Application of deep neural techniques in predictive modelling for the estimation of Escherichia coli growth rate-Reference-Cited by-同舟云学术

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Application of deep neural techniques in predictive modelling for the estimation of Escherichia coli growth rate

Published:2020-11-03 Issue:5 Volume:130 Page:1645-1655
ISSN:1364-5072
Container-title:Journal of Applied Microbiology
language:en
Short-container-title:J Appl Microbiol

Author:

Chitra M.¹^ORCID,Sutha S.¹,Pappa N.¹

Affiliation:

1. Department of Instrumentation Engineering Madras Institute of Technology (MIT) CampusAnna University Chennai India

Publisher

Wiley

Subject

Applied Microbiology and Biotechnology,General Medicine,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jam.14901

Reference48 articles.

1. Al‐Akhras M.A.H. Al Shorman M. Masadeh M.M. Aljarrah K. Ababneh Z.(2018)Modeling the effect of rose bengal on growth and decay patterns ofPseudomonas aeruginosa Escherichia coliandStaphylococcus aureus. In: IOP Conference Series: Materials Science and Engineering305 10–13.

2. Rethinking Tertiary Models: Relationships between Growth Parameters of Bacillus cereus Strains

3. A dynamic approach to predicting bacterial growth in food

4. Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

5. Application of recurrent neural network to predict bacterial growth in dynamic conditions

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2. Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA);Journal of Pure and Applied Microbiology;2023-04-13

3. Prediction of microbial growth via the hyperconic neural network approach;Chemical Engineering Research and Design;2022-10

4. Machine learning approach for predicting single cell lag time of Salmonella Enteritidis after heat and chlorine treatment;Food Research International;2022-06

5. Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning;Journal of Microbiological Methods;2022-01

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