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المؤلفBouchaala, Lobna
المؤلفBen Khedher, Saoussen
المؤلفMezghanni, Héla
المؤلفZouari, Nabil
المؤلفTounsi, Slim
تاريخ الإتاحة2023-06-01T07:31:32Z
تاريخ النشر2015
اسم المنشور2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 - Proceedings
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/SNPD.2015.7176180
معرّف المصادر الموحدhttp://hdl.handle.net/10576/43694
الملخصThe 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعantifungal activity
Bacillus amyloliquefaciens
Bayesian network
learning
response surface methodology
العنوانBayesian network and response surface methodology for prediction and improvement of bacterial metabolite production
النوعConference Paper
dc.accessType Abstract Only


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