The connections represent the information exchange between nodes

The connections represent the information exchange between nodes. The flow can be in one direction (unidirectional) and bidirectional when it flows in either direction.
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The behavior of the entire network depends on the individual interactions of nodes through the connections, which is not visible at the node level. The global behavior is emergent since the characteristics of the entire network supersede the characteristics of individual nodes resulting to a powerful tool.
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As a consequence is common for networks to be used for modeling in many areas including computer science.
\subsubsection{Artificial Neural Network}
An artificial neuron mimics biological neurons by performing computations, and an Artificial Neural Network(ANN) sees the nodes as artificial neurons.
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The Natural neurons in the brain use synapses on the dendrites of the neuron to receive signals . If the signal exceededs a threshold, the neuron is activated and sends a signal to another synapse using the axon.
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The artificial neurons have inputs and weights, a mathematical function, determines whether the neuron will be activated while a second function computes the output. The ANNs use the artificial neurons for information processing.
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The weight of the neuron determines the strength of the output as the input is multiplied by the weight, which can be amended accordingly in order to get the desired outcome.
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Since the inception of the ANN’s (McCulloch and Pitts (1943)) they have evolved into new models of learning like the back-propagation algorithm (Rumelhart and McClelland, 1986).

\subsubsection{Supervised Machine Learning}
Supervised machine learning models need adequate number of outcomes like audit results in order to perform the learning process. In case the number of audits performed are inadequate, deep neural network models cannot be used.
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Supervised learning, the machine learns the mapping function (f) after it has been provided with the inputs (X) and outputs (Y) variables. The machine is the student who is given the question (X) and the answer (Y) by the teacher (supervisor) .
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A model has been trained successfully if it can predict the output (Y) using the mapping function (f) on unseen data (X) accurately.
Y = f(X)