The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit® RS PO as matrix substance

Ibrić, Svetlana; Jovanović, Milica; Djurić, Z.; Parojčić, Jelena; Solomun, Ljiljana

doi:10.1016/s0168-3659(02)00044-5

Cited by 76 publications

(32 citation statements)

References 9 publications

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“…With pairs of feature values (TCTW and TVDI) derived from the Landsat images and θ v measurements were obtained, the TCTW and TVDI indices were introduced as the regressors with θ v as the output in an advanced machine learning algorithm, the general regression neural network (GRNN) [38,39]. Over the past decade, probabilistic neural networks like GRNN have been widely used for solving nonlinear problems and preforming predictions because of their flexible network structures, high fault tolerances, and robustness [39][40][41][42][43][44][45][46]. Additionally, the primary benefit of GRNN over a traditional back propagation neural network is its ability to obtain a satisfying fit of the data rapidly, with only a few training samples available, and without additional parameter inputs [39,44].…”

Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

“…Over the past decade, probabilistic neural networks like GRNN have been widely used for solving nonlinear problems and preforming predictions because of their flexible network structures, high fault tolerances, and robustness [39][40][41][42][43][44][45][46]. Additionally, the primary benefit of GRNN over a traditional back propagation neural network is its ability to obtain a satisfying fit of the data rapidly, with only a few training samples available, and without additional parameter inputs [39,44]. These features make GRNN a very efficient tool for constructing predictors for the desired variables.…”

Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

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Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Zhao

et al. 2016

Remote Sensing

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Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θ v ) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θ v . The results showed that the modeled θ v was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θ v estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θ v image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics.

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Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Zhao

et al. 2016

Remote Sensing

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“…The architecture is the combination of multilayer perceptrons and radial basis functions. GRNN is widely used in prediction problems due to its nonlinear mapping ability that enhances its approximation capabilities [27]. Here this approach is selected due to its nonlinear network structure, which is one pass learning network with high parallel structure.…”

Section: Grnn Modelmentioning

confidence: 99%

Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques

Majid

Ahsan

Tariq

2015

Applied Soft Computing

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“…For example, the reliability of ANNs in optimizing controlled release capsules and ketoprofen hydrogel ointment has been demonstrated by Hussain et al [54]. A trained ANN model has been successfully employed to predict release profile and optimize formulation of various drug formulations such as aspirin extended release tablets [55,56], diclofenac sodium sustained release matrix tablets [57], salbutamol sulfate osmotic pump tablets [58], transdermal ketoprofen hydrogel [59], and nimodipine floating tablet formulation [60].…”

Section: Optimization Of Pharmaceutical Formulationsmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

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Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit® RS PO as matrix substance

Cited by 76 publications

References 9 publications

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Modeling glass-forming ability of bulk metallic glasses using computational intelligent techniques

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

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