Jessie Bravo

Universidad Nacional Pedro Ruiz Gallo, Lambayeque -Perú, Graduate Student

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Papers by Jessie Bravo

Tabla 5. Información de artículos - Alfabetización Informacional

Zenodo (CERN European Organization for Nuclear Research), Oct 5, 2023

Tabla 5. Base de datos

Zenodo (CERN European Organization for Nuclear Research), Nov 15, 2023

Tabla 5. Digital and information literacy in basic education teachers

Zenodo (CERN European Organization for Nuclear Research), Jan 25, 2024

Integration of Technological Tools to Improve Communication and Monitoring in a Higher Educational Institution in e-learning Environments

Smart innovation, systems and technologies, 2024

Digital Competence of Mathematics teachers based on DigCompEdu in Regular Basic Education in the Lambayeque region

Una revisión sistemática de Modelos de clasificación de dengue utilizando machine learning

RISTI, Jun 29, 2023

Resumen: El dengue es una enfermedad arboviral que anualmente reporta un gran número de infectado... more Resumen: El dengue es una enfermedad arboviral que anualmente reporta un gran número de infectados en la costa norte y la selva peruana. Según las estadísticas, está aumentando cada año. Este artículo tiene como objetivo desarrollar una revisión sistemática de la literatura científica sobre las variables de estudio y los métodos de aprendizaje automático utilizados actualmente para detectar la infección por dengue. La metodología utilizada fue PRISMA, mapeando inicialmente la literatura de 274 artículos científicos, quedando seleccionados 33 artículos para la revisión sistemática. Los resultados obtenidos son que los algoritmos de aprendizaje automático más utilizados son las redes neuronales (NN) y support vector machine (SVM). Asimismo, se ha encontrado que los científicos tienden a realizar investigaciones con variables climáticas o demográficas para obtener mejores resultados. Se concluye que los métodos de aprendizaje automático que más se han utilizado son las redes neuronales de diferentes tipos: convolucional, recurrente, profunda y multicapa, y para la predicción de brotes de dengue predominaron los métodos de series de tiempo con LSTM y ARIMA, también se estableció que la tendencia es hacia la inclusión de variables climáticas y demográficas en los modelos de predicción. Palabras-clave: Dengue, detección, machine learning, métodos de clasificación, algoritmos de clasificación, random forest, support vector machine, artificial neural networks Abstract: Dengue is an arboviral disease that annually reports a large number of infected on the north coast and the Peruvian jungle. According to statistics, it is increasing yearly. This article aims to develop a systematic review of the scientific RISTI, N.º 50, 06/2023 Una revisión sistemática de Modelos de clasificación de dengue utilizando machine learning literature on the study variables and the machine learning methods currently used for detecting dengue infection. The methodology used was PRISMA, initially mapping the literature of 274 scientific articles, leaving 33 articles selected for the systematic review. The results obtained are that the most used machine learning algorithms are neural networks (NN) and support vector machine (SVM). Likewise, it has been found that scientists tend to carry out research with climatic or demographic variables to obtain better results. It is concluded that the machine learning methods that have been used the most are neural networks of different types: convolutional, recurrent, deep, and multilayer, and for the prediction of dengue outbreaks the time series methods with LSTM and ARIMA were the predominant ones, it was also established that the trend is towards the inclusion of climatic and demographic variables in the prediction models.

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Digital and information literacy in regular basic education teachers: a systematic review

Zenodo (CERN European Organization for Nuclear Research), Oct 1, 2023

Digital and Information Literacy in Basic-Education Teachers: A Systematic Literature Review

Education sciences, Jan 26, 2024

Contribution of the Synchronous and Asynchronous Mode in Virtual Education in a Peruvian Higher Educational Institution During the COVID-19 Pandemic

Springer eBooks, 2023

Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

by Jessie Bravo and Oscar Serquen

Sustainability, Jun 1, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more

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Analysis of Exchange Rate Volatility in Peru in the Presence of Structural Breaks

Journal of Hunan University Natural Sciences

The presence of structural breaks in the analysis of volatility in the financial field has been i... more The presence of structural breaks in the analysis of volatility in the financial field has been ignored in research done in Latin America. This paper fills this gap by analyzing the behavior of the dollar exchange rate in Peru by evaluating the impact of structural breaks in volatility forecasting. The return behavior was analyzed for the period 05/01/2010 to 09/30/2021. Econometric analysis was used, which consisted of: (1) use of the modified Iterative Cumulative Sum of Squares (ICSS) algorithm to determine the structural breakpoints; (2) estimation of GARCH Models for the subsamples originated by the identified breakpoints; and (3) comparison of alternative models with the GARCH(1,1) expanding window model for horizons of 1, 20, 60 and 120 days. The ICSS algorithm identified 8 breaks in volatility behavior. The models were compared based on out-of-sample forecast performance. It was determined that the GARCH model that considers structural breaks is only effective for a one-day h...

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Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru

Sustainability

Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and re... more Due to the COVID-19 pandemic, the tourism sector has been one of the most affected sectors and requires management entities to develop urgent measures to reactivate and achieve digital transformation using emerging disruptive technologies. The objective of this research is to apply machine learning techniques to predict visitors to tourist attractions on the Moche Route in northern Peru, for which a methodology based on four main stages was applied: (1) data collection, (2) model analysis, (3) model development, and (4) model evaluation. Public data from official sources and internet data (TripAdvisor and Google Trends) during the period from January 2011 to May 2022 are used. Four algorithms are evaluated: linear regression, KNN regression, decision tree, and random forest. In conclusion, for both the prediction of national and foreign tourists, the best algorithm is linear regression, and the results allow for taking the necessary actions to achieve the digital transformation to p...

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Key elements for UX-based design, applied in the improvement of the academic service of Peruvian university education in the context of COVID-19

2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education & Research (ICALTER), Nov 16, 2022

Analytical system based on Big Data for digital transformation in Higher Education

2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education & Research (ICALTER), Nov 16, 2022

Influence of Technological Factors in Teaching and Communication in Higher Education During the COVID-19 Health Crisis

Smart innovation, systems and technologies, Nov 23, 2022

Digital Transformation Model for the Reactivation of the Tourism Sector in the COVID-19 Environment of the Lambayeque Region

Proceedings of the 6th Brazilian Technology Symposium (BTSym’20), 2021

Globally, the tourism sector has been affected by COVID-19 pandemic, which has triggered an econo... more Globally, the tourism sector has been affected by COVID-19 pandemic, which has triggered an economic and social crisis. Many countries are currently generating alternatives to promote tourism and allow tourists to return and improve the current situation. In Peru, borders were closed, and therefore the different tourist sites and activities were closed, generating unemployment in the actors involved in the tourism sector, as well as the local communities that underpinned their economy in the activities of this area. Faced with this problem, it is evident that technology and the digitization of tourism activities will allow supporting this sector, so this article proposes a digital transformation model for the reactivation of tourism, taking the Lambayeque region as a case study. We have carried out a systematic investigation of tourism information and applied interviews to specialists in the sector and surveys of the actors involved. The model is based on five dimensions: technology, management, processes, tourism product, and digital tourist. For each dimension, we describe its purpose and the proposals applying solutions and technological tools that allow digital transformation. Our model was validated through an expert judgment, the result of which was acceptable, hoping that its implementation will contribute to the reactivation of tourism. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Perceived Effectiveness of Gender-Based Virtual Education in Times of COVID-19 Pandemic in University Higher Education

Smart innovation, systems and technologies, Jul 20, 2022

Raising Awareness in the Adoption of COVID-19 Preventive Measures in Higher Education Students through an Epidemiological Surveillance Mobile App

2021 International Congress on Educational and Technology in Sciences, CISETC 2021, 2021

COVID-19 disease has started several levels of alert and biosafety protocols to mitigate the risk... more COVID-19 disease has started several levels of alert and biosafety protocols to mitigate the risk of contagion everywhere, therefore causing universities to adopt the virtual environments in their teachings, but nowadays it became a necessity to return to classrooms in a short term basis;however, how prepared are we for this “new normal”? In this research, a validated questionnaire was used to measure the knowledge of COVID-19 and preventive measures adopted by university students, before and after the use of an epidemiological surveillance mobile app, developed as a tool to raise awareness in students. The research was quantitative, quasi-experimental, it was applied in a sample of 82 students, the analysis was global and gender inferential using the non-parametric T test for paired samples. The results indicated that the epidemiological surveillance mobile app made possible to raise awareness in the students in the adoption of preventive measures, proposing awareness strategies th...

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Digital Transformation Model for the Reactivation of the Tourism Sector in the COVID-19 Environment of the Lambayeque Region

Correlational analysis of incident factors in the academic performance of university higher education students under the context of COVID-19

2021 IEEE 1st International Conference on Advanced Learning Technologies on Education & Research (ICALTER)

The COVID-19 pandemic changed the teaching modality radically, which led to virtual education in ... more The COVID-19 pandemic changed the teaching modality radically, which led to virtual education in institutions, which had to quickly adapt as well as students. The research was carried out with a non-experimental, descriptive and correlational quantitative approach, applied to a sample of 1804 students from a public university, two statistical studies were carried out, one supported by the academic database and the second through the application of a validated instrument to identify which factors have the greatest influence on the academic performance of students in the virtual modality. In the statistical analysis, it was found that academic performance improved in the virtual semester with respect to the face-to-face one; demographic factors: gender, age, study cycle, and career groups had a significant influence on academic performance. Finally, no significant influence was found between the technological and methodological dimensions with the academic performance of the virtual semester.

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