Publication: Computational Intelligence Modeling of Pharmaceutical Properties
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2016-02
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Abstract
In the pharmaceutical industry, a good understanding of the casual relationship between product quality and
attributes of formulations is very useful in developing new products, and optimizing manufacturing processes.
Feature selection is mandatory due to the abundance of noisy, irrelevant, or misleading features. The selected
features will improve the performance of the prediction model and will provide a faster and more cost effective
prediction than using all the features. With the big data captured in the pharmaceutical product development
practice, computational intelligence (CI) models and machine learning algorithms could potentially be used to
identify the process parameters of formulations and manufacturing processes. That needs a deep investigation of
roller compaction process parameters of pharmaceutical formulations that affect the ribbons production. In this
work, we are using the bio-inspired optimization algorithms for feature selection such as (grey wolf, Bat, flower
pollination, social spider, antlion, moth-flame, genetic algorithms, and particle swarm) to predict the different
pharmaceutical properties.
Description
Proceedings of the First PhD Symposium on Sustainable Ultrascale
Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
Keywords
Computational Intelligence, Pharmaceutical Roll Compaction, Bio-inspired Optimization, Feature Selection
Bibliographic citation
Carretero Pérez, Jesús; et.al. (eds.). (2016). Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016). Timisoara, Romania. Universidad Carlos III de Madrid, ARCOS. Pp. 1-4.