Strategies to develop robust neural network models: Prediction of flash point as a case study

Analytica Chimica Acta
Amin Alibakshi

Abstract

Artificial neural network (ANN) is one of the most widely used methods to develop accurate predictive models based on artificial intelligence and machine learning. In the present study, the important practical aspects of developing a reliable ANN model e.g. appropriate assignment of the number of neurons, number of hidden layers, transfer function, training algorithm, dataset division and initialization of the network are discussed. As a case study, predictability of the flash point for a dataset of 740 organic compounds using ANNs was investigated via a total number of 484220ANNs to allow covering a wide range of parameters affecting the performance of an ANN. Among all studied parameters, the number of neurons or layers was found to be the most important parameters to develop a reliable ANN with low overfitting risk. To evaluate appropriate number of neurons and layers, a value of equal or greater than 10 for the ratio of the training samples to the ANN constants was suggested as a rule of thumb. More ever, a strategy for evaluation of the authentic performance of ANNs and deciding about the reliability of an ANN model was proposed which is applicable to other models developed by supervised learning. Based on the introduced c...Continue Reading

Citations

Nov 30, 2018·Molecular Informatics·H MirshahvaladM Malekzadeh Dirin
Jan 11, 2020·Journal of Biomolecular Structure & Dynamics·Zhe DuHong-Lin Zhai
Jul 19, 2019·Clinical Biochemistry·Carmen Peña-BautistaConsuelo Cháfer-Pericás
Apr 27, 2021·Analytica Chimica Acta·Lucas B AyresCarlos D Garcia
May 14, 2019·Journal of Proteome Research·Riccardo ConcuHumbert González-Díaz

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