El objetivo de esta investigación es mejorar la gestión de los tiempos en los procesos en el Ministerio de la Producción del Perú, con la finalidad de aumentar la eficiencia, eficacia y la productividad, implementando la metodología G&M, basado en BPM. En la actualidad el Ministerio de la Producción, no cuenta con un sistema de organización que le permita obtener resultados óptimos, los procedimientos se demoran demasiado por la existencia de pasos repetitivos y redundancia en las ejecuciones de sus procesos, la cadena de valor no contiene los procesos como los elementos principales, generando mayor asignación de recursos. El uso de esta metodología transforma la visión actual de los directivos y empleados a una perspectiva de visión horizontal de gestión por procesos, que está orientado a resultados globales, donde los colaboradores saben qué lugar ocupan dentro del proce...

“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 ...

“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 ...

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), K-Nearest-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 ac...

“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 identif...

“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 identif...

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 identifica...

Abstract—In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work identifies, classifies, and analyzes tweets related to real natural disasters through tweets with the hashtag #Nat-uralDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geo-located tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, ...

El gobierno electrónico se presenta como una oportunidad de herramienta que no solo posibilita la inclusión de valor en las actividades universitarias realizadas hasta ahora mediante el uso intensivo de las TIC, sino que también podría ser utilizada como estrategia de gestión para la modernización de las universidades, la necesidad de alcanzar la excelencia y una mayor transparencia en la gestión de sus tareas y esfuerzos para aumentar y mejorar la calidad, la responsabilidad y la democratización de la prestación del servicio y su relación con la sociedad. (Carrillo et al., 2004). Desde este punto de vista, se pretende considerar las consecuencias y posibilidades que puede representar la e-Gobernanza como estrategia para revalorizar los procesos de gestión en la mayoría de las universidades latinoamericanas que debieron cerrar aulas y pasillos durante meses sus instalaciones ...

Identifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed...