Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass

By Anil K. Giri, R. Patel, Siba Sankar Mahapatra. 2011

154 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Health, Toxicology and Mutagenesis, Pollution

View Paper: https://www.semanticscholar.org/paper/f877fe9812796bbf9ad213731a49123287d0666b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Chemistry

1. P. Das
2. Arpita Ghosh
3. K. Sinha

In Health, Toxicology and Mutagenesis

1. P. Das
2. Arpita Ghosh
3. K. Sinha

In Pollution

1. P. Das
2. Arpita Ghosh
3. K. Sinha

Citations in the corpus

• Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN) (2015, methodology): “The hidden layer then does all the data processing and produces output based on the sum of the weighted values from the input layer modified by a sigmoid transfer function (Giri et al. 2011).”