**2.1 Deep neural network (DNN)**

In DNN, there is multilayer perceptron or hidden layer between the input and output. All the layers are connected to previous layers; by going through each layer, the network estimates the exact output based on the weights and activation function. Through DNN, we can model any complex non-linear relation. The backbone of the DNN is the characteristic of learning about the feature that is most relevant to the targets [7]. The DNN has research gap in model selection, training dynamics, by using graph convolution neural network combination optimization, and Bayesian neural network for estimation of uncertainty. There are a lot of applications for DNN, that is, computer vision, machine translation, social network filtering, playing board, video games, and medical diagnosis (**Figure 2**).
