Classification of alcohols obtained by QCM sensors with different characteristics using ABC based neural network

Abstract

Alcohols with different structures are used frequently in hygiene products and cosmetics. It is desirable to classify these alcohols to evaluate their potential harmful effects using less costly methods. In this study, five different types of alcohol are classified using five QCM sensors with different structures. The main idea of the study is to determine the QCM sensor that makes the most successful classification. All the five of the QCM sensors gave successful results, but QCM12-constructed using only NP-was the most successful. ABC-based ANN is used for the classification, and the lowest MSE value in test dataset is obtained as 1.41E-16. The results of 300 different scenarios showed that different alcohols can be classified successfully by using ANN-ABC on the sensor data from QCM12. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.