Guevara Maldonado, César Byron
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Guevara Maldonado, César Byron
Main Affiliation
Quito
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cesarguevara@uti.edu.ec
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Scopus Author ID
56452706300

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Topological Analysis Techniques for Improving Neural Network Performance in COVID-19 Detection Using Persistent Homology

2024 , Israel Reyes , Karen Cáceres-Benítez , Ana Marcillo , Andre Vera , Carolina Cadena-Morejón , Fernando Villalba-Meneses , Guevara Maldonado, César Byron , Paulina Vizcaíno-Imacaña , Diego Almeida-Galárraga , Andrés Tirado-Espín

In this study, we employ topological data analysis techniques on neural networks applied in COVID-19 detection, aiming to improve their predictive power. Leveraging the power of persistent homology, a mathematical tool for extracting topological features from intricate datasets, we turned chest X-ray images into a representation of the topological features. This representation was used to train and test the ability of neural networks to learn topological properties from images. We examine neural networks trained on chest radiographs containing both COVID-19 positive and negative cases. Our results suggest that, by identifying specific topological features correlated with COVID-19 detection, we may enhance the performance of the neural network models and analyze the underlying factors contributing to high accuracy rate of detection. The findings from this study contribute as exploratory advance in the field of medical imaging analysis and disease detection, showcasing the potential of topological analysis within neural networks.

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Effect of Burnout Syndrome on work performance in administrative personnel

2024 , Verónica Adriana Freire Palacios , Sridam David Arévalo Lara , María Belén Espíndola Lara , Andrea Ramírez Casco , David Miguel Larrea Luzuriaga , Guevara Maldonado, César Byron

Burnout syndrome can negatively affect workers' performance. Objective: To determine the prevalence of Burnout Syndrome and its impact on the Administrative Performance of the Human Talent at the Chimborazo Sports Federation. This study is quantitative, descriptive, and cross-sectional, involving 21 administrative workers. The Maslach Burnout Inventory Questionnaire was used to measure burnout, and a Job Performance Questionnaire was applied. Descriptive and correlational analyses were conducted. Results showed that 10 % had high levels of burnout, 14 % medium, and 76 % low. The most affected dimensions were personal accomplishment and depersonalization. Job performance was mostly regular (90 %). A significant correlation was found between burnout and job performance (r=0,689, p=0,001). Burnout explained 41,7 % of the variability in performance. Conclusions: There is an inverse relationship between burnout syndrome and job performance in this group of workers. Preventive measures are recommended.

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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.

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COVID-19 spread algorithm in the international airport network-DetArpds

2023 , Guevara Maldonado, César Byron , Coronel D. , Maldonado B.E.S. , Flores J.E.S.

Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review. © 2023 Guevara et al.

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Multisensory learning system applying augmented reality

2020 , Guevara Maldonado, César Byron , Coronel, D.M.V.

Mathematics is essential in our daily life. However, traditional teaching methods are mainly limited to the use of textbooks, generating demotivation and low interest in learning the subject. The present study proposes the development of an augmented reality system for the multi-sensory learning of students in the field of mathematics. For the creation of this proposal, we have used a human–computer interface tool called Makey Makey. Besides, the Singapore method, which has produced excellent results in the learning of mathematics, is applied. Scratch, a programming language that allows people to develop applications without having deep knowledge of the code, was used for the development of the application. Scratch allows us to combine Singapore, Makey Makey, and Augmented Reality optimally for learning. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

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Comparative Analysis of Neural Networks and Data Processing Techniques for Parkinson’s Gait Classification

2024 , Israel Reyes , Francis Andaluz , Kerly Troya , Luis Zhinin-Vera , Diego Almeida-Galárraga , Carolina Cadena-Morejón , Andrés Tirado-Espín , Santiago Villalba-Meneses , Guevara Maldonado, César Byron

Parkinson’s disease (PD) is an advancing neurodegenerative condition characterized by motor symptoms, including disturbances in gait and varying degrees of severity, typically assessed using the Hoehn and Yahr stages. Precise classification of PD gait patterns and severity levels is of paramount importance for efficient diagnosis and continuous treatment monitoring. This research article presents a comprehensive assessment of the performance of three distinct Artificial Neural Network (ANN) models integrated with diverse data processing techniques, encompassing segmentation, filtration, and noise reduction, in the context of classifying PD severity. The classification is based on the vertical ground reaction force (VGRF) measurements obtained from both healthy individuals and those afflicted by Parkinson’s disease, sourced from a well-established database (GaitPDB, Physio Net). The study provides a comparative analysis of the efficacy of these models in accurately discriminating between various gait patterns and stages of disease severity, underscoring their potential to enhance clinical decision-making and patient care. Additionally, the study offers valuable insights into the impact of data processing methodologies on classification performance

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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

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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.

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STRATEGIC QUALITY MANAGEMENT OF PROCESSES IN NURSING SERVICES WITHIN INTERNAL AND GENERAL MEDICINE UNITS FOR A SUSTAINABLE FUTURE IN HEALTH SYSTEMS

2024 , Gaibor-González, Mariela , Bonilla-Jurado, Diego , Zumba-Novay, Ember , Guevara Maldonado, César Byron

Objective of the study: The study focuses on the importance of quality nursing care in internal medicine, especially for patient recovery in complex cases. Variability in nursing practices can lead to inconsistent outcomes, and Evidence-Based Practice (EBP) is suggested as a strategy to standardize care and improve quality of service. The study evaluates the quality of nursing care in the province of Tungurahua, Ecuador from the perspectives of nurses and patients. Materials and Methods: Using the SERVQUAL model, the study evaluates the quality of nursing services through surveys focused on dimensions such as tangibility, reliability, responsiveness, safety, and empathy. The HS-EBP questionnaire was adapted to measure EBP among nurses. The study included 137 patients and 12 nurses from the Internal and General Medicine Service. Results: A moderate positive correlation was found between nursing education and perceived quality of service (r = 0.430), and between the use of research and perceived reliability of care (r = 0.405). However, there are barriers to the systematic application of EBP, and the study emphasizes the need to focus on both technical evaluation and empathy to improve service quality. Conclusions: The integration of EBP is essential to improve the quality of nursing care in internal and general medicine, but it is also important to address the organizational and interpersonal factors that affect patients' perceptions. A holistic approach that combines professional development, evidence-based practices, and patient-centered care is recommended to improve standards in internal medicine.

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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. , Carrion-Jumbo J. , Riofrio-Luzcando D. , 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.

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