Although the AUC of 0.9 in our original report for both training and blinded verification was promising, we had to eliminate some markers, such as HSP90, due to preanalytic bias leading to site-to-site differences.
Each architecture uses a different combination of neurons and inputs and each network has its own training and verification set.
The best ANN structure was selected on the basis of the lowest error on the training and verification.
The input output patterns for the neural network training and verification are generated using orthogonal arrays and ASAP simulation.
The training and verification process for the model is then described using 60 real engineering documents as a sample.
Data remaining after extraction of the external validation set was further divided into a training pool (further divided into "training" and "verification" sets) and an external test set.
The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively.
Unless otherwise indicated, 165 networks were trained for each architecture (number of inputs and number of neurons) and the 33 having the best performance (combining training and verification statistics) were retained in the final ANNE for that architecture.
In addition, development through direct support to research and training, and verification of isotope hydrology methodologies through field projects are required by various researchers in the region, and constant efforts are essential to propagate the same to end-users.
In addition, we provide an analysis of the computational complexity associated with the i-vector and i-supervector at four different stages: loading matrix estimation, i-vector and i-supervector extraction, PLDA model training, and verification score calculation.
