La presente investigación corresponde a un estudio descriptivo cuyo propósito es obtener información y analizar las percepciones del personal docente, administrativo y directivo sobre los cinco componentes de la organización inteligente: el dominio personal, los modelos mentales, la visión compartida, el aprendizaje en equipo y el pensamiento sistémico. Se trabajó con toda la población que labora en la I.E. Nº 88047 "Augusto Salazar Bondy", conformado por 80 personas distribuidos entre el personal docente, directivo y administrativo. Se administró un cuestionario como instrumento para recolectar la información; el mismo que fue sometido a juicio de expertos para obtener validez, posteriormente, se aplicó el coeficiente Alfa de Cronbach para conocer su grado de confiabilidad. Una vez tomado el cuestionario y recogida la información, esta se analizó y procesó utilizando el p...

El propósito del informe doctoral fue proponer la organización inteligente para la acreditación de la IEPP. Santa Rosa de Lima, 2020. Se utilizó como metodología la investigación básica con diseño no experimental: descriptivo-simple. Los 100 responsables del proceso de acreditación fueron estimados como población y muestra. En los resultados, se utilizó el Alfa de Cronbach 0.885-confiabialidad; y la selección de los ítems quedó determinado por el criterio del análisis factorial, agrupándose en 9 factores, (Tabla 10: matriz del componente rotado). Además, en la presente investigación, se alineó los estándares del Modelo de acreditación para instituciones de Educación Básica del Sineace, y se los incrementó, con los componentes de la organización inteligente de Peter Senge, concretizándose en 3 dimensiones teóricas y 29 estándares de cumplimiento en la IE. Concl...

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...

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...

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...

Employee safety is paramount in the manufacturing industry to ensure their well-being and protection. Technological advancements, particularly convolutional neural networks (CNN), have significantly enhanced this safety aspect by facilitating object detection and recognition. This project aims to utilize CNN technology to detect personal protective equipment and implement a safety implement detection system. The CNN architecture with the YOLOv5x model was employed to train a dataset. Dataset videos were converted into frames, with resolution scale adjustments made during the data collection phase. Subsequently, the dataset was labeled, underwent data cleaning, and label and bounding box revisions. The results revealed significant metrics in safety equipment detection in industrial settings. Helmet precision reached 91%, with a recall of 74%. Goggles achieved 85% precision and an 87% reca...

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...

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...

El propósito del presente trabajo de investigación mejoró las competencias investigativas de los estudiantes del I-II ciclo de la FEYH- USP a través del uso de los blogs como recurso didáctico. Con respecto a la metodología fue de carácter explicativo con diseño cuasi-experimental. La población estuvo conformada por 180 de la FEYH con una muestra de 61 estudiantes que cursan el I y II de las carreras profesionales de inicial, primaria, secundaria, informática y especial seleccionadas en forma intencionada. El instrumento para medir la variable dependiente fue el Cuestionario dirigido a los estudiantes de las diversas escuelas profesionales de la Facultad de Educación y Humanidades de la Universidad San Pedro, el cual tuvo como objetivo recoger información válida que permitió mejorar las competencias investigativas de nuestros estudiantes de los primeros ciclos de estudio. A...

“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...