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

Phylogenic tree of A. radioresistens strain NS-MIE (Accesssion no: MK334657). The 16s rRNAs of A. radioresistens strain NS-MIE and Acinetobacter radioresistens strain NRBC 1024013 NR shows a high bootstrap value of 86 %.

BioMed Research International

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Figure 1

Figure 1: 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

Figure 1 | 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 