Our deep neural networks (DNN) model was developed using Keras on top of TensorFlow . This model consists of two hidden layers where the first and second hidden layers include 56, and 28 neurons, respectively. The rectified linear unit (ReLu) activation function was included for each hidden layer of neurons to shift the summed weighted inputs. As the model is a classifier, the output layer was connected with the sigmoid activation function. We observed that randomly dropping out 50% of the units of the hidden layers helps to reduce the overfitting significantly.