Schematic representation of the multiple-input algorithm. Input A goes through a fully connected neural network (Branch A) and comes out as a one-dimensional (1D) array of size [288], and Input B comes out of the convolutional Branch B as a 1D array of size [16], as implied by the parameters detailed in Table 2. They are concatenated before going through another fully connected neural network, whose output is of size 1.