated Bifidobacterium animalis subsp. animalis demonstrated potential for its use incorporated into foods, but it is necessary to improve viability conditions during storage and survival under gastric stress conditions.
Drivers of teleworker productivity: A systematic review of the empirical evidence Tapasco-Alzate, Omar; Giraldo-García, Jaime; Corpas-Iguarán, Eduardo; Garcés-Gómez, Yeison Alberto
Communications in Science and Technology Vol 9 No 2 (2024)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.9.2.2024.1406

Abstract

This paper aims to identify the factors influencing teleworker productivity by reviewing empirical evidence found in the scientific literature on the topic. A systematic review was conducted to gather and evaluate primary literature sources, complemented by a bibliometric analysis of the volume, distribution, and trends in scientific production over the past 24 years. The effects found are heterogeneous, narrow in scope, and sometimes contradictory. Telework significantly impacts productivity, with its effects varying based on intensity, the nature of the tasks performed, and individual, social, and situational factors. This manuscript provides a comprehensive review of the factors influencing teleworker productivity, analyzing 318 research articles to identify the key determinants of productivity in remote work environments. It systematically categorizes these factors into individual, social, and situational dimensions, offering valuable insights for organizations and individuals adapting to the evolving landscape of telework.
Teachers’ self-perception of scientific competences: a gender approach Garcés-Gómez, Yeison Alberto; Alzate, Valentina Cadavid; Rodríguez Ortiz, Angélica María; Lara Escobar, Rubén Darío
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i4.27724

Abstract

This study analyses the self-perception of 274 teachers from public, urban, and rural schools in Manizales, Colombia, using a Likert scale instrument developed considering the scientific competencies determined by UNESCO. In the analysis of the results, it was found that, even though in the sample analyzed, women have greater training in research and scientific competencies, their perception of their abilities in this aspect is lower than that of men. With the Mann-Whitney U test and rank-biserial correlation, it was possible to test the alternative hypothesis that the female self-perception of capabilities is lower than the male for each question. The instrument was validated with the internal consistency index with an α=0.98. Additionally, the instrument has been validated with a confirmatory factor analysis, obtaining values of comparative fit index (CFI) of 0.869 and Tucker-Lewis’s index (TLI) of 0.858 with RMSEA and SRMR of 0.103 and 0.063, respectively. The paper provides insights into the self-perception of scientific competencies among teachers, which can inform teacher training and professional development programs. The study highlighted the gender gap in self-perception of scientific competencies, which can inform policies and interventions to promote gender equity in science education.
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.
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