Air pollution is a major problem that affects both human health and the environment, causing millions of premature deaths annually worldwide and severely degrading the state of the planet. Exposure to fine particulate matter, which is highly hazardous, enables these particles to penetrate deeply into the lungs and lead to serious health issues, including a reduction in life expectancy by more than two years. In response to this problem, it is crucial to identify effective ways to monitor the levels of these pollutants in our daily surroundings. This article presents a case study conducted in the district of San Borja, Lima, Peru, where prediction models for PM2.5 and PM10 were implemented using the XGBoost and LightGBM algorithms. Employing data from the SENAMHI portal and a correlation analysis of variables, two different scenarios were developed for training the models. In scenario 1, ...

Las opiniones de los clientes sobre servicios en redes sociales son vitales para las empresas debido a que se pueden utilizar para mejorar y potenciar las oportunidades de negocio si los comentarios pueden analizarse a tiempo. El propósito de este trabajo es determinar los métodos de machine learning con mejor rendimiento para aplicar análisis de sentimientos y clasificar comentarios positivos y negativos sobre el servicio de restaurantes peruanos en Facebook. Como primera contribución en este proyecto, se crearon dos datasets de comentarios de publicaciones de cadenas de restaurantes peruanos en Facebook. La segunda contribución es la metodología propuesta dividida en dos etapas: en la primera etapa se aplicaron técnicas de Lenguaje Natural para el preprocesamiento de los comentarios; en la segunda etapa se analizó el desempeño de los algoritmos de Naive Bayes, Random Forest y ...

Air pollution is a major problem that affects both human health and the environment, causing millions of premature deaths annually worldwide and severely degrading the state of the planet. Exposure to fine particulate matter, which is highly hazardous, enables these particles to penetrate deeply into the lungs and lead to serious health issues, including a reduction in life expectancy by more than two years. In response to this problem, it is crucial to identify effective ways to monitor the levels of these pollutants in our daily surroundings. This article presents a case study conducted in the district of San Borja, Lima, Peru, where prediction models for PM2.5 and PM10 were implemented using the XGBoost and LightGBM algorithms. Employing data from the SENAMHI portal and a correlation analysis of variables, two different scenarios were developed for training the models. In scenario 1, ...

Face recognition has become relevant in the search for non-physical contact solutions in enclosed spaces for identity verification in the context of the SARS-CoV-2 pandemic. One of the challenges of face recognition is mask occlusion which hides more than 50 % of the face. This research evaluated four models pre-trained by transfer learning: VGG-16, RESNET-50, Vision Transformer (ViT), and Swin Transformer, trained on their upper layers with a proprietary dataset. The analysis obtained an accuracy of 24 % (RESNET-50), 25 % (VGG-16), 96 % (ViT), and 91 % (Swin) with unmasked subjects. While with a mask, accuracy was 32 % (RESNET-50), 53 % (VGG-16), 87 % (ViT), and 61 % (Swin). These percentages indicate that modern architectures such as the Transformers perform better in mask recognition than the CNNs (VGG-16 and RESNET-50). The contribution of the research lies in the experimentation wit...

Face recognition has become relevant in the search for non-physical contact solutions in enclosed spaces for identity verification in the context of the SARS-CoV-2 pandemic. One of the challenges of face recognition is mask occlusion which hides more than 50 % of the face. This research evaluated four models pre-trained by transfer learning: VGG-16, RESNET-50, Vision Transformer (ViT), and Swin Transformer, trained on their upper layers with a proprietary dataset. The analysis obtained an accuracy of 24 % (RESNET-50), 25 % (VGG-16), 96 % (ViT), and 91 % (Swin) with unmasked subjects. While with a mask, accuracy was 32 % (RESNET-50), 53 % (VGG-16), 87 % (ViT), and 61 % (Swin). These percentages indicate that modern architectures such as the Transformers perform better in mask recognition than the CNNs (VGG-16 and RESNET-50). The contribution of the research lies in the experimentation wit...