Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

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Computational intelligence models to predict porosity of tablets using minimum features.

P2860

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P2860

Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

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2013 nî lūn-bûn

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2013年の論文

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2013年論文

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2013年論文

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2013年論文

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2013年論文

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2013年論文

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2013年论文

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2013年论文

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2013年论文

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name

Application of physicochemical ...... ion of tablet characteristics.

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Item

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Application of physicochemical ...... ion of tablet characteristics.

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Application of physicochemical ...... ion of tablet characteristics.

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P1476

Application of physicochemical ...... tion of tablet characteristics

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Kitti Papós

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Klára Pintye-Hódi

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Péter Kása

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Stane Srčič

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Tamás Sovány

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P2860

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Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form.

Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

Comparison of the halving of tablets prepared with eccentric and rotary tablet presses.

Modeling of subdivision of scored tablets with the application of artificial neural networks.

Establishing and analyzing the design space in the development of direct compression formulations by gene expression programming.

Contribution of the physicochemical properties of active pharmaceutical ingredients to tablet properties identified by ensemble artificial neural networks and Kohonen's self-organizing maps.

Tableting process optimisation with the application of fuzzy models.

P2888

s12249-013-9932-6

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511-516

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scholarly article

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10.1208/S12249-013-9932-6

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English

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Ilija Ilić

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2013-02-15T00:00:00Z

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235630315

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1035500009

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23413109

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3665991

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Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

about

P2860

P2860

Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P407

P433

P478

P50

P577

P5875

P6179

P698

P932

P356

P1433

P1476

P2093

P2860

P2888

P304

P31

P356

P407

P433

P478

P50

P577

P5875

P6179

P698

P932