Examinando por Autor "Iparraguirre-Villanueva, Orlando"

Mostrando 1 - 20 de 20

Acceso abierto
Analysis of the Impact of the Pandemic on the Growth, Use, and Development of E-Business: A Systematic Review of the Literature
(MDPI, 2023-04-18) Ambrosio-Pérez, Milagros; Cabanillas-Carbonell, Michael; Iparraguirre-Villanueva, Orlando
The COVID-19 pandemic has affected various sectors in multiple countries, among them the economic sector has been one of the most affected, so the search for tools or measures for the continuation of sales and processes became recurrent, finding in e-business and its components precise tools to counteract the situation. Therefore, the present research aims to analyze the impact of the COVID-19 pandemic on the use, growth, and development of e-business by conducting a systematic literature review using the PRISMA methodology, collecting scientific articles covering the period of the pandemic from databases such as IEEE Xplore, ScienceDirect, Scopus, EBSCO, and IOPScience. Despite the limitations in access to scientific articles, it could be concluded that within the main characteristics identified, e-business tools in general allowed many businesses to continue subsisting and making sales thanks to the increase in online users due to the COVID-19 lockdowns. Although it was identified that the adoption of these tools lacked policies, limitations, and supports from governments, the perception of their use was positive in that they were considered safe and efficient.
Acceso abierto
Analysis of the Impact of the Pandemic on the Growth, Use, and Development of E-Business: A Systematic Review of the Literature
(MDPI, 2023-04-18) Ambrosio-Pérez, Milagros; Cabanillas-Carbonell, Michael; Iparraguirre-Villanueva, Orlando
The COVID-19 pandemic has affected various sectors in multiple countries, among them the economic sector has been one of the most affected, so the search for tools or measures for the continuation of sales and processes became recurrent, finding in e-business and its components precise tools to counteract the situation. Therefore, the present research aims to analyze the impact of the COVID-19 pandemic on the use, growth, and development of e-business by conducting a systematic literature review using the PRISMA methodology, collecting scientific articles covering the period of the pandemic from databases such as IEEE Xplore, ScienceDirect, Scopus, EBSCO, and IOPScience. Despite the limitations in access to scientific articles, it could be concluded that within the main characteristics identified, e-business tools in general allowed many businesses to continue subsisting and making sales thanks to the increase in online users due to the COVID-19 lockdowns. Although it was identified that the adoption of these tools lacked policies, limitations, and supports from governments, the perception of their use was positive in that they were considered safe and efficient.
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
“Breast Cancer Prediction using Machine Learning Models“
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Epifanía-Huerta, Andrés; Torres-Ceclén, Carmen; Ruiz-Alvarado, John; Cabanillas-Carbonel, Michael
Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.
Acceso abierto
“Breast Cancer Prediction using Machine Learning Models“
(Science and Information Organization, 2023) Iparraguirre-Villanueva, Orlando; Epifanía-Huerta, Andrés; Torres-Ceclén, Carmen; Ruiz-Alvarado, John; Cabanillas-Carbonel, Michael
Breast cancer is a type of cancer that develops in the cells of the breast. Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments. Detecting cancer at an early stage can save a person's life. Artificial intelligence (AI) plays a very important role in this area. Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers. This work aims to predict the probability of breast cancer in patients. Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF). The breast cancer diagnostic medical dataset from the Wisconsin repository has been used. The dataset includes 569 observations and 32 features. Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed. The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision. The training and results indicate that the six trained models can provide optimal classification and prediction results. The RF, GB, and AB models achieved 100% accuracy, outperforming the other models. Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB. Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively. Similarly, the last three models achieved an optimal yield close to 100%. Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.
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
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
Location-based Mobile Application for Blood Donor Search
(ASOCIACION ESPANOLES DE GEOGRAFIA, 2022) Iparraguirre-Villanueva, Orlando; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
Technological advances and the massive use of mobile devices have led to the exponential evolution of mobile applications in the health sector. Blood donation centers frequently suffer blood shortages due to lack of donations, which is why blood donation requests are frequently seen on social networks for blood donors in urgent need of a transfusion of a specific blood group. Mobile applications for blood donation are crucial in the health sector, since it allows donors and blood donation centers to communicate immediately to coordinate with each other, minimizing the time to perform the donation process. The present work was to develop a location-based mobile application for the search of blood donors, with the objective of increasing the number of donors, having a greater population reach, and reducing the time to search for blood donors. The results obtained show a significant increase of 39.58% in the number of donors, a reduction of 53.2% in the search time, and a greater population reach.
Acceso abierto
Location-based Mobile Application for Blood Donor Search
(Science and Information Organization, 2022) Iparraguirre-Villanueva, Orlando; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
Technological advances and the massive use of mobile devices have led to the exponential evolution of mobile applications in the health sector. Blood donation centers frequently suffer blood shortages due to lack of donations, which is why blood donation requests are frequently seen on social networks for blood donors in urgent need of a transfusion of a specific blood group. Mobile applications for blood donation are crucial in the health sector, since it allows donors and blood donation centers to communicate immediately to coordinate with each other, minimizing the time to perform the donation process. The present work was to develop a location-based mobile application for the search of blood donors, with the objective of increasing the number of donors, having a greater population reach, and reducing the time to search for blood donors. The results obtained show a significant increase of 39.58% in the number of donors, a reduction of 53.2% in the search time, and a greater population reach.
Acceso abierto
Productivity of incident management with conversational bots-a review
(Institute of Advanced Engineering and Science, 2023-01-30) Iparraguirre-Villanueva, Orlando; Obregon-Palomino, Luz; Pujay-Iglesias, Wilson; Sierra-Liñan, Fernando; Cabanillas-Carbonell, Michael
The use of conversational agents (bots) in information systems managed by company’s increases productivity in the development of activities focused on processes such as customer service, healthcare, and presentation. The present work is a systematic literature review that collects articles from 2019 to 2022 in the databases Scopus, Springer, Willey, Indexes-Csic, Taylor & Francis, Pubmed, and Ebsco Host. PRISMA methodology was used to systematize 47 relevant articles. As a result of the analysis, 2/19 very important benefits were obtained, which are: helping to obtain information and facilitating customer service; as for the types of conversational bots, a total of 9 types were found, of which conversational agents and chatbots with artificial intelligence (AI) are the most common; in the case of processes, 3/5 processes that optimize conversational bots were found, where the most prominent are: teaching process, health processes, and customer service processes. An architecture model for conversational bots in incident management is also proposed.
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
Text prediction recurrent neural networks using long shortterm memory-dropout
(Institute of Advanced Engineering and Science, 2022-10-29) Iparraguirre-Villanueva, Orlando; Guevara-Ponce, Victor; Ruiz-Alvarado, Daniel; BeltozarClemente, Saul; Sierra-Liñan, Fernando; Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
“Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability to learn long-term dependencies. The present research integrates the LSTM network and dropout technique to generate a text from a corpus as input, a model is developed to find the best way to extract the words from the context. For training the model, the poem ““La Ciudad y los perros““ which is composed of 128,600 words is used as input data. The poem was divided into two data sets, 38.88% for training and the remaining 61.12% for testing the model. The proposed model was tested in two variants: word importance and context. The results were evaluated in terms of the semantic proximity of the generated text to the given context.“
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. “