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>Actual versus RSM and ANN predicted values for chromium reduction by<i> A. radioresistens</i> strain NS-MIE.</p>

BioMed Research International

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Figure 2

Figure 2: 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 2 | 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 