Computational Intelligence Modeling of Pharmaceutical Properties

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ISBN: 978-84-608-6309-0
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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.
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
Computational Intelligence, Pharmaceutical Roll Compaction, Bio-inspired Optimization, Feature Selection
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Carretero Pérez, Jesús; (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.