Search

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or scarcity. Therefore, humanity is facing environmental challenges due to an increase in the occurrence and intensity of droughts. The forecast of droughts can be of great help when trying to reduce the adverse effects that the scarcity of water brings, particularly in agriculture. When evaluating the conditions of water scarcity, as well as in the identification and characterization of droughts, the use of predictive models of drought indices could be a very useful tool. In this research, the utility of Artificial Neural Networks with exogenous inputs was tested, with the aim of predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictor data from 1979 to 2014. The best model was found using the scaled conjugate gradient backpropagation algorithm as the optimization method and was set to the following architecture: 6-25-1 network. The correlation coefficient of predicted and observed Standardized Precipitation Index values for the test dataset was between 0.84 and 0.95. As a result, the Artificial Neural Network models performed successfully in predicting Standardized Precipitation Index at the four analyzed regions. The developed and tested Artificial Neural Network models in this research suggest remarkable prediction abilities of the monthly Standardized Precipitation Index in the study region. [ABSTRACT FROM AUTHOR]

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems. [ABSTRACT FROM AUTHOR]