Guevara Maldonado, César Byron

Preferred name

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Quito

cesarguevara@uti.edu.ec

ORCID

0000-0003-1571-5829

Scopus Author ID

56452706300

Now showing 1 - 10 of 47

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Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm
(2020)
Guevara Maldonado, César Byron
;
Penas M.S.
In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate - the key information to prioritize medical care - was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized. © 2013 IEEE.
Scopus© Citations 11 20
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Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model
(2023)
Sánchez-Real E.
;
Otuna-Hernández D.,
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Fajardo-Cabrera A.
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Davies-Alcívar R.
;
Madrid-Pérez M.
Keratoconus is an eye disease that distorts the shape of the cornea. This study aimed to analyze the effect of an increase in intraocular pressure applied to eyes with different severity of keratoconus disease using patient-specific models. Finite element models of the normal eye, eye with keratoconus, and eye with keratoglobus were constructed. The loading conditions considered the intraocular pressure increment as well as their physiological intraocular pressure. The analysis was performed with distinct materials for normal and keratoconic eyes. The finite element analysis revealed differences in the three models in terms of their deformation and maximum principal stress, and differences were observed in corneal curvature and thickness. These findings could enhance research in the biomechanical area, leading to more successful treatment options and a more individualized approach in the field of practical ophthalmology. Further investigation with larger sample sizes and more precise data on eye material would allow us to evaluate whether these disparities could inform the diagnosis of keratoconus. © 2023 by the authors.
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Emotion classification using EEG headset signals and Random Forests [Clasificación de emociones utilizando señales de auriculares EEG y Random Forests]
(2023)
Vasquez R.
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Carrion-Jumbo J.
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Riofrio-Luzcando D.
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Guevara Maldonado, César Byron
Emotions are one of the important components of the human being, thus they are a valuable part of daily activities such as interaction with people, decision making and learning. For this reason, it is important to detect, recognize and understand emotions using computational systems to improve communication between people and machines, which would facilitate the ability of computers to understand the communication between humans. This study proposes the creation of a model that allows the classification of people's emotions based on their EEG signals, for which the brain-computer interface EMOTIV EPOC was used. This allowed the collection of electroencephalographic information from 50 people, all of whom were shown audiovisual resources that helped to provoke the desired mood. The information obtained was stored in a database for the generation of the model and the corresponding classification analysis. Random Forest model was created for emotion prediction (happiness, sadness and relaxation), based on the signals of any person. The results obtained were 97.21% accurate for happiness, 76% for relaxation and 76% for sadness. Finally, the model was used to generate a real-time emotion prediction algorithm; it captures the person's EEG signals, executes the generated algorithm and displays the result on the screen with the help of images representative of each emotion. © 2023 ITMA.
22
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Analysis and design of an internal Top-Down network applying international standards [Análisis y diseño de una red interna Top-Down aplicando estándares internacionales]
(2023)
Almeida A.
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Suarez B.
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Guevara Maldonado, César Byron
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Coronel D.
;
Hidalgo J.
Telecommunications networks have become something essential within public or private companies, since they contribute to technological development. A network infrastructure with adequate cabling structured together with rules and standards enables the integration of multiple technologies and services. Currently, most institutions in Latin American countries do not apply norms, standards or good practices in their design, due to lack of knowledge or to save resources, without understanding that this generates an unreliable and unstable network infrastructure. Consequently, this study focuses on the design, architecture and administration of the network of a public institution in the city of Tulcán-Ecuador. The main objective of this proposal was the design of a network infrastructure that facilitates the administration of the network at a logical and physical level, taking into account the requirements and facilities of the institution. This network proposal applied the Top-Down Network Design By Cisco methodology to design a centralized, stable, flexible, and secure network. In addition, different international network design and management standards and regulations (ANSI/TIA/EIA/ISO) were used to generate a high-quality network. © 2023 ITMA.
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Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness
(2024)
Eugenia Ulzurrun
;
Ana Grande-Pérez
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Daniel del Hoyo
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Guevara Maldonado, César Byron
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Carmen Gil
Introduction: SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes. Materials and methods: Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations. Results: Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure. Discussion: Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.
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Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data
(2024)
Guevara Maldonado, César Byron
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Dennys Coronel
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Byron Salazar
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Jorge Salazar
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Arias Flores, Hugo Patricio
Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.
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Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance
(2023)
Pacheco-Mendoza S.
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Guevara Maldonado, César Byron
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Mayorga-Albán A.
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Fernández-Escobar J.
This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values < 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode. © 2023 by the authors.
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Accessibility assessment in mobile applications for android
(2020)
Acosta-Vargas, P.
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Salvador-Ullauri, L.
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Jadán Guerrero, Janio
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Guevara Maldonado, César Byron
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Sanchez-Gordon, S.
At present, the lack of adequate methods to test whether a mobile application is accessible has become a major challenge for accessibility experts. This study was applied to ten mobile applications, the most popular according to PCMAG. We propose to use the Web Content Accessibility Guidelines 2.1 through manual review and automatic review with the Google Play Store Accessibility Scanner validator for the Android. The evaluation results of the mobile applications indicate that the applications are not accessible because they do not comply with the minimum required level proposed by WCAG 2.1. The research proposes suggestions to improve and raise awareness among the designers of mobile applications, in such a way that more inclusive mobile applications accessible to all types of users are built. © Springer Nature Switzerland AG 2020.
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Predictive Model to Evaluate University Students' Perception and Attitude Towards Artificial Intelligence
(2024)
María Lorena Noboa Torres
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Daniela Alejandra Ribadeneira Pazmiño
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Daniela Paola Avalos Espinoza
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Guevara Maldonado, César Byron
Artificial Intelligence is emerging as a transformative tool impacting various industries, including education As Artificial Intelligence continues to develop and gain prominence in classrooms, understanding how students perceive this integration and how it affects their educational experience becomes crucial. The aim of this research was to develop a model to predict the perception of students at Bolívar State University regarding the use and potentialities of Artificial Intelligence in the educational field. The methodology employed a factorial analysis, which represents the relationships among a set of variables. From this, a logistic regression was performed, generating an equation to identify predictors that allowed understanding student behavior based on specific characteristics such as attitude, perception, and satisfaction. As a technique for information gathering, a questionnaire composed of 25 items on a Likert scale was used, statistically validated with a Cronbach's alpha value of 0.925. The results of the model show that all covariates, except "Insecurity and fear of using artificial intelligence tools", are significant (p < 0.001). This suggests that the remaining variables are related to the dependent variable "Positive Perception of the Usefulness of Artificial Intelligence in Learning". It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations. Training can help students learn about AI and how to use it effectively and ethically.
12
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Preparation of Higher Education Students in Ecuador: An Analysis Based on the Knowledge Economy
(2024)
Varela Lascano Darwin Marcelo
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Diego Fernando Salas Heredia
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Silvana Micaela Coloma Gudiño
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Guevara Maldonado, César Byron
The knowledge economy has emerged as a key paradigm in global socioeconomic development, highlighting the importance of higher education in the formation of human capital capable of generating, applying and disseminating innovative knowledge. This study aims to evaluate the preparation of higher education students in Ecuador, considering critical variables such as soft skills, perception of the knowledge economy, university-business linkage and internationalization, and their impact on academic training. The methodology employed was quantitative, using a multiple linear regression model to analyze the relationship between the independent variables and the academic formation of a sample of 205 students from two Ecuadorian universities. Advanced statistical techniques were applied to evaluate the significance and impact of each variable. The results indicate that soft skills (r = 0.713, p < 0.01), perception of the knowledge economy (r = 0.602, p < 0.01) and internationalization (r = 0.594, p < 0.01) have a significant and positive impact on academic training. However, university-business linkage showed a lower and non-significant correlation (r = 0.407, p < 0.01). In conclusion, academic training in Ecuador benefits significantly from the development of soft skills, a positive perception of the knowledge economy and internationalization. However, the lack of significant impact of university-business linkages suggests the need for future studies to explore barriers and improve these collaborations. These findings underscore the importance of educational policies that integrate these factors to improve the preparation of students in a global knowledge economy.
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Guevara Maldonado, César Byron

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