Artificial neural network-based pore size prediction of alginate gel scaffold for targeted drug delivery

Das, Raja; Bhasarkar, Jaykumar B.; Rastogi, Amol; Saxena, Raghav; Bal, Dharmendra Kumar

doi:10.1007/s00521-022-07958-z

Cited by 1 publication

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural network (ANN) is made of at least three main layers of neurons, including the input layer which receives data input, the hidden layer which performs the most computation, and the output layer which predicts the final output. [22][23][24][25][26][27] The activation function (AF) in ANN is used to connect input feeding to output to decide whether the neurons should be activated or not. There are several types of AF in ANN, such as linear, sigmoid, piecewise linear, and Elliot, and many different training algorithms with different characteristics and performances, such as forward propagation, backward propagation (backpropagation), resilient backpropagation (Rprop), and quick propagation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial neural network prediction of particle size of agglomerated polybutadiene latex

Zhao,

Lu,

Chen

et al. 2024

Journal of Polymer Science

View full text Add to dashboard Cite

Polymer agglomeration is a useful way to enlarge the particle size of polybutadiene latex (PBL). However, the agglomeration process is very complex and is affected by many factors. Therefore, it is difficult to predict the mean particle size and distribution of agglomerated PBL. This study proposed a novel simulation method based on the artificial neural network (ANN) model. Firstly, the influence of agglomeration conditions on the particle size of agglomerated PBL were investigated, including the particle size of pre‐agglomerate PBL, the amount, the α‐methacrylic acid (α‐MAA) content, the solid content and the particle size of agglomerating agent. It is found that these factors have a close nonlinear relationship with the mean particle size and polydispersion index (PDI) of agglomerated PBL. Then, a model containing 236 data points was built using ANN including these properties as input with mean particle size and PDI of agglomerated PBL as output. The coefficient of determination (R2) values predicted by ANN for training, validation, test and total data are 0.9879, 0.9964, 0.9930, and 0.9902, respectively. It indicates that there is a good agreement between experimental data and predicted results by ANN. This study demonstrates that a simple machine learning method has good performance in predicting the particle size of agglomerated PBL, which further improves the development efficiency of PBL polymer agglomeration.

show abstract