Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated<i> Acinetobacter radioresistens</i> Strain NS-MIE from Agricultural Soil

<p>ANN response surface for three different independent variables. Response surface between NB concentration versus pH, NB concentration versus chromium concentration, and pH versus chromium concentration.</p>

BioMed Research International

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Figure 4

Figure 4: Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated<i> Acinetobacter radioresistens</i> Strain NS-MIE from Agricultural Soil 

Figure 4 | Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated Acinetobacter radioresistens Strain NS-MIE from Agricultural Soil 