Authors:
Kumar Raj Dayalbagh Educational Institute Department of Chemistry, Faculty of Science Agra 282110 India

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Abhishek Kardam Dayalbagh Educational Institute Department of Chemistry, Faculty of Science Agra 282110 India

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Jyoti Arora Technical College, Dayalbagh Educational Institute Department of Mathematics Agra 282110 India

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Shalini Srivastava Dayalbagh Educational Institute Department of Chemistry, Faculty of Science Agra 282110 India

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Abstract  

The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer function. The data of fifty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments resulted into the performance evaluation based on considering 20% data for testing and 20% data for cross validation at 1,500 Epoch with 0.70 momentums The Levenberg–Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean squared error at the eighth place of the decimal for training (MSE) and cross validation.

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Journal of Radionalytical and Nuclear Chemistry
Language English
Size A4
Year of
Foundation
1968
Volumes
per Year
1
Issues
per Year
12
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0236-5731 (Print)
ISSN 1588-2780 (Online)