Guevara Maldonado, César Byron
Thumbnail Image
Preferred name
Guevara Maldonado, César Byron
Main Affiliation
Quito
Email
cesarguevara@uti.edu.ec
ORCID
Scopus Author ID
56452706300

Research Output

Filters

47
Reset filters

Settings
Now showing 1 - 10 of 47
No Thumbnail Available
Publication

Advancing University Education: Exploring the Benefits of Education for Sustainable Development

2024 , Diego Bonilla-Jurado , Ember Zumba , Araceli Lucio-Quintana , Carlos Yerbabuena-Torres , Andrea Ramírez-Casco , Guevara Maldonado, César Byron

This article addresses the integration of Education for Sustainable Development (ESD) in higher education institutions, exploring its effects on academic performance and students’ ability to address sustainability challenges. Using the PRISMA 2020 methodology for a systematic literature review, 50 relevant articles were selected from 543 records, providing data on the academic impacts of ESD through bibliometric approaches and surveys. The results revealed that ESD improves academic performance, motivation and engagement, as well as enhances students’ ability to solve complex problems sustainably. However, significant barriers, such as a lack of resources and adequate teacher training, hinder effective implementation. Approximately 60% of students in ESD programs show greater motivation and analytical abilities compared to 50% in traditional programs. ESD enriches academic training and equips students with essential practical skills, preparing them to be agents of positive change. Incorporating emerging technologies and participatory learning methods is crucial to enhancing ESD effectiveness. Greater investment in teacher training and standardized educational materials, along with the promotion of international collaboration to share resources and best practices, is required.

View
No Thumbnail Available
Publication

Diagnosis and Degree of Evolution in a Keratoconus-Type Corneal Ectasia from Image Processing

2023 , Otuna-Hernández D. , Espinoza-Castro L. , Yánez-Contreras P. , Villalba-Meneses F. , Cadena-Morejón C. , Guevara Maldonado, César Byron , Cruz-Varela J. , Tirado-Espín A. , Almeida-Galárraga D.

Keratoconus is a degenerative ocular pathology characterized by the thinning of the cornea, thus affecting many people around the world since this corneal ectasia causes a deformation of the corneal curvature that leads to astigmatism and, in more severe cases, to blindness. Treating physicians use non-invasive instruments, such is the case of Pentacam®, which takes images of the cornea, both the topography and the profile of the cornea, which allows them to diagnose, evaluate and treat this disease; this is known as morphological characterization of the cornea. On the other hand, Berlin/Ambrosio analysis helps in the identification and subsequent diagnosis since this analysis uses a mathematical model of linear progression, which identifies the different curves with the severity of the disease. Therefore, the aim of this study is to use the images provided by Pentacam®, Berlin/Ambrosio analysis, and vision parameters in a convolutional neural network to evaluate if this disparity could be used to help with the diagnosis of keratoconus and, consequently, generate a more precise and optimal method in the diagnosis of keratoconus. As a result, the processing and comparison of the images and the parameters allowed a 10% increase in the results of specificity and sensitivity of the mean and severe stages when combining tools (corneal profile and vision parameters) in the CNN reaching ranges of 90 to 95%. Furthermore, it is important to highlight that in the early-stage study, its improvement was around 20% in specificity, sensitivity, and accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

View
No Thumbnail Available
Publication

Development of a Convolutional Neural Network for Detection of Ovarian Cancer Based on Computed Tomography Images

2024 , Gabriela Narvaez-Chunillo , Ronny Ordoñez-Sanchez , Lizbeth Ortiz-Vinueza , Diego Almeida-Galárraga , Fernando Villalba-Meneses , Roberto Bravo-Freire , Andrés Tirado-Espín , Carolina Cadena-Morejón , Paulina Vizcaíno-Imacaña , Guevara Maldonado, César Byron

Ovarian cancer is one of the most frequent gynecologic malignancies in women, but it is often detected in late stage, leaving patients with little time to follow a successful therapy. Specialists have opted to use computer-aided diagnosis (CAD) for the detection of ovarian cancer through the analysis of computed tomography (CT) images, in which the professional examines the size, shape and different characteristics that enable a precise diagnosis in the ovary. This present project purposes a Convolutional Neural Network (CNN) which consist on four convolutional layers; including two pooling layer and two fully-connected layer. The cancerous ovaries images is selected from the Cancer Imaging Achive dataset for training and validation of the model. Moreover, the training of the CNN contain filters to ensure that all of the images are the same dimensions and pixel size. The testing results from the training of the images showed that the proposed model obtained a range of accuracy that goes from 90.0% to the best of the cases 98.85%. The variables obtained like the data of the pressure and loss of the training were compared with those of the validation, allowing for the determination of a successful CNN training.

View
No Thumbnail Available
Publication

An adversarial risk analysis framework for software release decision support

2025 , Refik Soyer , Fabrizio Ruggeri , David Rios Insua , Cason Pierce , Guevara Maldonado, César Byron

Recent artificial intelligence (AI) risk management frameworks and regulations place stringent quality constraints on AI systems to be deployed in an increasingly competitive environment. Thus, from a software engineering point of view, a major issue is deciding when to release an AI system to the market. This problem is complex due to, among other features, the uncertainty surrounding the AI system's reliability and safety as reflected through its faults, the various cost items involved, and the presence of competitors. A novel general adversarial risk analysis framework with multiple agents of two types (producers and buyers) is proposed to support an AI system developer in deciding when to release a product. The implementation of the proposed framework is illustrated with an example and extensions to cases with multiple producers and multiple buyers are discussed

View
No Thumbnail Available
Publication

Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance

2023 , Pacheco-Mendoza S. , Guevara Maldonado, César Byron , Mayorga-Albán A. , 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.

View
No Thumbnail Available
Publication

Editorial design of interactive picture book with mobile application based on uxd user experience design

2020 , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Amagua M.

This research presents an interactive human-computer learning system model applying adaptive editorial design. The proposal aims to generate an interactive picture book and a mobile application based on user experience design (UxD). The results will be obtained using UX metrics and will have the particularity of working with the technique of participatory design and reticular deconstruction. This book includes its presentation as an audiobook and an editorial composition with pop-ups and pages to paint. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

View
No Thumbnail Available
Publication

Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data

2024 , Guevara Maldonado, César Byron , Dennys Coronel , Byron Salazar , Jorge Salazar , 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.

View
No Thumbnail Available
Publication

Accessibility assessment in mobile applications for android

2020 , Acosta-Vargas, P. , Salvador-Ullauri, L. , Jadán Guerrero, Janio , Guevara Maldonado, César Byron , Sanchez-Gordon, S. , Calle-Jimenez, T. , Lara Álvarez, Patricio , Medina, A. , Nunes, I.L.

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.

View
No Thumbnail Available
Publication

Predictive Model to Evaluate University Students' Perception and Attitude Towards Artificial Intelligence

2024 , María Lorena Noboa Torres , Daniela Alejandra Ribadeneira Pazmiño , Daniela Paola Avalos Espinoza , 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.

View
No Thumbnail Available
Publication

Development of Behavior Profile of Users with Visual Impairment

2020 , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja M. , Fierro-Saltos W. , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

The interaction of the user with visual impairment with assistive technologies, and in particular with screen readers, generates a group of actions and events during their navigation. These interactions are defined as behavioral patterns, which have a sequence that occurs at specific time slot. Understanding user behavior by analyzing their interaction with applications, in addition, details the characteristics, relationships, structures and functions of the sequence of actions in a specific application domain. The objective of this document is to find activity patterns from a set of commands used by the user, combining data mining and a Bayesian model. This model calculates the probability of the functions used with the screen reader and generates a behavior profile to improve the user experience. For this study, the screen reader JAWS version 2018, the Open Journal Systems platform version 3.0.1 and a computer with Windows 10 operating system were used. During the first phase, command history used by the user by interacting with the Open Journal Systems platform were collected. The result is that the accessibility of users with visual impairment to interact with the computer and its applications has been improved by applying this model. © Springer Nature Switzerland AG 2020.

View