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The human brain on an average contains 86 billion neurons approximately [10]. A biological neuron consists of thin fibers, and those are known as dendrites. Dendrites receive incoming signals. The cell body, “soma” responsible for processing input signals and to decide firing/nonfiring of neurons to output signal. Processed signals output from neurons received by “axon” and passes it to relevant cells.

Artificial neuron called also as “perceptron” is a fundamental component of neural network, which is a mathematical function of a real-world problem with binary outputs. The neurons are systematically organized into two or more than two layers. One layer of neurons are connected to immediately preceding neurons layer and immediately succeeding neuron layer. The first (input) layer receives the external data, and the last (output) layer ultimately produces result. Each artificial neuron receives input from input layer, process the weights and sums and pass the sum through a nonlinear mathematical relation to produce output. In between them are one or more hidden layers (Figure 2.1). Weights are multiples of respective input values arranged in an array. To achieve a final value of prediction, bias is added to the weighted sum. The size of the correction values to adjust for errors by the model is known as a learning rate. Activation function decides whether or not a neuron is fired [11]. The neural network uses previous step output data values for the network training and minimizes the error between observed and estimated. The process readjusts the weights at each interaction of neuron. The training will stop after reaching the optimal learning rate [12, 13]. The higher learning rate reduces the time for training, and ultimate accuracy is low. Lower learning rate takes longer time and higher accuracy.

Figure 2.1 Architecture of artificial neural network (original figure).