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Smart Materials Congress 2019
Nano Research and Applications
ISSN: 2471-9838
Page 22
August 01-02, 2019
Dublin, Ireland
Smart Materials and
Structures
8
th
International Conference on
Machine learning techniques to predict the characteristics
of zeolite
Aiman Darwiche
Nova Southeastern University, USA
M
achine learning can predict characteristics of
material with high accuracy rates. Using Random
Forest, we built a prediction model that depends on the
geometric features, such as local geometry, structure,
and porosity of the material, to predict the bulk and shear
moduli of material with a high accuracy. The prediction
model will be used on zeolites to predict the elastic
response but can be extended to other materials. The
proposed model is evaluated by comparing its predictive
accuracy to those of extant methods.
Biography
AimanDarwiche has completed his PhD fromNova Southeast-
ern University in 2018. He is the Co-founder and Chief Data Sci-
entist at Compu-House, a startup that emphasizes on building
prediction models for life threatening health conditions before
their occurrences. Besides, he teaches undergraduate courses
at Northern Kentucky University. He has several papers.
ad1443@mynsu.nova.eduAiman Darwiche, Nano Res Appl 2019, Volume 05