where h is the learning rate, C is the loss function, x (t) is a stochastic term, and t is the propagation step. There are multiple choices available for the activation function associated with each neuron. We used the logistic sigmoid which is given by a(z) = 1 1+ e -z , where z is the quantity received by the neuron. We use the Keras python platform to implement the NN model. The network we have used for BR1 representation has 35 × 2 = 70 input nodes, 5 nodes in a single hidden layer, and one output node. In the BR2 representation, the input layer has 35 input nodes. The sigmoid activation function is used in the hidden layer, while the linear activation function is used for the input and output layers. The learning rate was 0.03. Support Vector Machine (SVM). This is a supervised learning method first introduced for classification models and then modified for regression problems. The SVM classifier performs the classification of the data set from selected subsets of samples, called support vectors, in which the characteristic information on class distinction is compressed. In the linear support vector regression problem which we utilize in the present work, the aim is to find the linear function f (x) = wx + b that approximates the output vector y with weights vector w such that the primal function J is minimized, which is given by Relevance Vector Machine (RVM). This is a sparse version of the support vector machine which attempts to amend several of the shortcomings of SVM, such as nonprobabilistic predictions, low sparsity generating a tendency to overfit data, and the presence of the two fitting parameters C and e which require cross validation. The RVM increases the sparsity of SVM and introduces a probabilistic weighting of the model weights based on Bayes' rule, assuming a Gaussian distribution of weights.