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Shekofteh, Y., Almasganj, F. and Daliri, A. (2015) MLP-Based Isolated Phoneme Classification using Likelihood Features Extracted from Reconstructed Phase Space. Engineering Applications of Artificial Intelligence, 44, 1-9.
https://doi.org/10.1016/j.engappai.2015.05.001

has been cited by the following article:

• TITLE: Experimental Investigation and Modeling of Activity Coefficient at Infinite Dilution of Solutes Using Dicationic Solvent Based on Pyrrolidinium as a New Stationary Phase in Gas Chromatography

  AUTHORS: Ali Yahyaee, Mina Nazifi, Mohsen Kianpour, Kurosh Tabar Heidar

  KEYWORDS: Gas Chromatography, Ionic Liquid, Activity Coefficients at Infinite Dilution, Artificial Neural Network (ANN), Thermodynamic Modeling

  JOURNAL NAME: American Journal of Analytical Chemistry, Vol.9 No.4, April 26, 2018

  ABSTRACT: Activity coefficients at infinite dilution, γ ∞ i, were calculated for 12 solutes, with organic solutes including linear alcohols (methanol, ethanol, propanol), linear alkanes (heptane, octane), benzene, toluene, cyclohexane, 1, 2-dichloroethane, trichloroethylene, acetonitrile and carbon tetrachloride. The values of γ ∞ i were determined via either thermodynamic or artificial neural network modelling at different temperatures. A comparison between extracted results from these two methods confirmed that experimental and predicted results are roughly the same. The accuracy of predicted results proves this model is fully compatible with a wide range of solutes, and it can readily be used as an alternative to conventional gas-liquid chromatography for the measurements of activity coefficient at infinite dilution.