Examinando por Autor "Ruiz-Alvarado, John"

Mostrando 1 - 4 de 4

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
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“