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Natural neurons receive signals through the synapses which are on the dendrites of the neuron. When a strong signal is received (exceeding a threshold), the neuron activates and sends a signal through the axon to another synapse.

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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 strngth of the output (the input is multiplied by the weight) and 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 is not adequate, 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 both the question (X) and the answer (Y) by the teacher (supervisor) .

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When the model predicts the output variables (Y) for new input data (X) using the mapping function with high accuracy, the training was successful.

Y = f(X)