Examinando por Autor "Beltozar-Clemente, Saul"

Mostrando 1 - 8 de 8

Acceso abierto
Development and evaluation of a didactic tool with augmented reality for Quechua language learning in preschoolers
(Institute of Advanced Engineering and Science, 2023-01-09) Zapata-Paulini, Joselyn; Beltozar-Clemente, Saul; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
“It is important to preserve our cultural identity through the preservation of our mother tongue, contributing to its dissemination. Augmented reality (AR) is a great ally of education that provides efficiency, and productivity and increases the interest of students in their academic activities. An AR application was developed for learning Quechua in preschool children, thus improving their learning, satisfaction, and preference compared to traditional teaching. Previously, learning styles were identified for better coverage of the application; the design thinking methodology was applied for the development of the application, then the respective tests were conducted where it was obtained that the children's performance improved by 28.3% more compared to traditional teaching, with an average satisfaction of 89% of the classrooms, and 81% of students' preference. It was concluded that the proposed application considerably favors the written and audiovisual learning of the Quechua language in preschool students. “
Acceso abierto
“Digital Tool for Film Promotion Through the Use of Augmented Reality“
(International Association of Online Engineering, 2023-01) Beltozar-Clemente, Saul; Sierra-Liñan, Fernando; Pucuhuayla-Revatta, Félix; Zapata-Paulin, Joselyn; Cabanillas-Carbonell, Michael
“After the isolation caused by the pandemic, the entertainment sector has been forced to change the way it markets its products and services, and users have been adapting to the change. The objective of this research article is to provide added value and innovation to the advertising promotions of movies in the premiere, through the development of a mobile application based on augmented reality to improve customers’ shopping experience. For which the Agile Mobile-D Methodology was used for the development of the application and the results were studied using 5 indicators: Time to choose a movie, where an improvement of 38.26% was obtained compared to the Pre-Test; regarding the indicator time to acquire information about the movie, an improvement of 81.33% was obtained. 33%; concerning the third indicator, time to initiate the purchase, an improvement of 88% was obtained; concerning the fourth indicator, time to obtain a reminder, an improvement of 83.33% was obtained, making the time with the use of the application shorter; finally, concerning the customer satisfaction indicator, 73% of the users who used the application rated their experience as between satisfied and very satisfied.“
Acceso abierto
Mobile Application with AR as a Strategy to Improve the Marketing Process in a Dental Center
(International Association of Online Engineering, 2023-02-06) Beltozar-Clemente, Saul; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
The post-pandemic period brought with it new challenges for both businesses and private health centers, many of which were affected by the loss of customers. In the case of dental centers, many were affected by the distrust of customers, since activities performed in the oral cavity exposed them to the contagion of Covid-19. This research work proposes the implementation of a mobile application with Augmented Reality (AR) as a strategy for digital marketing immersion, with the aim of achieving a dynamic approach to the services provided in the dental center to customers, this is through the use of this technology in conjunction with social networks, contributing to the improvement of the business and building trust with customers. The application was developed under the Mobile-D methodology with a layered system development architecture, having as indicators the time of elaboration of the advertisement, the cost of information material, the time to inform the services, and the level of customer satisfaction. Finally, the results revealed that the time of elaboration of the advertisement decreased from 25 hours to 14 hours, the cost of informative material was considered “low“ since the implementation of the application turns out to be economic, and the time to inform the services in its marketing process went from 30 min to 19 min with the use of the application, finally, the customer satisfaction increased being considered in 87% between “Good“ and “Excellent“.
Acceso abierto
“Search and classify topics in a corpus of text using the latent dirichlet allocation model“
(Institute of Advanced Engineering and Science, 2022-11-18) Iparraguirre-Villanueva, Orlando; Sierra-Liñan, Fernando; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Pucuhuayla-Revatta, Félix; Zapata-Paulin, Joselyn; Cabanillas-Carbonell, Michael
“This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 ““curriculum““ documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.“
Acceso abierto
Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm
(Science and Information Organization, 2022) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Sierra-Liñan, Fernando; Beltozar-Clemente, Saul; Cabanillas-Carbonel, Michael
Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.
Acceso abierto
Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm
(Science and Information Organization, 2022) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Sierra-Liñan, Fernando; Beltozar-Clemente, Saul; Cabanillas-Carbonel, Michael
Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.
Acceso abierto
“The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
(MDPI, 2023-01-31) Iparraguirre-Villanueva, Orlando; Alvarez-Risco, Aldo; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Zapata-Paulini, Joselyn; Yáñez, Jaime A.; Cabanillas-Carbonell, Michael
“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “
Acceso abierto
“The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model“
(MDPI, 2023-01-31) Iparraguirre-Villanueva, Orlando; Alvarez-Risco, Aldo; Herrera Salazar, Jose Luis; Beltozar-Clemente, Saul; Zapata-Paulini, Joselyn; Yáñez, Jaime A.; Cabanillas-Carbonell, Michael
“Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus. “