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

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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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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.

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Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

2024 , Villalba-Meneses F. , Guevara Maldonado, César Byron , Lojan A.B. , Gualsaqui M.G. , Arias-Serrano I. , Velásquez-López P.A. , Almeida-Galárraga D. , Tirado-Espín A. , Marín J., Marín J.J.

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura–Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP. © 2024 by the authors.

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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.

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Mathematical model of intrusion detection based on sequential execution of commands applying pagerank

2020 , Guevara Maldonado, César Byron , Hidalgo, J. , Yandún, M. , Arias Flores, Hugo Patricio , Zapata-Saavedra, L. , Ramirez-Morales, I. , Aguilar-Galvez, F. , Chalco-Torres, L. , Ortiz, D.P.

Cybersecurity in networks and computer systems is a very important research area for companies and institutions around the world. Therefore, safeguarding information is a fundamental objective, because data is the most valuable asset of a person or company. Users interacting with multiple systems generate a unique behavioral pattern for each person (called digital fingerprint). This behavior is compiled with the interactions between the user and the applications, websites, communication equipment (PCs, mobile phones, tablets, etc.). In this paper the analysis of eight users with computers with a UNIX operating system, who have performed their tasks in a period of 2 years, is detailed. This data is the history of use in Shell sessions, which are sorted by date and token. With this information a mathematical model of intrusion detection based on time series behaviors is generated. To generate this model a data pre-processing is necessary, which it generates user sessions (Equation presented), where u identifies the user and m the number of sessions the user u has made. Each session (Equation presented) contains a sequence of execution of commands (Equation presented), that is (Equation presented), where n is the position in wich the C command was executed. Only 17 commands have been selected, which are the most used by each user u. In the creation of the mathematical model we apply the page Rank algorithm [1], the same that within a command execution session (Equation presented), determines which command (Equation presented) calls another command (Equation presented), and determines which command is the most executed. For this study we will perform a model with sb subsequences of two commands, (Equation presented), where the algorithm is applied and we obtain a probability of execution per command defined by (Equation presented). Finally, a profile is generated for each of the users as a signal in time series, where maximum and minimum normal behavior is obtained. If any behavior is outside those ranges, it is determined as intrusive behavior, with a detection probability value. Otherwise, it is determined that the behavior is normal and can continue executing commands in a normal way. The results obtained in this model have shown that the proposal is quite effective in the testing phase, with an accuracy rate greater than 90% and a false positive rate of less than 4%. This shows that our model is effective and adaptable to the dynamic behavior of the user. On the other hand, a variability in the execution of user commands has been found to be quite high in periods of short time, but the proposed algorithm tends to adapt quite optimally. © Springer Nature Switzerland AG 2020.

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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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Development of an accessible video game to improve the understanding of the test of honey-alonso

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

When evaluating the learning styles of several individuals using the Honey-Alonso test, some users did not understand the meaning of several of the questions. This may be due to problems of context, tiredness in front of the extension of the test, lack of understanding or disinterest. The Honey-Alonso test consists of four groups of twenty questions each. Each group of questions allows identifying the level that an individual possesses on each one of the four learning styles. These styles are: active, reflective, theoretical and pragmatic. Answering a questionnaire of eighty questions is not an easy task from an andragogical point of view. This article proposes the creation of an educational video game designed with a script based on the questions of the Honey-Alonso test. The answers selected by the player are taken as a condition to determine the order of the next questions presented to the player. © Springer Nature Switzerland AG 2020.

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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.

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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. , Suarez B. , Guevara Maldonado, César Byron , 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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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.

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