RT Conference Proceedings
T1 Computational Intelligence Modeling of Pharmaceutical Properties
A1 Zawbaa, Hossam M.
A2 Carretero Pérez, Jesús
A2 García Blas, Javier
A2 Petcu, Dana
AB In the pharmaceutical industry, a good understanding of the casual relationship between product quality andattributes 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 selectedfeatures will improve the performance of the prediction model and will provide a faster and more cost effectiveprediction than using all the features. With the big data captured in the pharmaceutical product developmentpractice, computational intelligence (CI) models and machine learning algorithms could potentially be used toidentify the process parameters of formulations and manufacturing processes. That needs a deep investigation ofroller compaction process parameters of pharmaceutical formulations that affect the ribbons production. In thiswork, we are using the bio-inspired optimization algorithms for feature selection such as (grey wolf, Bat, flowerpollination, social spider, antlion, moth-flame, genetic algorithms, and particle swarm) to predict the differentpharmaceutical properties.
SN 978-84-608-6309-0
YR 2016
FD 2016-02
LK https://hdl.handle.net/10016/22890
UL https://hdl.handle.net/10016/22890
LA eng
NO Proceedings of the First PhD Symposium on Sustainable UltrascaleComputing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.
NO European Cooperation in Science and Technology. COST
NO This work was supported by the IPROCOM Marie Curie initial training network, funded through thePeople Programme (Marie Curie Actions) of the European Union’s Seventh Framework ProgrammeFP7/2007-2013/ under REA grant agreement No. 316555. In addition, this work was partially supportedby NESUS.
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