EMPLOYING THE ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN FORECASTING TO REDUCE THE SPREAD OF EPIDEMICS WITH A PRACTICAL APPLICATION

The development in the methods used in forecasting has made countries compete to take their place in optimization the use of these methods, especially under the spread of the new Covid-19 epidemic, so in this research we applied the Adaptive Neuro Fuzzy Inference System (ANFIS) to data on infection numbers for the Corona pandemic for all Governorates of Iraq, and the time series data is usually collected over time either for equal intervals or for unequal intervals [Ravichandran et al. (2012)]. And the forecasting results indicated that the forecasting data follow the same path as the actual data. The hybrid algorithm in the ANFIS showed good forecasting results through five independent inputs and a dependent variable that represents the number of injuries. © 2021 DAV College. All rights reserved.

OPTIMIZING THE FORECASTING BY THE USE OF ARTIFICIAL NEURAL NETWORKS TO REDUCE THE SPREAD OF EPIDEMICS WITH A PRACTICAL APPLICATION

Artificial neural networks are of common use in the areas of artificial intelligence because they simulate the thinking of the human mind. In this research, artificial neural networks were used to improve the accuracy of forecasting to reduce the spread of epidemics. The results of the study showed the optimal use of the artificial neural network, as it gave accurate results and close to the actual data. Recent research activities in forecasting with artificial neural networks (ANN) suggest that ANN can be a promising alternative to the traditional linear methods.