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Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks

Authorized Users Only
2018
Authors
Antanasijević, Davor
Antanasijević, Jelena
Pocajt, Viktor
Article (Published version)
Metadata
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Abstract
A dataset containing transition temperature values for 243 bent-core liquid crystal (LC) compounds was used to develop quantitative structure property relationship (QSPR) models using only 2D molecular descriptors and general regression neural network (GRNN). Beside a standard analogue GRNN model, another GRNN model with fuzzy digital response was created with the aim to estimate the prediction error for each compound. Two approaches for the selection of most relevant subset of descriptors, namely the partial mutual information (PMI) and self-organizing maps combined with chi square ranking, were also compared. The best results were obtained using analogue GRNN model based on PMI selected subset (R-2 = 0.91), with the mean absolute error (MAE) lower in comparison with previously published corresponding QSPR models. The digital PMI-GRNN model enabled distinction between high and low accurate predictions, i.e. ones with absolute error higher than mean absolute error (MAE) and others with... absolute error lt = MAE, with the accuracy of 81%.

Keywords:
Digital GRNN model / Prediction error estimation / SOM feature selection / QSPR
Source:
Engineering Applications of Artificial Intelligence, 2018, 71, 251-258
Publisher:
  • Pergamon-Elsevier Science Ltd, Oxford
Funding / projects:
  • Development and Application of Methods and Materials for Monitoring New Organic Contaminants, Toxic Compounds and Heavy Metals (RS-172007)
  • Study of the Synthesis, Structure and Activity of Natural and Synthetic Organic Compounds (RS-172013)

DOI: 10.1016/j.engappai.2018.03.009

ISSN: 0952-1976

WoS: 000436213000020

Scopus: 2-s2.0-85044440493
[ Google Scholar ]
2
2
URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3873
Collections
  • Radovi istraživača / Researchers’ publications (TMF)
Institution/Community
Tehnološko-metalurški fakultet
TY  - JOUR
AU  - Antanasijević, Davor
AU  - Antanasijević, Jelena
AU  - Pocajt, Viktor
PY  - 2018
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3873
AB  - A dataset containing transition temperature values for 243 bent-core liquid crystal (LC) compounds was used to develop quantitative structure property relationship (QSPR) models using only 2D molecular descriptors and general regression neural network (GRNN). Beside a standard analogue GRNN model, another GRNN model with fuzzy digital response was created with the aim to estimate the prediction error for each compound. Two approaches for the selection of most relevant subset of descriptors, namely the partial mutual information (PMI) and self-organizing maps combined with chi square ranking, were also compared. The best results were obtained using analogue GRNN model based on PMI selected subset (R-2 = 0.91), with the mean absolute error (MAE) lower in comparison with previously published corresponding QSPR models. The digital PMI-GRNN model enabled distinction between high and low accurate predictions, i.e. ones with absolute error higher than mean absolute error (MAE) and others with absolute error  lt = MAE, with the accuracy of 81%.
PB  - Pergamon-Elsevier Science Ltd, Oxford
T2  - Engineering Applications of Artificial Intelligence
T1  - Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks
EP  - 258
SP  - 251
VL  - 71
DO  - 10.1016/j.engappai.2018.03.009
ER  - 
@article{
author = "Antanasijević, Davor and Antanasijević, Jelena and Pocajt, Viktor",
year = "2018",
abstract = "A dataset containing transition temperature values for 243 bent-core liquid crystal (LC) compounds was used to develop quantitative structure property relationship (QSPR) models using only 2D molecular descriptors and general regression neural network (GRNN). Beside a standard analogue GRNN model, another GRNN model with fuzzy digital response was created with the aim to estimate the prediction error for each compound. Two approaches for the selection of most relevant subset of descriptors, namely the partial mutual information (PMI) and self-organizing maps combined with chi square ranking, were also compared. The best results were obtained using analogue GRNN model based on PMI selected subset (R-2 = 0.91), with the mean absolute error (MAE) lower in comparison with previously published corresponding QSPR models. The digital PMI-GRNN model enabled distinction between high and low accurate predictions, i.e. ones with absolute error higher than mean absolute error (MAE) and others with absolute error  lt = MAE, with the accuracy of 81%.",
publisher = "Pergamon-Elsevier Science Ltd, Oxford",
journal = "Engineering Applications of Artificial Intelligence",
title = "Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks",
pages = "258-251",
volume = "71",
doi = "10.1016/j.engappai.2018.03.009"
}
Antanasijević, D., Antanasijević, J.,& Pocajt, V.. (2018). Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks. in Engineering Applications of Artificial Intelligence
Pergamon-Elsevier Science Ltd, Oxford., 71, 251-258.
https://doi.org/10.1016/j.engappai.2018.03.009
Antanasijević D, Antanasijević J, Pocajt V. Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks. in Engineering Applications of Artificial Intelligence. 2018;71:251-258.
doi:10.1016/j.engappai.2018.03.009 .
Antanasijević, Davor, Antanasijević, Jelena, Pocajt, Viktor, "Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks" in Engineering Applications of Artificial Intelligence, 71 (2018):251-258,
https://doi.org/10.1016/j.engappai.2018.03.009 . .

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