Examinando por Autor "Zapata-Paulini, Joselyn"

Mostrando 1 - 11 de 11

Acceso abierto
Augmented reality for innovation: Education and analysis of the glacial retreat of the Peruvian Andean snow-capped mountains
(Elsevier B.V., 2023-09) Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael; Iparraguirre-Villanueva, Orlando; Sierra-Liñan, Fernando; Baltozar-Clemente, Saul; Alvarez-Risco, Aldo; Yáñez, Jaime A.
Mountain glaciers are considered great reservoirs of water, and their importance lies in the fact that many of our ecosystems and numerous communities depend on them; Peru has one of the largest extensions of Andean snow-capped mountains, which have been affected by the decline in their glacier coverage and that is warned, will disappear due to environmental conditions and alterations in the current global temperature. This problem has increased due to ignorance, misinformation, indifference, and lack of solidarity on the part of the population who favors this discouraging situation. Taking advantage of the current technological immersion, in which we live, the development of a mobile application was proposed as a pedagogical resource to raise awareness among educational institutions about the glacial retreat of the Peruvian Andean snow-capped mountains, showing the current situation of some of the snow-capped mountains of the Andes that have suffered a greater impact, implementing augmented reality technology to obtain an interactive link. To provide greater detail of the situation, previous studies were carried out on glacial retreats in two Peruvian snow-capped mountains over the last 40 years, where it was found that, of the snow-capped mountains considered, Chicon had a decrease of 32.5% of its glacier cover, and Pumahuanca had a decrease of 56.9%. Such results are exposed within the application to provide realistic data on the glacial conditions of both Peruvian snow-capped mountains, as well as the consequences and conservation techniques to mitigate and cope with deglaciation. Taking into consideration that environmental education from an early age turns out to be key to forming an informed and participatory society about climate change.
Acceso abierto
“Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education“
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Torres-Ceclén, Carmen; Epifanía-Huerta, Andrés; Castro-Leon, Gloria; Melgarejo-Graciano, Melquiades; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), KNearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. “
Acceso abierto
“Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education“
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Torres-Ceclén, Carmen; Epifanía-Huerta, Andrés; Castro-Leon, Gloria; Melgarejo-Graciano, Melquiades; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), KNearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. “
Acceso abierto
Convolutional Neural Networks with Transfer Learning for Pneumonia Detection
(Science and Information Organization, 2022) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Roque Paredes, Ofelia; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.“
Acceso abierto
Convolutional Neural Networks with Transfer Learning for Pneumonia Detection
(Science and Information Organization, 2022) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Roque Paredes, Ofelia; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“Pneumonia is a type of acute respiratory infection caused by microbes, and viruses that affect the lungs. Pneumonia is the leading cause of infant mortality in the world, accounting for 81% of deaths in children under five years of age. There are approximately 1.2 million cases of pneumonia in children under five years of age and 180 000 died in 2016. Early detection of pneumonia can help reduce mortality rates. Therefore, this paper presents four convolutional neural network (CNN) models to detect pneumonia from chest X-ray images. CNNs were trained to classify X-ray images into two types: normal and pneumonia, using several convolutional layers. The four models used in this work are pre-trained: VGG16, VGG19, ResNet50, and InceptionV3. The measures that were used for the evaluation of the results are Accuracy, recall, and F1-Score. The models were trained and validated with the dataset. The results showed that the Inceptionv3 model achieved the best performance with 72.9% accuracy, recall 93.7%, and F1-Score 82%. This indicates that CNN models are suitable for detecting pneumonia with high accuracy.“
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
Disease Identification in Crop Plants based on Convolutional Neural Networks
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Torres-Ceclén, Carmen; Ruiz-Alvarado, John; Castro-Leon, Gloria; Roque-Paredes, Ofelia; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identification and classification, slightly higher than the ResNet-50 model, which achieved an accuracy of 97%, thus demonstrating an effective and promising approach, being able to learn relevant features from the images and classify them accurately. Overall, ML in conjunction with CNNs proved to be an effective tool for identifying and classifying diseases in crop plants. The CNN models used in this work are a very good choice for this type of tasks, since they proved to have a very high performance in classification tasks. In terms of accuracy, all three models are very accurate in image classification, with an accuracy of over 96% with large data sets“
Acceso abierto
Disease Identification in Crop Plants based on Convolutional Neural Networks
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Torres-Ceclén, Carmen; Ruiz-Alvarado, John; Castro-Leon, Gloria; Roque-Paredes, Ofelia; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identification and classification, slightly higher than the ResNet-50 model, which achieved an accuracy of 97%, thus demonstrating an effective and promising approach, being able to learn relevant features from the images and classify them accurately. Overall, ML in conjunction with CNNs proved to be an effective tool for identifying and classifying diseases in crop plants. The CNN models used in this work are a very good choice for this type of tasks, since they proved to have a very high performance in classification tasks. In terms of accuracy, all three models are very accurate in image classification, with an accuracy of over 96% with large data sets“
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
“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. “