European Journal of Molecular & Clinical Medicine

Recognizing emotions at better accuracy is very challenging task. Therefore, in recent time, the human-machine interaction technology has gained so much success for recognizing the emotional states depending on physiological signals. The human emotional states can be detected by using facial expressions, but sometimes the accurate results are not achieved. Therefore in proposed work, the emotions are recognized using Electroencephalogram (EEG) which work on the basis of brain signal. Here, the human emotional states data is collected using DEAP Dataset and Artificial Neural Network (ANN) is used as classifier. Five time domain features namely correlation, average, variance, kurtosis and skewness are calculated for three frequency bands theta, alpha and beta. The data for two emotional dimensions valence and arousal is taken from DEAP Dataset. The proposed work gives better recognition results for valence and arousal dimensions which are 85.60 % and 87.36 % respectively. So we get the success in achieving significant accuracy.   

In recent years, Artificial Neural Networks (ANN) was widely implemented for
developing predictive and estimation models to estimate the needed parameters. As the
Coronavirus disease 2019 (COVID-19) case numbers are rising internationally as
uncontrolled outbreaks, it is important to better understand what factors promote the super
spreading events. In this paper, the use of Multi-Layer Perceptron (MLP) and Radial Basis
Function (RBF) of ANN for COVID-19 spread and death contributing factors in America
was described. A comparison was made by using a dataset of COVID-19 cases and deaths
reported from 49 states in America during April 2020. Seven covariates used in the network
which are High Temperature, Low Temperature, Average Temperature, Population,
Percentage of Cases over Population, Percentage of Death over Population, and Total
Cases. However, the performance of MLP and RBF networks may be evaluated relatively
similar. It was found that both MLP and RBF proved that the Population, Percentage of
cases over population, and Total cases are the most contributing factors towards COVID-19
spread and death in America particularly.