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AuthorBouchaala, Lobna
AuthorBen Khedher, Saoussen
AuthorMezghanni, Héla
AuthorZouari, Nabil
AuthorTounsi, Slim
Available date2023-06-01T07:31:32Z
Publication Date2015
Publication Name2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 - Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.1109/SNPD.2015.7176180
URIhttp://hdl.handle.net/10576/43694
AbstractThe optimization of antifungal activity production by Bacillus amyloliquefaciens was carried out using Response Surface Methodology (RSM) in two steps. The first step involved the screening of cultural parameters affecting the production. The second step involved the optimization of significant ones. In this study, we used Bayesian network to predict the results of the experiments required for the second step. Then, by RSM, using the predicted values by BN, we defined the composition of a culture medium allowing 56% improvement in antifungal activity production over the basal medium. Such medium composition and improvement were shown to be similar to that obtained in the previous study demonstrating that, when coupled with RSM, BN permitted improvement of antifungal activity production with a much reduced number of experiments.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectantifungal activity
Bacillus amyloliquefaciens
Bayesian network
learning
response surface methodology
TitleBayesian network and response surface methodology for prediction and improvement of bacterial metabolite production
TypeConference Paper
dc.accessType Abstract Only


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