In the present study, the Feedforward Neural Network (FNN) architecture is employed , with its general form of three hidden layers shown in Figure . An FNN is a distinct variety of ANN where information proceeds unidirectionally-from the input nodes to the output nodes-without any cyclic paths. The fundamental component of an FNN is the artificial neuron. The process begins by sequentially accepting inputs, each of which is multiplied by a corresponding weight, assigned during the training phase of the neural network based on its learning algorithm and optimization objective.