<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Theodore E. Simos</style></author><author><style face="normal" font="default" size="100%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Spyridon D. Mourtas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A multi-input with multi-function activated weights and structure determination neuronet for classification problems and applications in firm fraud and loan approval</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Soft Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Firm fraud classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Loan approval classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">WASD neuronet</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S1568494622005130</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">109351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets such as slow training speed and local minimum. A multi-input multi-function activated WASD neuronet (MMA-WASDN) model is introduced in this paper, combined with a novel multi-function activated WASD (MA-WASD) algorithm, for handling binary classification problems. Using multiple power activation functions, the MA-WASD algorithm finds the optimal weights and structure of the MMA-WASDN and uses cross-validation to address bias and prevent being stuck in local optima during the training process. As a result, neuronets trained with the MA-WASD algorithm have higher precision and accuracy than neuronets trained with traditional WASD algorithms. Applications on firm fraud and loan approval classification validate our MMA-WASDN model in order to demonstrate its outstanding learning and predicting performance. Since these applications use real-world datasets that include strings and missing values, an algorithmic method for preparing data is also suggested to make them manageable from the MMA-WASDN. A comparison of the MMA-WASDN model to five other high-performing neuronet models is included, as well as a MATLAB package that is publicly available through GitHub to support and promote the findings of this research.</style></abstract></record></records></xml>