Validation of the Prediction of Effectiveness of Statistical Time Series Models Using an Artificial Neural Network Model

The objective of this project was to predict the number of cases of infections and deaths from covid-19 through the application of artificial intelligence techniques in order to validate the effectiveness of a statistical model and counteract congestion in the health area within the territory. Ecuadorian, the rapid spread that caused serious consequences in the health systems and the virus triggered a global health crisis, the drastic impact on people's lives caused the application of Artificial Neural Networks-RNA techniques to obtain rapid diagnoses and effective. Historical data from the Ecuadorian state about the infections and deaths recorded per day were taken, the data was processed using the time series statistical method technique and later in the RNA models for the generation of the prediction and validation of the statistical method, the results obtained from each of the neural networks provided a feasible forecast that was close to the real values. The main conclusions show that the techniques applied in this project are efficient when predicting the number of cases of infection and death from covid-19 based on historical data and that the use of neural networks is very useful for solving various predictive problems. © 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Detection of Covid-19 Through the Analysis of Radiographic Images of the Chest using Convolutional Neural Networks

There are different ways to detect Covid-19, which have emerged so far giving an effective response in detecting the disease, the PCR test is a reliable diagnostic method, which requires a well-equipped laboratory to obtain results, which can take hours or days. Another detection technique for this disease is by analyzing the chest image;This technique is used as a diagnostic tool in emergency areas in health centers, because it can reveal characteristics related to lung involvement. For this reason, it is important to develop an automatic detection system, as an alternative diagnosis option for Covid-19. Deep Learning techniques can help detect the SARS-CoV-2 virus by analyzing chest radiographic images. Thanks to the high availability of the datasets available, and using convolutional neural networks, the analysis is carried out by classifying images. In this research, two CNN models were created whose outputs are normal or covid19, the same ones that were trained with two datasets from public research repositories. The performance of the models trained in Pytorch were compared with the models trained in Keras under similar conditions of parameters and hyperparameters, obtaining a higher performance with Pytorch however since the two types of models have learned adequately with an accuracy that is above the 90% recommended the use of both models. © 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.