5 The Dual Impact of Lockdown on Curbing COVID-19 Spread …
131
Fig. 5.12 Prediction of Sarimax model with 0, 0, 1 order
and intelligent high-level computations are just an extended version of conventional
computing. They are basically roots of the fifth generation. The ANN approach is
inspired from the Neurons of the Nervous system of the human brain where there
are numerous numbers of interconnected systems along with fast, rapid and parallel
execution is going on. ANN is also a data-driven model based on mathematics and
the large interconnected architecture. It basically learns from the data by training
and giving better results over time and training letting it to think in a rational way
to do things with accuracy. The main goal is to understand the complex relationship
between the input and the output node. It mainly consisted of three layers: Input,
Hidden and Output layers. Neural networks get the knowledge by identifying the
patterns as well as insights in data. The MLP is composed of multiple layers of
operation nodes that derive the data and are connected to the input and output layers
in a directed graph combination. The hidden layers are responsible to perform the
various calculations and make predicted outputs. The finer the layers become the
more accurate the output is tended to be. The number of layers is not fixed and
depends totally on the problem. There are other two terms, i.e. weights and biases.
Each node is connected and weight, i.e. coefficients determine the impact of input
features which constitute its structure.
RNN is basically a type of Neural Network. Recurrent Networks is able to process
a successive recursion with the help of transition function to internal hidden vector
state of the input.
It is an MLP where the earlier hidden unit activations have a loop rather feedback
loop which goes into the neural network along with the input features. The input at
instance t has some previous data which is at time t − 1. The cyclic connections are
the ultimate power of RNN which makes it more powerful than the normal neural
131
Fig. 5.12 Prediction of Sarimax model with 0, 0, 1 order
and intelligent high-level computations are just an extended version of conventional
computing. They are basically roots of the fifth generation. The ANN approach is
inspired from the Neurons of the Nervous system of the human brain where there
are numerous numbers of interconnected systems along with fast, rapid and parallel
execution is going on. ANN is also a data-driven model based on mathematics and
the large interconnected architecture. It basically learns from the data by training
and giving better results over time and training letting it to think in a rational way
to do things with accuracy. The main goal is to understand the complex relationship
between the input and the output node. It mainly consisted of three layers: Input,
Hidden and Output layers. Neural networks get the knowledge by identifying the
patterns as well as insights in data. The MLP is composed of multiple layers of
operation nodes that derive the data and are connected to the input and output layers
in a directed graph combination. The hidden layers are responsible to perform the
various calculations and make predicted outputs. The finer the layers become the
more accurate the output is tended to be. The number of layers is not fixed and
depends totally on the problem. There are other two terms, i.e. weights and biases.
Each node is connected and weight, i.e. coefficients determine the impact of input
features which constitute its structure.
RNN is basically a type of Neural Network. Recurrent Networks is able to process
a successive recursion with the help of transition function to internal hidden vector
state of the input.
It is an MLP where the earlier hidden unit activations have a loop rather feedback
loop which goes into the neural network along with the input features. The input at
instance t has some previous data which is at time t − 1. The cyclic connections are
the ultimate power of RNN which makes it more powerful than the normal neural